# Tax Planning and Multinational Behavior∗

> Briefs, arguments, decisions, and more.

URL: https://www.frixlaw.com/law-library/documents/agency%3Airs%3Aeb16ecc5aa080e78

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- **Collection:** Agency decision
- **Document type:** Agency decision

## Text

Tax Planning and Multinational Behavior∗
Rosanne Altshuler
Lysle Boller
Rutgers University Penn Wharton Budget Model
Juan Carlos Suárez Serrato
Stanford GSB & NBER
February 2024
Abstract
We study the adoption and use of a specific form of tax planning by US multinational
corporations (MNCs). Using IRS data, we identify “hybrid” tax planning structures (HTPs)
which can be used to avoid corporate income tax by targeting mismatches between US and
Irish, Dutch, and Luxembourgish tax law. By 2016, more than 35% of the foreign profits
of US MNCs were linked to HTPs. Difference-in-differences models comparing adopting
and non-adopting MNCs reveal that after HTP adoption, MNCs intensify behaviors related
to profit shifting, significantly increasing related-party loans, foreign intangible assets, and
profits held abroad. These changes result in stark reductions in foreign effective tax rates.
Adopting MNCs also experience larger increases in foreign tangible assets and in global
R&D, payroll, and investment.
Keywords: international taxation, profit shifting, Double Irish, Reverse Hybrid Mismatch
JEL Codes: D22, H25, H26, H32
This research was conducted as part of a Joint Statistical Research Program with the Internal Revenue Service
(IRS). Any views expressed are those of the authors and not those of the IRS. The statistics reported in this
paper have been reviewed and cleared for disclosure by the IRS.

∗

We are very grateful for comments from David Agrawal, Hunt Allcott, Alan Auerbach, Michael Best, Jeff
Clemens, Julie Cullen, Dhammika Dharmapala, Rebecca Diamond, Tim Dowd, Clare Doyle, Jesse Drucker, Naomi
Feldman, Roger Gordon, Mindy Herzfeld, Jim Hines, Chad Jones, Wojciech Kopczuk, Wouter Leenders, Rebecca
Lester, Antoine Levy, Li Liu, Clément Malgouyres, Peter Merrill, Mathilde Muñoz, Karthik Muralidharan, Paul
Oosterhuis, Mathieu Parenti, Emmanuel Saez, Navodhya Samarakoon, Shoshanna Vasserman, Daniel Xu, Danny
Yagan, Gabriel Zucman, and from seminar participants at CESifo, Columbia University, the Federal Reserve
Bank of San Francisco, the National Tax Association Annual Meetings, the NBER Public Economics Meetings,
the Office of Tax Analysis of the US Treasury Department, the Oxford University Centre for Business Taxation,
the Paris School of Economics, the Penn Wharton Budget Model, Rutgers University, Stanford University, UC
Berkeley, UC San Diego, the University of Illinois at Urbana-Champaign, the University of South Carolina,
and the Statistics of Income unit of the Internal Revenue Service. We are grateful to Arnold Ventures, the
International Tax Policy Forum, and the NBER for their support of this project. Special thanks to Chloe Gagin,
Nuria McGrath, Melanie Patrick, Ralph Rector, Michael Weber, and Jason Wenrich for assistance with data
access. Clare Doyle, Jason Harrison, and Kevin Roberts provided excellent research assistance. All errors remain
our own.

1

Introduction

The last quarter century has seen a remarkable increase in the complexity of tax planning
by multinational corporations (MNCs). Document leaks and special government reports have
revealed the existence of tax planning strategies that are designed to avoid corporate income
taxes in multiple jurisdictions by leveraging mismatches in tax laws across countries. While
media attention following these revelations and dissatisfaction with the current system have
motivated important international tax policy changes and multilateral projects to address tax
avoidance, little is known about the importance of these tax planning strategies.1 How prevalent
are these strategies among MNCs? Do they facilitate profit shifting and lower foreign effective
tax rates (ETRs)? Does tax planning influence the real economic activity of MNCs?
This paper uses tax data from the Internal Revenue Service (IRS) to answer these questions by
analyzing the adoption and use of a set of complex tax planning strategies that target mismatches
between US and Irish, Dutch, and Luxembourgish tax law. We first use data from multiple IRS
tax forms to reconstruct the ownership networks of the foreign affiliates of US MNCs. We then
identify when a US MNC creates an ownership structure that can leverage mismatches in tax
laws across these countries. Data from the tax returns of US MNCs is crucial for the purposes
of identifying the adoption of these tax planning structures and understanding how MNCs use
them to shift profits across countries. This paper is the first to systematically uncover these tax
planning structures and to study how their adoption is related to changes in tax avoidance and
real economic activity.
We use these administrative data to document the growth and prevalence of these tax planning strategies. Although these strategies were extremely rare in the early 1990s, they were
gradually adopted by MNCs following the 1997 regulations known as “Check the Box” (CTB),
which facilitated this form of tax planning. By 2016, 17.5% of US MNCs in our sample had
adopted at least one of the structures we identify, and these companies were responsible for
more than 60% of foreign profits. Data on the foreign operations of adopting MNCs show that
more than 50% of their foreign profits flow through one of these structures and that more than
35% of the foreign profits of all MNCs in our sample are linked to these structures. MNCs
that use these strategies are also responsible for significant shares of domestic economic activity,
1

These changes and projects include important aspects of the recent Tax Cuts and Jobs Act (TCJA) of 2017,
the European Anti-Tax Avoidance Directive, and the OECD’s Base Erosion and Profit Shifting project.

1

Figure 1: Comparison of Foreign Effective Tax Rates

20%

10%
Other MNCs
Hybrid Tax Planners
0%
1995

2000

2005

2010

2015

Notes: This figure compares the aggregate annual foreign ETR for two groups of US MNCs. The light green line
shows the ETR for a group of MNCs that eventually adopt at least one of the hybrid tax structures described in
Section 2. The dark blue line shows the ETR for MNCs that did not adopt any of these structures during the
sample window. The combined sample includes most large US C corporations as described in Section 3.

including 25% of domestic corporate payroll and 15% of domestic capital investment.
Relative to MNCs that never adopt these structures, those that do engage more intensely
in the kinds of financial transactions that could be used to shift profits to low-tax countries:
they increase their foreign holdings of intangible capital, they have larger loan balances between
related foreign affiliates, and they collect more royalty income and accumulate more cash abroad.
Most strikingly, as shown in Figure 1, over the period during which these structures are gradually
put in place, the foreign ETRs of adopting MNCs experience a dramatic decline. By 2016, the
foreign ETR of adopters was roughly half that of other MNCs.
We use difference-in-differences event study regressions to conduct more formal comparisons.
The models measure changes in firm outcomes surrounding the adoption of hybrid tax planning
structures, relative to non-adopting firms. These models confirm that the marked increase in
behaviors related to profit shifting coincides with the adoption of these structures and that these
mechanisms of tax avoidance lead to significant declines in foreign ETRs, as in Figure 1.
Concurrent with declining foreign ETRs, we estimate that adopting US MNCs have larger
increases in foreign investment and accumulate 40% (p < 0.01) more depreciable capital in
foreign affiliates than MNCs that do not adopt one of these structures. We also estimate that
the adoption of these tax planning structures is followed by a 20% (p < 0.01) increase in domestic
payroll and a 50% (p < 0.01) increase in expenditures on research and development (R&D).
2

We develop our results in three steps. First, we reconstruct the foreign ownership structures of
US MNCs using information from three key tax forms contained in the IRS data files. Parent-level
data from Form 1120 and related tax forms provide information on domestic activity, including
assets, payroll, and domestic investment. Parents also file an information form (Form 5471) for
each of their controlled foreign corporations (CFCs), which includes data on foreign assets, taxes,
and earnings and profits (E&P), as well as related transactions between CFCs. With the advent
of CTB, US MNCs could elect to “disregard” their foreign affiliates. These foreign disregarded
entities (FDEs) are hybrid structures that are considered corporations in the host country but
pass-through entities from the US perspective, making them transparent to the US Treasury.
The IRS collects information about these entities in a separate information return, Form 8858.
A novel aspect of this paper is the integration of FDE data with their CFC owners.
Second, with this information in hand, we flag MNCs that adopt tax planning structures
that have previously only been revealed in leaks and special government reports. These planning
structures use entities that facilitate tax planning by creating mismatches in their tax treatment
between the United States and foreign countries. The first structure we identify, known as
the Double Irish, uses FDEs to leverage aspects of Irish and US tax law and shift profits out
of high-tax foreign countries into tax havens. The second and third structures use a strategy
known as a Reverse Hybrid Mismatch to route foreign profits to low-tax foreign affiliates. We
identify usage of Reverse Hybrid Mismatches for foreign affiliates located in the Netherlands
and in Luxembourg. These three hybrid tax planning structures are well known and have been
targeted by European countries through legal investigations.2 They have also motivated policy
agendas such as Action 2 of the OECD Base Erosion and Profit Shifting (BEPS) project, which
aims to close down hybrid mismatch arrangements.
Using these unique indicators of tax planning, we measure the growth, prevalence, and importance of these hybrid tax planning (HTP) structures relative to the aggregate economic activity
of US MNCs. As mentioned above, the three structures we identify are connected to large shares
of aggregate foreign activity by US MNCs by 2016, the end of our sample period. Although there
have been numerous investigations and case studies that reveal how these arrangements work,
this paper is the first attempt to systematically measure their magnitude.
2

Use of hybrid tax planning structures by US MCNs has been previously reported by Drucker (2010), Duhigg
and Kocieniewski (2012), Guardian (2019), Guardian (2018), and Kleinbard (2013).

3

In our third and final step, we compare the foreign and domestic operations of US MNCs
that adopt one of these strategies to those of MNCs that do not. In the early 1990s, these two
groups of firms experience similar trajectories with regard to their foreign ETRs and measures of
domestic and foreign activity. After the 1997 CTB regulations are put in place, foreign affiliates
of MNCs that engage in hybrid tax planning experience significantly larger declines in ETRs;
more rapid foreign and domestic growth; and sharp increases across several proxies for profit
shifting and tax deferral, including related-party loans, intangible assets, payments related to
cost sharing agreements, royalty income, and cash held abroad.
To ensure that these results are tied to the adoption of HTPs, we estimate staggered differencein-differences models around the first year that a given MNC adopts an HTP. Estimates using
a “stacked” difference-in-differences estimator (e.g., as in Cengiz, Dube, Lindner and Zipperer,
2019) provide evidence that these structures were put in place for tax avoidance purposes.3
Specifically, we show that MNCs engage in more financial transactions that can be used to
shift profits and reduce foreign ETRs following the adoption of an HTP. We also find that
adopting MNCs experience larger increases in foreign capital, domestic payroll, and R&D than
non-adopting MNCs in the years surrounding the adoption of an HTP. One potential concern is
that these complex tax structures are adopted by specific types of firms that were also subject to
other macroeconomic shocks over this time period. We address this concern by showing that we
obtain similar results when we include industry-by-year fixed effects, when we flexibly control
for firm size bins interacted with year fixed effects, and when we flexibly control for differences
in intangible assets across firms. We also obtain similar results when we additionally use inverse
propensity score weights to attain more similar firm-to-firm comparisons, when we use a two-way
fixed effects (TWFE) specification, and when we use an alternative staggered estimator from Sun
and Abraham (2021).
Estimates of our staggered difference-in-differences models have a causal interpretation under
the assumption that the outcomes of MNCs that adopted an HTP would have otherwise trended
similarly to those that did not adopt such a structure. Event study results generally show that
important outcomes for adopting MNCs, such as growth in payroll, foreign assets, R&D, and
foreign ETRs, follow similar patterns to those of non-adopting MNCs prior to HTP adoption.
These results suggest that MNCs do not select into HTPs based on prior trends in economic
3

Throughout the paper we use tax avoidance to refer to legal strategies used to minimize tax obligations.

4

outcomes. However, it is possible that MNCs select into HTPs based on the gains from adopting
an HTP, such that adopting MNCs would have different post-period trends absent an HTP.
Interpreting these results through the lens of an economic model could help decompose firmlevel changes into two components, one that captures selection on gains from tax planning and
another that captures the effects of the HTP tax advantage.
Overall, our results provide the first systematic documentation of the prevalence of hybrid
tax planning structures among US MNCs. We show that, by 2016, these structures accounted for
about a third of their foreign profits and that these MNCs represent a large fraction of domestic
corporate activity. We estimate that adoption of these strategies is followed by large increases
in financial transactions that can be used to shift profits to low-tax countries and by declines in
foreign ETRs. We also estimate significant relative changes in domestic and foreign economic
activity following the adoption of an HTP.
This paper contributes to studies that quantify the importance of profit shifting. In a seminal
contribution, Hines and Rice (1994) describe and measure the importance of tax havens to
the operations of US MNCs. Clausing (2016) uses the sensitivity of reported profits to tax
rate differentials to estimate the magnitude of profit shifting of US MNCs. Tørsløv, Wier and
Zucman (2018) use macroeconomic data and differences in the profitability of different affiliates
to estimate the magnitude of profits shifted to tax havens. Bilicka (2019) uses tax data from
the UK to argue that the large differences in the profitability between domestic UK firms and
the affiliates of foreign MNCs (in the UK) are driven by profit shifting. In a recent survey,
Dyreng and Hanlon (2021) highlight the cross-sectional variation in tax avoidance and conclude
that a large portion of the variation in tax avoidance remains unexplained. Our focus on HTPs
contributes to understanding the importance of specific tax planning strategies.
While several papers document the existence of profit shifting, the magnitude of this problem has been hard to pin down. Using tax data from US firms, Dowd, Landefeld and Moore
(2017) argue that the sensitivity of reported profits to tax differentials can be non-linear and
that accounting for non-linearities increases estimates of profits shifted to low-tax countries.
In contrast, Blouin and Robinson (2020) argue that prior estimates using tax and survey data
can be plagued by double-counting of profits and that accounting for direct investment income
between affiliates can significantly lower estimates of profit shifting. Following the suggestions
in Blouin and Robinson (2020), we subtract dividend income from related foreign corporations
5

when computing aggregate foreign earnings for US MNCs. Rather than provide estimates of
profit shifting, we document the prevalence of widely used tax planning structures, show that
MNCs use them in transactions that are likely related to profit shifting, and find that close to
one third of the foreign profits of US MNCs in our sample flow through these structures by 2016.
The characterization of the structure of the foreign activities of US MNCs prior to the passage
of the Tax Cuts and Jobs Act of 2017 (TCJA) is an important contribution of this paper, as the
response of US MNCs to the many changes and new incentives in the TCJA likely depends on
these preexisting structures.4
We also contribute to our understanding of CTB regulations by directly examining the adoption and consequences of complex tax structures facilitated by the policy. Using tax return data
from US MNCs, Altshuler and Grubert (2006) find reductions in foreign effective tax rates after
the enactment of CTB in 1997 that are consistent with the use of the tax planning strategies we
examine. Mutti and Grubert (2009) use multiple data sources to show that, after the implementation of CTB, MNCs increased profit shares in low-tax jurisdictions and transferred intangible
assets abroad. Blouin and Krull (2014) show that MNCs had more tax haven affiliates and
longer ownership chains after the enactment of CTB. Faulkender, Hankins and Petersen (2019)
study the 440% increase in cash held abroad by US MNCs between 1998 and 2008 and argue
that this rise is driven by tax factors, including CTB. While prior research assumed that MNCs
disregarded foreign affiliates following the CTB regulations, ours is the first paper to use tax
information to confirm when an affiliate is disregarded and to systematically identify MNCs that
use a specific set of tax planning structures. In a contemporaneous paper, Samarakoon (2022)
uses tax data to identify firms that use a Double Irish structure and examines how the closure
of this structure impacts repatriation of deferred earnings by MNCs.5 Our results show that the
bulk of the decrease in foreign ETRs and increase in cash held abroad over the sample period
4

The TCJA lowered the corporate tax rate to 21% and made significant changes to US taxation of international
income. It also introduced four new provisions. First, due to the transition to territorial taxation, US MNCs
now can deduct dividends received from foreign affiliates from their US taxable income, thereby eliminating any
repatriation tax. Second, the TCJA introduced a new tax on Global Intangible Low-Taxed Income (GILTI)—
defined as income that includes low-tax foreign income exceeding 10% of an MNC’s tangible foreign capital
investment (adjusted for depreciation). Third, a new category of income—Foreign-Derived Intangible Income
(FDII)—is subject to a reduced tax rate. FDII encompasses income derived from intellectual property held in
the United States that generates foreign sales. Finally, the TCJA introduced the Base Erosion and Anti-Abuse
Tax (BEAT) to curb the erosion of the tax base by both US and foreign MNCs.
5
Hardeck and Wittenstein (2018) use data from the Luxembourg Leaks to identify firms with hybrid tax
structures and find that hybrid tax structures reduce MNC tax rates, as measured by financial statements data.

6

was driven by MNCs that adopted a particular set of tax planning structures.
Finally, our paper contributes to the literature on how profit shifting impacts real behavior.
Grubert and Slemrod (1998) study profit shifting opportunities through Puerto Rico and argue
that US MNCs changed their investment decisions in response to these opportunities. Suárez
Serrato (2018) studies the reduction in profit shifting opportunities through Puerto Rico and
shows that US MNCs decrease their domestic investment in response.6 Albertus (2019) uses
Bureau of Economic Analysis data to compare US MNCs with different average foreign tax
rates prior to the implementation of CTB. He finds that MNCs with higher initial tax rates
experienced a larger decline in average rates and increased their R&D intensity after 1996. By
using tax data to identify specific tax planning structures and to demonstrate how they are used
for profit shifting, we reveal substantial heterogeneity regarding the types of firms that benefited
from CTB and shed light on the mechanisms through which CTB lowered foreign ETRs and
affected real economic activity.
The remainder of the paper is organized as follows. Section 2 discusses how CTB facilitated
the creation of foreign disregarded entities and describes the three hybrid tax planning structures
we examine. Section 3 provides an overview of the data. Section 4 discusses how the creation
of foreign disregarded entities and hybrid tax planning structures has grown over time. Section
5 estimates firm-level changes in foreign tax rates and economic activity of US MNCs following
the adoption of tax planning strategies. Section 6 concludes. We conduct additional analyses
in the appendices. Appendix A provides robustness checks for our main analyses. Appendix B
discusses the measurement of foreign earnings and taxes. Appendix C studies how MNCs can
structure cost sharing agreements to shift profits from the US parent to foreign affiliates.

2

Hybrid Tax Planning Structures and Check The Box

This section describes the three hybrid tax planning structures that we study. We first describe
the “Check the Box” regulations that facilitated their adoption and then describe the structures
in detail.

6

de Mooij and Liu (2018) study the impact of transfer pricing regulations and show that these policies can
reduce investment. Bilicka, Qi and Xing (2019) show that a worldwide debt cap that limited interest stripping
as a form of profit shifting also impacts the investment decisions of UK MNCs.

7

2.1 Check The Box Regulations
During the period we study, the United States imposed a corporate tax on the worldwide income
of US corporations, with a credit for foreign taxes paid to avoid double taxation. The credit was
limited to what US tax would have been on the foreign income. Taxes were not due on active
foreign business income until it was repatriated to the US parent corporation. This deferral
feature of the US tax code made it attractive to hold income generated abroad in tax havens.
To prevent profit shifting, deferral was not extended to certain types of “tainted income”
under what is generally referred to as Controlled Foreign Corporation rules. These rules are
contained in Subpart F of the tax code, and foreign income that is subject to current US tax
is referred to as “Subpart F” income. Tainted income includes passive portfolio income and
the payment of interest, dividends, and royalties from one CFC to a related CFC in another
jurisdiction.
In 1996, the US Treasury promulgated regulations effective on January 1, 1997, that made
it easier for US corporations to change the entity classification (e.g., pass-through or corporate)
of domestic and foreign affiliates. This policy change became known as Check the Box, referring
to the ease with which US corporations could change entity classifications. CTB was originally
intended to simplify tax filing for domestic firms. However, it also facilitated certain types of
international tax planning strategies that leverage mismatches in tax laws across countries. These
strategies make use of foreign affiliates referred to as “hybrid entities” that are treated differently
for tax purposes at home and in host countries.
Below we discuss how tax planning structures that use hybrid entities allow US companies
to avoid US tax levied on intercompany payments such as dividends, interest, and royalties, and
how these structures leverage mismatches in tax laws across countries to lower foreign tax bills.

2.2 Hybrid Tax Planning Strategies
CTB facilitates tax planning by allowing MNCs to easily create “hybrid” entities. A hybrid
entity is a business operation that is incorporated from the foreign country point of view and
a pass-through (unincorporated branch of another corporation) from the US point of view (or
vice versa, in which case it is referred to as a “reverse” hybrid). Since 1997, an MNC can simply
check a box on a tax form to disregard a foreign corporation, thereby creating an FDE, a type of
pass-through entity. If an entity is disregarded, the transactions with its entity parent and with
8

other FDEs owned by the same parent become transparent to the US Treasury, as they are all
viewed as part of one consolidated corporation.7
The simplest hybrid tax planning structure allows MNCs to take large deductions for interest
in high-tax jurisdictions through the use of tax haven finance affiliates. Consider the following
planning structure to finance a subsidiary in a high-tax country. Instead of funding the high-tax
subsidiary directly, the parent injects equity into a tax haven affiliate, which lends to the hightax subsidiary. The high-tax subsidiary then pays interest to the tax haven affiliate. This profit
shifting strategy is commonly known as “interest stripping.” Though the interest is deductible
abroad against taxable income, it remains subject to immediate US tax under the CFC rules.
Prior to 1997, CFC rules made the use of a tax haven financing affiliate unattractive for tax
purposes. Since 1997, however, the parent can check the box on the high-tax affiliate, making it
a hybrid FDE. From the US point of view, the high-tax CFC is an unincorporated branch of the
tax haven FDE; the interest payment is thus transparent to the US Treasury, which regards the
combined tax haven/high-tax operation as one consolidated corporation. The interest payment
therefore avoids Subpart F taxes and the company can defer US income tax by holding profits
in the tax haven.
Panel A in Figure 2 depicts this simple hybrid tax planning structure using a tax haven
affiliate. The green box around the two entities (the tax haven CFC and the high-tax FDE)
indicates that the structure is consolidated from the US point of view. This simple structure
allows the parent to capitalize a foreign affiliate through a tax haven while making intercompany
payments transparent, thereby avoiding any current US tax on interest.
While this structure avoids Subpart F tax and defers US income tax, the MNC would still
be subject to corporate income tax in the tax haven (if it exists) and potentially to foreign
withholding tax on the interest payments between affiliates.8 Moreover, to combat interest
stripping, many countries have adopted “thin-capitalization” rules that limit the tax deductibility
of interest payments, reducing the attractiveness of this option.

7

Although it was possible for MNCs to create foreign disregarded entities prior to 1997, there were strict rules
regarding what types of entities could be declared as such. In particular, such entities had to demonstrate that
they possessed at least three of four characteristics associated with partnerships. In practice, we observe very few
of these entities prior to the implementation of CTB in 1997.
8
After the adoption of “look-through” rules passed as part of the Tax Increase Prevention and Reconciliation
Act of 2005, MNCs could avoid Subpart F taxation on distributions of interest, rents and royalties across CFCs
without relying on FDEs.

9

2.2.1

CTB and Cost Sharing Agreements

Another form of income shifting is available to MNCs with intellectual property (IP). This
method uses cost sharing agreements (CSAs) to develop IP that can be licensed abroad. These
agreements are particularly tax advantageous when combined with CTB.
Under a cost sharing agreement, the tax haven affiliate makes a “buy-in payment” that funds
a part of the parent’s R&D project. This gives the affiliate the right to license resulting IP to
other foreign subsidiaries in exchange for royalty payments. Royalty payments are not subject to
current tax under Subpart F if the parent checks the box on the affiliate making the payment.9
The key is that with CTB, any payments for the use of the IP abroad are contained within one
consolidated company from the view of the US Treasury. This structure has the same foundation
as in Panel A of Figure 2, but replaces the equity injection with a transfer of IP (via a cost sharing
agreement) and uses royalty payments instead of interest to shift profits.
It is important to note that determining the right arm’s length payment for the buy-in is
usually quite difficult. Typically the IP is not fully developed at the time the buy-in payment is
made, so there is uncertainty regarding future profits. While the US has rules under which buy-in
payments must be adjusted if the profits associated with the IP are too high relative to payments,
it is still possible for MNCs to underprice the IP. This allows US MNCs to shift income to lowtax affiliates. MNCs are then able to use hybrid tax planning structures, as discussed below, to
minimize tax on the foreign profits generated from their IP.
Even if IP is not underpriced, MNCs have historically attempted to strategically allocate
allowable costs to generate tax savings through cost sharing agreements. We discuss these cost
allocation strategies as well as their legal challenges in Section 5.3 and Appendix C.
As with interest stripping, royalty payments may still be subject to corporate income taxes in
a tax haven and to withholding taxes that are meant to prevent profit shifting between countries.
We now describe complex tax planning strategies that aim to reduce exposure to income and
withholding taxes across multiple jurisdictions, including the US.

9

Profits could be further accumulated in a tax haven if MNCs overprice the royalty. The absence of comparable
transactions makes it hard for tax authorities to value intellectual property and correctly price royalty payments.

10

Figure 2: Diagrams of Hybrid Tax Planning Structures
(A) Simple CTB Structure

(B) Double Irish CTB with Cost Sharing
Parent MNC

Parent MNC

Transfer IP

Equity

Consolidated
Corporation

Irish
Holding Company
(M&C in Bermuda)

Tax Haven CFC

Loan

License

Interest

Royalty

Irish
FDE A

High-Tax
FDE

Sales outside of US

(C) Double Irish with Dutch Sandwich
Parent MNC
Transfer IP

(D) Reverse Hybrid Mismatch:
Dutch CV-BV
US #1

US #2

Managing
Partner

Silent
Partner

Transfer IP
Royalty
Dutch
Conduit
FDE

Irish
Holding Company
(M&C in Bermuda)

Dutch CV
(Partnership
Ltd.
Co.)

License
Royalty
License

License
Irish
FDE
A

Royalty
Dutch BV
(Private Ltd.
Liability Co.)

Sales outside of US

Sales outside of US

Notes: Panel A of Figure 2 depicts a hypothetical financing CTB structure; Panel B describes a Double Irish cost
sharing structure with CTB; Panel C illustrates the Double Irish with a Dutch Sandwich; and Panel D describes
a Reverse Hybrid Mismatch structure, otherwise known as a CV-BV (or SCS-SARL in the case of Luxembourgish
entities). In each of these diagrams, the green rectangle depicts the combined structures as perceived by the IRS,
and squares denote corporations. Squares with circles inside denote hybrid entities, which are corporations in the
local country but disregarded for US purposes. In Panel D, the CV (or SCS if Luxembourg entities are used) is
shown as a triangle to denote that it is a reverse hybrid: is it a partnership for Dutch purposes but a corporation
for US purposes.

11

2.2.2

CTB, Ireland, and Intellectual Property

The first tax planning strategy we study is known as the Double Irish and involves setting up
a network of affiliates in Ireland and a tax haven country such as Bermuda. To motivate this
structure, consider a parent MNC that develops IP in the US that it wants to sell around the
world. The parent can transfer the IP to a holding company in a tax haven using a cost sharing
arrangement. The tax haven holding company then licenses the IP to an Irish CFC (CFC A),
which pays royalties back to the holding company from the sales revenue it receives selling the
IP abroad.
This initial cost sharing structure creates three tax problems for the parent. First, the parent
will be subject to Subpart F taxes (current US tax) on the royalties paid from Irish CFC A to
the tax haven holding company. Second, taxes will be due in Ireland on any profits that remain
in CFC A after royalties are paid to the haven holding company. Finally, the parent will owe
Irish withholding taxes on the royalty transfers to the haven.
The first two of these tax problems can be solved using CTB and a Double Irish tax planning
structure, as summarized in Panel B of Figure 2. In this structure, the parent transfers the
IP to a holding company managed and controlled in a tax haven (e.g., Bermuda) but legally
incorporated in Ireland. Though the US considers this an Irish holding company, under Irish
tax law, the holding company is a Bermuda company and therefore not subject to Irish tax.
The parent also checks the box on Irish CFC A to avoid current US tax on the royalties: the
CFC becomes an FDE and is therefore fiscally transparent to the US Treasury. This eliminates
Irish tax on any profits remaining in Ireland (i.e., the holding company) after royalties are paid,
as well as current US taxes on the royalties. However, the transfer from the Irish FDE to the
IP-holding company still generates Irish withholding tax.
MNCs can eliminate this withholding tax, thus solving the final tax problem, by inserting a
Dutch conduit—a “Dutch Sandwich”—between the Irish affiliates. With the Dutch conduit in
place, the parent owes no withholding tax on payments between the conduit and the Irish affiliate
(FDE A), as no withholding taxes are due between European Union companies. Further, no
withholding taxes will accrue on the royalties between the Dutch conduit and the Irish holding
company because no withholding tax is imposed on these transfers under Dutch law. To avoid
Subpart F taxes on these royalty payments, the parent also checks the box on the Dutch conduit,

12

making it an FDE. This “Double Irish with a Dutch Sandwich” hybrid tax planning structure,
as shown in Panel C of Figure 2, solves all three tax problems we identified above.
2.2.3

Reverse Hybrid Mismatch

The final two tax planning structures we examine use a strategy known as a Reverse Hybrid
Mismatch. While this strategy can be employed using affiliates in different countries, we describe
a common structure using Dutch companies. To set up this structure, a US MNC creates two
US-based affiliates to act as managing/silent partners in a Dutch closed limited partnership called
a CV (commanditaire vennootschap in Dutch). The partnership is a “reverse hybrid” entity: it is
treated as a pass-through company by the Netherlands and as a corporation by the US. The CV
owns a Dutch private limited liability company, called a BV (besloten vennootschap in Dutch),
which acts as a holding company. The BV owns foreign (non-US) subsidiaries (e.g., in Europe).
The BV also holds the license for the US IP, sells the IP to foreign companies, and pays royalties
to the CV. Panel D of Figure 2 depicts this structure.
The Reverse Hybrid Mismatch allows the MNC to avoid tax on foreign income by solving
three tax problems. First, tax may be due in the Netherlands. By Dutch tax law, the CV is a
pass-through entity, so corporate tax is not levied in the Netherlands. Second, payments from
BV to CV can generate Subpart F tax. The (reverse hybrid) CV is a corporation from the US
perspective, and if the parent “checks the box” to disregard the (hybrid) BV, the US sees the
two entities as a consolidated operation. Thus, no Subpart F tax will be due on the royalties.
Finally, payments from the BV to the CV could trigger Dutch withholding tax. However, during
our period of analysis, a 2005 decree by the Dutch Finance Ministry exempted US-based CVBVs from withholding tax. With this Reverse Hybrid Mismatch structure in place, profits from
US-developed IP sold abroad were not subject to corporate tax in the Netherlands and enjoyed
indefinite deferral from US tax (under pre-TCJA law). The CV-BV structure is effectively a
“sink” for foreign profits.
A Reverse Hybrid Mismatch structure can be set up through other countries. In particular, a combination of two types of Luxembourgish companies, known respectively as SCS and
SARL, yields a structure similar to the Dutch CV-BV.10 We study both CV-BV and SCS-SARL
structures for the purpose of our analyses.
10

SCS and SARL are short for société en commandite simple and société à responsabilité limitée, respectively.
In this case, the SCS is the reverse hybrid company and the SARL is disregarded from the US perspective.

13

While the description of these structures emphasizes their potential to minimize tax obligations, it is important to note that tax planning is also costly. MNCs have to pay for accounting
and legal advice and to engage in transactions to form the structures. In addition, company
executives differ in their perceived cost of adopting tax-aggressive positions. To the extent that
MNCs incur these costs to avoid paying taxes, tax planning is distortionary from an economic
perspective.

3

Data and Sample Construction

3.1 IRS Business Tax Data
We rely primarily on several IRS datasets for our analysis. These administrative datasets provide
parent and (both CFC- and FDE-level) affiliate-level information disclosed in tax returns that
allows us to measure the domestic and foreign activity of a large sample of US corporations, both
private and public.
The first dataset, commonly referred to as the Statistics of Income (SOI) Corporate Sample,
is an annual stratified sample of US corporations that SOI uses to produce publicly available
aggregated business income statistics.11 The SOI Corporate Sample contains information from
unaudited tax returns for approximately 100,000 US corporations annually, and has been used
in the business tax literature to study the behavior of domestic firms (e.g., as in Yagan, 2015;
Zwick and Mahon, 2017). Our data focus on C corporations that were sampled between 1992 and
2016. The data primarily contain information from Form 1120, the US Corporate Income Tax
Return, as well as some information from related forms. In our analysis, we also use information
from Form 6765, which is used to claim the R&D tax credit, and Form 4562, which is used to
calculate tax deductions for depreciation on capital assets.
The second dataset, which reports information related to foreign affiliates of US corporations,
is used by SOI to publish aggregate statistics for international business taxes (IRS, 2022a). This
dataset contains a subset of C corporations from the SOI Corporate Sample that file Form 5471
or Form 8858: Form 5471 provides financial information and activity of CFCs, and Form 8858
provides similar information for FDEs, the entity type enabled by CTB. We refer to this sample
as the “SOI International Business Tax Sample.” Unlike the SOI Corporate Sample, which is
provided annually, CFC data is collected only in even years. FDE data is collected for four of
11

Statistics are available at IRS (2022b), and the sampling procedure is described in IRS (2011).

14

Table 1: Data Sources and Selected Outcomes
SOI Corporate Sample
Form Description

Selected Outcomes

1120

Corporate Income Tax Return

Domestic Assets
Domestic Wages

6765

R&D Tax Credit

Domestic R&D Expenses
Domestic R&D Wages
R&D Tax Credit

4562

Depreciation and Amortization

Capital Investment

SOI International Business Tax Sample
Form Description

Selected Outcomes

5471

CFC Information Return

Country of Incorporation
Foreign Assets
Foreign E&P
Foreign Taxes
Transactions Between CFCs
Transactions Between US Parent and CFCs

FDE Information Return

Country of Incorporation
Date Disregarded
Foreign Assets
Foreign E&P
Pass-through Owners
Tax Owner

8858

Compustat Data
Description

Selected Outcomes

Consolidated Public MNC Data

Deferred Foreign Taxes (txdfo)
Foreign Taxes (txfo)
Net Income (ni)
Pretax Foreign Income (pifo)
R&D Expense (xrd)
Revenue (sale)
Total Assets (at)

the years in our sample period (2006, 2008, 2012, and 2016). Prior to 2004, SOI statistics only
included information related to CFCs for large MNCs with more than $500 million in assets.
Starting in 2004, the sampling procedure became much broader.
In some instances, we also supplement the IRS datasets with financial statements data on
15

Table 2: Sample Sizes
(1)
(2)
Int’l. Business Sample SOI Corp. Sample

(3)
Stable Sample

20,029
322,538
57,685

3,635
43,941
53,141

MNC Count
CFC Count
FDE Count

23,222
333,438
58,690

Notes: This table provides the size of three different samples of US MNCs, along with their related controlled
foreign corporations (CFCs) and foreign disregarded entities (FDEs). Column (1) provides sample sizes using all
MNCs in the SOI International Business Tax Sample. Column (2) provides sample sizes after removing MNCs
that were not C corporations. Column (3) applies a size filter that removes smaller MNCs from the sample so
that the sample composition is similar in earlier and later years.

public companies from Compustat. Table 1 summarizes the tax forms described above along
with selected outcomes that we use in our analysis, both from SOI data and from Compustat.

3.2 Sample Construction
Table 2 shows the size of several different samples of MNCs, along with their foreign affiliates
(CFCs and FDEs). We consider a firm to be an MNC if it files Form 5471 for at least one
CFC. Column (1) reports the number of MNCs that have coverage in our data from the SOI
International Business Tax Sample. Column (2) shows the number of MNCs from Column (1)
that are C corporations contained in the SOI Corporate Sample. As mentioned above, there was
a sampling change in the international business tax study starting in 2004 that resulted in a large
increase in the sample, especially for smaller MNCs. To stabilize the firm sampling distribution
between earlier and later years, we remove MNCs that did not have at least one CFC with $50
million in foreign assets as well as those with fewer than $500 million in domestic assets. Column
(3) shows sample sizes after applying this filter. Our analysis primarily uses the sample shown in
Column (3) to study firm-level outcomes. Summary statistics for this stable sample are provided
in Table A.1.

3.3 Measuring Foreign Effective Tax Rates
One possible concern when measuring the income of foreign affiliates of corporations relates to
the measurement of foreign earnings. Blouin and Robinson (2020) suggest that aggregated IRS
statistics may inadvertently double count foreign earnings. This is due to accounting quirks of
16

MNCs. Consider a hypothetical US firm with two CFCs (A and B). Suppose that CFC A is a
holding company that holds a 100% stake in CFC B and has no economic purpose other than to
collect dividends from its subsidiaries, and further suppose that CFC B discloses E&P of $100
million, which is issued as a dividend to CFC A. CFC A will then also report E&P of $100 million.
A simple aggregation of the firm’s foreign profits will result in an estimate of $200 million in
foreign E&P even though the true figure is $100 million. If firms with hybrid structures tend to
issue more dividends between their foreign affiliates, then this exercise would overestimate their
E&P and in turn underestimate the ETR. However, each CFC must also file an attachment to
Form 5471 that discloses transactions between the focal CFC and related CFCs, including any
dividends that the CFCs may transfer to each other.
To ensure that there is no double-counting of foreign profits, we subtract these related dividends from our calculations. Specifically, we compute the firm-level foreign ETR as
ET R =

Foreign Taxes
,
Foreign Taxes + Foreign Earnings and Profits

where total foreign tax payments are taken from Form 5471, Schedule E.12 To calculate pretax
foreign earnings and profits, we obtain pretax E&P for each affiliated CFC (using Schedules H
and E from Form 5471). Following the suggestion of Blouin and Robinson (2020), we remove
dividends received from related CFCs from E&P (using Schedule M from Form 5471).
In Appendix B, we examine the performance of this correction. We construct a proxy for aggregation error by generating a link between IRS data, which provide disaggregated information
about foreign affiliates, and Compustat, which provides data from public disclosures of consolidated MNCs. By comparing the disaggregated data to the consolidated figures, we can quantify
the extent to which commonly-used aggregation techniques may result in double-counting of
foreign earnings in tax data.
This book-tax comparison reveals that aggregation error has been increasing over time, likely
because the mechanism by which this error occurs suggests that it grows as MNCs create more
complicated affiliate networks. The linked sample also reveals large inconsistencies in the reporting of corporate income tax across firms’ books and tax filings. These inconsistencies are
particularly noticeable in extractive and financial industries.13 Applying the correction proposed
12

There does not appear to be any such double-counting concern related to the payment of foreign taxes. We
exclude unprofitable firm-years from this calculation.
13
Extractive industries often operate under contracts with foreign governments that include forms of revenue
sharing, which can be misreported as a corporate income tax.

17

by Blouin and Robinson (2020) yields a 30% reduction in the magnitude of foreign earnings as
measured in tax data in 2016 and significantly reduces book-tax differences. Furthermore, as
shown in Figures B.7 and B.8, this correction breaks the systematic relationship between booktax differences and the size of multinationals’ foreign affiliate networks. Unadjusted book-tax
differences are increasing over time. After applying the correction, this is no longer true. Both of
these exercises indicate that the correction appears to significantly reduce measurement error.14

3.4 Measuring Changes in International Corporate Structures
Both the SOI Corporate Sample and the International Business Tax Sample have been used to
study domestic and international business taxation. Relatively little work, however, has utilized
the wealth of information regarding FDEs.15 Although data from Form 8858 are collected less
frequently than other samples, they allow us to observe two important features of US MNC
structures. First, they reveal the date when an entity was first disregarded by an MNC, which
allows us to measure adoption of CTB among MNCs. Second, they allow us to observe the
tax ownership structure of each CFC along with its FDEs. These ownership structures reveal
important cross-national linkages within MNCs and, most importantly, allow us to identify CFCs
and FDEs that have particular structures associated with the tax planning strategies described
in Section 2.
3.4.1

Detecting the Double Irish

As described previously, the Double Irish involves two Irish entities—a top-level entity that is
incorporated in Ireland, but managed and controlled in another low-tax foreign country, and a
lower-level Irish entity that merchandises the IP and pays a royalty. Typically, the lower-level
entity is “checked” and is classified as an FDE for US tax purposes. Alternatively, the MNC
may “check” both types of entities which are then classified as FDEs under the “tax ownership”
of a separate CFC. As a result, we flag two types of CFCs that could be used in a Double Irish
arrangement. First, we flag any CFC that is incorporated in Ireland and that checks the box on
an Irish FDE. Second, we flag any CFC that checks the box on two separate Irish FDEs. Note
14

In Appendix B.3, we also provide corrected estimates of the elasticity of foreign earnings with respect to
foreign tax rates following the methodology of Dowd, Landefeld and Moore (2017). We show that their general
finding that earnings are more sensitive to rates in haven jurisdictions is robust to this correction.
15
A notable exception is a recent working paper, Samarakoon (2022), that examines the impact of the closure
of the Double Irish tax structure in Ireland.

18

that this classification method flags “simple” Double Irish arrangements that involve a direct link
between two Irish entities, but also more complex arrangements, such as the Double Irish with
a Dutch Sandwich, that might involve intermediary affiliates through which profits are routed.
3.4.2

Detecting Reverse Hybrids

The other type of structures we consider are Reverse Hybrid Mismatch arrangements common
in the Netherlands and Luxembourg. This arrangement, also described in Section 2, involves a
top-level entity that is classified as a partnership and a bottom-level entity that is classified as
a private limited company (PLC) in the associated country of incorporation. SOI data typically
provide the acronym that is associated with the management form of foreign affiliates on Form
5471 and 8858. In the Netherlands, for example, partnerships are associated with the acronym CV
and the equivalent form of a PLC is associated with the acronym BV. The equivalent acronyms
in Luxembourg are SCS (for a partnership) and SARL (for a PLC). To classify potential reverse
hybrid structures, we flag any CFC classified as a CV (incorporated in the Netherlands) or SCS
(incorporated in Luxembourg) that check the box on an FDE classified as a BV (Netherlands)
or SARL (Luxembourg). We also flag any CFC that checks the box on a CV-BV or SCS-SARL
pair of FDEs.
For both the Double Irish and Reverse Hybrid Mismatch arrangements, we use the first
date that all flagged FDEs were disregarded to measure the year that an MNC first adopted a
particular structure.

4

Adoption and Prevalence of CTB and Hybrid Tax Planning
Structures

Below, we describe how US MNCs used CTB starting in 1997. We show that after its implementation, usage of tax-transparent FDEs quickly became widespread among US MNCs. A large
share of these entities are connected to well known tax havens.
Next, we focus on the tax planning structures described in Section 2. We show that MNCs
gradually adopted these structures in the decade after the implementation of CTB. By the 2010s,
MNCs that adopted at least one of these structures generated a majority of foreign earnings
among firms in our sample and within these MNCs, a majority of foreign earnings were connected
to these structures. We show that these MNCs also represent a large share of domestic corporate
19

Figure 3: Adoption of Foreign Disregarded Entities
(A) Cumulative Number of FDEs

Cumulative FDEs

60,000

40,000

20,000

0
1993

1997

2001

2005

2009

2013

2017

(B) Prevalence of FDEs among US MNCs

Share of MNC Profits

100%

80%

60%

40%

20%

0%
1995

2000

2005

Share of MNC Foreign Profits

2010

2015

Share of MNCs

Notes: Panel A of Figure 3 plots the cumulative number of foreign disregarded entities (FDEs). There were fewer
than 50 FDEs prior to 1997 and this number grew rapidly following the adoption of CTB regulations. Panel B
plots the fraction of US MNCs with an FDE, as well as the share of foreign profits that accrue to US MNCs with
FDEs. By 2008, close to 80% of US MNCs have a FDE, and these MNCs account for close to 100% of foreign
profits.

20

Table 3: Disregarded Entities by Country of Incorporation
Country Name

Unadj. E&P (billions)

Num. FDEs

Ireland
Netherlands
United Kingdom
Switzerland
Cayman Islands
Singapore
Luxembourg
Bermuda
Canada
Australia

224
136
130
82
71
68
53
51
41
34

4,844
12,236
26,982
2,138
5,277
3,458
4,592
2,126
7,427
6,780

Notes: This table shows the largest ten countries by total foreign earnings generated by foreign disregarded
entities. Column (2) provides aggregate unadjusted E&P, generated by FDEs in the country listed in Column
(1). This includes E&P for all years that we observe Form 8858 filings (2006, 2008, 2012, and 2016). Column (3)
shows the number of unique entities across all years of this sample.

activity, generating 20% of domestic payroll and holding 15% of domestic capital assets by 2016
among US C corporations.

4.1 Adoption of Check the Box
Panel A of Figure 3 shows the cumulative number of FDEs created between 1992 and 2016. Prior
to 1997, usage of these transparent entities was relatively rare—the IRS used a resource-intensive
system that required firms to show that their affiliates possessed a set of characteristics that
were more consistent with either a partnership (transparent) or corporation (non-transparent)
classification. Starting in 1997, the Treasury relaxed these restrictions, as described in Section
2. As a result, usage of FDEs became widespread over the next two decades, with over 60,000
foreign affiliates classified as FDEs by the end of 2016. Panel B shows that by 2008, about 80%
of MNCs used CTB to declare at least one FDE, and that these MNCs generated nearly all of
foreign E&P.
Table 3 shows the largest ten countries according to total earnings generated by FDEs. The
Netherlands and Ireland are some of the largest domiciles for these types of foreign affiliates.
FDEs also generate large amounts of earnings in well known tax havens, such as the Cayman
Islands and Bermuda.

21

4.2 Adoption of Hybrid Tax Planning Structures
Panel A of Figure 4 shows the evolution of the share of MNCs in our sample that adopted
particular hybrid tax planning structures. After the implementation of CTB, there was steady
adoption of these structures, with more than 17.5% of MNCs adopting at least one by 2016.
Panel B demonstrates that by 2008, these MNCs generated a majority of foreign E&P. Panel C
shows that the CFCs linked to HTPs generated the majority of profits within MNCs that use
them by 2016. Panel D shows the share of foreign E&P linked to HTP structures relative to
aggregate foreign E&P for MNCs in our sample—by 2016, more than 35% of all foreign E&P of
US MNCs was routed through an HTP.
Figure 5 shows that MNCs with tax planning structures comprise a large share of domestic
economic activity. As a share of all C corporations in the SOI Corporate Sample, which includes
domestic corporations as well as MNCs, MNCs with tax planning structures paid more than 20%
of domestic wages and accounted for about 15% of domestic investment by 2010.
The results in this section demonstrate that several hybrid tax planning structures became
widely adopted by US MNCs after the implementation of CTB, with large shares of foreign
profits flowing through these structures in the decades after adoption.

22

Figure 4: Adoption of Hybrid Tax Planning (HTP) Structures
(B) Share of Foreign Profits
in MNCs with HTP Structure
Share of MNC Foreign Profits

(A) Share of MNCs with an
HTP Structure

Share of MNCs

15%

10%

5%

0%
2000

SCS−SARL

2005

CV−BV

2010

20%

Double Irish

2015

1995

Multiple

2000

SCS−SARL

2005

CV−BV

2010

Double Irish

2015

Multiple

(D) Share of Foreign E&P Connected
to an HTP Structure (All MNCs)

Share of Foreign E&P

(C) Share of Foreign E&P Connected
to an HTP Structure (HTP MNCs)

Share of Foreign E&P

40%

0%
1995

40%

20%

0%
1995

60%

30%

20%

10%

0%
2000

2005

2010

2015

SCS−SARL

CV−BV

Double Irish

Multiple

1995

2000

2005

2010

2015

SCS−SARL

CV−BV

Double Irish

Multiple

Notes: These figures show that a growing share of US MNCs have adopted hybrid tax structures over time (Panel
A) and that these MNCs are responsible for a large share of the overall foreign E&P of US MNCs (Panel B).
Panel C shows that a large share of foreign E&P within adopting MNCs is connected to HTPs. Panel D shows
the share of foreign E&P that is connected to an HTP relative to all the MNCs in our sample. In each panel, the
blue area comprises MNCs that have adopted more than one structure.

23

Figure 5: Hybrid Tax Planning Structures and Domestic Economic Activity
(A) Share of Corporate Domestic Payroll

Share of Domestic Wages

25%

20%

15%

10%

5%

0%
1995

2000

SCS−SARL

2005

CV−BV

2010

Double Irish

2015

Multiple

Share of Domestic Investment

(B) Share of Corporate Domestic Investment

15%

10%

5%

0%
1995

2000

SCS−SARL

2005

CV−BV

2010

Double Irish

2015

Multiple

Notes: These figures show the share of domestic wages (Panel A) and domestic capital investment (Panel B)
paid by US MNCs that adopt one of the hybrid tax structures described in Section 2. This share is computed
as a fraction of all domestic wages and capital investment among C corporations in the IRS Statistics of Income
Corporate Sample. See Section 3 and Table 2, Column 2 for a description of the sample.

24

Figure 6: Predicted Probability of HTP Adoption by Industry

Information

Manufacturing

Professional, Scientific, and Technical Services

Wholesale Trade

Retail Trade

Management of Companies (Holding Companies)

Mining

Finance and Insurance
0.0

0.1

0.2

Notes: This figure reports the predicted probability of HTP adoption by sector according to a simple logit model.
We remove industries with fewer than ten MNCs in either group.

4.3 Which Firms Adopt Hybrid Tax Planning Structures?
To examine characteristics of MNCs that adopt hybrid structures, we estimate a series of simple
logistic regressions that predict HTP adoption and estimate industry shares within adopting and
non-adopting groups. These analyses support anecdotes that aggressive tax planning MNCs tend
to be larger firms that operate in industries with large amounts of IP. We also examine whether
a set of additional characteristics are predictive of adoption and discuss patterns that emerge
from this analysis.
We start by examining industry variation in the adoption of hybrid tax planning by sector.16
Figure 6 reports the predicted probability of adoption by sector using estimates from a simple
logit model with industry dummies. There is considerable variation in adoption across different
sectors. For example, MNCs that are classified within the Information sector are more than twice
as likely to adopt HTP structures compared to MNCs classified under Finance and Insurance.
16

We use IRS industry classifications that are analogous to 2-digit SIC codes. For consistency, we use the most
recently observed industry classification for each MNC across all years.

25

Figure 7: Sector Shares, HTPs vs. Other MNCs
Manufacturing

Other

Information

Wholesale Trade

Professional, Scientific, and Technical Services

Finance and Insurance

Retail Trade

Management of Companies (Holding Companies)

Mining
0%

10%

20%

Hybrid Tax Planners

30%

40%

50%

Other MNCs

Notes: This figure reports industry shares by sector. Sectors with fewer than 10 firms in either group are collected
into the “Other” category.

Figure 7 shows industry shares for MNCs that adopt HTPs and for those that do not. A plurality of MNCs are classifed within the Manufacturing sector. MNCs that adopt HTP structures
disproportionately come from the Information and Manufacturing sectors.
Figure 8 provides predicted probability estimates from a logit model that is analagous to
Figure 6 but for subindustries.17 Adoption is stronger within subindustries that contain tech and
pharmaceutical firms (e.g., data processing and chemical manufacturing). Other IP-intensive
industries, such as publishing, also adopt HTP structures at relatively high rates. Figure 9
displays subindustry shares for adopting and non-adopting MNC groups, providing a similar
takeaway.

17

These IRS subindustry classifications are roughly equivalent to 3-digit SIC codes.

26

Figure 8: Predicted Probability of HTP Adoption by Subindustry
Data Processing, Hosting, and Related Services
Other Information Services
Miscellaneous Manufacturing
Publishing Industries (except Internet)
Merchant Wholesalers, Nondurable Goods
Chemical Manufacturing
Computer and Electronic Product Manufacturing
Primary Metal Manufacturing
Food Manufacturing
Professional, Scientific, and Technical Services
Machinery Manufacturing
Electrical Equipment, Appliance, and Component Manufacturing
Fabricated Metal Manufacturing
Transportation Equipment Manufacturing
Management of Companies (Holding Companies)
Merchant Wholesalers, Durable Goods
Mining
0.0

0.2

0.4

0.6

Notes: This figure reports the predicted probability of adoption by major group according to a simple logit model.
We remove groupings with fewer than ten MNCs in either group.

Figure 9: Major Shares, HTPs vs. Other MNCs
Computer and Electronic Product Manufacturing
Chemical Manufacturing
Professional, Scientific, and Technical Services
Publishing Industries (except Internet)
Merchant Wholesalers, Durable Goods
Merchant Wholesalers, Nondurable Goods
Machinery Manufacturing
Miscellaneous Manufacturing
Transportation Equipment Manufacturing
Management of Companies (Holding Companies)
Mining
Food Manufacturing
Fabricated Metal Manufacturing
Electrical Equipment, Appliance, and Component Manufacturing
Primary Metal Manufacturing
Data Processing, Hosting, and Related Services
0%

5%

Hybrid Tax Planners

10%

Other MNCs

Notes: This figure reports industry shares by major group. We remove groupings with fewer than ten MNCs in
either group.

27

Figure 10: Predicted Probability of HTP Adoption by Firm Size

0.15
Domestic Assets
Domestic Sales
Foreign Assets

0.10

Foreign Sales

0.05
0−25th Percentile

25−50th Percentile

50−75th Percentile

75−100th Percentile

Notes: This figure reports the predicted probability of adoption by size according to four simple logit models that
examine four different measures of firm size. We remove industry groupings with fewer than ten MNCs in either
group.

Figure 10 provides estimates of the predicted probability of HTP adoption according to
several different measures of MNC size. Each color provides estimates computed from a separate
logit model corresponding to a different measure of size (domestic assets, domestic sales, foreign
assets, and foreign sales). We bin the MNCs into size quartiles. All of these measures indicate
that larger MNCs tend to adopt HTP structures at higher rates.
In addition to industry and firm size, we examine whether a set of other observable characteristics is predictive of HTP adoption. Table A.4 reports coefficient estimates for a battery of logit
regressions that predict HTP adoption based on whether an MNC claims a tax credit for R&D;
its age (binned by quartile with the youngest firms set as the reference category); the average
statutory foreign ETR that it faces (and its share of foreign sales in jurisdictions with unobserved
statutory rates); a measure of its geographic exposure to Check the Box; whether it operated in
in Ireland, the Netherlands, or Luxembourg prior to adoption; whether it has negative domestic
earnings; and its advertising to sales ratio (a proxy for intangibles used in, e.g., Grubert and
Slemrod, 1998).18 These models use observations from adopting MNCs in the period prior to
18

To compute a firm-level measure of exposure to Check the Box, we first compute a country-level measure of
exposure γc = πcp /πc , where πcp are aggregate foreign earnings generated by pass-through foreign affiliates (FDEs)
for country c, and πc are aggregate foreign earnings for all foreign affiliates in country c for the years that we
observe FDE earnings (2006, 2008, 2012, and 2016). Next, for each MNC i we compute the share of foreign
sales by country sic in the period prior to adoption. Finally, for each
∑ firm, we compute the exposure measure
as a weighted average of the country-level exposure measures, γi = c∈Ci γc sic for the set of countries Ci where
MNC i has positive sales in the period prior to adoption, where the weights sic are the firm-level country shares
calculated in the second step. For firms that never adopt, we compute this exposure measure in every year.

28

adoption, and include all observations for never-adopters. Table A.5 combines the variables from
these regressions into a single logit regression. Both tables provide specifications with year fixed
effects only and with interactions between industry, sales quartile bins, asset quartile bins, and
year fixed effects.19 Finally, Table A.6 provides logit estimates from a set of MNCs that also
appear in Compustat to examine whether the identity of firms’ auditors plays a role in adoption
of hybrid structures.20
Of the characteristics listed above, only a few appear to be predictive of HTP adoption. Unsurprisingly, MNCs that previously operated in the jurisdictions where we detect HTP structures
(Ireland, the Netherlands, and Luxembourg) are more likely to adopt them. More generally, the
geographic distribution of MNC activity is predictive of adoption—MNCs that operate in countries where Check the Box is more heavily used may be able to shift income more easily, which
may explain why this measure of exposure is predictive of adoption. Finally, MNCs that are in a
domestic loss position are less likely to adopt HTP structures—this also has an intuitive explanation as these firms may have tax credits that offset income tax levied on repatriated foreign
earnings. Confidence intervals for these estimates are generally wider in the combined regression,
and when more granular fixed effects are included. These tables show that, even though some
characteristics are predictive of adopting an HTP, most variables are not statistically significant
after the inclusion of industry and size fixed effects. While some variables statistically correlate
with adoption, it is hard to predict which specific firms will adopt an HTP, even conditional on
a rich set of covariates.
Overall, the results in this section confirm the conventional wisdom that larger firms in
industries that rely on intellectual property are more likely to adopt HTPs. At the same time,
there remains considerable unexplained variation in HTP adoption even after controlling for a
large set of firm characteristics that have been used as proxies for tax planning, indicating the
existence of idiosyncratic costs and benefits of adoption that vary across firms.

19

Note that the number of observations goes down in the second Column (from 8,608 to 4,920). This is because
to be included, each bin must have both HTP adopters and non-adopters.
20
We divide the firms into three groups, multinationals with a Big 4 auditor (Ernst & Young, PWC, Deloitte,
and KPMG), medium size auditors, and small auditors. These results suggest that auditors do not play a
significant role in the adoption of HTP structures.

29

5

Hybrid Tax Planning Structures and Multinational Activity

Below, we examine changes in domestic and foreign economic activity of adopting MNCs. We
start with a set of descriptive facts that compare select aggregate outcomes for MNCs that
utilize HTPs to other MNCs that do not rely on these arrangements. We then estimate staggered
difference-in-differences models that control for firm characteristics in order to compare outcomes
for MNCs that do and do not adopt hybrid tax structures. These models allow us to tie changes
in firm activity to the timing of HTP adoption.

5.1 Comparison of Aggregate Trends
While the structures we study have been suspected of being used for profit shifting, lack of tax
data prevented prior researchers from confirming this role in a systematic manner. We therefore
start by examining whether hybrid tax planning firms engage in the kinds of transactions that are
associated with profit shifting. Figure 11 compares hybrid tax planners to other MNCs along a
number of these dimensions. For a given outcome, this and related figures plot selected outcomes
as a percentage of 1996 levels. The secondary y-axis to the right of each graph indicates the level
values for the group of hybrid tax planning firms. In Panel A, we first document that hybrid
tax planning firms generate a much larger aggregate loan balance between their related CFCs
when compared to other MNCs. These balances may be related to interest stripping strategies,
as discussed in Panel A of Figure 2.
Panel B of Figure 11 shows that hybrid tax planning MNCs experience a much faster rise in
the book value of foreign intangible assets when compared to non-HTP MNCs. This growth is
consistent with the use of HTP structures to shift income generated by intangible assets. Panel
C shows that these MNCs also increased compensation for services paid by CFCs to parent
companies—which includes cost sharing payments that are used to transfer intangible assets
from the US to foreign affiliates. Starting in 2008, Schedule G of Form 5471 allows us to observe
whether a parent had any cost sharing agreements with one of its CFCs; Panel D shows that
hybrid tax planning firms are also more likely to engage in these agreements.
Figure 1 compares average foreign ETRs of hybrid tax planners with that of other US MNCs
in our sample. At the beginning of the sample period, both types of MNCs paid taxes on foreign
E&P at similar rates. Starting in 2002, however, there is a striking divergence in the evolution of

30

Figure 11: Mechanisms for Profit Shifting
(A) Loans Between Related CFCs

(B) Foreign Intangible Assets
400

1,750

2,000%

2,000%

Other MNCs
1,500

Hybrid Tax Planners
1,500%

Hybrid Tax Planners

1,250

200
1,000%

500

500%

$ billions

750

300

1,500%

$ billions

1,000
1,000%

Other MNCs

100

500%

250
0%

0%

0
1995

2000

2005

2010

2015

(C) Compensation for Services
800%

0
1995

2000

2005

2010

2015

(D) Share with Cost Sharing Agreements

30

30%

Other MNCs
Hybrid Tax Planners

600%

400%

$ billions

20

Other MNCs

20%

Hybrid Tax Planners

10

10%

0

0%

200%

0%
1995

2000

2005

2010

2015

2008

2010

2012

2014

2016

Notes: These figures show the evolution of aggregate loans between related CFCs (Panel A), foreign intangible
assets (Panel B), payments from CFCs to US parent companies for technical services (Panel C), and the share
of MNCs in each group with active cost sharing agreements with a CFC (Panel D). For comparability, aggregate
values for both groups are normalized to 100% as of 1996 for Panels A through C. The light green line shows
values for a group of MNCs that eventually adopt at least one of the hybrid tax structures described in Section
2. The dark blue line shows aggregate values for MNCs that did not adopt any of these structures during the
sample window. For most outcomes, the right-hand axis displays dollar value in billions, relative to the aggregate
1996 dollar value for HTP MNCs.

each group’s ETR. By 2016, hybrid tax planners faced a foreign ETR that was about half of that
incurred by other MNCs. Figure 11 provides important context for the decline in foreign ETRs.
While one may suppose that declining statutory rates around the world may be responsible for
this decline, Figure 11 shows that MNCs with the largest reduction in foreign ETRs were also
engaging in behavior that has been linked to aggressive tax planning. Indeed, as we see in Figure
1, firms that did not adopt HTP structures experienced a much smaller decline in their foreign
ETR during our sample period.
Having shown that hybrid tax planning structures are likely used for profit shifting, we now
examine whether hybrid tax planning firms also deferred more income abroad. Panel A of Figure
12 shows that, relative to foreign E&P, hybrid tax planning firms saw faster declines in royalty

31

Figure 12: Evidence of Deferral
(A) Royalties to Parent

(B) Cash Held Abroad

150%
1,500%

700
Other MNCs
600

Hybrid Tax Planners

500
1,000%
400
300

50%

500%

$ billions

100%

200

Other MNCs

100

Hybrid Tax Planners
0%

0%
1995

2000

2005

2010

2015

0
1995

2000

2005

2010

2015

Notes: These figures show the evolution of royalty payments from CFCs to domestic parent entities as a share
of foreign E&P (Panel A) and of aggregate foreign cash balances (Panel B) for two groups of US MNCs. For
comparability, aggregate values for both groups are normalized to 100% as of 1996 in Panel B. The light green
line shows values for a group of MNCs that eventually adopt at least one of the hybrid tax structures described
in Section 2. The dark blue line shows aggregate values for MNCs that do not adopt any of these structures
during the sample window. For most outcomes, the right-hand axis displays dollar value in billions, relative to
the aggregate 1996 dollar value for HTP MNCs.

payments from CFCs to parents. This result is consistent with a transition away from undeferred
royalty income. Consistent with this interpretation, Panel B also shows that hybrid tax planning
firms saw large increases in cash held abroad compared to MNCs that did not adopt HTPs.
While the fact that MNCs accumulated cash abroad during the last two decades is well known,
this figure shows that the bulk of this growth occurred among the 300 firms that we observe with
hybrid tax planning agreements through Ireland, Netherlands, and Luxembourg.
The results in Figures 11 and 12 provide prima facie evidence of the specific mechanisms
through which hybrid tax planning strategies operate. The ability to shift profits to lower-tax
countries allows MNCs to avoid foreign income tax and defer US income tax.
These figures also highlight the value of using tax data, because they allow us to (i) identify the
adoption of specific tax planning structures (Figure 4), (ii) link the adoption of HTP structures to
specific profit shifting mechanisms (Figures 11 and 12), and (iii) measure the associated impact
on ETRs (Figure 1).
We now examine whether firms that benefited from tax planning also experienced differential
evolution in their real operations. Figure 13 shows that, while hybrid tax planning firms and
non-HTP MNCs had similar patterns of economic activity prior to 1997, their economic activities
diverged over the same time period that hybrid tax planning strategies were adopted. Panel A
shows that hybrid tax planners had larger increases in domestic capital assets; Panel B shows

32

Figure 13: Hybrid Tax Planning and Real Economic Activity
(A) Domestic Capital Assets

(B) Foreign Capital Assets
250%

1,000

200%

500

100%

500

50%

200%
400
150%
100%

250

Other MNCs

300
200
Other MNCs

50%

Hybrid Tax Planners
0%
2000

2005

2010

100

Hybrid Tax Planners
0%

0
1995

$ billions

750

$ billions

150%

2015

0
1995

2000

2005

2010

2015

(C) Domestic Wages
300%
250
200
150
100

100%
Other MNCs

$ billions

200%

50

Hybrid Tax Planners
0%

0
1995

2000

2005

2010

2015

Notes: These figures show the evolution of aggregate domestic capital assets (Panel A), foreign capital assets
(Panel B), and domestic wages (Panel C) for two groups of US MNCs. For comparability, aggregate values for
both groups are normalized to 100% as of 1996. The light green line shows aggregate values for a group of MNCs
that eventually adopt at least one of the hybrid tax structures described in Section 2. The dark blue line shows
aggregate values for MNCs that do not adopt any of these structures during the sample window. The right-hand
axis displays dollar value in billions relative to the aggregate 1996 dollar value for HTP MNCs.

that these firms also accumulated more foreign capital assets; and Panel C shows larger increases
in domestic payroll. Across all of these measures, declines in foreign ETRs were accompanied by
increases in foreign and domestic economic activity.

5.2 Estimating Staggered Difference-in-Differences Models
We now show that the changes in firm outcomes described above are closely tied to the adoption
of HTPs. Recent literature has provided several alternative models that researchers may use
to produce difference-in-differences estimates in staggered contexts. We provide estimates for
three of these models. Our main specification relies on the “stacked” design from Cengiz, Dube,
Lindner and Zipperer (2019). We choose this estimator as our main specification because it lends

33

itself easily to the addition of propensity score weights, which we describe later in this section.
The stacked design creates a data set for each cohort. This dataset includes MNCs that adopt
a hybrid structure, as well as MNCs that do not adopt a hybrid structure in the six years before
and after the adoption year c. Non-adopting MNCs may therefore be repeated in the regression
dataset as comparison units for different cohorts. Formally, the stacked design estimates the
regression equation,
Yict = αic + λct +

∑

µℓ 1 {t − c = ℓ} + vict ,

(1)

ℓ

where i indexes MNCs, c is the year in which a particular cohort first adopts a hybrid structure,
and t indexes years. ℓ is an indicator for the relative number of periods after MNC i adopts a
foreign tax planning structure.21 αic and λct are MNC-by-cohort and year-by-cohort fixed effects.
We estimate this regression for various outcomes Yict . In all specifications, we cluster standard
errors at the MNC level.
We interpret results of Equation 1 as measuring dynamic changes in firm-level outcomes of
adopting MNCs relative to non-adopting MNCs. Relative to the results in the prior section,
these estimates help tie changes in firm outcomes to the timing of adoption. This approach also
addresses the concern that firm outcomes are driven by concomitant shocks to firms with characteristics that are related to tax planning (e.g. larger firms, more IP-intensive firms, or firms
in different industries). To do so, we estimate alternative specifications that interact year fixed
effects with a set of pre-adoption covariates for MNCs to allow for time-varying heterogeneity
across industries, across foreign and domestic firm sales bins, and across foreign and domestic
bins for intangible assets.22 As a robustness check, we also estimate effects using the stacked specification with inverse probability weights (IPW) as well as an alternative specification proposed
by Sun and Abraham (2021) and a standard TWFE estimator.
While these firm-level comparisons are informative of the role of HTPs in driving the aggregate
changes described in the previous section, a key question is whether the estimates of Equation 1
can be interpreted as causal effects of HTPs. The usual assumptions for a causal interpretation
21

For comparison, a standard two-way fixed effects specification does not use repeated comparison units and
instead estimates the regression equation
∑
µℓ 1 {t − c = ℓ} + vit .
(2)
Yit = αi + λt +
ℓ
22

To be precise, this implies an augmented version of Equation 1 where λct is replaced by
G is a set of groups for which we include group-by-cohort-by-year fixed effects.

34

∑

g∈G λgct , where

include parallel trends and no anticipatory behavior. The descriptive evidence provided in the
previous section shows that the evolution of outcomes for adopting and non-adopting MNCs was
strikingly similar prior to the bulk of adoptions in the mid-2000s. Additionally, pre-trend coefficients in the event study plots provided below are generally insignificant. Regarding anticipation,
recall that our data only provides observations in even years. MNCs would therefore have to
adjust behavior two years in advance for this form of bias to be present. Because the parallel
trends assumption is inherently untestable and that HTP adoption is an endogenous choice of
the firm, it is important to consider that MNCs may select into HTPs because they have more
to gain from tax planning.
5.2.1

Estimates of Changes in Financial and Tax Outcomes

Figure 14 plots estimates of Equation 1 for a set of financial and tax outcomes. We report two
specifications for each outcome. Specification 1 (in black) does not include additional controls.
Specification 2 (in orange) includes year-by-cohort-by-industry and year-by-cohort-by-group fixed
effects, where groups include domestic and foreign sales quartiles and domestic and foreign intangible asset quartiles. Quartiles are computed using the period prior to adoption for each
cohort.
Panel A provides estimates for the log of the balance of loans between CFCs, Panel B for the
log of intangibles held abroad, and Panel C for the log of cash held abroad. Across these three
outcomes, we observe similar trends for HTP-adopting and non-adopting MNCs prior to the
adoption of a hybrid structure followed by relative increases for HTP-adopting MNCs after the
period of adoption. Consistent with these mechanisms and the result of Figure 1, Panel D shows
that the foreign ETRs of HTP-adopting MNCs gradually declined relative to non-adopting MNCs
following adoption. Six years after adoption, MNCs experience a reduction in their foreign ETR
of between three and four percentage points. For all of these outcomes, we find that inclusion
of size-bin-by-cohort-by-year fixed effects and industry-by-cohort-by-year fixed effects does not
significantly impact the estimates.
Table 4 provides aggregated estimates using a similar specification that replaces relative
time dummies with pre and post dummies. The estimates in Column (1) indicate that loans
between CFCs increased by 40%, foreign intangible assets increased by 68%, and cash held
abroad increased by 56% on average relative to the period prior to adoption. We also estimate

35

Figure 14: Profit Shifting Mechanisms, Deferral, and Foreign ETRs
(B) Foreign Intangible Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Loans Between Related CFCs
1.0

0.5

0.0

−0.5

−6

−4

−2

0

2

4

1.0

0.5

0.0

6

−6

−4

Time Relative to Adoption

0

2

4

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

6

(D) Foreign Effective Tax Rate
Relative Change in Outcome

(C) Cash Held Abroad
Relative Change in Logged Outcome

−2

Time Relative to Adoption

1.0

0.5

0.0

0.05

0.00

−0.05

−0.5
−6

−4

−2

0

2

4

6

−6

Time Relative to Adoption

−4

−2

0

2

4

6

Time Relative to Adoption

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

Notes: This figure provides estimates of µℓ from Equation 1 for the corresponding foreign outcome listed in
each panel, using 2-year pooled average data from Compustat and dropping odd years to match the IRS SOI
International Business Tax Sample. R&D Intensity (Panel A) is calculated as the ratio of annual R&D to the
MNC’s most recent sales value pre-adoption, and is restricted to be less than 1. Specification 1 (in black) does
not include additional controls. Specification 2 (in orange) includes year-by-cohort-by-industry fixed effects and
year-by-cohort-by-group fixed effects, where groups include domestic and foreign sales quartiles and domestic and
foreign intangible asset quartiles, and where quartiles are computed using pre-adoption values for each cohort.

an average decline in foreign ETRs of 3.9 percentage points in the period after adoption. Column
(2) shows that these estimates are stable across specifications that include interactions between
bins of firm size and year fixed effects, bins of intangible assets interacted with year fixed effects,
and industry-by-year fixed effects, suggesting that our results are not driven by comparisons
across firms in different industries, in different domestic and foreign size categories, or that are
more or less dependent on intellectual property.
36

Table 4: Profit Shifting Mechanisms and Foreign ETRs
(1)

(2)

(3)

(4)

(5)

0.681*** 0.735***
(0.172)
(0.177)
248
248
1524
1490

0.637***
(0.193)
208
1221

0.590***
(0.171)
250
1532

0.709***
(0.175)
229
1757

0.563*** 0.555***
(0.133)
(0.136)
257
257
2054
2000

0.469**
(0.149)
214
1518

0.539**
(0.173)
257
2023

0.472**
(0.144)
252
2037

0.401*
(0.157)
233
1263

0.524**
(0.161)
233
1240

0.505**
(0.174)
197
1041

0.344+
(0.176)
238
1294

0.491**
(0.163)
205
1597

-0.039**
(0.015)
257
2100

-0.032*
(0.016)
257
2043

-0.024
(0.018)
214
1529

-0.037*
(0.017)
257
2046

-0.042**
(0.015)
252
2042

Yes
Stacked

Yes
Stacked

Yes
Yes
Stacked

Yes
SA

Yes
TWFE

Panel A
Foreign Intangibles
Num. Treated
Num. Control
Panel B
Foreign Cash
Num. Treated
Num. Control
Panel C
Rltd. CFC Loans
Num. Treated
Num. Control
Panel D
Foreign ETR
Num. Treated
Num. Control
MNC & Year x Cohort FEs
MNC & Ind., Size x Yr FEs
MNC & Ind., Size x Yr x Cohort FEs
Inverse Prob. Weights
Model

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Notes: This table provides estimates of the difference-in-differences model discussed in Section 5.2 for the corresponding foreign outcome listed in each panel, where Columns (1) - (3) use Equation 1 and Columns (4) and
(5) use Equation 2. Column (1) does not include additional controls. Columns (2) - (5) include year-by-cohortby-industry and year-by-cohort-by-group fixed effects, where groups include domestic and foreign sales quartiles
and domestic and foreign intangible asset quartiles, and where quartiles are computed using pre-adoption values.
Column (3) uses inverse probability-weighted data. Column (4) estimates an alternative specification from Sun
and Abraham (2021). Column (5) estimates a standard TWFE specification.

We conduct an additional exercise to provide an alternative summary of the average change in
the foreign effective tax rate to reduce potential measurement error. In contrast to the estimates
above, which use year-by-year foreign ETRs, Table 5 shows pooled estimates where ETRs are
37

Table 5: Pooled Foreign ETRs

Unweighted Foreign ETR
Weighted Foreign ETR
Num. Treated
Num. Control
Sample
Outcomes
Years Included

(1)

(2)

−0.035+
(0.021)
−0.092**
(0.034)

−0.032*
(0.016)
−0.061*
(0.028)

155
1101
SOI
SOI
Even

83
308
Compustat
Compustat
All

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Notes: This table estimates a difference-in-differences model using a two-period specification. Foreign effective
tax rates are computed using aggregate taxes and earnings from each period to reduce measurement error
relative to an annual specification. Column (1) provides estimates using measures from SOI data. Column (2)
provides estimates using measures from Compustat. Results are provided for an unweighted specifciation as well
as a weighted specification where firms are weighted by aggregate pre-period earnings. Firms are only included
if they have positive earnings in both pre and post periods.

computed at the firm level for all pre and post periods. We include firms that have positive
aggregate earnings in both periods.23 This reduces year-to-year volatility in the firm-level ETR
that can be generated by losses and tax credits. We winsorize these rates to ensure they do not
exceed 100% so that outliers do not skew the average. Finally, we compute the average foreign
ETR using two different measures. The first measure, which is also used for Figure 14 above, uses
SOI tax data to compute the foreign ETR following the methodology described in Section 3.3.
The second measure uses foreign taxes and pretax income as reported in Compustat following the
method used to calculate foreign effective tax rates in Dyreng, Hanlon, Maydew and Thornock
(2017). There are advantages and disadvantages to both measures—SOI data provide a larger
sample of multinationals, but only even years are present in our data. Compustat has smaller
coverage, excluding private firms, but these firms are observed annually. Finally, we include two
specifications for each measure—the first is an unweighted regression and the second weights firms
by aggregate pre-period foreign income. Results are shown in Table 5. The unweighted pooled
results are broadly similar to the event study estimates, showing declines of between 3.2 and
23

We use the stacked panel in this analysis in order to create distinct pre and post periods for the comparison
group of firms that do not adopt hybrid structures.

38

3.5 percentage points among firms that adopt hybrid structures. Weighted results demonstrate
much stronger reductions in foreign ETRs after adoption of hybrid structures, between 6.1 and
9.2 percentage points depending on the measure used. These results suggest that although firms
generally experienced reductions in foreign tax rates after adopting hybrid structures, these
reductions were concentrated in the largest adopting firms.
5.2.2

Estimates of Changes in Real Economic Activity

To measure changes in real activity, we first estimate Equation 1 for a set of outcomes that
includes foreign assets, domestic wages, and domestic capital assets. Foreign assets are reported
on IRS Form 5471, domestic wages and capital assets are reported on Form 1120, and domestic
investment is measured as the sum of reported assets placed into service on Form 4562. Figure
15 reports the results of the event study analyses for these outcomes. As with the financial and
tax outcomes, estimates are not significantly different from zero in the pre-adoption period for
any of the real outcomes. Following the adoption of a hybrid tax planning structure, we observe
significant relative increases in foreign and domestic capital assets and domestic wages.
Table 6 summarizes these estimates using an aggregated specification. The specification with
more granular controls (Column (2)) reports an increase in domestic investment of 27% and a
corresponding increase in domestic capital assets of 16%. Panel C reports a 40% increase in
foreign capital assets, while Panel D reports a 20% increase in domestic payroll.

39

Figure 15: Event Studies: Hybrid Tax Planning and Real Economic Activity
(B) Domestic Capital Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Domestic Investment
0.6

0.4

0.2

0.0

−6

−4

−2

0

2

4

0.3

0.2

0.1

0.0

−0.1

6

−6

−4

Time Relative to Adoption

4

MNC & Year x Cohort FEs
MNC & Industry, Sales, Asset x Yr x Cohort FEs

0.25

0.00

−0.25

−2

0

2

6

(D) Domestic Wages
Relative Change in Logged Outcome

Relative Change in Logged Outcome

2

MNC & Industry, Sales, Asset x Yr x Cohort FEs

0.50

−4

0

MNC & Year x Cohort FEs

(C) Foreign Capital Assets

−6

−2

Time Relative to Adoption

4

6

0.4

0.2

0.0

−6

Time Relative to Adoption

−4

−2

0

2

4

6

Time Relative to Adoption

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

Notes: This figure provides estimates of µℓ from Equation 1 for the corresponding outcome listed in each panel.
Specification 1 (in black) does not include additional controls. Specification 2 (in orange) includes year-bycohort-by-industry fixed effects and year-by-cohort-by-group fixed effects, where groups include domestic and
foreign sales quartiles and domestic and foreign intangible asset quartiles, and where quartiles are computed
using pre-adoption values for each cohort.

40

Table 6: Hybrid Tax Planning and Real Economic Activity
(1)

(2)

(3)

(4)

(5)

0.170*
(0.079)
241
1802

0.267**
(0.088)
241
1756

0.307**
(0.109)
209
1472

0.285**
(0.104)
242
1798

0.206*
(0.086)
240
1919

0.098*
(0.046)
250
1882

0.156**
(0.051)
250
1828

0.147**
(0.056)
214
1528

0.076
(0.048)
252
1856

0.105*
(0.049)
247
1964

0.351*** 0.404***
(0.101)
(0.100)
257
257
1906
1852

0.377***
(0.106)
214
1492

0.340** 0.366***
(0.111) (0.105)
257
247
1866
1978

0.150**
(0.055)
250
1872

0.204***
(0.059)
250
1819

0.182**
(0.062)
214
1519

0.186*
(0.078)
252
1850

0.148*
(0.060)
247
1961

Yes
Stacked

Yes
Stacked

Yes
Yes
Stacked

Yes
SA

Yes
TWFE

Panel A
Domestic Investment
Num. Treated
Num. Control
Panel B
Domestic Capital
Num. Treated
Num. Control
Panel C
Foreign Capital
Num. Treated
Num. Control
Panel D
Domestic Wages
Num. Treated
Num. Control
MNC & Year x Cohort FEs
MNC & Ind., Size x Yr FEs
MNC & Ind., Size x Yr x Cohort FEs
Inverse Prob. Weights
Model

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Notes: This table provides estimates of the difference-in-differences model discussed in Section 5.2 for the corresponding outcome listed in each panel, where Columns (1) - (3) use Equation 1 and Columns (4) and (5)
use Equation 2. Column (1) does not include additional controls. Columns (2) - (5) include year-by-cohortby-industry and year-by-cohort-by-group fixed effects, where groups include domestic and foreign sales quartiles
and domestic and foreign intangible asset quartiles, and where quartiles are computed using pre-adoption values.
Column (3) uses inverse probability-weighted data. Column (4) estimates an alternative specification from Sun
and Abraham (2021). Column (5) estimates a standard TWFE specification.

41

Figure 16: Event Studies: Hybrid Tax Planning and R&D Data from Compustat
(B) Log R&D
Relative Change in Outcome

Relative Change in Outcome

(A) R&D Intensity
0.06

0.04

0.02

0.00

−0.02
−6

−4

−2

0

2

4

0.25

0.00

−0.25

6

−6

Time Relative to Adoption

−4

−2

0

2

4

6

Time Relative to Adoption

MNC & Year x Cohort FEs
MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Year x Cohort FEs
MNC & Industry, Sales, Asset x Yr x Cohort FEs

Notes: This figure provides estimates of µℓ from Equation 1 for the corresponding R&D outcome listed in each
panel. R&D Intensity (Panel A) is calculated as the ratio of annual R&D to the MNC’s most recent sales value
pre-adoption, and is restricted to be less than 1. Specification 1 (in black) does not include additional controls.
Specification 2 (in orange) includes year-by-cohort-by-industry fixed effects and year-by-cohort-by-group fixed
effects, where groups include domestic and foreign sales quartiles and domestic and foreign intangible asset
quartiles, and where quartiles are computed using pre-adoption values for each cohort.

5.2.3

R&D Activity

To examine R&D activity, we merge in data from Compustat, which gathers R&D expenditures
from MNCs’ consolidated financial statements. Because this procedure introduces sampling
attrition, as not all MNCs in the tax data are public, we separate this analysis from the estimates
contained above that rely on administrative tax data.
In order to match the biannual frequency of observations in the SOI sample, we create a
two-year pooled average for each Compustat variable of interest and focus the estimation on the
same years available in the tax data. We study two measures of R&D using the same event study
model discussed above. The first measure, R&D intensity, is computed as the ratio of R&D to
revenue. We fix the denominator to the period prior to HTP adoption. A valuable feature of
this measure is that it does not exclude firm-years in which zero R&D expense is reported. The
second measure is the log of R&D. This measure leads to further sample attrition because of the
large number of firm-years that report zero R&D expenditures.
The results of these estimations are shown in Figure 16. As with other measures of real
economic activity, this figure shows that adopting firms experience a significant increase in both
R&D intensity and log R&D following HTP adoption. These results are summarized in Table 7.
42

Table 7: Hybrid Tax Planning and R&D Data from Compustat
(1)

(2)

(3)

(4)

(5)

0.026**
(0.008)
136
452
0.051
0.054

0.026**
(0.010)
124
385
0.051
0.054

0.032**
(0.010)
113
335
0.047
0.054

0.033***
(0.009)
124
380
0.051
0.054

0.034***
(0.009)
124
380
0.051
0.054

0.319*** 0.285***
(0.068)
(0.078)
89
79
298
252

0.362***
(0.077)
73
235

0.241***
(0.069)
79
266

0.268**
(0.084)
79
266

Yes
Stacked

Yes
Yes
Stacked

Yes
SA

Yes
TWFE

Panel A
R&D Intensity
Num. Treated
Num. Control
Avg. R&D Intensity (All Firms)
Avg. R&D Intensity (Treated Firms)
Panel B
Log R&D
Num. Treated
Num. Control
MNC & Year x Cohort FEs
MNC & Ind., Size x Yr FEs
MNC & Ind., Size x Yr x Cohort FEs
Inverse Prob. Weights
Model

Yes
Stacked

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Notes: This table provides estimates of the difference-in-differences model discussed in Section 5.2 for the
corresponding R&D outcome listed in each panel, where Columns (1) - (3) use Equation 1 and Columns (4) and
(5) use Equation 2. Estimations use 2-year pooled average data from Compustat, where odd years are dropped
to match the IRS SOI International Business Tax Sample. R&D Intensity (Panel A) is calculated as the ratio of
annual R&D to the MNC’s most recent sales value pre-adoption, and is restricted to be less than 1. Column (1)
does not include additional controls. Columns (2) - (5) include year-by-cohort-by-industry and
year-by-cohort-by-group fixed effects, where groups include domestic and foreign sales quartiles and domestic
and foreign intangible asset quartiles, and where quartiles are computed using pre-adoption values. Column (3)
uses inverse probability-weighted data. Column (4) estimates an alternative specification from Sun and
Abraham (2021). Column (5) estimates a standard TWFE specification.

Column (2) reports that R&D intensity increased by 0.026, which corresponds to a 48% increase
relative to the average R&D intensity of 0.054 for HTP-adopting firms. Panel B shows that log
R&D increased by 0.285 following adoption of an HTP. The larger estimate for R&D intensity
suggests that incorporating extensive-margin responses is important in this setting and leads to
larger estimates of the change in R&D activity following the adoption of an HTP structure.

43

5.2.4

Further Robustness Checks

To ensure that we are comparing similar firms in our regressions, we extend the estimation
of Equation 1 by applying inverse probability weighting to the sample of MNCs. A standard
approach to this form of weighting in an unstaggered difference-in-differences setting estimates a
single propensity score for each unit. These scores are canonically estimated using data in periods
prior to HTP adoption. In staggered designs, however, control units may serve as comparisons for
multiple treated cohorts, and there is not a well defined pre-period. The advantage of the stacked
estimator is that it can be thought of as combining a set of unstaggered difference-in-differences
datasets, one for each treated cohort.
As shown in Section 4.3, we find evidence for considerable variation in rates of HTP adoption
by industry and by firm size, which motivates our usage of industry and firm-size fixed effects in
the preceding difference-in-differences estimates to control for time-varying heterogeneity along
these dimensions. Section 4.3 also shows that several other observable characteristics predict
MNC adoption of hybrid tax structures (see Tables A.4 and A.5). Propensity score weighting
offers a convenient way to control for potential selection bias that may be introduced by a lack
of balance along these observable dimensions between adopting and non-adopting MNCs.
We approach the computation of propensity scores with a similar logic to the design of the
stacked estimator by computing a set of propensity scores for each treated cohort and nevertreated observations that are used as cohort-level comparisons. We use the same predictor
variables examined in Section 4.3 and Tables A.4 and A.5.24 This gives us a set of propensity
scores for never-treated units that are used as comparison units for multiple cohorts. These
scores increase comparability within cohorts across adopting and non-adopting MNCs for other
observable characteristics. The score incorporates pre-adoption measures of R&D activity; firm
age; average foreign statutory rates; exposure to geographies where it may have been easier to
use Check the Box; prior activity in Ireland, the Netherlands, and Luxembourg; whether or not
the MNC had negative domestic income; and the firm’s advertising intensity relative to sales.
Because the stacked estimator also explicitly duplicates never-treated units when they are used
as comparisons for multiple cohorts, we can apply these sets of propensity scores directly in
24

In contrast to the logit estimates shown in Section 4.3, we use a random forest model to compute propensity
scores. In our context, random forest models outperform logit models from a predictive standpoint, and can
flexibly account for non-linearities. They are also theoretically invariant to transformations such as the natural
log, which would require dropping observations with non-positive values.

44

the stacked design in a similar way to an unstaggered propensity score weighted difference-indifferences model.
Columns (3) of Tables 4, 6, and 7 show that we obtain similar estimates of changes in firm
outcomes when we expand the model in Equation 1 by including inverse probability weights. See
Figure A.1 and A.2 for corresponding event studies that also use IPW.
We also explore the robustness of our results to using alternative estimators. Columns (4)
of Tables 4, 6, and 7 show that we generally obtain similar estimates when using the estimator
of Sun and Abraham (2021). Columns (5) of Tables 4, 6, and 7 show that we also find similar
effects when using a simpler two-way fixed effects estimator.25
Overall, the results in this section show that the adoption of HTP structures precedes large
increases in tax avoidance behavior as well as increases in real economic activity, both at home
and abroad.

5.3 Domestic to Foreign Profit Shifting and the Role of Cost Sharing
Agreements
In Section 2.2.1, we briefly discuss how MNCs can use a contract known as a cost sharing
agreement (CSA) to transfer IP to foreign affiliates. In Section 5.1, we show that HTPs utilize
CSAs at a much higher rate than non-HTP MNCs. One underlying reason for this is that MNCs
with hybrid tax planning structures may be able to use CSAs to shift profits from the US to
their low-tax hybrid entities.
Note that this behavior may in some instances be permitted under US tax law. One such
legal approach to profit shifting, which is described in publicly available legal briefs, was used
by MNCs in the early 2000s. During this period, MNCs were able to exclude certain R&D
costs that the IRS argued were subject to a CSA (and therefore, would have been allocated to
an MNC’s foreign affiliates). Specifically, 1995 regulations issued by the IRS did not explicitly
require stock-based compensation to be covered by CSAs.
After extensive litigation, a technology firm won a ruling in the US Tax Court that permitted
the exclusion of these costs. Although the US Treasury attempted to clarify its regulations to
disincentivize this type of tax planning, other MNCs continued to engage in similar strategies
during a long period of regulatory uncertainty. Appendix C provides additional institutional
25

See Figure A.3 and A.4 for event studies using the estimator of Sun and Abraham (2021) and Figure A.5
and A.6 for event studies using the TWFE estimator.

45

detail about this strategy, along with empirical analysis that demonstrates a substantial behavioral shift by MNCs that held CSAs during the mid-2000s. We find evidence that MNCs
exposed to this regulatory uncertainty expanded their use of stock-based compensation, both
in absolute terms and as a fraction of their overall wage bill. We also find evidence that these
MNCs increased their overall R&D activity and reported larger amounts of their wage expenses
as eligible R&D costs for a domestic tax credit.

6

Conclusion

Complex tax planning strategies have been a focus of media attention and have played an important role in motivating international tax reforms. Despite this focus, policy makers, practitioners,
and academics lack a comprehensive understanding of the prevalence of these strategies and their
role in explaining some key facts surrounding international taxation.
Using an unique integration of tax data covering the domestic and foreign operations of US
MNCs, we help to fill this gap by reconstructing the ownership networks of foreign affiliates of
US MNCs. This allows us to identify the adoption of three important tax planning strategies:
the Double Irish and two forms of Reverse Hybrid Mismatch arrangement, one through the
Netherlands, the other through Luxembourg. We show that these structures account for a
significant fraction of the foreign profits of US MNCs. We also link the use of these structures
to financial transactions that companies could use to shift profits to low-tax (and tax haven)
countries. Although only 17.5% of US MNCs adopt these structures, those that do obtain
a significant tax advantage over other MNCs. Remarkably, this small fraction of companies
generates a majority of foreign earnings by US MNCs and is responsible for the bulk of the
increase in cash held abroad over the period we study.
Our analyses use this tax data to reveal that the adoption of hybrid tax planning structures
is accompanied by significant changes in real economic activity. We find that firms adopting
these structures also have experience significant foreign and domestic growth.
While our analysis sheds light on multinational tax planning behavior over the last several
decades, a large number of policy changes have reshaped the incentives and feasibility of tax
planning in recent years. As new data becomes available, researchers should be able to determine
how firms have reacted to the new landscape and the extent to which current policy attempts to
curb profit shifting and tax avoidance have been successful.
46

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49

Appendices
These appendices include supplemental information and additional analyses. Appendix A
provides additional tables and figures related to the main analyses. Appendix B provides an
overview of measurement issues in several different sources of multinational data. Appendix C
describes how multinationals with cost sharing agreements may have used a legal strategy to
reclassify foreign costs as domestic costs for tax planning purposes.

A Additional Tables and Figures

50

Table A.1: MNC Summary Statistics
Domestic Outcomes
Tangible Assets
Wages
Investment
R&D

All MNCs
Mean
3024
583
267
73

Foreign Outcomes

51

Tangible Assets
Intangible Assets
Pretax E&P
Income Taxes
Cash

Sample Sizes

P25 P75
SD
226 1981 9977
58 447 1636
10 141 1619
0
38 343
All MNCs

Mean
886
355
279
54
496

Foreign Affiliate Counts
FDE Count
CFC Count

Hybrid Tax Planners

P25
16
0
2
0
5

P75
409
136
105
22
125

P25
4
2

P75
34
12

3635

P25 P75
SD Mean P25 P75
SD
341 2894 14023 2659 201 1762 8557
114 928 2440
440
49 366 1308
17 216 1242
243
9 125 1704
0 106
644
44
0
27 184

Hybrid Tax Planners
SD
3874
1856
1607
284
4579

All MNCs
Mean
38
13

Mean
4407
1125
356
179

Mean
1706
898
771
117
1429

P25
56
6
12
4
21

P75
811
462
323
59
430

Mean
86
34

P25
16
4

P75
81
33

512

Other MNCs

SD Mean P25
6598
668
11
3467
211
0
3062
148
1
378
38
0
8368
250
4

Hybrid Tax Planners
SD
103
41

Other MNCs

SD
188
86

P75
SD
313 2692
91 1039
74 840
16 250
90 2775

Other MNCs
Mean P25
23
4
10
2

P75
24
10

SD
48
26

3123

Notes: This table contains summary statistics for our stable sample as described in Section 3.2. This sample was created by identifying MNCs with
coverage in both the SOI Corporate and SOI International Business Tax samples, not including MNCs that did not have at least $500 million in domestic
assets as well as at least one CFC with $50 million in foreign assets. To ensure we do not disclose information about individual MNCs, the values listed
for P25 and P75 are the means of ten observations surrounding a given percentile.

Figure A.1: Event Studies: Profit Shifting Mechanisms and Foreign ETRs (IPW)
(B) Foreign Intangible Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Loans Between Related CFCs
1.0

0.5

0.0

−0.5

−6

−4

−2

0

2

4

1.2

0.8

0.4

0.0

−0.4

6

−6

−4

Time Relative to Adoption

0

2

4

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

(C) Cash Held Abroad

6

(D) Foreign Effective Tax Rate

1.0

Relative Change in Outcome

Relative Change in Logged Outcome

−2

Time Relative to Adoption

0.5

0.0

0.05

0.00

−0.05

−0.5
−6

−4

−2

0

2

4

6

−6

Time Relative to Adoption

−4

−2

0

2

4

6

Time Relative to Adoption

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

Notes: This figure provides estimates of µℓ from Equation 1 for the corresponding foreign outcome listed in each
panel, using inverse probability-weighted data. Specification 1 (in black) does not include additional controls.
Specification 2 (in orange) includes year-by-cohort-by-industry fixed effects and year-by-cohort-by-group fixed
effects, where groups include domestic and foreign sales quartiles and domestic and foreign intangible asset
quartiles, and where quartiles are computed using pre-adoption values for each cohort.

52

Figure A.2: Event Studies: Hybrid Tax Planning and Real Economic Activity
(IPW)
(B) Domestic Capital Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Domestic Investment
0.50

0.25

0.00

−6

−4

−2

0

2

4

0.3

0.2

0.1

0.0

−0.1

6

−6

−4

Time Relative to Adoption

4

MNC & Year x Cohort FEs
MNC & Industry, Sales, Asset x Yr x Cohort FEs

0.25

0.00

−0.25
−2

0

2

6

(D) Domestic Wages
Relative Change in Logged Outcome

Relative Change in Logged Outcome

2

MNC & Industry, Sales, Asset x Yr x Cohort FEs

0.50

−4

0

MNC & Year x Cohort FEs

(C) Foreign Capital Assets

−6

−2

Time Relative to Adoption

4

6

0.4

0.2

0.0

−6

Time Relative to Adoption

−4

−2

0

2

4

6

Time Relative to Adoption

MNC & Year x Cohort FEs

MNC & Year x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

MNC & Industry, Sales, Asset x Yr x Cohort FEs

Notes: This figure provides estimates of µℓ from Equation 1 for the corresponding outcome listed in each panel,
using inverse probability-weighted data. Specification 1 (in black) does not include additional controls. Specification 2 (in orange) includes year-by-cohort-by-industry fixed effects and year-by-cohort-by-group fixed effects,
where groups include domestic and foreign sales quartiles and domestic and foreign intangible asset quartiles, and
where quartiles are computed using pre-adoption values for each cohort.

53

Figure A.3: Event Studies: Profit Shifting Mechanisms and Foreign ETRs
(Sun Abraham)
(B) Foreign Intangible Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Loans Between Related CFCs

0.5

0.0

−0.5

−6

−4

−2

0

2

4

1.2

0.8

0.4

0.0

−0.4

6

−6

−2

0

2

Time Relative to Adoption

MNC & Year FEs

MNC & Year FEs

MNC & Industry, Sales, Asset x Yr FEs

MNC & Industry, Sales, Asset x Yr FEs

(C) Cash Held Abroad

4

6

(D) Foreign Effective Tax Rate
0.05

1.0

Relative Change in Outcome

Relative Change in Logged Outcome

−4

Time Relative to Adoption

0.5

0.0

0.00

−0.05

−0.5
−6

−4

−2

0

2

4

6

−6

−4

−2

0

2

Time Relative to Adoption

Time Relative to Adoption

MNC & Year FEs

MNC & Year FEs

MNC & Industry, Sales, Asset x Yr FEs

MNC & Industry, Sales, Asset x Yr FEs

4

6

Notes: This figure provides estimates of µℓ from Equation 2 for the corresponding foreign outcome listed in each
panel, using the staggered estimator from Sun and Abraham (2021). Specification 1 (in black) does not include
additional controls. Specification 2 (in orange) includes year-by-cohort-by-industry fixed effects and year-bycohort-by-group fixed effects, where groups include domestic and foreign sales quartiles and domestic and foreign
intangible asset quartiles, and where quartiles are computed using pre-adoption values for each cohort.

54

Figure A.4: Event Studies: Hybrid Tax Planning and Real Economic Activity
(Sun Abraham)
(B) Domestic Capital Assets
Relative Change in Logged Outcome

Relative Change in Logged Outcome

(A) Domestic Investment
0.6

0.4

0.2

0.0

−6

−4

−2

0

2

4

0.2

0.1

0.0

−0.1

6

−6

2

MNC & Year FEs

MNC & Industry, Sales, Asset x Yr FEs

MNC & Industry, Sales, Asset x Yr FEs

0.25

0.00

−0.25

−2

0

2

4

4

6

4

6

(D) Domestic Wages
Relative Change in Logged Outcome

Relative Change in Logged Outcome

0

MNC & Year FEs

0.50

−4

−2

Time Relative to Adoption

(C) Foreign Capital Assets

−6

−4

Time Relative to Adoption

6

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Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/agency%3Airs%3Aeb16ecc5aa080e78. Public record. Not legal advice.