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Using IRS Data to Identify Income Shifting to Foreign Affiliates
Lisa De Simone Stanford Graduate School of Business
Lillian F. Mills* The University of Texas at Austin
Bridget Stomberg Indiana University [email protected]
November 2018
Acknowledgements: We thank Kathleen Andries, Ben Ayers, Mary Barth, Phil Berger, John Campbell, Tim Dowd (discussant), Nadine Ebert, Matthew Ege, Paul Fischer, Robert Holthausen, Ross Jennings, Rebecca Lester, Petro Lisowsky, Tracie Majors, Kevin Markle, Ed Maydew, Peter Merrill, Tom Neubig, John McInnis, Kathleen Powers, John Robinson, Leslie Robinson, Doug Shackelford (discussant), Jeri Seidman, Joel Slemrod, Jacob Thornock, Eric Toder, Robert Ullmann, Luke Watson (discussant), Ryan Wilson (discussant), Brian Williams (discussant), Yong Yu, and workshop participants at the University of Texas at Austin, University of North Carolina, the 2017 IRS-TPC Joint Research Conference, Stanford Accounting Summer Camp 2015, the EIASM 2nd Workshop on Current Research in Taxation, the 2012 AAA Annual Meeting, the 2014 National Tax Association Annual Meeting, the 2012 Oxford Doctoral Symposium, and the 2013 UNC Tax Doctoral Conference for valuable comments and suggestions on prior versions of the paper that did not include IRS data. We also thank Lynn Willden, VP of Tax at Insight Enterprises, for helpful discussions. De Simone acknowledges funding from the Stanford Graduate School of Business. Mills acknowledges funding from the Red McCombs School of Business at the University of Texas at Austin and the Beverly H. and William P. O’Hara Chair in Business. The Internal Revenue Service (IRS) provided confidential tax information to one of the authors pursuant to provisions of the Internal Revenue Code that allow disclosure of information to a contractor to the extent necessary to perform a research contract for the IRS. None of the confidential tax information received from the IRS is disclosed in this treatise. Statistical aggregates are used so that a specific taxpayer cannot be identified from information supplied by the IRS. All opinions are those of the authors and do not reflect the views of the IRS.
Outbound Scores for a sample of Compustat firms are available on the authors’ websites (e.g., http://web.stanford.edu/~lnds/OutboundScores.html). Keywords: IRS audit, tax avoidance, international tax, income shifting
*Corresponding author: Mills can be contacted at (512) 471-4607, The University of Texas at Austin, McCombs School of Business, 2110 Speedway, Stop B6400, Austin, TX 78712.
Using IRS Data to Identify Income Shifting to Foreign Affiliates
ABSTRACT:
Income shifting is a significant source of tax planning for U.S. corporations. We use confidential Internal Revenue Service (IRS) data to develop a firm-year measure of income shifting. Our measure captures the relative extent of U.S. multinational entity (MNE) net intercompany payments out of the U.S to CFCs. Our data show that the majority of sample firms report net inbound intercompany payments on average. Sample firms report nearly $830B of outbound payments and over $1T of inbound payments in total. Companies reporting net outbound payments are smaller and operate in high tech industries. Supplemental analyses show that firms with outbound intercompany payments have a lower rate of IRS audit, and are no more likely to be assessed additional taxes upon audit. Our study provides a measure based on publicly available data that researchers, investors, and policymakers can use to infer outbound income shifting.
Keywords: income shifting, tax avoidance, international tax, IRS audit
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1. INTRODUCTION
Outbound income shifting is one of the most substantial ways U.S. multinational entities
(MNEs) lower their worldwide income tax burdens, resulting in more than an estimated $2T of
profits trapped overseas (Rubin 2015). The sheer magnitude of purported income shifting
worldwide spurred initiatives by the Organization of Economic Cooperation and Development
(OECD), the European Union (EU), and numerous countries to curb this activity. In the U.S.,
widespread concern over outbound income shifting sparked international tax reform to reduce
corporate taxpayers’ incentives to report income overseas. Congress recently aimed to curtail the
tax benefits associated with outbound intercompany payments through the Base Erosion and
Anti-Abuse Tax (BEAT). Consequently, researchers, policy makers, and investors are interested
in better understanding the pervasiveness, determinants, and consequences of outbound shifting.
A robust academic literature identifies firm-level and macroeconomic factors that
influence income shifting (e.g., Klassen, Lang and Wolfson 1993; Hines and Rice 1994; Collins,
Kemsley and Lang 1998; Grubert and Slemrod 1998; Klassen and Laplante 2012a; Dyreng and
Markle 2016; De Simone, Klassen and Seidman 2017). Although no uniform definition of
income shifting exists, most prior literature aims to identify tax-motivated pricing of
intercompany transfers. One limitation of many such studies, however, is that they infer tax-
motivated income shifting based on assumptions about reported profitability across jurisdictions.
To the extent these studies rely on incorrect assumptions about expected profitability, they
measure tax-motivated income shifting with error.1
Our study extends this literature by developing a firm-year measure of the relative extent
to which U.S. MNEs shift income out of the U.S. via intercompany payments to controlled
1 Studies using intrafirm trade or price data are notable exceptions. In particular, Clausing (2003) observes prices charged between related and unrelated companies to more directly estimate tax-motivated transfer pricing.
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foreign corporations (CFCs) for goods, services, intangibles, and capital. We view the act of
changing the location where income is reported via intercompany payments as “income
shifting.” Intercompany payments can have two components: an arm’s-length component that
reflects the economics of the transaction (and may be at least partially tax-motivated), and a non-
arm’s-length component that is solely tax-motivated.2 Measuring both components is useful
because even the arm’s-length component changes where income is reported. For example, arm’s
length interest charges on loans between affiliates can be tax-motivated and shift income out of
high-tax countries to low-tax countries. Because total outbound shifting is economically
important and it is inherently difficult to isolate the tax motivations of intercompany transactions,
we measure a broader construct of income shifting that includes, but is not limited to, tax-
motivated income shifting.
Tax return data from Schedule M of Form 5471 allow us to directly observe the total
magnitude of intercompany payments between U.S. MNEs and their CFCs. Intercompany
payments represent a substantial percent of U.S. international trade (Clausing 2006), and are
economically material.3 In our sample of 4,266 tax return years matched to Compustat, we report
nearly $2T of intercompany payments between 2005 and 2014. These data also allow us to
provide new descriptive evidence on the types of intercompany payments between U.S. MNEs
and their CFCs. For example, we observe net inbound shifting via intercompany payments on
average, and also for the vast majority of MNEs we study (63 percent report net inbound
payments). Although total intercompany payments declined in absolute magnitude during the
2 Prior literature examines non-tax motivations for foreign investment, production locations, and reported income, which can give rise to intercompany payments that shift income (e.g., Bartik 1985; Allred and Park 2007; Busse and Hefeker 2007; Lewin, Massini and Peeters 2009; Alcácer and Zhao 2012; and Huang, Krull, and Ziedonis 2015). 3 Using Bureau of Economic Analysis data from 2000, Clausing (2006) estimates intrafirm trade represented 41 percent of U.S. international trade. This estimate likely understates the current portion given recent rising globalization and increases in cross-jurisdictional tax-motivated income shifting.
3
financial crisis, we document an overall increasing trend in net outbound payments during our
sample period, consistent with findings by Klassen and Laplante (2012b) that firms are more
aggressively shifting income out of the U.S. over time. Companies reporting net outbound
payments are also smaller on average and operate in high tech industries. Finally, although the
absolute magnitude of disaggregated inbound and outbound payments is increasing in firm size,
net outbound intercompany payments as a percentage of worldwide sales are generally
decreasing in firm size. Because firm size is a significant determinant of IRS audit, this pattern of
results could reflect IRS audit probabilities deterring income shifting (e.g., Hoopes, Mescall and
Pittman 2012).
With respect to the types of intercompany transactions that facilitate outbound payments,
we find taxpayers make net outbound payments on average for cost sharing agreements, services,
and commissions. We find net inbound payments for most other transactions including sales of
tangible assets, and rents and royalties. Further, inventory transactions dominate all other
transaction types in terms of the absolute magnitude of inbound and outbound payments.
Together, these findings suggest that the majority of intercompany payments do not arise from
transactions related to intellectual property, which are often considered to provide the most
flexibility to shift income out of the U.S.
To develop our firm-year measure of relative outbound income shifting, we estimate an
ordered logistic regression for the decile of net outbound payment intensity as a function of
constructs that prior studies have shown to be determinants of income shifting. We construct
proxies for each of these constructs using publicly available financial statement data so that our
measure is useful to researchers, investors, analysts, policymakers and tax authorities other than
the IRS. We use R&D, advertising, SG&A expense, balance-sheet intangible assets, “soft”
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assets, and an estimate of Tobin’s q as proxies for intangible-intensity (Morck and Yeung 1991,
1992; Harris, Morck, Slemrod and Yeung 1993; Klassen and Laplante 2012a; Griffith, Miller
and O’Connell 2014). We use gross profit margins and membership in a high tech industry as
proxies for unique offerings because they imply innovative products, non-routine returns, excess
profits or monopoly rents due to patent, trademark and copyright protections (Bain 1941; Pines
1952; Nakamura 2001; Tomohara 2004, 2007). We use foreign sales intensity, domestic and
foreign profitability, and domestic and foreign sales growth to capture the impact of global
footprint on income shifting (Hines and Rice 1994; Desai, Foley and Hines 2006). We include
the difference between the U.S. statutory tax rate and the average foreign effective tax rate as a
proxy for tax incentives to shift income out of the U.S. (e.g., Klassen et al. 1993; Hines and Rice
1994; Collins et al. 1998). Because debt facilitates income shifting via intercompany interest
payments (e.g., Huizinga, Laeven and Nicodeme 2008), we include total leverage and interest
expense. We also consider firm size and whether the firm is audited by a Big 5 audit firm to
account for economies of scale in tax planning (e.g., Armstrong, Blouin and Larcker 2010).
To construct a parsimonious measure useful to researchers, we apply the parameter
estimates from our ordered logit to firm-year characteristics to create a score increasing in the
relative magnitude of net outbound intercompany payments. We then use the resulting score to
examine the IRS audit outcomes of income shifting. On one hand, the increase in income shifting
scrutiny over our sample period suggests that firms with larger net outbound intercompany
payments could face higher IRS audit rates and proposed tax deficiencies. On the other hand,
firms making greater net outbound intercompany payments could be uniquely positioned to
defend these payments such that they do not incur negative tax outcomes. We find that firms
with net outbound intercompany payments are less likely to be audited or assessed additional
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taxes upon audit than other MNEs during our sample period. These results validate recent
changes to the IRS’ audit strategy, such as adopting a risk-based (as opposed to size-based) audit
approach that focuses on specific transactions and issues including related party payments.
Our study contributes to the income shifting literature in three ways. First, we focus on a
broader notion of income shifting that encompasses tax-motivated and non-tax motivated
activities that shift income to lower-tax jurisdictions. Second, we use confidential IRS data to
provide large-sample descriptive evidence about intercompany payments between U.S. MNEs
and CFCs. This descriptive analysis allows stakeholders to better understand the nature and
magnitude of cross-border related-party transactions. Third, we develop a measure that captures
the relative extent of U.S. MNE net intercompany payments out of the U.S. Our measure is
useful because it relies on directly observing total inbound and outbound payments between U.S.
MNEs and their CFCs. Thus, we overcome limitations of existing measures that: (i) cannot
determine whether estimated deviations in profitability from expected amounts arise from
intercompany payments, which we believe is a key element of income shifting, (ii) rely on
potentially strong assumptions when developing expectations about profitability, and (iii) often
ignore many non-tax reasons profitability can vary by country.
However, to the extent researchers are interested in capturing other dimensions of global
income shifting by MNEs, our measure may be of more limited use because we do not examine
intercompany payments between foreign affiliates of U.S. MNEs or among foreign MNEs.
Further, because we have access only to aggregated data at the firm-year level, we cannot
separately investigate intercompany payments at the country-subsidiary level. We are also unable
to observe increases in foreign profitability unrelated to intercompany payments. Finally,
because our measure captures total (tax-motivated and non-tax motivated) income shifting, we
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caution researchers that it is a noisy proxy for strategic transfer pricing.
Researchers can use our measure in tax and non-tax settings to investigate consequences
of income shifting and address research questions about corporate governance, supply chain
decisions, corporate investment, etc. For example, income shifting can create excess foreign cash
holdings (De Simone, Piotroski and Tomy 2018) and agency conflicts between managers and
shareholders, leading to distorted investment (e.g., Hanlon, Lester and Verdi 2015; Edwards,
Kravet and Wilson 2016; De Simone, Klassen and Seidman 2018), lower values of foreign cash
(Chen 2015; Campbell, Dhaliwal, Krull and Schwab 2016), less transparent information
environments (Gallemore and Labro 2015), constrained internal capital markets (Blouin, Krull
and Robinson 2016; De Simone and Lester 2018), and higher costs of debt (Blaylock, Downes,
Mathis and White 2016). Our firm-year measure of outbound shifting will be useful for
investigating many of these unexplored tax and non-tax consequences of income shifting.4
2. BACKGROUND AND RELATED LITERATURE
Income Shifting
Although frequently the subject of media coverage, political scrutiny and academic
research, “income shifting” is not consistently defined. We view income shifting as the act of
changing where income is reported, and intercompany payments are a key mechanism for
achieving such income shifting. Some prior studies focus exclusively on tax-motivated transfer
pricing as a mechanism for income shifting. However, it is inherently difficult for researchers to
distinguish between the tax and non-tax motivations of intercompany payments. Every payment
4 Because we construct our measure using a regression of net outbound payment intensity on firm-year determinants of income shifting, we caution researchers about using the measure as an outcome variable. As with similarly constructed measures (e.g., tax shelter propensity, financial constraint scores, etc.), using our measure as a dependent variable could lead to incorrect inferences if a true determinant of the outcome variable is uncorrelated with the variables used in the prediction model or vice versa.
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includes an arm’s-length component that reflects the economics of the transaction (i.e., the price
that would be charged between unrelated parties), and can also include a tax-motivated
component that prices the transaction as tax-efficiently as possible given tax rules. However,
even the arm’s length component shifts income from one affiliate to another, and could be at
least partially tax-motivated. For example, consider a U.S. MNE that locates its IP in a low-taxed
foreign subsidiary. The arm’s length royalty payment from the U.S. parent to the foreign
subsidiary is $11, but the MNE can justify any royalty between $1 and $20 to the tax authorities.
In our view, any royalty greater than zero constitutes income shifting because it shifts income
from the U.S. to the foreign subsidiary. Thus, the entire intercompany royalty payment is a
component of total income shifting. Congress also appears to view the entire intercompany
payment as income shifting as evidenced by recent U.S. tax reform provisions (i.e., the BEAT)
that curtail tax benefits arising from total outbound intercompany payments and not only the
non-arm’s-length tax-motivated piece. Additionally, it is the entire intercompany payment that
increases foreign profits and cash holdings.
Predicting Outbound Income Shifting
Over the last 30 years, U.S. MNEs had strong tax incentives to shift income to other
countries in response to one of the highest corporate statutory tax rates in the world. Indeed,
outbound income shifting is one of the most substantial ways U.S. MNEs lower their income tax
burdens, and Klassen and Laplante (2012b) document that as regulatory and enforcement costs
decreased over time, the magnitude of MNE income shifting increased. Although prior research
identifies several determinants of income shifting (e.g., Harris 1993; Harris et al. 1993; Klassen
et al. 1993; Hines and Rice 1994; Collins et al. 1998; De Simone, Huang and Krull 2018), using
publicly-available data to estimate the extent to which specific firms shift income out of the U.S.
8
in any year remains a challenge. Our study takes a new approach to overcome this challenge.
Because intercompany transfer prices are not publicly available, many studies in
accounting and economics infer tax-motivated income shifting as any positive deviation from
expected country-level (or domestic versus foreign) profitability that is associated with tax
incentives. These studies test for tax-motivated income shifting indirectly, using country-level or
non-U.S. profitability as the outcome of interest.5 For example, Hines and Rice (1994) use a
Cobb-Douglas production function and assume the association between profits and factors of
production (tangible assets, labor, and productivity) is constant across countries absent tax-
motivated shifting. Collins et al. (1998) assume that, absent tax incentives, U.S. and foreign rates
of profitability should be equal. During their sample period, the U.S. statutory rate was lower
than that of many other countries. Thus, they use financial statement data to model consolidated
foreign pre-tax return on sales as a function of worldwide pre-tax return on sales. If the residual
from this model is negative and the firm’s average foreign tax rate exceeds the U.S. statutory rate
in that year, they classify the firm-year as one in which the firm shifted income into the U.S. in
response to tax incentives. Klassen and Laplante (2012b) update the Collins et al. (1998) model
by demonstrating that averaging returns on sales and tax incentives over a three-year period
provides better identification of outbound tax-motivated income shifting for a sample of U.S.
MNEs.6
Although these approaches attempt to capture tax-motivated income shifting, as Clausing
(2003) notes, “[d]ue to data limitations, [these studies are] necessarily indirect, relying on
5 See, e.g., Klassen et al. (1993), Hines and Rice (1994), Huizinga and Laeven (2008), Huizinga, Laeven and Nicodeme (2008), Dharmapala and Reidel (2013), Dyreng and Markle (2016) and De Simone, Klassen and Seidman (2017). 6 Chen, Hepfer, Quinn and Wilson (2017) build on Klassen and Laplante (2012b) to develop a continuous firm-year measure of outbound tax-motivated income shifting. In a concurrent working paper, De Simone, Klassen, and Seidman (2018) use separate entity financials of global MNEs to estimate a firm-specific measure.
9
statistical relationships between country tax rates and affiliate profitabilities” (p. 2210). Thus,
these models ignore whether the variation in profitability arises from intercompany payments
and rely on assumptions about expected profitability. These assumptions can lead to spurious
inferences if inaccurate. Indeed, many companies enter new markets precisely because they can
achieve cost savings and/or higher revenue growth.
Noting the limitations of these profitability-based measures, Clausing (2001) and
Clausing (2003) more directly estimate tax-motivated transfer pricing using proprietary data on
intercompany trades and prices. Clausing (2001) uses net intrafirm trade aggregated across firms
at the country level and so cannot estimate any firm-level measures. Clausing (2003) estimates
tax-motivated transfer pricing by benchmarking related party prices against prices charged to
unrelated parties, which is a substantial strength over other work. However, Clausing (2003) is
unable to develop a firm-year measure of tax-motivated income shifting with these data. She
compares import and export unit prices charged between affiliated and unaffiliated entities, but
she does not observe total imports and exports by firm or net intrafirm trade.
Our use of data from IRS Form 5471 is similar to Clausing’s (2001, 2003) use of
intercompany trade and unit price data to examine income shifting.7 Our data allow us to
examine how companies change where income is reported via intercompany payments, which is
a key component of income shifting. We use these data to predict the relative extent of net
outbound intercompany payments by U.S. MNEs and propose a new firm-year measure to
capture outbound income shifting by U.S. MNEs via intercompany payments with CFCs.
Our approach has several advantages. We directly observe net intercompany payments.
We therefore do not impose assumptions about the location of income or relative profitability
7 Collins and Shackelford (1998) also use confidential tax return information on intercompany payments to examine which types of transactions are most sensitive to explicit tax rate differentials across jurisdictions.
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across jurisdictions. Further, these detailed data allow new descriptive evidence on the
magnitude and nature of intercompany payments for a large sample of U.S. MNEs.
Our approach also has limitations. First, we observe only the magnitude of intercompany
payments between U.S. MNEs and their CFCs; we do not observe intercompany payments
between CFCs or those by foreign MNEs. Second, the IRS provided us only with aggregated
data on intercompany payments at the firm-year level. Thus, we are not able to separately
investigate intercompany payments at the country-subsidiary level. Third, our measure does not
capture higher-than-expected foreign profitability attributable to factors other than intercompany
payments. Although much of the literature views income shifting as how firms change the
location of reported profits via intercompany payments, we acknowledge some researchers may
take a different view. Finally, we are unable to observe whether and to what extent the reported
intercompany payments are tax-motivated. As such, our measure incorporates non-tax and all
tax-motivated income shifting (rather than just strategic transfer pricing). However, it is
important to capture both components of the intercompany payment because even the arm’s
length component changes the location of reported income and can therefore garner tax benefits.
3. OUTBOUND INTERCOMPANY PAYMENTS
Net Intercompany Payments
We observe the magnitude of intercompany payments between U.S. MNEs and CFCs
reported on IRS Form 5471 Schedule M (“Schedule M”), aggregated at the consolidated tax
return level. We provide a copy of Schedule M in the Appendix. Schedule M requires the
majority U.S. owner to provide information on transactions between the CFC and its
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shareholders or other related persons.8
Schedule M categorizes transactions in multiple ways. First, U.S owners must provide
information about the magnitude of both inflows to and outflows from the CFC. Second, U.S.
owners must provide inflows and outflows separately for multiple types of transactions including
inventory transfers, royalty payments, etc. Third, the columns of Schedule M separately identify
transactions between the CFC and various related entities including the U.S. person filing the
return and any domestic corporation or partnership controlled by the U.S. person filing the
return. We combine all intercompany payments between the CFC and related domestic entities to
obtain the total U.S.-to-overseas payments by type.
The IRS provided us taxpayer-level data that aggregates payments by type across all
CFCs the taxpayer controls each year. We total these payments to determine whether they are
payments from the CFC to the U.S. (Total In) or from the U.S. to the CFCs (Total Out). To
determine net payments, we subtract Total In from Total Out. We then scale the net amount of
intercompany payments by worldwide sales (from Compustat) to control for potential differences
in intercompany payments based on firm size. The resulting measure, Net Payments, is therefore
net intercompany payments reported on Form 5471 Schedule M scaled by worldwide sales.
Positive Net Payments represent net outbound payments from the U.S. to its CFCs. Negative Net
Payments represent net inbound payments. We use the confidential Net Payments data to develop
a firm-year measure of relative net outbound intercompany payments that researchers can
generate using publicly-available data, as discussed in Section 4.
8 In general, any U.S. person that owns more than 50 percent of the total combined voting power of all classes of stock of a foreign corporation entitled to vote, or more than 50 percent of the total value of shares of all classes of stock of a foreign corporation, is required to file Schedule M. A U.S. person includes a citizen or resident of the United States, a domestic partnership, a domestic estate, some trusts, and any other entity not characterized as a foreign person.
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Characteristics that Facilitate Income Shifting
We draw on prior literature that suggests intangible intensity, unique product and service
offerings, global footprint, tax incentives, debt and economies of scale in tax planning are all
associated with increased income shifting. We discuss proxies for each construct below. Our aim
is not to develop new constructs or proxies to explain income shifting, but rather to use these
variables to develop a new, powerful measure of income shifting.
Intangible Intensity
Intangible-intensive firms have greater opportunities for income shifting because they can
more easily change the location of valuable assets than capital-intensive firms can (Harris 1993;
Harris et al. 1993; Klassen and Laplante 2012a). We use R&D (RD) and advertising (AD)
expenditures to capture current-period investments in intangible assets, such as self-created IP
and brand value that are not capitalized for book purposes. We include SG&A expense (SGA)
because it captures the extent to which investments in intangibles generate expenses related to
administrative support, such as legal costs associated with patent and trademark applications and
defense. We use intangible assets from the balance sheet (Intangibles) to capture purchased and
capitalized expenditures and an estimate of Tobin’s q (Tobin’s Q) to capture uncapitalized
expenditures. Finally, because intangible intensity is often measured relative to investments in all
assets, we include capital expenditures (CapEx) and “soft” assets (Soft Assets) measured as total
assets less net property, plant and equity and cash. We expect capital expenditures to be
negatively associated with net outbound payments and soft assets to be positively associated. We
measure all variables in year t and scale all variables by total worldwide sales in year t.
Unique Offerings
Firms with unique products and services have greater discretion in setting favorable
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transfer prices to shift income because tax authorities find it difficult to obtain comparable arm’s-
length prices to challenge the taxpayer (Holmstrom and Tirole 1991; Brauner 2008; OECD 2014;
De Simone 2016; De Simone and Sansing 2018). We use gross profit percentage (GP%) as an ex
post measure of product uniqueness that allows companies to earn non-routine returns, excess
profits or monopoly rents through patent, trademark and copyright protections (Bain 1941; Pines
1952; Nakamura 2001; Tomohara 2004, 2007). GP% is gross profit scaled by total worldwide
sales in year t. We also use industry membership because technological innovations that generate
distinctive products and services are more common in certain industries. High Tech equals one
for firms in the following three-digit SIC codes 283, 357, 360-368, 481, 737, and 873, and zero
otherwise (Francis and Schipper 1999; Core, Guay and Van Buskirk 2003).
Global Footprint
A global footprint allows firms to shift income by locating key operations outside of the
U.S., thereby setting the foundation for intercompany transactions with foreign affiliates to shift
income. However, to the extent direct sourcing of foreign sales through operational decisions
provides substantial tax savings, firms may have less incentive to use intercompany payments to
further shift income.9 It is therefore unclear ex ante how the extent of foreign operations will
affect outbound U.S. shifting via intercompany payments in our sample. We measure foreign
sales intensity (Foreign Sales%) as foreign sales reported in the Compustat Segments database
scaled by total worldwide sales in year t.10 We include foreign (domestic) pre-tax returns on sales
(FROS and DROS) measured as foreign (domestic) pre-tax book income PIFO (PIDOM) scaled
9 Generating income directly in foreign locations can play a significant role in a firm’s ability to shift income to low-tax foreign operations, making the firm less willing to expend resources to shift further income via intercompany payments with CFCs. Other firms aggressively pursue both activities. 10 Although some prior research uses foreign assets to capture the extent of firms’ foreign operations, foreign assets are not widely reported in our sample period (Oler, Shevlin, and Wilson 2007).
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by foreign (domestic) sales from the Compustat Segments database. We calculate the three-year
foreign (domestic) sales growth (FSales Growth and DSales Growth) as the average one-year
percent change in foreign (domestic) sales in years t, t-1, and t-2 using foreign (domestic) sales
reported in the Compustat Segments database.
Tax Incentives
Following Collins et al. (1998) and Klassen and Laplante (2012b), we measure tax
incentives (FTR) as the U.S. statutory tax rate of 35 percent less the firm’s average foreign
effective tax rate (TXFO plus TXDFO, scaled by PIFO). Because we have access only to
aggregate 5471 data per firm, we cannot use country-level statutory tax rates to determine
shifting incentives. To mitigate that foreign effective tax rates are endogenous to income
shifting, we measure FTR in year t-1. However, all results are qualitatively similar when using
income tax rate differentials measured in year t.
Debt
Because debt provides a tax shield, the marginal benefit of income shifting via
intercompany payments could be smaller for firms with significant debt in the U.S. On the other
hand, intercompany debt gives rise to interest payments that MNEs can use to shift income out of
relatively high-tax jurisdictions (Huizinga et al. 2008). If firms with high worldwide leverage are
more sophisticated about their use of debt, they could also engage in more related party loans for
tax purposes. To account for both effects, we include total long-term debt (DLTT) and total
interest expense (XINT). We scale both variables by worldwide sales and measure the variables
in year t. We set missing values of debt and interest to zero.
Tax Planning Opportunities
Finally, we include proxies to capture cross-sectional differences in firms’ abilities to
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identify and implement tax planning opportunities. First, we include firm size (Size) calculated as
the log of total worldwide sales. Rice (1992), Rego (2003), and others provide evidence that
multinational tax avoidance increases in firm size, consistent with larger firms achieving
economies of scale in tax planning. However, Zimmerman (1983) provides evidence that larger
firms avoid less income tax because they face greater political costs. Thus, the effect of firm size
on outbound income shifting, one type of potentially controversial and politically-sensitive tax
avoidance, is unclear. Second, we account for professional accounting expertise (Big5) by
including an indicator variable equal to one if the firm has a Big 5 auditor and zero otherwise.
4. RESEARCH METHOD
Relative Outbound Income Shifting Prediction Model
To generate our measure, we first estimate an ordered logistic regression for firm f in year
t. Our dependent variable is the decile rank of Net Payments.11
Ln["#(&'()*'+,-.+/01|3)"#(&'()*'+,-.+/51|3)
]=αm - βX + ε (1≤ 𝑚<M)
where m is a category, α is threshold, and
βX = β*Intangible Intensityf,t + β*Unique Offeringsf,t + β*Global Footprintf,t + β*Tax Incentivesf,t + β*Debtf,t + β*Tax Planning Opportunitiesf,t + β*Non-Crisisf,t + IndustryFE (1)
In addition to the variables defined in Section 3, we include Non-Crisis, an indicator
variable equal to one for years outside of the financial crisis of 2008 (i.e., all years other than
2008 through 2010) to allow different magnitudes of net outbound payments during times of
economic downturn (Dyreng and Markle 2016). We also include industry fixed effects
11 We rank by deciles because linear hypothesis tests of alternative ordered logistic regression models (e.g., using quartile or quintile ranks) reveal significant violations of the proportional odds assumption of ordered logistic regressions across several of our determinants. When ranking Net Payments into deciles, linear hypothesis tests suggest that the proportional odds assumption may be violated only for Tobin’s Q. However, because we aim to provide a measure that is parsimonious for other researchers to estimate, we require equal slopes for Tobin’s Q to eliminate the need for researchers to estimate ten possible scores per firm-year observation.
16
corresponding to the Fama-French 12 industry classifications to control for variability in
reporting by similarly-situated firms. We adjust these classifications to remove three-digit SIC
codes included in High Tech. We winsorize all continuous variables at 1 and 99 percent and
cluster standard errors by firm.12
Sample
We estimate equation (1) on a sample of 4,266 observations. We begin with U.S. return-
years from 2005 through 2014 for Large Business & International taxpayers (i.e., total assets
greater than or equal to $10M) with Form 1120 in the IRS’s Business Returns Transaction File
(BRTF) and filing at least one 5471 Schedule M that reports intercompany payments. We choose
our sample period because IRS tax return data are best populated for these years.13
We next match these tax return data to the IRS Audit Information Management System
(AIMS) database. Tax returns appear in this database after the IRS has decided whether to audit
the return and, if audited, the exam portion of the audit is finalized such that there is a non-
missing disposal code. We match these IRS data to Compustat using the employer identification
number (EIN) and require the fiscal year end in Compustat (DATADATE) to match the tax
period end date in the BRTF. We confirm a one-to-one match. Consistent with prior research, we
omit financial firms and utilities. We next require observations to have information available in
the Compustat Segments database to calculate variables that require foreign sales data. Finally,
we restrict observations to those where FTR lies within [-1,1] to ensure reasonable income tax
incentives for income shifting. Table 1 describes our sample selection criteria.
12 The version of SAS software on IRS computers (which must be used to access and analyze confidential data) does not allow two-way clustering of standard errors. Because clustering standard errors by firm or by firm and time yields unbiased standard errors when a sample has many firm observations over a limited number of years (Peterson 2009), we do not believe inferences are sensitive to clustering. 13 Data availability for Schedule M increased significantly in 2005 after the IRS implemented the modernized e-File system. Prior to 2005, we can obtain data for only five Schedule M’s from the IRS.
17
To evaluate the completeness of our sample relative to all MNEs in Compustat, we
consider firm-year observations from Compustat that have all financial statement variables
required for our analysis and that report non-zero values of foreign sales and pre-tax foreign
income. These 11,630 observations (untabulated) represent all MNE firm-years in Compustat
from 2005 through 2014 that could potentially be included in our analysis. Based on information
provided by the IRS, between approximately 46 and 68 percent of all corporate taxpayers filing
Form 5471 also file Schedule M.14 Thus, we would expect to match about 5,300 to 7,900 MNEs
from Compustat to the IRS 5471 Schedule M data. We match 7,180 (62 percent) of Compustat
MNEs to the Form 5471 Schedule M database, so we believe our sample captures a substantial
portion of the population of Compustat MNEs able to shift income.
Descriptive Statistics on Intercompany Payments
Table 2 describes the sample we use to predict the relative magnitude of net
intercompany payments. We show the magnitude of intercompany payments by year, industry,
firm size, and transaction type. Panel A provides descriptive statistics for the full sample. Recall
Total In (Out) is the dollar magnitude of inbound (outbound) intercompany payments reported
on Schedule M. Inbound (outbound) payments average about $194M ($249M) in our sample,
which aggregates to almost $830B ($1,062B) across the entire sample of 4,266 returns. Net
Payments is the magnitude of net intercompany payments reported on Form 5471 Schedule M as
a percent of total worldwide sales from Compustat. Positive (negative) Net Payments represent
net intercompany payments out of (into) the U.S. For our sample, the mean (median) value of
Net Payments is -0.86 (-0.64) percent of sales, suggesting that the average aggregate net effect of
14 Only those U.S. persons who had control of a foreign corporation for at least 30 days during the accounting period of the foreign corporation are required to file Schedule M. Therefore, taxpayers filing Form 5471 but not filing Schedule M include MNEs that do not meet these ownership requirements.
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intercompany transactions during our sample period results in net inbound payments of $128B to
the U.S. This finding is consistent with U.S. transfer pricing rules requiring inbound
remuneration for the substantial activities performed in the U.S. by U.S. MNEs (Dyreng and
Markle 2016). In untabulated results, we find that 37.4 percent of returns report net outbound
payments (i.e., Net Payments is positive). For the subsample with net outbound payments,
outbound payments average 7.9 (median 3.6) percent of worldwide sales. Using the mean value
of worldwide sales in this subsample ($3.3B), we estimate each return with net outbound
payments represents roughly $255M of payments to CFCs. In aggregate across 1,596 returns
reporting net outbound payments, this equates to over $408B.
Panel B describes Net Payments by year. With the exception of the financial crisis (2008-
2010), Net Payments increase over time. Because this finding is consistent with Klassen and
Laplante (2012b), who show that tax-motivated income shifting by U.S. MNEs is increasing over
time, it helps validate the use of net intercompany payments as a proxy for income shifting. We
also observe significantly higher mean and median values of Net Payments after the financial
crisis (2011-2014) relative to before (2005-2007) or during (2008-2010) the crisis.
Panel B also graphs the sample median Total In and Total Out by year. These payments
generally move in concert; when inbound payments increase so do outbound payments.
However, the difference between median inbound and outbound payments narrows over time,
with median inbound payments ultimately dropping below median outbound payments in 2014.
This graph therefore suggests the increase in Net Payments we observe over time is driven by a
decrease in inbound payments relative to outbound payments. For example, median outbound
intercompany payments remain relatively constant: $40M in 2007-2008 as well as in 2014. But
median inbound intercompany payments decline from $68M in 2007-2008 to only $33M in
19
2013-2014. Finally, both inbound and outbound intercompany payments decreased in magnitude
during the financial crisis. We observe a similar pattern of results using means instead of
medians.
Panel C reports information by industry. Consistent with expectations and providing
further validation that Net Payments is associated with previously documented determinants of
income shifting as predicted, we observe outbound net payments for the average high-tech firm.
Using the mean value of sales in our sample ($3.5B), we estimate the average high tech firm
reports $44M of net outbound intercompany payments each year. We observe average net
inbound payments among most other industries, consistent with high-tech firms more
successfully shifting income out of the U.S. than firms in other industries. When we graph mean
Total In and Total Out by industry, we observe the highest average magnitudes of unscaled
outbound payments (solid bars) in the Consumer Durables industry and the highest average
magnitude of unscaled inbound payments (striped bars) among Energy, Oil, and Gas firms.15 The
industries with the largest differences between inbound and outbound payments are Telephone
and TV, and Chemicals, where unscaled inbound intercompany payments exceed outbound
payments by over $170M on average per return.
Panel D describes net payments based on firm size using (rounded) asset quartiles.16
Unscaled intercompany payments increase across size quartiles as expected. Whereas firms with
less than $350M in assets report less than $25M in inbound and outbound payments on average
per return, firms with over $3.5B in assets report total outbound payments of $586M, on average
15 Net Payments is calculated per return as total outbound payments less total inbound payments, scaled by worldwide sales. Thus, it is possible to observe a positive value of average Net Payments for high-tech firms (i.e., net outbound intercompany payments for the average high-tech firm) while simultaneously observing average unscaled Total In exceeding average unscaled Total Out for high-tech firms. 16 To preserve the anonymity of firms in the IRS sample, we are unable to provide explicit asset cutoffs. We therefore create four subsamples, each containing roughly 25 percent of sample observations, using rounded asset thresholds.
20
per return, and total inbound payments of $760M on average per return. This pattern underscores
the need to control for size in our analysis; thus, we scale net payments by sales. Indeed, we
observe Net Payments (i.e., scaled net outbound intercompany payments) is decreasing in firm
size. Only firms with total assets less than $350M have net outbound payments on average. In
untabulated analyses, we find that although almost 46 percent of returns in the smallest asset
group report net outbound payments, only 30 percent of the largest asset group does. Mean
differences in Net Payments between the smallest and largest groups are statistically significant.
Given IRS audit probability increases in firm size (e.g., Hoopes et al. 2012), this pattern of
results could reflect IRS audit probability deterring outbound income shifting.
In Panel E, we exploit the rich detail of Schedule M to provide information on the nature
of intercompany payments based on the type of transaction. We confirm that the mean values of
each line-pair component of Net Payments are statistically different from zero with the exception
of intercompany inventory transfers. The most frequent type of intercompany transaction is
services; 84 percent of returns report non-zero amounts. Services generate net outbound
payments, on average, equal to roughly one percent of sales or $36M per return. Cost sharing
agreements, although rare (three percent of the sample) also result in net outbound payments, on
average, as do commissions. Net dividend payments generate the largest source of inbound
payments, on average. Perhaps surprisingly, the largest intercompany payments relate to
inventory transactions ($218M Total Out and $180M Total In, on average) and the smallest
amounts relate to cost sharing agreements (only $2.8M Total Out).
We observe the largest disparity between inbound and outbound payments for Rents &
Royalties ($61.7M Total In versus only $5.6M Total Out, on average) and Dividends ($75.6M
Total In versus only $1.07M Total Out, on average). Thus, we observe relatively few and smaller
21
intercompany outbound payments related to intangible assets or intellectual property in our
sample. Together, this descriptive evidence suggests intercompany transfers are not dominated
by transactions considered to provide the most flexibility to engage in outbound income shifting.
5. RESULTS
Characteristics of Firms by Outbound Rank
The dependent variable in the ordered logistic regression is Outbound Rank, the pooled
decile rank of Net Payments.17 Table 3 Panel A provides descriptive statistics of Net Payments
by Outbound Rank numerically and graphically. An examination of the range of values of Net
Payments within each rank yields several observations.
First, Net Payments is monotonically increasing in Outbound Rank by construction.
Second, all firm-years with Outbound Ranks of seven to nine (i.e., the highest three ranks of
Outbound Rank) make net outbound payments (i.e., Net Payments are greater than zero). Third,
consistent with our sample making net inbound payments at the mean and median, Net Payments
is less than zero for a majority of deciles (Outbound Ranks zero to five). Finally, we observe
greater variation in the magnitude of net payments among returns with the largest net outbound
payments. In untabulated tests, 52 percent of sample MNEs with at least two concurrent years of
data are in the same decile of Outbound Rank in t and t-1, which suggests the magnitude and
direction of net intercompany payments is relatively stable over time.
In Panel B, we compare mean differences in regression variables across sample firms
grouped by Outbound Rank. For parsimony, we use information from Panel A to group the
sample into three partitions based on Outbound Rank. Group 1 includes firm-years with
Outbound Rank less than or equal to two (i.e., the bottom 30 percent of the distribution of Net
17 In untabulated analyses, we rank Net Payments by year. The decile rank by year is significantly correlated with Outbound Rank at 0.987. We confirm results are robust to ranking by year.
22
Payments), all of which report net inbound payments. We observe net inbound payments of 4.3
to 21.6 percent of sales in the group, on average. Group 2 includes firm-years with Outbound
Rank equal to three to six (i.e., the middle 40 percent of the distribution of Net Payments), where
we observe smaller values of net inbound payments (0 to 2 percent of sales) and very low
variation in each decile. Group 3 includes firm-years with Outbound Rank greater than or equal
to seven (i.e., the top 30 percent of the distribution of Net Payments). Return-years in these
deciles report net outbound payments ranging from 1.6 to 21.8 percent of sales.
These three groups are different at the mean across a number of dimensions. Consistent
with our analysis in Table 2, we find that Group 3, composed entirely of return-years reporting
net outbound payments, is smaller than Groups 1 and 2 both in terms of assets and sales.
Comparing Group 3 to Group 1 (and thus comparing the top and bottom three deciles of Net
Payments), Group 3 has greater intangible intensity than Group 1 as evidenced by higher mean
R&D and more intangible assets. Firms in Group 3 are also more likely to be from high tech
industries. However, Group 3 reports lower advertising and SG&A expense, and a lower gross
profit percentage. Turning to global footprint, Group 3 reports smaller foreign sales, as well as
lower foreign and domestic returns on sales but higher foreign sales growth. Group 3 also has a
greater tax incentive to shift income abroad.
Main Results
Table 4 presents results of estimating the ordered logistic regression. Similar to OLS
goodness-of-fit measures, the Nagelkerke or max-rescaled R2 ranges from zero to one, with larger
values indicating better model fit. Our model explains approximately 10.7 percent of the variation
in the data. The likelihood ratio tests the statistical significance of the overall model by testing
whether the coefficients are jointly different from zero. Our model’s likelihood ratio is 476.1,
23
suggesting that our predictors improve model fit and we can reject the hull hypothesis that a model
including only the intercepts is the “correct” model.
We estimate many coefficients with the predicted sign. However, the coefficient
estimates are not always statistically significant at conventional levels, which could be due to
high multicollinearity among the determinants in the model. Because our aim is to develop a
model that predicts relative magnitude of net outbound income shifting – and not to examine the
effect of any one determinant – this multicollinearity is not a concern. With respect to intangible
intensity, we estimate positive coefficients on five of the seven proxies, consistent with
intangible assets and intellectual property facilitating income shifting. Unique product offerings
measured using membership in a high tech industry are positively associated with the relative
magnitude of net outbound intercompany payments, as expected. Contrary to expectations,
however, gross profit percentage is negatively associated with Outbound Rank.18
With respect to global footprint, results are mixed. We estimate positive coefficients on
foreign return on sales and both domestic and foreign sales growth but these coefficients are not
significant at conventional levels. In contrast, foreign sales intensity and domestic return on sales
are negatively associated with Outbound Rank. The negative coefficient on Foreign Sales%
could reflect the fact that firms with significant foreign sales and profitability can garner tax
benefits by generating more income in the location of their operations and customers rather than
through outbound intercompany payments. As expected, tax incentives to shift income to foreign
jurisdictions are positively associated with Outbound Rank.
We estimate positive coefficients on Interest and Big5, the latter result consistent with
18 In additional tests (untabulated), we confirm this negative relation holds in univariate correlations, as well as in subsample regression analyses after partitioning the sample into high-tech and non-high-tech firms. We also estimate the regression by asset quartile and document a negative (insignificant) coefficient for firms in the bottom three quartiles (top quartile). Thus, this unexpected finding is robust.
24
access to sophisticated tax service providers increasing the magnitude of net outbound
intercompany payments. We estimate negative coefficients on Leverage and Size. Consistent
with evidence in Table 2, we find greater net outbound payments in years without an economic
downturn. Industry membership in manufacturing, chemicals and retail is negatively associated
with net outbound payments, all else equal.
Construction and Validation of Outbound Score
To construct Outbound Score, a parsimonious measure useful to researchers, we apply the
parameter estimates from our ordered logit to publicly available data on firm-year characteristics
from Compustat.19
Outbound Score = 0.6933*RD - 1.8854*AD + 0.4377*SGA + 0.2634*Intangibles + 0.0197*Tobin’s Q + 0.4057*CapEx - 0.1447*Soft Assets - 2.2314*GP% + 0.6527*High Tech - 1.3845*Foreign Sales% - 0.5382*DROS + 1.4334*FROS + 0.0772*FSales Growth + 0.2470*DSales Growth + 0.3329*FTR - 0.2477*Leverage + 1.5965*Interest - 0.0414*Size + 0.0451*Big5 + 0.1615*Non-Crisis + 0.0968*Non-Durables - 0.1232*Durables - 0.2811*Manufacturing - 0.2629*Oil & Gas - 0.5852*Chemicals + 0.0975*Bus. Equip. - 0.1633*Telecom - 0.3829*Shops + 0.2095*Healthcare (2)
This approach is similar to that used by Hadlock and Pierce (2010).20 Panel B of Table 4 describes
the resulting measure produced using equation (2). Outbound Score is negative and increasing in
the magnitude of net outbound payments. To validate Outbound Score, we plot the mean and
median values of the score by Outbound Rank. Because both measures are increasing in outbound
shifting, we expect the graph to display positive slopes. Consistent with this expectation, we
observe increasing values of Outbound Score as we move across the deciles of Net Payments.
19 To facilitate computation, a database of scores for a sample of U.S. multinational firms on Compustat and more detailed instructions on how to calculate the score are available on the authors’ websites. 20 For parsimony and consistent with Hadlock and Pierce (2010), we calculate a score. The alternative is to identify the most likely Outbound Rank for each firm-year. Given that we rank our dependent variable into deciles, identifying the most likely rank requires calculating the probability associated with each of the ten ranks per firm-year, then identifying the rank associated with the maximum of the ten probability calculations. Calculating a single probability as in Wilson (2009) or Lisowsky (2010) is not possible in our setting because we use an ordered logit.
25
To compute Outbound Score, we recommend researchers restrict the sample to firms
meeting the following criteria: corporations incorporated in the U.S. (FIC=USA) (i.e., the
measure is not appropriate for foreign-incorporated MNEs), total assets greater than $10M, in
industries other than Financial (SIC = 6000-6900) or Utilities (4900-4999). Observations should
also have data available to compute all determinants in equation (1). Finally, observations should
have FTR between one and negative one, inclusive. In untabulated analysis, we find the model
has similar explanatory power for firms in both high tech and non-high tech industries. We
therefore believe it appropriate to use the score in samples that include all industries other than
financial services and utilities. We also find, however, that the explanatory power of the model is
greatest when estimated using firms with greater than $1B in total assets. Therefore, researchers
should consider gauging the sensitivity of results to restricting the sample to the largest firms.
To our knowledge, the measure developed by Collins et al. (1998) and its variants (e.g.,
Klassen and Laplante 2012b) are the only published firm-year measures of income shifting for
U.S. MNEs. To assist researchers in identifying situations in which one measure might be better
suited for their research question, we next compare observations where the measures generate
different or similar inferences about outbound shifting. First, we examine observations for which
our score predicts the greatest amount of net outbound payments (i.e., Outbound Score ≥ 7) but
which the Collins et al. (1998) measure does not classify as shifting income, and vice versa
(untabulated). Second, we examine overlapping observations to identify characteristics that
predict income shifting across both measures.21
21 We consider the Collins et al. (1998) model to classify an observation as shifting income if the resulting residual is negative and the firm’s average foreign tax rate is less than the U.S. statutory tax rate. We repeat these comparisons described above replacing Outbound Score with the IRS payments data (Outbound Rank). Inferences remain unchanged, which provides further validation that our score successfully captures the information contained in the IRS payments data.
26
Outbound Score uniquely identifies outbound income shifting for observations that: (i)
are younger and have reported foreign income for fewer years, (ii) have a smaller foreign
footprint (i.e., lower foreign profitability), (iii) have lower profits and profit margins (i.e.,
worldwide profitability, and gross profit margins), (iv) are more likely to be in high tech
industries, (v) have higher sales growth, and (vi) have higher R&D spending. These observations
also report more outbound payments for services, borrowing, and other non-IP-related
transactions. These differences suggest our measure better detects less profitable firms in the
earlier stages of global expansion. These firms report lower foreign profits than worldwide
profits, but the foreign profits nonetheless include significant payments from U.S. affiliates.
Although Collins et al. (1998) intend their measure to detect tax-motivated pricing of
intercompany transfers, it uniquely identifies firms with large and profitable foreign footprints,
which could also stem from (tax and non-tax motivated) investment and location decisions. We
conclude that Outbound Score isolates outbound income shifting attributable to intercompany
payments. In contrast the Collins et al. (1998) measure identifies firms that report a larger-than-
expected amount of profitability in lower-tax jurisdictions – regardless of whether that
profitability stems from intercompany payments. Finally, the overlapping observations reveal
that both measures identify shifting firms as being larger with more growth, a larger foreign
footprint, and more tax incentives to shift income overseas relative to other firms. Researchers
can use both measures to develop a more comprehensive understanding of how firms achieve
greater profitability abroad.
6. IRS ENFORCEMENT OF INTERCOMPANY PAYMENTS
In this section, we investigate whether the probability of IRS audit varies with the
magnitude of Outbound Score and whether, conditional upon audit, the probability of the IRS
27
proposing a deficiency varies with Outbound Score. The IRS heavily scrutinized income shifting
for much of our sample period and researchers, policy makers and the general public often view
income shifting as an aggressive strategy through which U.S. MNE’s avoid substantial amounts
of U.S. tax. We therefore exploit our unique dataset to answer the question of whether and how
the magnitude of reported net outbound intercompany payments is associated with audit
outcomes. In doing so, we also demonstrate how researchers can use our measure to answer
questions about the consequences of income shifting. Further, these tests provide an opportunity
to validate Outbound Score because we compare the results of tests using this measure to results
using Net Payments and Outbound Rank.
On the one hand, the IRS is keenly aware of potentially abusive tax avoidance through
income shifting and has increased efforts at detection. For example, the IRS finalized transfer
pricing service regulations in 2006 that impose stringent rules and documentation requirements
for the pricing of intercompany services. In addition, the IRS changed the cost-sharing
agreement regulations to limit the ability of U.S. MNEs to use these arrangements to transfer
valuable IP to low-tax jurisdictions. In a December 2008 speech, IRS commissioner Doug
Schulman named transfer pricing as one of the three most important international issues for the
IRS and in December 2010, he announced that the IRS would establish a “Transfer Pricing
Practice” to administer transfer pricing policies.22 Also in 2010, the IRS began to require
Schedule UTP (Uncertain Tax Positions), on which taxpayers must describe material uncertain
federal tax positions for which they have accrued financial statement reserves. The most
common uncertain tax position identified by taxpayers in 2010 was transfer pricing, which
represented 20 percent of all positions reported (Coder 2011).
22 See www.transferpricing.com/ustransferpricing for a summary of news related to U.S. enforcement of international transactions since 2003.
28
However, Kleinbard (2012) argues that an economically significant portion of income
shifting occurs through legal channels, suggesting that firms with more net outbound
intercompany payments can shift income without incurring incremental negative tax outcomes.
Similarly, De Waegenaere, Sansing and Wielhouwer (2006) theorize that inconsistency in
transfer pricing rules across countries can decrease expected tax liabilities when taxpayers
engage in substantial income shifting. Anecdotal evidence suggests that taxpayers claiming
multiple positions within a jurisdiction negotiate with tax authorities upon audit and concede
benefits claimed on one position to maintain others. It is therefore possible that claiming multiple
positions within and across jurisdictions allows firms to sustain greater income shifting. Due to
competing predictions, we test whether firms with more net outbound intercompany payments
face more IRS audit scrutiny relative to other firms under a null hypothesis.
To assess how representative our sample is of the population of large corporations, Table
5, Panel A graphs audit rates by year for all sample returns, the average audit rate in our pooled
sample across all years, and audit rates for all LB&I Forms 1120 as disclosed in the IRS Data
Book. For completeness, we present all sample years but expect audit rates to decline for
recently filed returns because they are less likely to have audits closed (Gleason and Mills 2011).
Firms in our sample face higher audit rates relative to the population of LB&I corporate returns.
The difference shrinks over time but begins to diverge again beginning with 2012 tax returns.
This pattern is consistent with IRS budget cuts forcing a decrease in audit activity (Nessa et al.
2018) that is offset in recent years by IRS’ transfer pricing initiatives such as creating Director
level positions for International Business Compliance and Transfer Pricing in 2012.
To examine how audit rates in our sample vary with the magnitude of net outbound
intercompany payments, Panel B graphs audit rates by Outbound Rank. The IRS is less likely to
29
audit returns in the highest decile of Net Payments than other returns in our sample. This pattern
could reflect the fact that it is the smallest firms in our sample that engage in the most outbound
shifting, but IRS audit rates are increasing in firm size. For example, TRAC data reveal the
lowest rate of audit for firms with less than $250M of assets, and we observe average net
outbound payments only among firms in our sample reporting less than $350M of assets.
In Panel C, we analyze audit rates in a regression framework. Audit is an indicator
variable equal to one when the tax return was subject to some type of examination, zero
otherwise.23 We include control variables from Mills (1998) and Lisowsky (2010) and cluster
standard errors by firm. Controlling for these other audit determinants, we find evidence that the
IRS is less likely to audit return-years reporting more outbound intercompany payments during
our sample period. Estimating consistent results across all three measures provides validation
that Outbound Score is a good representation of the underlying IRS data we use to construct it.24
These results support the IRS’ recent decision to eliminate the CIC program, for which firm size
was a significant determinant of participation (Ayers, Seidman and Towery 2018), in favor of a
“campaign” approach that targets specific tax avoidance transactions. These results further
support the IRS’ recent initiatives to focus on transfer pricing, particularly outbound
intercompany payments. We acknowledge that audits can take several years to be resolved and if
audits of returns reporting outbound payments are more likely to be ongoing as of the end of our
sample period, our results could understate the true audit rate for these returns. We confirm
results in Panel C are robust to dropping 2013 and 2014 tax returns from the sample to allow
23 We define Audit based on codes in the AIMS database that indicate how a tax return case was closed. We set Audit equal to one when the code indicates any examination was conducted (e.g., “No Change”, “Agreed”, “Appealed” and “Petitioned”). We set Audit equal to zero when the code indicates the return was accepted as filed or without an examination (e.g., “Accepted as Filed” and “Survey,” where the latter indicates the IRS initially selected a return for examination but closed the return without contacting the taxpayer). 24 We estimate consistently-signed coefficients across all three measures of Outbound. The two-tailed p-value on the estimate for Outbound Rank is 0.1069, reflecting marginal significance at conventional levels.
30
more time for audit completion (untabulated).25
In Panel D, we examine the likelihood that the IRS assesses a tax deficiency, conditional
upon audit.26 Deficiency is an indicator variable equal to one if a deficiency was proposed by the
IRS following the decision to audit the return, and zero otherwise. Controlling for the same
determinants of audit discussed above, we find no evidence of a relation between net outbound
payments and the likelihood of being assessed a tax deficiency upon audit. Similar to the
likelihood of audit tests, we note that we estimate consistent results across all three measures.
Together, we take these results as validation that Outbound Score is a good representation of the
underlying IRS data we use to construct it.
In untabulated analysis, we exploit the detailed line items on Schedule M to investigate
whether some types of net outbound payments are more associated with audit likelihoods or
proposed deficiencies. At conventional significance levels (two-tailed), return-years reporting net
outbound commissions, interest, and insurance payments are more likely to be audited, whereas
return-years with net outbound tangible payments are less likely to be audited. Table 2 reports
that only six percent of Schedule M’s show non-zero intercompany insurance payments,
suggesting that these payments are particularly salient to the IRS. Conditional upon audit, returns
reporting net outbound commissions, rents and royalties, dividends, and interest payments are
more likely to be assessed a deficiency.
7. CONCLUSIONS
We use confidential IRS data on the magnitude of U.S.-foreign intercompany payments
25 We also identify potentially aggressive shifters as those reporting lower-than-expected inbound payments (or higher than expected outbound payments) based on the residual from an OLS regression of Net Payments on the determinants included in our equation (1). We find no evidence that the IRS is more likely to audit these potentially aggressive shifters (untabulated). 26 We acknowledge this analysis likely suffers from selection bias, however our IRS software does not allow for tests addressing selection bias when both the selection model and the model of interest have binary dependent variables.
31
to develop a measure of the extent to which U.S. MNEs shift income out of the U.S. via
intercompany payments. The measure is useful to researchers, policy makers, and financial
statement users. Estimating a firm’s relative magnitude of net outbound intercompany payments
is important because (i) the magnitude of income shifting activity is economically significant and
gaining increasing attention at a global scale, (ii) there are several policy efforts to mitigate base
erosion and profit shifting in Europe and the U.S., and (iii) the potential consequences of income
shifting include investment distortions, hoarding of excess foreign cash, and uncertainty in the
valuation of global versus domestic earnings and assets. Properly identifying the relative extent
to which firms engage in net outbound intercompany payments is a first step in better
understanding the determinants and magnitude of income shifting, assessing potential policy
efforts to mitigate the phenomenon, and examining the consequences of income shifting.
The data we use to construct our measure also allow us to provide new descriptive
evidence on the nature and magnitude of intercompany payments. For example, we observe net
inbound intercompany payments on average, and among the majority of sample firms. We find
net outbound intercompany payments on average only among the smallest firms in our sample
and firms in high-tech industries. The largest magnitude of intercompany payments relates to
inventory sales. In contrast, transactions related to IP are relatively rare and of smaller
magnitude. We also document increasing outbound intercompany payments over time. Finally,
we find no evidence that the probability of IRS audit is increasing in net outbound intercompany
payments. These results provide justification for the IRS’ recent decision to adopt a risk-based
audit approach that focuses on specific transactions that afford opportunities for tax avoidance,
including intercompany payments.
Several provisions of the recent tax legislation could impact our measure. First, the lower
32
corporate tax rate reduces incentives for U.S. MNEs to shift income out of the U.S. However,
because many countries still offer tax rates lower than 21 percent, U.S. MNEs will continue to
shift income abroad. Second, the BEAT provision potentially subjects outbound intercompany
payments to an alternative minimum U.S. tax, with the goal of incentivizing firms to reduce
intercompany payments and curb tax-motivated transfer pricing. Alternatively, firms can reduce
intercompany payments by making their foreign supply chain less dependent on U.S. operations.
The Global Intangible Low-Taxed Income (GILTI) and Foreign-Derived Intangible Income
(FDII) provisions impose U.S. taxes on foreign or domestic income, respectively, that are
decreasing in investments in foreign tangible business assets. As a result, these provisions
encourage increased foreign investments by U.S. MNEs, which increases opportunities for firms
to shift income abroad.
The net effect of these provisions depends on the facts and circumstances of each U.S.
MNE. However, because we measure the relative extent of firms’ net outbound intercompany
payments, our measure’s ability to predict the rank order of net outbound payments should not
drastically change with the new tax law. In particular, we do not expect firms with greater
opportunities and incentives to shift income abroad to shift less income relative to other firms in
our sample following the new tax bill, nor do we expect them to become inbound income shifters
in absolute terms. We look forward to future research on the impact of the new tax law on net
outbound shifting of U.S. MNEs.
33
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36
Appendix: Form 5471 Schedule M
SCHEDULE M (Form 5471) (Rev. December 2012)
Department of the Treasury Internal Revenue Service
Transactions Between Controlled Foreign Corporation and Shareholders or Other Related Persons
a Information about Schedule M (Form 5471) and its instructions is at www.irs.gov/form5471. a Attach to Form 5471.
OMB No. 1545-0704
Name of person filing Form 5471 Identifying number
Name of foreign corporation EIN (if any) Reference ID number (see instructions)
Important: Complete a separate Schedule M for each controlled foreign corporation. Enter the totals for each type of transaction that occurred during the annual accounting period between the foreign corporation and the persons listed in columns (b) through (f). All amounts must be stated in U.S. dollars translated from functional currency at the average exchange rate for the foreign corporation’s tax year. See instructions.Enter the relevant functional currency and the exchange rate used throughout this schedule a
(a) Transactions of
foreign corporation (b) U.S. person
filing this return
(c) Any domestic corporation or
partnership controlled by U.S. person filing
this return
(d) Any other foreign corporation or
partnership controlled by U.S. person filing
this return
(e) 10% or more U.S. shareholder of
controlled foreign corporation (other than the U.S. person filing
this return)
(f) 10% or more U.S. shareholder of any
corporation controlling the
foreign corporation
1 Sales of stock in trade (inventory) . . .
2 Sales of tangible property other than stock in trade . . . . . . . . . .
3 Sales of property rights (patents, trademarks, etc.) . . . . . . . .
4 Platform contribution transaction payments received . . . . . . . . . .
5 Cost sharing transaction payments received . . . . . . . . . .
6 Compensation received for technical, managerial, engineering, construction, or like services . . . . . . . . .
7 Commissions received . . . . . .8 Rents, royalties, and license fees received
9 Dividends received (exclude deemed distributions under subpart F and distributions of previously taxed income)
10 Interest received . . . . . . . .
11 Premiums received for insurance or reinsurance . . . . . . . . .
12 Add lines 1 through 11 . . . . . .13 Purchases of stock in trade (inventory) .
14 Purchases of tangible property other than stock in trade . . . . . . . . .
15 Purchases of property rights (patents, trademarks, etc.) . . . . . . . .
16 Platform contribution transaction payments paid . . . . . . . . . . .
17 Cost sharing transaction payments paid .
18 Compensation paid for technical, managerial, engineering, construction, or like services . . . . . . . . .
19 Commissions paid . . . . . . .20 Rents, royalties, and license fees paid .21 Dividends paid . . . . . . . .22 Interest paid . . . . . . . . .23 Premiums paid for insurance or reinsurance 24 Add lines 13 through 23 . . . . . .
25 Amounts borrowed (enter the maximum loan balance during the year) — see instructions . . . . . . . . .
26 Amounts loaned (enter the maximum loan balance during the year) — see instructions
For Paperwork Reduction Act Notice, see the Instructions for Form 5471. Cat. No. 49963O Schedule M (Form 5471) (Rev. 12-2012)
37
Table 1: Sample Selection
Public U.S.-Incorporated tax return-years in IRS Business Returns Transaction File and IRS Audit Information Management System databases filing Form 5471 Schedule M for tax years between 2005 and 2014, matched to Compustat on EIN and year end
7,649
Less: Observations in financial industries (SIC 6000-6999) or utilities (SIC 4900-4999) (576)
Less: Observations missing required data for control variables (2,311) Less: Observations where FTR outside [-1,1] (496) Sample 4,266
Our sample contains 4,266 tax return-year observations with data required for estimation. The IRS Business Returns Transaction File (BRTF) contains data from Corporate Federal Income Tax Form 1120. We match these data to a custom IRS database of Form 5471 Schedule M filings reporting information on intercompany transactions. We then match these data to the IRS AIMS database, which includes tax returns where the IRS has decided whether to audit the return and, if audited, the exam portion of the audit is finalized such that there is a non-missing disposal code. Finally, we match to Compustat. To match IRS data to Compustat, we require the Employer Identification Number (EIN) on the tax return and SEC Form 10-K to be the same. We also require the fiscal year and tax year to be the same. We eliminate financial firms (SIC Codes 6000-6999) and utilities (SIC Codes 4900-4999) as is common in many studies of tax avoidance. FTR is the difference between the top U.S. statutory corporate tax rate of 35 percent and the firm’s average foreign tax rate in year t-1, calculated as total foreign tax expense (TXFO + TXDFO) divided by foreign pre-tax income (PIFO).
38
Table 2: Description of Intercompany Payments Panel A: Descriptive Statistics of Regression Variables
Variable Mean Std Dev P25 P50 P75
Total In 194.55 696.38 5.272 26.374 109.38 Total Out -249.04 755.62 -155.3 -39.741 -8.0675 Net Payments -0.0086 0.1135 -0.0437 -0.0064 0.0150 Assets* 3961.3 8535.9 330.0 1140.0 3410.0 Sales* 3502.6 7599.7 320.0 1020.0 3100.0 PI* 327.07 965.65 3.000 60.00 250.00 Intangible Intensity RD 0.0619 0.1069 0.0000 0.0171 0.0792 AD 0.0099 0.0262 0.0000 0.0000 0.0063 SGA 0.2843 0.2148 0.1266 0.2322 0.3856 Intangibles 0.0247 0.1059 0.0000 0.0000 0.0000 Tobin’s Q 2.3300 1.1613 1.6130 1.9973 2.6226 Capex 0.0448 0.0859 0.0108 0.0244 0.0445 Soft Assets 1.0567 0.7853 0.5987 0.8750 1.2873 Unique Offerings GP % 0.4150 0.2076 0.2578 0.3836 0.5614 High Tech 0.3104 0.4627 0.0000 0.0000 1.0000 Global Footprint Foreign Sales% 0.4163 0.2306 0.2319 0.4067 0.5787 FROS 0.0328 0.0650 0.0042 0.0229 0.0580 DROS 0.0104 0.1752 -0.0121 0.0282 0.0763 FSales Growth 0.1973 0.5194 0.0186 0.0996 0.2219 DSales Growth 0.1001 0.2312 -0.0120 0.0609 0.1570 Tax Incentives FTR 0.1128 0.2283 0.0012 0.1008 0.2330 Debt Leverage 0.1331 0.2586 0.0000 0.0004 0.1699 Interest 0.0134 0.0240 0.0000 0.0046 0.0161 Tax Planning Opportunities Size* 20.723 1.654 20.000 21.000 22.000 Big5 0.8537 0.3534 1.0000 1.0000 1.0000
Total In (Out) is the dollar magnitude of inbound (outbound) intercompany transactions reported on IRS Form 5471 Schedule M. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). The following variables are from Compustat. Assets is total assets (AT) reported in USD millions. Sales is total worldwide sales (SALE) reported in USD millions. PI is pre-tax income (PI) reported in USD millions. RD is R&D expenditures (XRD) scaled by Sales. AD is advertising expense (XRD) scaled by Sales. SGA is selling, general and administrative expense (XSGA) scaled by Sales. Intangibles is balance sheet intangible assets (INTAN) scaled by Sales. Tobin’s Q is Assets plus market value of equity (PRCC_F*CSHO) scaled by Assets. Capex is capital expenditures (CAPX) scaled by
39
Sales. We reset missing RD, AD, SGA, and Capex to zero. Soft Assets is Assets less net property, plant, and equipment (PPENT) and cash (CH), scaled by Sales. GP% is gross profit (GP) scaled by Sales. High Tech=1 for firms in the following three-digit SIC codes, following Francis and Schipper (1999) and Core, Guay and Van Buskirk (2003): 283, 357, 360-368, 481, 737, and 873. Foreign Sales% is the ratio of foreign sales reported in the Compustat Segments database (Foreign Sales) to total sales reported in the Compustat Segments database. If Foreign Sales are missing and pre-tax foreign income (PIFO), current foreign tax expense (TXFO) and deferred foreign tax expense (TXDFO) are all zero or missing, we set Foreign Sales% to zero. FROS is foreign pre-tax income (PIFO) scaled by Foreign Sales in year t. DROS is domestic pre-tax income (PIDOM) scaled by domestic sales in year t, where domestic sales are obtained from the Compustat Segments database (Domestic Sales). FSales Growth is average annual percent change in Foreign Sales from t-2 to t. DSales Growth is average annual percent change in Domestic Sales from t-2 to t. FTR is the difference between the U.S. statutory tax rate of 35 percent and the average foreign tax rate (TXFO+TXFED)/PIFO)) in t-1 such that the tax incentive to shift income out of the U.S. is increasing in FTR. Leverage is long-term liabilities (DLTT) scaled by Sales. Interest is interest expense (XINT) scaled by Sales. Size is the natural log of Sales. Big5 is an indicator variable equal to 1 if the firm is audited by a Big 5 audit firm in year t.
* We round these Compustat variables to maintain taxpayer privacy.
40
Table 2 (cont.): Description of Intercompany Payments Panel B: Intercompany Payments by Year
This table presents descriptive statistics for Net Payments by year.
Net Payments
Year N Mean P25 P50 P75 2005 107 -0.0326 -0.0601 -0.0158 0.0063 2006 403 -0.0091 -0.0395 -0.0054 0.0145 2007 453 -0.0143 -0.0426 -0.0059 0.0117 2008 450 -0.0179 -0.0468 -0.0078 0.0081 2009 444 -0.0175 -0.0492 -0.0107 0.0064 2010 670 -0.0083 -0.0441 -0.0098 0.0117 2011 727 -0.0042 -0.0421 -0.0049 0.0218 2012 660 0.0069 -0.0341 -0.0036 0.0300 2013 292 -0.0121 -0.0511 -0.0065 0.0158 2014 60 0.0069 -0.0422 -0.0028 0.0573 Total 4,266 -0.0086 -0.0425 -0.0064 0.0550
This graph presents mean and median Net Payments by year.
-0.04
-0.03
-0.03
-0.02
-0.02
-0.01
-0.01
0.00
0.01
0.01
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Mean Net Payments Median Net Payments
Net Inbound
Net Outbound
41
Table 2 (cont.): Description of Intercompany Payments Panel B (cont.): Intercompany Payments by Year
This graph presents median values of Total In and Total Out by year.
Panel B presents information about intercompanay payments by year. We first report descriptive statistics for Net Payments by year, where Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). We then graph Net Payments by year to better illustrate time-series trends. Finally, we graph Total In and Total Out by year. Total In (Out) is the dollar magnitude of inbound (outbound) intercompany transactions reported on IRS Form 5471 Schedule M.
0
10
20
30
40
50
60
70
80
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Median Total In Median Total Out
42
Table 2 (cont.): Description of Intercompany Payments Panel C: Intercompany Payments by Industry
This table presents Net Payments by Fama-French 12 industry. Net Payments N Mean P25 P50 P75 High Tech (A) 1,324 0.0126 -0.0495 0.0009 0.0745 Non-High Tech: 1 Consumer Non-Durables 203 -0.0061 -0.0448 -0.0098 0.0088 2 Consumer Durables 181 -0.0118 -0.0350 -0.0110 0.0110 3 Manufacturing 921 -0.0234 -0.0408 -0.0111 0.0044 4 Energy, Oil and Gas 161 -0.0280 -0.0374 -0.0092 -0.0070 5 Chemicals 264 -0.0362 -0.0661 -0.0316 -0.0024 6 Business Equipment 272 -0.0200 -0.0597 -0.0092 0.0234 7 Telephone and Television 55 -0.0160 -0.0378 -0.0093 0.0006 9 Wholesale, Retail and Services 272 -0.0242 -0.0287 -0.0046 0.000 10 Healthcare, Medical Equipment and Drugs 195 -0.0104 -0.0941 -0.0096 0.0560 12 Other 418 0.0016 -0.0140 -0.0042 0.0005 Total Non-High Tech (B) 2,942 -0.0181 -0.0419 -0.0090 0.0039 Mean Differences (A) – (B) 0.0306***
This graph presents mean values of Total In and Total Out by industry.
Panel C presents information using the Fama-French 12 industry classifications after excluding High Tech SIC codes. High Tech firms are in the following three-digit SIC codes, following Francis and Schipper (1999) and Core, Guay and Van Buskirk (2003): 283, 357, 360-368, 481, 737, and 873. We first report Net Payments, the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). We then graph Total In and Total Out by year. Total In (Out) is the dollar magnitude of inbound (outbound) intercompany transactions reported on IRS Form 5471 Schedule M.
0
50
100
150
200
250
300
350
400
450
High Tech
Non-D
urable
s
Durable
s
Manufa
cturin
g
Energy
, Oil &
Gas
Chemica
ls
Busines
s Equ
ip.
Teleph
one &
TV
Wholes
., Ret.
& Svcs
Health
& Drug
sOthe
r
Mean Total Out
Mean Total In
43
Table 2 (cont.): Description of Intercompany Payments Panel D: Intercompany Payments by Firm Size
This graph presents Total In and Total Out based on firm size.
This graph presents Net Payments based on firm size.
Panel D presents information based on firm size. We first graph Total In and Total Out by year. Total In (Out) is the dollar magnitude of inbound (outbound) intercompany transactions reported on IRS Form 5471 Schedule M. We then graph Net Payments, the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE).
0
100
200
300
400
500
600
700
800
Assets < $350M $350M ≤ Assets < $1B
$1B ≤ Assets < $3.5B $3.5B ≤ Assets
Mean Total Out
Mean Total In
0
0
0
0
0
0
0
Assets < $350M $350M ≤ Assets < $1B $1B ≤ Assets < $3.5B $3.5B ≤ Assets
Mean Net Payments
44
Table 2 (cont.): Description of Intercompany Payments Panel E: Intercompany Payments by Transaction Type
This table presents descriptive statistics for Net Payments by type of intercompany payment.
Transaction Sch. M Lines N
Pct. of Sample Mean
P25 P50
P75
(1) (2) (3) (4) (5) (6) (7) (8) Inventory 1/13 2,998 70.3 -0.0016 -0.0288 -0.0020 0.0171 Tangible assets 2/14 876 20.5 -0.0020 -0.0014 -0.0002 0.0000 Intangible property 3/15 289 6.77 -0.0004 -0.0006 0.0000 0.0002 Platform contributions 4/16 130 3.05 -0.0077 -0.0110 -0.0033 -0.0006 Cost sharing agreements 5/17 129 3.02 0.0019 0.0001 0.0004 0.0031 Services 6/18 3,572 83.7 0.0105 -0.0039 -0.0002 0.0062 Commissions 7/19 1,480 34.7 0.0063 0.0000 0.0003 0.0035 Rents & Royalties 8/20 2,306 54.1 -0.0129 -0.0126 -0.0033 -0.0003 Dividends 9/21 1,495 35.0 -0.0168 -0.0199 -0.0056 -0.0014 Interest 10/22 2,944 69.0 -0.0010 -0.0013 -0.0002 0.0000 Insurance 11/23 259 6.07 -0.0001 -0.0002 0.0000 0.0000 Total 12/24 4,266 100 -0.0086 -0.0437 -0.0064 0.0150
This graph presents Total In and Total Out by type of intercompany payment.
Panel E presents information based on the type of intercompany transaction on Form 5471 Schedule M. We first report descriptive statistics for Net Payments by transaction type including the number of sample return years reporting non-zero amounts. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). We then graph mean values of Total In and Total Out by transaction type. Total In (Out) is the dollar magnitude of inbound (outbound) intercompany transactions reported on IRS Form 5471 Schedule M.
0
25
50
75
100
125
150
175
200
225
Invent
ory
Tangibl
e
Intang
ible
Platfor
m CSA
Service
s
Commiss
ions
Rents/R
oyalt
ies
Dividen
ds
Intere
st
Insura
nce
Mean Total Out
Mean Total In
45
Table 3: Descriptive Statistics by Outbound Rank Panel A: Net Payments by Outbound Rank
Outbound Rank Min Mean P50 Max Max-Min
0 -0.5398 -0.2166 -0.1798 -0.1194 0.4203 1 -0.1192 -0.0819 -0.0795 -0.0566 0.0625 2 -0.0566 -0.0438 -0.0437 -0.0329 0.0237 3 -0.0327 -0.0232 -0.0229 -0.0157 0.0170 4 -0.0157 -0.0110 -0.0109 -0.0064 0.0093 5 -0.0064 -0.0033 -0.0032 -0.0008 0.0057 6 -0.0007 0.0017 0.0014 0.0057 0.0065 7 0.0057 0.0158 0.0150 0.0307 0.0250 8 0.0307 0.0588 0.0568 0.0987 0.0679 9 0.0991 0.2178 0.1848 0.6524 0.5534
This panel presents descriptive statistics of Net Payments by Outbound Rank. Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE).
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0 1 2 3 4 5 6 7 8 9
Mean Net Payments Median Net Payments
46
Table 3: Descriptive Statistics by Outbound Rank (continued) Panel B: Differences in Means by Outbound Rank
Group: 1 2 3 Outbound Rank: [0,2] [3,6] [7,9] Differences Variable Mean Mean Mean (2)-(1) (3)-(1) (3)-(2) Net Payments -0.1141 -0.0090 0.0974 0.1051 *** 0.2114 *** 0.1063 *** Assets 4145.8 4268.0 3368.1 122.21 -777.69 -899.90 *** Sale 3378.5 4110.5 2816.3 732.05 *** -562.22 *** -1294.3 *** PI 379.78 324.86 277.32 -54.924 -102.46 -47.531 Intangible Intensity RD 0.0795 0.0252 0.0931 -0.0543 *** 0.0136 *** 0.0679 *** AD 0.0132 0.0087 0.0081 -0.0045 *** -0.0051 *** -0.0005 SGA 0.3356 0.2121 0.3291 -0.1235 *** -0.0064 *** 0.1170 *** Intangibles 0.0192 0.0313 0.0213 0.0121 *** 0.0021 *** -0.0100 *** Tobin's Q 2.5139 2.0727 2.4890 -0.4413 *** -0.0249 *** 0.4163 *** Capex 0.0452 0.0472 0.0411 0.0019 -0.0041 -0.0061 ** Soft Assets 1.1884 0.8906 1.1464 -0.2978 *** -0.0420 *** 0.2558 *** Unique Offerings GP% 0.4813 0.3536 0.4304 -0.1277 *** -0.0509 *** 0.0768 *** High Tech 0.3039 0.1940 0.4719 -0.1099 *** 0.1680 *** 0.2779 *** Global Footprint Foreign Sales% 0.4974 0.3466 0.4282 -0.1508 *** -0.0693 *** 0.0815 *** FROS 0.0443 0.0247 0.0320 -0.0196 *** -0.0124 *** 0.0073 *** DROS 0.0134 0.0298 -0.0186 0.0164 *** -0.0319 *** -0.0483 *** FSales Growth 0.1597 0.2075 0.2214 0.0478 *** 0.0617 *** 0.0139 DSales Growth 0.0948 0.0894 0.1198 -0.0054 0.0250 0.0304 ***
Tax Incentives FTR 0.1149 0.0872 0.1448 -0.0277 *** 0.0299 *** 0.0576 ***
Debt 0.0000 0.0000 0.0000 Leverage 0.1354 0.1432 0.1174 0.0078 -0.0180 -0.0258 ***
Interest 0.0127 0.0151 0.0119 0.0024 *** -0.0007 *** -0.0031 ***
Tax Planning Opportunities Size 20.661 21.082 20.308 0.4207 *** -0.3527 *** -0.7734 ***
Big5 0.8703 0.8617 0.8266 -0.0086 -0.0438 -0.0351 ***
This panel presents means and differences in means by Outbound Rank. Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). Total Out (In) is the dollar magnitude of outbound (inbound) intercompany transactions reported on IRS Form 5471 Schedule M. The following variables are from Compustat. RD is R&D expenditures (XRD) scaled by Sales, where Sales is total sales from Compustat (SALE). AD is advertising expense (XRD) scaled by Sales. SGA is selling, general and administrative expense (XSGA) scaled by Sales. Intangibles is balance sheet intangible assets (INTAN) scaled by Sales. Tobin’s Q is Assets plus the market value of equity (PRCC_F*CSHO) scaled by Assets. Capex is capital expenditures (CAPX) scaled by Sales. We reset missing RD, AD, SGA and Capex to zero. Soft Assets is total assets less net property, plant, and equipment (PPENT) and cash (CH). GP% is gross profit (GP) scaled by Sales. High Tech=1 if firm is in a high technology industry defined following Francis and Schipper (1999), zero otherwise. Foreign Sales% is the ratio of foreign sales reported in the Compustat Segments database (Foreign Sales) to total sales reported in the Compustat Segments database. If Foreign Sales are missing and pre-tax foreign income (PIFO), current foreign tax expense (TXFO) and deferred foreign tax expense (TXDFO) are all zero or missing, we set Foreign Sales% to zero. FROS is foreign pre-tax income (PIFO) scaled by Foreign Sales in year t-1. DROS is
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domestic pre-tax income (PIDOM) scaled by domestic sales in year t-1, where domestic sales are obtained from the Compustat Segments database (Domestic Sales). FSales Growth is average annual percent change in Foreign Sales from t-2 to t. DSales Growth is average annual percent change in Domestic Sales from t-2 to t . FTR is the difference between the U.S. statutory tax rate of 35 percent and the average foreign tax rate (TXFO+TXFED)/PIFO)) in t-1 such that the tax incentive to shift income out of the U.S. is increasing in FTR. Leverage is debt (DLTT) scaled by Sales. Interest is interest expense (XINT) scaled by Sales. Size is the log Sales. Big5 is an indicator variable equal to 1 if the firm is audited by a Big 5 audit firm in year t. Non-Crisis is an indicator variable equal to one for all years except 2008 to 2010, zero otherwise. We include fixed effects corresponding to the Fama-French 12 industry classifications after removing High Tech industries from each group; industry 12 (Other) is represented by the intercept. We present standard errors in parentheses. ***, ** and * represent significance at 1%, 5% and 10%, respectively.
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Table 4: Construction of Outbound Score Panel A: Ordered Logistic Regression Results
Variable Pred. Est. Std. Error Intangible Intensity
RD + 0.6933 0.7328 AD + -1.8854 1.8860 SGA + 0.4377 0.4735 Intangibles + 0.2634 0.2766 Tobin’s Q + 0.0197 0.0486 Capex - 0.4057 0.5404 Soft Assets + -0.1447 0.0701 **
Unique Offerings GP% + -2.2314 0.4597 ***
High Tech + 0.6527 0.1629 ***
Global Footprint Foreign Sales% ? -1.3845 0.2286 ***
DROS ? -0.5382 0.3227
FROS ? 1.4334 0.8616
FSales Growth ? 0.0772 0.0650 DSales Growth ? 0.2470 0.1822 Tax Incentives FTR + 0.3329 0.1464 **
Debt Leverage ? -0.2477 0.1874 Interest ? 1.5965 2.1728 Tax Planning Opportunities Size ? -0.0414 0.0382 Big5 + 0.0451 0.1504 Year and Industry Effects Non-Crisis + 0.1615 0.0499 ***
Non-Durables ? 0.0968 0.2580 Durables ? -0.1232 0.2586 Manufacturing ? -0.2811 0.1536 *
Oil & Gas ? -0.2629 0.2608 Chemicals ? -0.5852 0.2026 ***
Bus. Equip. ? 0.0975 0.2555 Telecom ? -0.1633 0.2898 Shops ? -0.3829 0.2079 *
Healthcare ? 0.2095 0.2988
N 4,266 Max-rescaled R2 0.1067 Likelihood Ratio 476.14
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Table 4: Construction of Outbound Score (continued) Panel B: Descriptive Statistics of Outbound Score
This table presents descriptive statistics for Outbound Score
N Mean Std. Dev. P25 P50 P75
4,266 -1.9615 0.6129 -2.3630 -1.9936 -1.5840
This graph plots Outbound Score by Outbound Rank
This table describes the construction of Outbound Score. Panel A presents estimated coefficients and standard errors from pooled cross-sectional ordered logistic regressions with Outbound Rank as the dependent variable. Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). The following variables are from Compustat. RD is R&D expenditures (XRD) scaled by Sales, where Sales is total sales from Compustat (SALE). AD is advertising expense (XRD) scaled by Sales. SGA is selling, general and administrative expense (XSGA) scaled by Sales. Intangibles is balance sheet intangible assets (INTAN) scaled by Sales. Tobin’s Q is Assets plus the market value of equity (PRCC_F*CSHO) scaled by Assets. Capex is capital expenditures (CAPX) scaled by Sales. We reset missing RD, AD, SGA and Capex to zero. Soft Assets is total assets less net property, plant, and equipment (PPENT) and cash (CH). GP% is gross profit (GP) scaled by Sales. High Tech=1 if firm is in a high technology industry defined following Francis and Schipper (1999), zero otherwise. Foreign Sales% is the ratio of foreign sales reported in the Compustat Segments database (Foreign Sales) to total sales reported in the Compustat Segments database. If Foreign Sales are missing and pre-tax foreign income (PIFO), current foreign tax expense (TXFO) and deferred foreign tax expense (TXDFO) are all zero or missing, we set Foreign Sales% to zero. FROS is foreign pre-tax income (PIFO) scaled by Foreign Sales in year t-1. DROS is domestic pre-tax income (PIDOM) scaled by domestic sales in year t-1, where domestic sales are obtained from the Compustat Segments database (Domestic Sales). FSales Growth is average annual percent change in Foreign Sales from t-2 to t. DSales Growth is average annual percent change in Domestic Sales from t-2 to t . FTR is the difference between the U.S. statutory tax rate of 35 percent and the average foreign tax rate (TXFO+TXFED)/PIFO)) in t-1 such that the tax incentive to shift income out of the U.S. is increasing in FTR. Leverage is debt (DLTT) scaled by Sales. Interest is interest expense (XINT) scaled by Sales. Size is the log Sales.
-2.5
-2
-1.5
-1
-0.5
00 1 2 3 4 5 6 7 8 9
Mean Outbound Score Median Outbound Score Linear (Mean Outbound Score)
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Big5 is an indicator variable equal to 1 if the firm is audited by a Big 5 audit firm in year t. Non-Crisis is an indicator variable equal to one for all years except 2008 to 2010, zero otherwise. We include fixed effects corresponding to the Fama-French 12 industry classifications after removing High Tech industries from each group; industry 12 (Other) is represented by the intercept. We present standard errors in parentheses. ***, ** and * represent significance at 1%, 5% and 10%, respectively, using two-tailed tests. Panel B first presents descriptive statistics of Outbound Score, which is the estimated coefficients of our prediction model applied to sample firm-year attributes, and then graphs Outbound Score by Outbound Rank.
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Table 5: Analysis of Audit Rates Panel A: Audits Rates by Year
This panel presents the audit rates by year for sample firms by and for all large corporations as provided in the IRS Data Book. Audit is an indicator variable equal to one if the return was audited by the IRS, and zero otherwise. The Sample Firms – Average series plots the pooled average audit rate of sample firms across all sample years.
Panel B: Audits Rates by Outbound Rank
This panel presents the audit rates by Outbound Rank for sample firms. Audit is an indicator variable equal to one if the return was audited by the IRS, and zero otherwise. The Sample Firms – Average series plots the pooled average audit rate of sample firms across all sample years. Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE).
0%
10%
20%
30%
40%
50%
60%
70%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Sample Firms Sample Firms - Average All Large Corporations
0%
10%
20%
30%
40%
50%
60%
70%
0 1 2 3 4 5 6 7 8 9
Sample Firms Sample Firms - Average
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Table 5 (cont.): Analysis of Audit Rates Panel C: Audit Likelihood Model
Dependent Variable: Audit Outbound: Net Payments Outbound Rank Outbound Score
Variable Intercept ? -11.463 -11.553 -11.547 (0.816) (0.712) (0.711) Outbound ? -0.7826 * -0.0260 -0.2202 ** (0.415) (0.016) (0.093) Size + 0.5457 *** 0.5447 *** 0.5302 *** (0.037) (0.037) (0.037) Big5 + -0.0732 -0.0713 -0.0764 (0.159) (0.159) (0.160) ROA + 1.8040 *** 1.8077 *** 1.5801 *** (0.427) (0.427) (0.437) NOL - 0.4078 ** 0.4086 ** 0.4216 ** (0.171) (0.171) (0.172) Leverage - -1.2415 *** -1.2429 *** -1.3071 *** (0.317) (0.318) (0.318) Observations 4,266 4,266 4,266
AUC 0.737 0.737 0.739 This panel presents estimated coefficients from pooled cross-sectional logistic regressions with Audit as the dependent variable. Audit is an indicator variable equal to one if the return was audited by the IRS, and zero otherwise. The variable of interest (Outbound) is Net Payments in Column (1), Outbound Rank in Column (2), and Outbound Score in Column (3). Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Outbound Score is the estimated coefficients of our Outbound prediction model applied to sample firm-year attributes. Size is the log of total worldwide sales in year t, where sales are reported in Compustat (SALE). Big5 is an indicator variable equal to 1 if the firm is audited by a Big 5 audit firm in year t. ROA is worldwide pre-tax income reported in Compustat (PI) scaled by worldwide assets reported in Compustat (AT). NOL is tax loss carryforwards scaled by total assets reported in Compustat (TLCF/AT). Leverage is total debt scaled by worldwide sales in year t reported in Compustat (DLTT/SALE). We present standard errors in parentheses and the Area under the Receive Operator Characteristic (ROC) Curve (AUC). ***, ** and *
represent significance at 1%, 5% and 10%, respectively, using two-tailed tests.
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Table 5 (cont.): Analysis of Audit Rates Panel D: Deficiency Likelihood Model
Dependent Variable: Deficiency Outbound: Net Payments Outbound Rank Outbound Score
Variable Intercept ? -3.2618 -3.3233 -3.2844 (0.816) (0.816) (0.814) Outbound ? -0.3447 -0.0164 -0.0518 (0.556) (0.021) (0.111) Size + 0.1292 *** 0.1289 *** 0.1260 *** (0.041) (0.041) (0.042) Big5 + -0.1820 -0.1840 -0.1869 (0.213) (0.213) (0.213) ROA + 4.8171 *** 4.8077 *** 4.7632 *** (0.670) (0.668) (0.677) NOL - -0.1372 -0.1365 -0.1325 (0.207) (0.207) (0.207) Leverage - -0.6112 -0.6144 -0.6293 (0.437) (0.438) (0.438) Observations 1,991 1,991 1,991
AUC 0.636 0.635 0.636 This panel presents estimated coefficients from pooled cross-sectional logistic regressions with Deficiency as the dependent variable. Deficiency is an indicator variable equal to one if a deficiency was proposed by the IRS, and zero otherwise. The variable of interest (Outbound) is Net Payments in Column (1), Outbound Rank in Column (2), and Outbound Score in Column (3). Net Payments is the dollar magnitude of outbound less inbound intercompany transactions reported on IRS Form 5471 Schedule M scaled by total worldwide sales reported in Compustat (SALE). Outbound Rank is the sample decile rank of Net Payments and ranges from zero to nine. Outbound Score is the estimated coefficients of our Outbound prediction model applied to sample firm-year attributes. Size is the log of total worldwide sales in year t, where sales are reported in Compustat (SALE). Big5 is an indicator variable equal to 1 if the firm is audited by a Big 5 audit firm in year t. ROA is worldwide pre-tax income reported in Compustat (PI) scaled by worldwide assets reported in Compustat (AT). NOL is tax loss carryforwards scaled by total assets reported in Compustat (TLCF/AT). Leverage is total debt scaled by worldwide sales in year t reported in Compustat (DLTT/SALE). We present standard errors in parentheses and the Area under the Receive Operator Characteristic (ROC) Curve (AUC). ***, ** and * represent significance at 1%, 5% and 10%, respectively, using two-tailed tests.
