WORKING PAPER SERIESInternational tax spillovers
and tangible investment,with implications
for the global minimum tax

Michael Keen (Tokyo College, The University of Tokyo)
Li Liu (International Monetary Fund)
Hayley Pallan (World Bank)
April 18, 2023
TC-E-23-1

Tokyo College, The University of Tokyo

TC-E-21-1

International tax spillovers and tangible investment,with implications for the global minimum tax
Michael Keen, Li Liu, and Hayley Pallan∗
18th April 2023

Abstract
This paper articulates and, using newly-assembled data, explores how international taxation affects aggregate tangible cross-border investment. Spillovers
from statutory tax rates abroad seem: As sizable as effects from the host’srate; larger than previous consensus values (attributed to a systematic bias
from FDI data); and consistent with ‘implicit’ profit shifting through real
investment (rather than ‘paper’ profit shifting). Contrary to much policy discussion, the results also imply that: Host countries’ marginal effective tax rates
have at best a weak effect on real investment; those elsewhere have none; and,applied to the prospective global minimum tax, inward tangible investment inmost sample countries will increase.

Keywords: Corporate Taxation, International Tax, Multinational Investment, Foreign Direct Investment
JEL Classification: H21, H25, H32∗
Keen: Tokyo College, University of Tokyo; Institute for Fiscal Studies, London;
CERDI, Université Clermont Auvergne; Center for Business Taxation, University of Oxford;(michael.keen@mail.u-tokyo.ac.jp). Liu: International Monetary Fund (lliu@imf.org). Pallan:
World Bank (hpallan@worldbank.org). We are grateful to Ruud de Mooij, Michael Devereux,
Dinar Prihardini, Andrzej Stasio and participants in seminars at the IMF, Mannheim, NBER, andOxford for helpful comments. All mistakes are our own. The views expressed are the authors’ anddo not necessarily represent the views of the IMF, its Executive Board, or IMF management. The
findings, interpretations and conclusions expressed in this paper are entirely those of the authors
and should not be attributed to the World Bank, its Executive Directors, or the countries they
represent.

11

Introduction

International spillovers in corporate taxation have become a prominent policy concern inrecent years, reflecting increased recognition of the impact that each country’s tax policies
may have on both activity and tax revenues in others, and of the potential this creates forpotentially damaging tax competition. These concerns prompted the G20/OECD-led projecton Base Erosion and Profit Shifting (BEPS) which has now culminated in the genuinely
historic agreement among nearly 140 countries, in October 2021, on fundamental reform of
the international tax architecture.1 A key and unprecedented element of this is agreement
on a global minimum effective rate of corporate tax of 15 percent, due to take effect in 2024.
One might have expected these policy debates to have been informed by, and generate,
close analyses of the effects of international tax reform on cross-border investment. Afterall, attracting inward investment, including through tax system design, is evidently a major
concern in many countries, seeing this as a route to both employment and growth-enhancing
knowledge spillovers.2 And there is indeed a large literature on the topic: large enough tohave begun generating meta-studies many years ago (De Mooij and Ederveen (2003), Feldand Heckemeyer (2011)). Over the last few years, however, there has been a shift of interest,
both academic and in the OECD/G20 discussions, towards analyzing the anatomy and, inparticular, the tax revenue consequences of ‘profit shifting’ (meaning artificial transactions
intended simply to reduce total tax liability).3 Empirical understanding of tax effects onreal cross-border investment, however, has not progressed as rapidly.
The recent preoccupation with the revenue consequences of profit shifting, also stressedby Suárez Serrato (2019), is indeed likely one reason for this. But there is perhaps a more
1

OECD (2020a,b).

2
There is ample evidence on the positive spillover effects from foreign direct investment on the accumulation of know-how (see for instance Baldwin, Braconier and Forslid (2005) and Keller (2010)), economic
growth for capital-importing countries (Bosworth and Collins (1999), Javorcik (2004), and Alfaro et al.
(2004)), and domestic capital formation in capital-exporting countries (Desai, Foley and Jr. (2005)).
3

Recent reviews are in Beer, De Mooij and Liu (2020) and Dharmapala (2019). The exact magnitudeof profit shifting remains contentious, but has been estimated as dissipating 5-10 percent of total corporate
income tax revenue in advanced and emerging economies (OECD (2015) and Tørsløv, Wier and Zucman(2018)), a third or more of all U.S corporate tax revenue (Clausing (2020)), and up to 1.3 percent of GDP
in developing countries (Crivelli, De Mooij and Keen (2016)).

2

fundamental explanation than shifting interests. This is that a great deal of the literature
has used Foreign Direct Investment (FDI) data, enthusiasm for which has been dimmed by
the increasing recognition of a point first made long ago: “FDI does not correspond directly
to any measure of real investment [...] It is more accurately thought of as a measure offinancial flows...”4 Measured FDI includes, importantly, flows that for tax reasons (routing
intra-group payments so as to take advantage of reduced withholding tax rates and othertax or treaty provisions), and perhaps for other reasons too, pass through some ‘conduit’jurisdiction while generating real investment somewhere else. This means that the sameunderlying investment can be recorded multiple times as it passes through conduits, giving
rise to a form of double counting. And such conduit flows cannot be unravelled from the
FDI data, which typically report the location of affiliates’ immediate parents, not that of
the ultimate parent company. The importance of these points jumps out from the data,in the existence of ‘investment hubs’ with inward investment clearly far in excess of levels
that are plausible for domestic investment, given the size of the domestic economy.5 Suchinvestments, with no real links to the local economy, may account for around 40 percent of
global FDI.6 Relying on FDI data to infer tax effects on cross-border real investment is thus,
at best, highly problematic.
There are of course analyses that do not use aggregate FDI data, with much attention
coming to be focused on firm-level data (as for example in Liu (2020) and Millot et al. (2020)).
While this has evident importance and appeal, results from aggregate FDI-based studies
continue to shape views on tax-responsiveness; and untangling what FDI data do or do not
say about tax effects has its own importance from the wider macroeconomic perspective ofunderstanding and managing cross-border capital flows. Moreover—even leaving aside theproblems with FDI data—much of the literature focuses only on the impact on inward FDIof host country taxation, and so simply does not speak to the key issue of cross-country4

Slemrod (1990), p.83.

5

See for instance International Monetary Fund (2014), Blanchard and Acalin (2021), Damgaard, Elkjaer
and Johannesen (2019) and Coppola et al. (2021).
6

Aykut, Sanghi and Kosmidou (2017), Damgaard, Elkjaer and Johannesen (2019).

3

spillovers from one country’s tax system to real investment elsewhere.7
Against that backdrop, the core purpose of this paper is to make further progress on what
thus remains, somewhat surprisingly, one of the least-understood aspects of international tax
arrangements: the impact on real investment. The results then enable us to address a key
but somewhat neglected aspect of the prospective impact of the global minimum tax: its
effects on real investment, both by country and in aggegate.
The fundamental problems associated with the use of FDI data are overcome here by using
a newly-constructed dataset on Foreign Affiliate Investment (FAI). These data explicitly
record the acquisition of tangible assets (new and old) by foreign affiliates in each hostcountry,8 and see through conduit flows to identify the country of the ultimate parent.
Consequently, neither of the main shortcomings of FDI data applies. Estimation can explore
the effects on host-parent bilateral FAI flows of tax systems in the host country, parent
country and other potential host countries. Our dataset covers inward investment from 187
parent countries into affiliates in 32 (advanced) host countries from 2003-2016, along withdetail on a range of activity variables. other information on activities. The rich informationin this dataset helps disentangle the effect of taxes from unobserved, confounding factors on
inward foreign investment. The limitation of these data to tangible assets is a significant
one, given the increasing importance of intangibles in multinational activity; but there canbe little doubt as to the importance and salience of tangible investment for the formation of
tax and other policies.
Armed with these data, the conceptual challenge is then to elucidate the precise channels
by which taxation might affect inward FAI. Candidates explored in previous work, sometimes
somewhat haphazardly, include statutory tax rates, average effective tax rates (along the linesof Devereux and Griffith (2003)) and marginal effective tax rates of the kind developed in
7

The semi-elasticity on which the meta-analyses of De Mooij and Ederveen (2003) and Feld and Heckemeyer (2011) focus is that with respect to the host country rate. Some studies do construct simple tax
differentials: Bénassy-Quéré, Fontagné and Lahrèche-Révil (2005), for example, examine the effect of tax
differentials on bilateral FDI, though the differential is in this case simply that between host and parent
statutory rates.
8

They thus put to rest the lament with which the quotation from Slemrod (1990) continues: “Unfortunately, no data exist on real investment made by foreign branches and subsidiaries.”4

closed economy contexts (classically, as in King and Fullerton (1984))9 . The precise routes bywhich these effects are expected to operate are often not clearly spelt out, either in analyticalwork or public debate. The reduced marginal effective rates associated with the 2017 Tax
Cuts and Jobs Act in the United States, for example, prompted concerns in Canada with
the impact on investment there. But how exactly the marginal effective rate in one country,
constructed with a closed economy in mind, should impact investment in another is by nomeans obvious. Similar doubts apply more generally to the helpfulness of the much-notedcross-country ‘league tables’ of marginal effective tax rates10 in understanding cross-borderinvestment decisions.
The approach taken here is to ground the empirics in a simple but reasonably generalmodel of the investment decisions of a multinational that potentially operate in several countries. This generates a sufficient statistic which summarizes the impact of the internationaland domestic tax system on inward investment, encapsulating effects through statutory tax
rates, in both the host country and alternative locations abroad, and King-Fullerton-type
marginal effective tax rates. In doing so, it highlights one form of spillover that has been
largely neglected in the international tax literature: the direct effect that investment in one
affiliate within a multinational may have on the profitability of others. It might be, forexample, that investing in one, and expanding its sales, reduces the price at which others
can sell; or expanding one may require using scarce expertise within the group and so reduce
the profitability of others. We refer to this, for want of a better term, as ‘implicit’ profit
shifting, and distinguish it from ‘paper’ profit shifting of the kind that, as mentioned above,
has become the primary focus of attention. In the empirics, we allow for, and attempt
to differentiate between, these two forms of profit shifting and the tax effects that operate
through them.
The paper is structured as follows. Section 2 sets out the broad framework that articulates
potential channels for tax spillovers on real investment and guides the empirical work. Section
9

The King-Fullerton marginal effective tax rate is the wedge between the pre- and post-tax returns on anunderlying investment which just yields the investor their required post-tax return (conventionally expressed
as a proportion of the former).
10

As, notably, Mintz (2018) and Bazel and Mintz (2020).

5

3 elaborates on the fundamental differences between FAI and FDI data and Section 4 thendevelops an estimation strategy. Section 5 sets out baseline results, explores their robustness
and some extensions, and explores directly the empirical consequences of using FAI rather
than FDI data. Section 6 applies the results to assess the implications for real inward
investment of the prospective global corporate minimum tax rate. Section 7 concludes.

2

Channels of Tax Spillover

To fix ideas, consider a multinational potentially investing through controlled affiliates in N
countries. Suppose too, for now, that profit shifting through artificial transactions between
them is not possible: relaxing this will be one of the later extensions. Denoting by Kj ≥ 0 thetangible capital that the multinational invests in its affiliate in jurisdiction j, and by Fj (Kj )some output associated with that capital, we suppose taxable receipts of the affiliate in the
typical host jurisdiction h to be of the form Rh [F1 (K1 ), .., Fh (Kh ), .., FN (KN )].11 The key
feature this allows for is the possibility that receipts of an affiliate located in any country h
may depend on the production of related affiliates in other countries. This serves to capturea range of possible patterns of multinational activity.
The simplest such possibility is that each affiliate serves its own local and competitivemarket, selling its product at price Ph , so that Rh = Ph .Fh (Kh ); this is in effect the familiar
closed economy case, with no cross effects across the distinct affiliates. But there are manyother possibilities captured by this structure. One, a case of particular interest in analyzing
the consequences of closer economic integration, is that the affiliates sell a homogeneous
product into a single integrated market within which the multinational has some degreePof market power, so that Rh = P ( N
i=1 Fi )Fh , where P (.) denotes inverse demand, with
P 0 < 0.12 In this case, each cross effect ∂Rj /∂Fh − P 0 Fj is strictly negative: increased
output in h reduces the price at which all affiliates sell, and so reduces revenue in all countries
other than h itself. Another possibility is that expanding output in affiliate h diverts scarce
11

The Fj (Kj ) are assumed increasing and strictly concave; intermediate goods supplied by third parties
is taken to be subsumed in the revenue functions.
12

Derivatives are indicated by a prime for a function of one variable.

6managerial resources away from other affiliates, again implying a negative cross-effect. There
may also be circumstances, however, in which cross effects are positive, reflecting someform of cross-border complementariness across host countries. (The idea of cross-country
complementarities has attracted significant attention in the literature, but with a focus
quite different from that here: there it is on the relationship between domestic investment
and investment abroad;13 here it is on the relationships between inward investment into
alternative locations). In the analysis that follows, while the possibility is left open thatcross effects may indeed be positive between h and some affiliates in the group, it is assumed,for definiteness, that the aggregate cross effect over affiliates is strictly negative, so thatPN
j6=h ∂Rj /∂Fh < 0.
The tax system in each country j has two components. One captures the marginal
effective tax rate in the tradition of King and Fullerton (1984); we refer to this as the
KF -M ET R. Familiar from the analysis of investment in closed economies as a sufficient
statistic for tax effects on investment, this is modeled as tax of Mj (expressed as a proportionof the required return, which is denoted by ρ and taken to be fixed) applied to the use ofreal capital in j. The other is a ‘source-based’ statutory tax rate on the profit earned oninvestments in j (against which Mj is deductible) at rate Tj . The multinational’s aggregate
after-tax profit is thusΠ(K1 , .., KN ) =

N
X





(1 − Tj ) Rj F1 (K1 ), .., FN (KN ) − ρ(1 + Mj ) ,

(1)

j=1

it being assumed that the required return to capital is deductible where the associated capitalis located. All this, it should be noted, relates to a multinational parented in some particularcountry p; that could be recognized by the clutter of an additional subscript, but the point
only becomes material if residence-based taxes apply, and the implications of that are more
readily developed when it comes to the later empirics.
13

See for example Becker and Riedel (2012), using firm level data for EU firms, and Desai, Foley andHines (2009) for the United States. Note too that, being rooted directly in commercial relations between
affiliates, the complementariness in mind here differs from that which can arise indirectly as tax changes act
on the cost of capital by affecting the gains from ‘paper’ profit shifting—that is the mechanism at work in
Suárez Serrato (2019), for example, and is addressed in Section 5 below.

7

From (1), the necessary condition14 on the multinational’s choice of investment in the
typical host country h, Kh , is

N
X
∂Rj
∂Rh 0
0
(1 − Th )Fh − ρ(1 + Mh ) + Fh≤0
(1 − Tj )
∂Fh
∂Fh
j6=h


(2)

with an internal solution for Kh iff the equality holds. The first term here is familiar fromthe closed economy setting, being the post-tax marginal revenue product of capital in h
net of the tax-inclusive cost of capital. If there were no second term, this would have thefamiliar implication that the KF -M ET R is a sufficient statistic for tax effects on investment
in h, with the statutory rate itself having no independent impact. It is the second term
which is the novelty introduced by the revenue structure introduced above, capturing theimpact on the incentive to invest in h of the consequent cross effect on the net revenues ofaffiliates elsewhere. If, for example, that aggregate cross-effect (after tax) is negative, thenfollowing the closed-economy rule of equating the marginal revenue product of capital to the
KF -M ET R-inclusive costs of capital will lead (given the assumed negativity of the overall
cross-effect) to too high a level of investment in h, because doing so ignores the adverse
impact of investing there on the profitability of other affiliates in the multinational group.
The key point is that interdependence in the circumstances of the affiliates within themultinational group means that one aspect of investing in h is that may serve as an ‘implicit’
profit shifting device, in the sense of moving taxable profits across countries. Throughthis route the attractions of investing in h depend (through the terms multiplying Fh0 ) on
statutory tax rates in the host country relative to those h and elsewhere. This has evident
similarities with the more traditional idea of what we refer to here as ‘paper’ profit shifting
as a matter of tax avoidance realized through artificial transactions. Here, however, theprofit shifting is tied directly to, and implicit in, the real investment decisions that are themultinational’s core business.15
14

It is assumed that Π is concave over non-negative K ≡ {K1 , ..., Kh }.

15

One other aspect of the difference between ‘implicit’ and ‘paper’ profit shifting as the terms are used here
is worth noting: while both shift profits in the sense of increasing them in some countries while reducing themin others, implicit profit shifting will generally affect the multinational group’s consolidated profit, whereas
the essence of ‘paper’ profit shifting is that (apart from any cost associated with the artificial arrangements

8

The way in which taxation consequently affects the levels and allocation of investment
across the multinational group more generally can be most clearly seen by recasting (2)into the familiar structure of a direct characterization of the equilibrium marginal revenue
product of capital in h, the relevant marginal revenue now being that of the group:
Proposition 1 The multinational’s real investment decisions are characterized, for all h =1, ...N , by
∂R 0
.F (Kh ) ≤ ρ(1 + I-M ET Rh )
∂Fh h

(3)

where T denotes the N -vector of statutory tax rates,
I-M ET Rh ≡Mh − ∆h (δ, T ),1 + ∆h (δ, T )(4)in whichP

j6=h Tj δhj − Th∆h (δ, T ) ≡ αhwith

1 − Th,XN∂Rj /∂Fh /(∂R/∂Fh ) ≥ 0
αh ≡ −

(5)

(6)

j6=h

and
∂Rj /∂Fh.
δhj ≡ PN
i6=h ∂Ri /∂Fh

(7)

Proof: This is the special case of a more general result in Appendix A obtained by settingΩh there (defined after (10) below) to zero.
There is a lot to unpack in Proposition 1.16 Its highest level implication is that, as thenotation is intended to suggest, the I-M ET Rh combines the N statutory rates and theusual closed economy marginal effective rate in the host country into what can be thoughtof as a generalization to an international setting of the standard KF -M ET R. That is, justthemselves) it does not.
16

Matters are evidently more complex, for instance, than the simple view, often heard in policy contexts,
that multinationals’ location decisions are driven by cross-country comparisons of some kind of average
effective tax rate, along the lines of Devereux and Griffith (2003), while the level of investment, conditional
on location, then depends on the KF -M ET R in the host country. The difficulty with that intuition is thatthe first part comes from viewing investment as fixed in scale, while the latter presumes it to have variable
scale.

9as KF -M ET Rh summarizes all relevant tax effects for an enterprise operating in just one
country, so the set of all I-M ET Rh is sufficient to characterize tax effects on investment
in all countries in which the multinational might operate. This is so in terms of both theintensive margin (the level of investment in host country h, conditional on some investment
taking place there) and, given the assumed absence—for now—of locational fixed costs,
for the extensive margin (whether to invest in h at all).17 The critical difference between
the I-M ET R and the familiar KF -M ET R is that the former allows for interactions in
production and revenues across the affiliates within a multinational group:18 if there arenone, the I-M ET R reduces to the standard KF -M ET R.19
More precisely, the structure of I-M ET Rh shows that taxation affects the multinational’s
investment in h through two channels. The first is through the term ∆h (δ, T ), which we referto as the ‘(statutory) rate differential’. This, in turn, serves as a sufficient statistic for the
impact of statutory tax rates, in all jurisdictions, on cross-border investment decisions. And,beneath the somewhat heavy notation, it has a straightforward structure.
In the numerator of ∆h is the excess of a weighted average20 of the statutory tax rates
in countries j other than the host h over that in the host itself, with the weight δhj attachedto the statutory tax rate in j being the proportion of the additional revenue to the rest of
the multinational group that is generated by increased investment in h which accrues to theaffiliate in j. It is this rate differential term that captures the implicit profit shifting inherent
in the cross-country allocation of investment.
Through ∆h , an increase in the tax rate abroad, in some j 6= h, tends21 to increase investment in the host country h if, and only if, ∂Rj /∂Fh < 0, so that the cross effect is negative:
17

How the latter part of this conclusion is affected by the presence of locational fixed costs is taken up in
Section 5.
18

A further difference, of course, is that while the KF -M ET R depends only on tax parameters the
I-M ET R also depends on the structure of the multinational’s operations.
19

Because then αh = 0.

20

We use this term for brevity, thought is should be noted, however, that since it may that ∂Rj /∂Fh > 0
for some j 6= h (even though, by assumption, this cannot be so in the aggregate), the weights may not all
lie between zero and one.
21

In the informal comparative statics that follow we simplify by treating the various derivatives of theaffiliates’ revenues as constants.

10

intuitively, an increase in Tj then makes it more attractive to invest in h because doing so
reduces the amount of profit in j that has now become more heavily taxed. Complementaries
in production across the two affiliates, on the other hand, would imply reduced investment
in h when Tj increases.
An increase in the tax rate in the host country itself, Th , on the other hand, unambiguously reduces investment there directly through the numerator of ∆h . This may though be
mitigated by an effect through the denominator, which arises (recalling for instance equation(2)) from the increased value of deducting the financial costs of investing in h. Under theseemingly weak condition that the weighted average of foreign tax rates is less than unity,
however, the overall effect is readily seen to be that an increase in the host’s tax rate reduces
inward investment.
A final implication of ∆h is that the effects on real investment of the difference between
rates in the host country and abroad are greater the larger is αh : that is, as one might
expect, the stronger are the spillover effects of additional investment in h on other affiliates’
taxable receipts relative to the impact on the multinational’s overall revenue.
The second and more familiar channel of tax effect captured by the I-M ET R is throughMh , the KF -M ET R in the host country, increases in which lead to lower investment there.22
If corporate income tax (CIT) rates are the same in all countries, or investment in h has norepercussions for revenues in any other affiliate, this is the only tax consideration at work.
More notable, as being in contrast to much public commentary, is that there is no directcross-border effect on investment in h from the the KF -M ET R in any other jurisdiction j.
One can, however, imagine—and we will later consider— circumstances in which such effects
might arise, as for example if the multinational faced a binding limit on the overall amountP
23
of capital available to it, Na lower KF -M ET R in the United States might
j=1 Kj ≤ K:
then indeed reduce investment in Canada.
Note too that these two channels of effect interact: the larger is ∆h , and hence the22

It is assumed in what follows that 1 + ∆h > 0; this, it seems, is not theoretically assured, but serves asa stability-type condition without which tax effects may be perverse. It is, in any event, satisfied at all data
points in our sample.
23

Adding such a constraint to the setting above, the direct effect of an increase in Mj for any j 6= h isreadily seen to be an increase in Kh .

11

more favorable to h is the statutory rate differential the smaller is the adverse impact on
investment there of an increase in the KF -M ET R.
The empirics that follow below explore the role of the I − M ET R, and its two interacting
components, the rate differential term ∆h and the KF -M ET Rh , as the key channels throughwhich taxes affect investment. It also considers one further implication of (4) (and reasons
why it might fail): for countries in which some investment occurs, there is, in the absence ofany capital constraint on the multinational, no spillover effect on investment in h from the
KF -M ET R in any other country.

3

Foreign Affiliate Investment

This section describes the FAI data used in the empirics, comparing it both conceptuallyand empirically with the more commonly employed FDI data.

3.1

Understanding Foreign Affiliate Investment (and FDI) data

The two crucial features of FAI data are that they report cross-border investment in physicalcapital and that in doing so it looks through conduit structures to identify the ultimate parent
country. In contrast, as stressed at the outset, much of the empirical work on taxation andcross-border investment has used data not on FAI but on foreign direct investment (FDI),24
which differ from FAI data in both respects.
First, whereas FAI data capture real investment, FDI data capture funds received bya foreign affiliate that may or may not correspond to tangible investment. These funds
comprise both direct net transfers from the immediate parent company (not necessarily
the ultimate parent), whether through equity or debt, and earnings retained by the foreign
24
Some studies have used foreign affiliate statistics for other purposes, including Tørsløv, Wier and Zucman(2018) to assess the extent of profit shifting by multinationals (for 2017), and by Fukui and Lakatos (2012)
and Ramondo, Rodrı́guez-Clare and Tintelnot (2015) to study the pattern of multinational production
activities. For US-based multinationals, the Bureau of Economic Analysis (BEA) foreign affiliates data has
been widely used (one notable instance being Desai, Foley and Hines Jr (2004)), as it will be below. The
OECD’s Activities of Multinational Enterprises (AMNE) Database has also compiled inward and outward
foreign affiliate statistics for 31 OECD countries between 2008 and 2016, as described in Cadestin et al.
(2018).

12affiliate. And these amounts appear as FDI regardless of their end use. Retentions that arerecorded as FDI may, for example, be kept within the foreign affiliate as cash reserves, withno addition to real investment. Or the funds received may be passed on to another affiliate
in the same multinational group. In such cases, inward FDI into a jurisdiction will exceed
real investment there. On the other hand, inward FDI will understate foreign affiliates’
real investment to the extent that their tangible investment is financed in ways other thanby cross-border transfers or retentions (but instead by, instance, local borrowing or local
issuance of shares). FAI data does not suffer from either form of misstatement of tangible
investment.
The second and closely related key difference25 is that FAI data see through to the locationof the ultimate parent company, whereas in FDI data the ‘parent’ country typically refers to
the location of the immediate investor.26 This reporting convention for FDI creates additionaland serious issues for the measurement of investment: whenever funds pass through anintermediate country the same underlying funds will be recorded twice in the FDI statistics,
with the intermediate country as the destination country for the first observation and as
the ‘parent’ country for the second. FDI data will thus double count (or more) investments
whenever conduit transactions arise between the ultimate parent and the final host country.
This double counting, which is rooted in the equity method of accounting, also gives rise
to double counting of profits for any company with intermediate affiliates at the microlevel (Blouin and Robinson, 2019). At the global level, double counting of multinational
investments would imply a misleadingly high level of aggregate FDI, inflated by pass-throughfunds that are recorded multiple times.
An example illustrating these core differences between FAI and FDI data—which will also
prove helpful in understanding some later results—is provided in Figure 1, with implications
summarized in Table 1. It takes the case of an ultimate parent in country UP which has
25

There is a third difference, which is in the threshold for ownership/control: FDI data comprise all foreigninterests with 10 percent or more voting power, while the FAI statistics have a higher threshold of 50 percent.
26

There are some other data available by ultimate investors, for example the OECD’s Inward “FDI by
immediate and by ultimate investing country dataset” and from UNCTAD. For the former, however, coverage
is limited and the distinct series by ultimate and investing countries are for stocks (not flows); for the latter,bilateral FDI by ultimate owner and host country is not publicly available after 2012 (Casella (2019)).

13

Table 1. FDI vs. FAI - Stylized Comparison

FDI
FAI

UP to HUP to IPIP to H

Total

2
3

1
0

1
0

4
3

a controlled affiliate in a country of intermediate ownership IP, that affiliate in turn being
the immediate parent of another affiliate in host country H. The ultimate parent injects $2of equity directly into the affiliate in the host country, and $1 of equity into it indirectly
through the affiliate in IP, possibly through a loan.
Total inward FDI in country H is then $3, comprising $1 FDI from country IP and $2
FDI from country UP. But there is also another $1 of FDI recorded as a flow into country
IP from country UP, even though those funds only pass through to H, without any real
activities taking place in IP. Aggregate FDI is thus $4, double counting all the cross-borderfunds that pass through intermediaries.
In contrast, the total amount of inward FAI in H will be between $0 and $3, depending on
how much these funds are invested in physical capital goods—and it is that invested amountwhich is recorded in the FAI data. All the FAI would be recorded in country H, with FAI
in country IP being zero. Supposing, for instance, that all the funds go to real investment,
FAI will be recorded as $3 in H and zero elsewhere.
The two sets of statistics diverge further if there is local financing of real investment since,
as noted, real investment supported by a local injection of funds is included in FAI but notin FDI. Local borrowing in H to finance real investment of $0.5, for example, increases FAI
into H by $3.5 but leaves measured FDI unaffected. On the other hand, any changes in theamount of retained earnings in the host affiliate that is not invested in real assets wouldchange FDI, but not FAI: with uninvested retained earnings in H of 0.8, for instance, FAI in
H remains at 3.5, while FDI into H increases to 3.8 and global FDI to 4.8.

3.2

The FAI Dataset

The FAI dataset used here has been constructed by combining information on inward real
investment from the Foreign Affiliate Statistics (FAS) provided by Statistics Canada, the14Bureau of Economic Analysis (BEA) of the US, and Eurostat.27 The unit of observation is at
the host/ultimate parent/year level, reporting the sum, over all multinationals with ultimate
parents in a particular country, of their gross investment into foreign affiliates in each of thehost countries covered by these sources (together with their turnover, employment, exports,
and other features). More precisely, these data are for foreign affiliates’28 gross investment
in tangible assets, both new and existing (Eurostat, 2012).
The sample covers inward FAI into 32 host countries from 187 parent countries over theperiod 1998-2016 for the United States and 2003-2016 for all other countries: this makes atotal of 1,630 observations in the main sample. The sample of host countries is mainly limited
to advanced economies, and all but four are EU members.29 Collectively, they account for
more than half of the global FDI stocks for each year in our sample.
Figure 2 compares levels of inflows of FAI and FDI over the period 2003-12, both for all
host countries in the sample and for the subset of EU members.30 What emerges—beyond
an impression of the importance of EU hosts in our dataset—is that, both in general andfor EU hosts in particular, FAI is far lower than FDI, suggesting that double counting is
indeed extensive in the latter, and is also less volatile. Notable too is that the gaps between
the FDI and FAI have widened substantially. In our sample of host countries, over 10 years
in which FAI and FDI data overlap, aggregate FAI was about 50 percent of FDI, broadly
consistent with recent findings: as mentioned above, ‘phantom’ investments seem to account27

These can be accessed at, respectively: https://www150.statcan.gc.ca/n1/pub/13-607-x/2016001/
145-eng.htm, https://www.bea.gov/data/intl-trade-investment/activities-us-affiliates-foreign-mnes, andhttps://ec.europa.eu/eurostat/web/structural-business-statistics/data/database.
28

Meaning entities that are majority owned by ultimate parents that reside in a different country and candetermine those affiliates’ general policies.
29
The host countries covered (with the * indicating non-EU members) are: Austria, Belgium, Bosnia
and Herzegovina (*), Bulgaria, Canada (*), Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands,
Norway(*), Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, the United Kingdom and theUnited States (*). All those listed as EU members were members throughout the sample period used for
estimation.
30

Here and in results reported below we combine FDI data from the IMF Coordinated Direct Investment
Survey (CDIS) and UNCTAD. Bilateral CDIS data are available starting in 2009; these are stock data, fromwhich we back-out flows using the perpetual inventory method, as It = Kt −δKt−1 , with δ = 0.195. Bilateral
UNCTAD data are publicly available only to 2012.

15

for around 40 percent of FDI.
Figure 3 compares FDI and FAI from different types of parent country into the hostcountries in our sample, expressing each series in shares relative to the total. The key
differences highlighted above—that FAI is reported by ultimate parent and reflects real
investment, whereas FDI is reported by immediate parent and reflects financial flows—
appear evident in the data. Over the sample period, low tax countries, defined as those witha statutory tax rate in the first quartile of the distribution (which means a statutory tax
rate of less than about 20 percent), account for a greater share of FDI than of FAI (Panel(a)). The same is true, and even more marked, in relation to investment hubs, defined ascountries with an average FDI/GDP ratio of above 150 percent (Panel (b)).
Importantly, many of the observations in the FAI dataset—around 34 percent—are zero.31

4

Empirical Strategy

This section describes the approach taken to estimation.

4.1

Specification and Other Data

The focus of interest to which the analysis in Section 2 points is the impact of the I −M ET R and, more generally, of its key components: the rate differential ∆h , the host country
KF -M ET R and—operating through some channel not captured in the modeling above—
the parent country KF -M ET R. Taking 1/(1 + ∆h ) ≈ 1 − ∆h , and now with many parent
countries and time periods, we take as our most general estimating equation:
E(F AIhpt ) = exp(β∆ ∆hpt + βM H Mht + βM P Mpt + βM ∆ (Mht × ∆hpt )(8)+ β∆2 ∆2hpt + β10 xht + β20 zpt + β30 gph ),where F AIhpt denotes bilateral investment Ihpt in tangible capital goods in host country h (ofwhich there are 32) from ultimate parent country p (of which there are 187) in year t (runningfrom 1998 to 2016), scaled by the lagged capital stock in h attributable to investment from
31

This is also the case for FDI, with around 43 percent of zeroes.

16

p, Khp,t−1 .32 The tax terms ∆hpt , Mht , and Mpt are as defined in Section 2, now adding
subscripts as appropriate. So, for instance, the critical tax differential term isP∆hpt = αhpt

j6=h Tjt δhjpt − Tht

1 − Tht.

(9)The controls in (8) comprise time-varying macro variables in the host (xht ) and parent
countries (zpt ), along with host-parent time-invariant bilateral variables (gph ). We include
among these controls a set of host country-year (aht ) and parent country (cp ) fixed effects,
except that when estimating the impact of the host country King-Fullerton marginal effective
tax rate Mht we replace aht by year fixed effects (bt ). We cluster standard errors by hostcountry to account for correlations within host countries over time.
The expected signs of the tax terms in (8) are implied by the analysis in Section 2. All
else equal, an increase in the rate differential ∆hp —reflecting a reduction the statutory rate in
the host country h and/or an increase in the weighted average of statutory rates elsewhere33

5

Cross-border Tax Effects on Inward FAIThis section presents baseline results from the use of the FAI data just described, looks attheir robustness and some extensions, draws lessons, and then explores further the differences
arising from the use of data on FAI rather than FDI.

5.1

Baseline Results

Table 2 reports baseline results from the estimation of equation (8) on the full sample. The
first column enters separately the various tax terms other than the parent KF -M ET R,
which is taken up later. The coefficients are evidently significantly different from each other,so that sufficiency of the I-METR is rejected. In terms of its underlying components, thecoefficient on the rate differential term is highly significant, has the expected sign, and islarge. The host country KF -M ET R enters with the expected (negative) sign but is not
32

For this purpose, the bilateral stock of FAI is imputed using the perpetual inventory method, using anassumed depreciation rate of 0.195. The results this gives for the US are very close to the direct reports in
the BEA data, giving some confidence that the estimates are reasonably accurate.

17

statistically significant; the same is true of the squared rate differential term.34 Contrary
to expectation, the interaction term, intended to capture possible non-linear effects, enters
negatively, and with some significance.
The remaining columns of Table 2 focus more closely on effects through the rate differential, ∆hpt and the host KF -M ET Rh , with corresponding adjustments to fixed effects andcontrol variables.
Columns (2)-(4) focus on possible spillovers from statutory corporate tax rates, in all cases
including host country-year and parent country fixed effects. Column (2) shows that the rate
differential term ∆hpt has a positive and statistically significant relationship with FAI. And
the effect is large, with an implied semi-elasticity with respect to the rate differential, β∆ , of
2.7. For interpretation, and comparison with previous work, it is helpful to translate this intoP
a semi-elasticity with respect to the difference in statutory rates themselves, i6=h Ti δi − Th ,which, recalling (5), requires multiplying β∆ by αh /(1 − Th ). The median value of αh in thedata being 0.98, and the average statutory rate around 25 percent, for simplicity we reportsemi-elasticities with respect to the underlying difference in statutory rates as β∆ /(1−0.25).35
For column (2), for example, this gives an estimated semi-elasticity of 3.6. The implicationis that a one percentage point decrease in the host country statutory tax rate or increasein the weighted average of statutory rates in other investment locations is associated with a
3.6 percent increase in the ratio of FAI to (lagged) capital in the host country.
Column (3) allows for distinct effects of the host’s statutory rate and the weighted averageof those elsewhere by decomposing ∆hpt into its two components, one relating to the weighted
average tax rate outside the host country h and the other to the statutory rate in h: werefer to these as “non-host” and “host” components respectively. Both effects have theexpected sign and, as theory predicts, are not significantly different from each other. And
the implied effects are again large, with semi-elasticities (again scaling each by αh /(1 − Th ))34
This is in contrast to the finding of non-linear effects (typically focusing on the higher order terms ofthe tax rates) in, for example, Bénassy-Quéré, Fontagné and Lahrèche-Révil (2005), Dowd, Landefeld andMoore (2017) and Bratta, Santomartino and Acciari (2021).
35

For the impact on FAI of changes in the host country rate, this ignores the effect operating through thedenominator of ∆hpt . This results in an under- (over-)statement of the impact on FAI where the host rate
is below (above) the weighted average elsewhere, but on average this effect should be negligible.

18

of around 4. This is noticeably larger than the values commonly cited as consensus views, apoint we return to later. The negativity of the non-host effect, it is also worth emphasizing,runs counter to the possibility of strong cross-border complementarities in production acrossalternative locations for inward investment (since in that case, with positive weights δhj , one
would have expected to find a positive impact of rates abroad on FAI in the host country.)
The use of sales-based weights in constructing the tax differential term seems most immediately plausible when potential hosts operate in an integrated market, which for empirical
purposes is in turn most appealing as a benchmark for the subset of EU members. Whenconsidering investing into some EU host country, for example, and whatever the parent
country, statutory tax rates elsewhere within the EU may matter more than those outside
the single market. To explore this, we construct distinct rate differential terms for EU
and non-EU countries. Specifically, for each EU host country we measure its scale relative to the EU market for all foreign affiliates from a given parent country, and for each
non-EU host country we measure its scale relative to the non-EU market: that is, we takePP
δhjpt,1{h∈EU } = Saleshpt / j∈EU Salesjpt , and δhjpt,1{h∈EU
Salesjpt , re/} = Saleshpt /j ∈EU/spectively. The results of doing so, in column (4), point to tax spillovers on FAI that arestrongest for investment entering the single market, though still both large and significant
outside it.
Columns (5)-(6) of Table 2 focus on the impact of the host country KF -M ET R, Mh ,adjusting fixed effects and control variables as described above. The coefficient βM H on Mhthus captures the average effect of the host country’s KF -M ET R on inward FAI with theinclusion of time-variant host-country controls.
In column (5), the host country KF -M ET R enters with the expected negative sign, but
is far from significance. The statutory rate differential, however, remains highly significant,though the semi-elasticity now falls to around 2.3.
Adding in Column (6) the interaction between the host country KF -M ET R and thestatutory rate tax differential, this again proves statistically significant and of unexpected
sign, just as in column (1). Assessing the full effects of the rate differential ∆hpt and thehost KF -M ET R thus requires taking account of the interaction between the two. Thisis done in Figure 4. The upper panel shows that throughout the sample range the rate19

differential tends, overall, to have both a positive and a statistically significant effect on FAI;
the only exceptions are for host countries with extremely large KF -M ET Rs (10 percentage
points or more above the average rate differential term). For the host KF -M ET R, in thelower panel, the sign of the overall effect flips depending on the initial level of the statutory
rate differential. For host countries with relatively large rate differentials (that is, when thehost country rate is relatively low), an increase in the host KF -M ET R does indeed havea negative effect on inward FAI. At the average level of the rate differential, however, theeffect of the host KF -M ET R remains insignificant; when the differential is below average,
the effect is near positive but remains insignificant. These results thus suggest that FAI inhost countries with lower KF -M ET Rs and higher rate differentials behaves most closely inline with the predictions from Section 2. Why the interaction operates with the opposite
sign to that theoretically expected, however, remains for now unclear.
The analysis in Section 2, reflected in the estimating equation (8) gives no role to any
KF -M ET R outside the host country h, whether in the parent country or elsewhere. Nevertheless, as also seen, cross-country comparisons of KF -M ET Rs often serve as benchmarks inevaluating national policies and reforms, and one can think of reasons why marginal effective
rate outside the host country might have some impact on inward investment there: perhaps
through capital constraints, as noted above, or perhaps though locational fixed costs, discussed later. The empirical question is thus whether inward real investment flows into a hostcountry h are truly affected by KF -M ET Rs anywhere else.
This issue is explored in Table 3. The sample is now considerably smaller than for
the results above, since observations on the KF -M ET R are available for only 46 parent
countries. To isolate the implications of this, Column (1) of Table 3 reports the same specification as Column (1) in Table 2 using this reduced sample: the results are essentiallyunchanged. Addressing the question at hand, column (2) adds the KF -M ET R in the parent country. The coefficient on this proves to be negative—the opposite of what, if anything,might be expected—and insignificant, while that on the rate differential is essentially unchanged. Column (3) adds the average KF -M ET R across all countries other than the host;the coefficients on both non-host KF -M ET Rs are insignificant. And using the lowest of all
KF -M ET Rs among all non-host countries, in column (4), the effect is again insignificant.
20

There is thus simply no sign of cross-border tax spillovers operating through the KF -M ET R
in countries other than the host. Spillovers through the statutory rate differential, meanwhile, remain sizable and strongly significant throughout Table 3.

5.2

Extensions

This subsection extends the discussion and analysis to distinguish between extensive andintensive margins of the FAI decision, allows for ‘pure’ profit shifting through devices that
do not in themselves affect multinationals’ consolidated profits, and compares the effects oftax measures for real investment of worldwide and territorial parents.
‘Pure’ profit shifting
The analytical framework of Section 2 that has underpinned the empirics so far precludes
pure profit shifting in the sense of wholly artificial arrangements intended to reduce themultinational group’s total tax liability. To the extent that the amount of profit shifted
through such arrangements is directly affected by real production decisions, however, theirimpact can be captured in that framework. Suppose for instance that the affiliate in hproduces an intermediate input used by that in j, so that Rj = r(Kj , Fh ) − c.Fh , for somefunction r(.), where c denotes the internal transfer price. Then ∂Rj /∂Fh will differ from zeroto the extent that the transfer price differs from the marginal product of the intermediate
in its use by j; if the transfer price is set higher than that, for instance, then ∂Rj /∂Fh < 0.
Many other forms of profit shifting, however, are not directly related to production: debt
shifting, for example, and routing through tax-favored conduit countries. The latter, as
stressed above, is particularly relevant here as being a key driver of the distinction between
FAI and FDI data. While FAI data looks though pure conduits to identify the ultimate parentof any affiliate in some host country, the real investment decisions of the multinational canof course nonetheless be expected to reflect its awareness of any tax savings that it can enjoy
by the use of such conduits. This means that the potential use of conduits as means of tax
avoidance cannot be ignored in understanding FAI data. Importantly, however, if the use of
conduits is not connected to real investment decisions, then it reduces average effective rates
but has no effect on the marginal effective rates that guide real investment decisions.
21

It may be, nonetheless, that real production can itself facilitate transactions-based tax
avoidance, for example by meeting ‘substance’ tests required to draw on advantageous treaty
provisions. To allow for this, it has become standard practice in the literature to suppose
there to be some real cost associated with deploying devices of pure profit shifting. Suppose,for example, that shifting profits of Sj into country j requires costs of the form (Sj )2 /(2φρKj )(with cost parameter φ > 0), capturing the idea that these costs are lower to the extent that
there is real production activity in j.36 Adding the possibility of such explicit profit shifting
to the setting above, it is shown in Appendix A that (3)-(7) of Proposition 1 continue tohold, but now withMh − ∆h (δ, T ) − φΩh (ω, T )(10)
1 + ∆h (δ, T )PN
P2
where Ωh (·) ≡ ( N
i=1 Ki . The distinction between
j=1 Tj ωj −Th ) /(2(1−Th )) and ωj ≡ Kj /I-M ET Rh ≡the two forms of profit shifting entering (10), ∆h and Ωh , is evidently subtle. The former,‘implicit’ profit shifting, captures the role of production decisions in directly affecting theallocation of taxable profits; the latter captures the role of production in facilitating the useof other instruments to shift profits.
While conceptually subtle, the distinction nevertheless has clear empirical implications,through the squaring of the statutory rate differential in the pure profit shifting term Ωhbut not in the term ∆h that relates to implicit shifting. Loosely, implicit profit shifting
increases investment in low tax countries and reduces it in high tax ones; pure profit shifting,in contrast, increases investment in all countries, including low tax ones (because investing
there, given the assumed avoidance technology, makes it easier to escape taxation in the hightax ones). For example, it was seen in Section 2 that an increase in Tj for some j 6= h will
increase investment in h through the implicit profit shifting channel route if ∂Rj /∂Fh < 0;through the pure profit shifting channel, however, it will have the opposite effect, tending
to reduce investment in h, if Th is initially above the weighted average of all statutory rates
elsewhere.
36

This approach dates back to Hines and Rice (1994), with recent applications including Suárez Serrato(2019) and Beer et al. (2023).

22

Exploring this, Table 4 reports an alternative specification which includes the pure profit
shifting term Ωhp .37 This enters, counter to expectation, with negative sign, and is far
from significance. The coefficients and significance of the rate differential and host country
KF -M ET R, meanwhile, are broadly unchanged relative to most of the baseline results inTable 2. Statutory rate differentials thus leave a clearer mark on real investment throughtheir impact on implicit profit shifting than they do through explicit forms of profit shiftingthat have received more attention.
Worldwide vs. territorial tax systems
The framework of Section 2 also presumed all national tax systems to be ‘territorial,’ inthe sense that the only taxes levied are those of the host country. The polar opposite caseis that of ‘worldwide’ taxation, meaning—in its purest form—that the parent country will
top up any taxes paid in the host country to the level applied by the country in which it
resides, applying a credit for any taxes paid to the host country. The implication is thatthe tax paid to the host country is less relevant for inward FAI when the parent resides in acountry applying worldwide taxation, because the final tax paid will be that of the residence
country. In practice, for reasons touched on in a moment, the distinction between territorial
and worldwide systems is not as sharp as this sounds. Nevertheless, a meaningful distinctionof this kind can be made, and we do so by constructing a list of territorial corporate tax
system using PWC World Tax Summaries.38 Table 5 reports results allowing tax effects todiffer according to the regime applied by the parent country.
In column (1), the coefficient of the rate differential is large and highly significant whether
the parent is from a worldwide or a territorial country—but somewhat smaller (and lesssignificant), as the considerations above would suggest, in the worldwide case. A similarstory emerges in column (2), where the rate differential is split into its host and non-hostcomponents and the effects of each allowed to differ according to the regime applied by
the residence country. There the effect of the host rate is both smaller and less significant
37

Since Mh and −Ωh enter identically into the I-M ET R, and recalling the definition of Ωh following (10),the ratio βΩ /βM h in principle identifies the shifting cost parameter φ; we do not, however, pursue this here.
38

Which countries we designate as having territorial or worldwide systems is indicated in Supplementary
Appendix Tables C.2 and C.3.

23

when the parent is from a worldwide country. That it is not zero is consistent with two
respects on which worldwide taxation in practice differs from its textbook form. One isthat application of residence country taxation can often be avoided, not least by deferring
repatriation of profits to the parent: this has been common practice, for instance, among
US multinationals. The other is that host country tax remains final if it is above the tax
due under residence country rules: no refund is paid to offset the excess.39 The important
point, however, is that the impact of the rate differential remains large and significant for
FAI from parents in worldwide countries, and that host and non-host components continue
to be large and of similar magnitude, though significance is somewhat less.
Distinguishing extensive and intensive margins of FAI
A disadvantage of the PPML estimator, noted above, is that it elides investment effects
across the extensive and intensive margins.40 Given the prevalence of zeroes in the data, thedistinction is evidently one of some importance and interest. In the framework of Section
2, such zeroes arise when the (group-wide) marginal revenue product of the first unit ofinvestment in some country is negative. An alternative modeling approach is to suppose
there to be some fixed cost Fj of investing in country j. Denoting after-tax profit in country
j by πj (Kh , K−h ) ≡ (1 − Ti ) Ri (Kh , K−h ) − ρ(1 + Mi )Ki j − Fj , where K−h denotes the(N − 1)-vector of capital invested in all jurisdictions other than h (and, for simplicity, thefixed cost is assumed to be tax-deductible), a necessary condition for the multinational toinvest in h is then that doing so yields group profits at least as high as can be obtained byinvesting only elsewhere, so thatmax

{Kh ,K−h }NX

πj (Kh , K−h ) ≥

j=1

max

{K−h }NX

πj (0, K−h ).

(11)

j6=h

Denoting by Ki∗ and Ki∗∗ , respectively, the solutions to the problems on the left and right39

Some sign of this effect is at work emerges on comparing results across subsamples with positive andnegative rate differentials (indicating host rates below and above that in the ultimate parent respectively):
the effect of the rate differential is lager in the former case: see Supplementary Appendix Figures C.1 and
C.2.
40

The distinction between the extensive and intensive margins has been made in previous work using FDI
data: examples include Davies and Kristjánsdóttir (2010) and Canton, Solera et al. (2016).

24

of (11), and decomposing the proportional difference in affiliate net revenue into systematic∗and idiosyncratic components as, in obvious notation, Rj∗∗ = (1 + λR + uRj )Rj and similarly∗
Kj∗∗ = (1 + λK + uKj )Kj , rearranging (11) gives the condition

Rh∗ − ρ(1 + Mh )Kh∗

P



X  (1 − Tj )(1 + Mj ) Th − j6=h Tj ωjRK∗≥ λ R 1+− λ ρKωjK + uh (12)1 − Th
1 − ThR∗

j6=hwhere Rj∗ ≡ Rj (Kh∗ , Kh∗ ), R∗ ≡

∗
R
∗
∗
∗
∗
K
j6=h Rj and ωj ≡ Rj /R (with Kj , K , and ωj definedP

analogously), and uh absorbs the idiosyncratic elements in the comparison with the counterfactual of investment in h being constrained to zero.41
The key implication of (12)-(??) is that the location decision in this setting depends
not only on the statutory rate differential and host KF -M ET R, much as above, but also(through the second term on the right of 12) the capital-weighted average of KF -M ET Rs
outside the host country. This opens up a route through which King-Fullerton effective
rates elsewhere may have cross-border spillover effects even in the absence of a group-wide
capital constraint: while having no impact on the intensive margin, in the presence of fixedlocation costs they may impact the extensive margin. This, intuitively, is because theyaffect the overall average effective tax rates available on investment elsewhere. Through thisroute, and just as in which the multinational is capital-constrained the impact of higher
KF -M ET Rs outside host country h would be expected to be increased investment in h.
To assess intensive and extensive margins separately, Table 6 reports results from estimating this structure as a Heckman-type self-selection model, including fixed effects at bothstages and controls as described above. The second stage (intensive margin) specification
for ln(F AI) is much as above, now with inclusion of the inverse Mills ratio. The first stage(extensive margin) is as in (12)-(??) with the inclusion of the inverse Mills ratio. For identification purposes, we impose the theory-based exclusion restriction that the weighted average
KF -M ET R enter the first but not the second stage; we consider also the exclusion of personal income tax and VAT rates along with the ratio of tax revenue to GDP. Throughout,
41

Specifically,X  1 − Tj 
X
Fh
∗
R
∗
K (1 − Tj )(1 + Mj )
+R
uj
− ρK
uj
.
uh ≡
1 − Th
1 − Th
1 − Th
j6=h

j6=h

25

the rate differential term is calculated using (lagged) sales as weights.
In columns (1) and (2) of Table 6, the average of non-host KF -M ET Rs is weighted
by (lagged) capital stocks, as (11) suggests. Under each set of exclusion restrictions, therate differential is significant and with expected sign at both stages. The host KF -M ET R
enters (only) the second stage with significance, but with unexpected sign. Using instead
sales weights, in columns (3) and (4), the rate differential is again highly significant at bothstages and somewhat larger, especially at first stage, while the host KF -M ET R enters thesecond stage with both significance and the expected sign.
Across all columns, the non-host weighted average KF -M ET R is insignificant in all but
one case, in which it takes counter-intuitive sign; this reinforces our earlier finding of nocross-border spillovers from this source, though it also weakens faith in our main exclusion
restriction.
Interpreted, for this and other reasons,42 with some caution, we take these results to bebroadly consonant with the findings above of effects from the host KF -M ET R that are atbest ambivalent, of no cross border spillovers from KF -M ET Rs elsewhere and of significant
and sizable effects from the statutory rate differential. For the last of these, there are perhaps
some signs of a stronger impact on the extensive than on the intensive margin. That is as
might be suggested by simple views of investment decisions depending only on domestic tax
considerations once location decisions, reflecting assessment of tax and other options over arange of countries, have been taken. But the signs here are that international statutory rate
differentials nonetheless matter a good deal at the intensive margin too, consistent with thepotential importance of the kind of production-related implicit profit shifting highlighted in
Section 2 above. Across both stages, the semi-elasticity implied by the results in Table 6 arevery, and perhaps implausibly large, ranging from 4.4 to over 10.43
42

For instance, we ignore tax effects through the (always highly significant) inverse Mills ratio, and whether
the uh can reasonably be taken to have a common normal distribution (after normalization for scale) is
unclear.
43

Calculated from column (1), for example, as (1.792+1.490)/(1-0.25) = 4.38.

26

5.3

Robustness

This subsection explores various dimensions of the robustness of the key results from thebaseline analysis.
Investment dynamics
Adjustment costs of various kinds can give rise to more complex dynamic responses of FAI
to taxation than the immediate adjustment presumed above. To allow for this, Table 7
reports in columns (1) and (2) results when including the lag of FAI in columns (1) and(2); including the differential term in the former, and its host- and non-host components
separately in the latter. The coefficient on the rate differential, which now captures a short
run impact, is highly significant and large at around 2.81. The same is true of the host andnon-host components, which also remain essentially equal in magnitude respectively). Thelong run effects are of course even larger, with a semi-elasticity on the rate differential incolumn (1) of 5.25.44
These results need to be interpreted with particular caution, given the prospect of bias
from the combination of lagged dependent variable and fixed effects. Efforts to address this,however (including for instance by the method of Wooldridge (2005)), ran into convergenceissues of a type that are not uncommon with Poisson regression.45
Timing of tax effects
It could also be that FAI responds to taxation not (only) contemporaneously but (also)with some lag or that it reflects anticipated future tax policies. Various such possibilities
are explored in the right hand part of Table 7. The results there show that lagged andfuture tax variables are indeed statistically significant (in columns (3) and (4) respectively),with the impact of the contemporaneous rate differential becoming smaller and somewhat
less significant when both are included (in column (5)). For a fully anticipated permanentchange, the semi-elasticity with respect to the rate difference is again much larger than the44

Calculated as (2.811/(1-0.286)).(1-0.25).

45

See for instance Santos Silva and Tenreyro (2011).

27

baseline, at around 6.1.46
Alternative weighting schemes and scaling
The focus in the benchmark analysis above was on the case in which the rate differential term
∆hp reflects operation in an integrated market, which lent some persuasiveness to its construction by using sales as weights. But there are other possible structures of multinational
activity consistent with the analysis in Section 2, each potentially pointing to a different setof weights.
The first two columns of Table 8 instead use capital weights, as an alternative approachto capturing interactions across affiliates directly related to their production: that is, we
now take δhjpt = Kjpt /(Kpt − Khpt ). In column (1) the impact of the rate differential issignificant and noticeably larger than in the baseline results of Table 2. In column (2),however, splitting into host and non-host components the former becomes insignificant andtakes counter-intuitive sign, while the latter, with with the expected sign, becomes extremely
large.
An alternative view of the interactions between affiliates might be in terms of vertical
integration across the multinational as a whole, with affiliates producing at various stagesof the supply chain and trading with one another. Weighting tax rates by sales (including
within the group) would seem appropriate in this case too, but we also explore in Columns(3) and (4) the alternative approach of weighting statutory tax rates by bilateral-exports:
in obvious notation, δhjpt = EXjpt /(EXpt − EXhpt ). The implied effect of the statutory rate
differential measured in this way, however, is insignificant; and while the separated host andnon-host components have the expected signs and are fairly close in magnitude, they too are
insignificant.
We have also conducted but for brevity do not report robustness checks in which FAI isscaled by value-added, rather than by the capital stock. The rate differential term remains
positive and statistically significant.

46

Ignoring the insignificant lagged effect, this is calculated as (2.139+2.666)/(1-0.25).

28

Endogeneity
A concern with regressions of the kind above is that countries’ tax policies are potentiallyendogenous to the state of FAI: host countries may, for instance, reduce tax rates in order
to boost inward investment that for some unobserved reason, is languishing. If so, thelikelihood is of biases towards zero of the coefficient on the rate differential and that on thehost rate alone: the risk, that is, is likely to be that our large tax effects found above areunder-estimates.
The issue, nonetheless, is evidently an important one. Table 9 reports results including
as instruments in all cases the first and second lags of the corresponding tax variable, taking,given the nonlinearity of the conditional mean, a control function approach (that is, including
at the second stage residuals from the first stage estimates). In column (1), the coefficient on
the rate differential is noticeably smaller and less significant than in the baseline results ofTable 2 while the host KF -M ET R appears in much the same way; from the insignificanceof the residuals, however, there is little sign that results are in this case contaminated byendogeneity. Splitting the rate differential into host and non-host components, however,there are in column (2) signs of endogeneity; and, as the considerations above would suggest,the coefficient on the host component becomes markedly large while that on the non-hostpart remains much as found above. The same is true in column (3), where the instrument
set is expanded to include shares of personal income tax and VAT in total revenue and the
population of the host country. Overall, the impression is that in so far as endogeneity is an
issue the main implication is indeed likely to be some understatement of investment effects
from the host country statutory rate.
An alternative structure of fixed effects
It has become common in the trade literature around gravity models to include host-parent
fixed effects, as for example in Glick and Rose (2016) and Head and Mayer (2014). We
have not done so above because this impedes identification of the effect of the host country KF -M ET R, which shows relatively little variation over time; when excluding the host
KF -M ET R we have instead used host-year and parent effects. In Table 10 we take thealternative approach of using host-parent and year effects, looking in turn at the rate differ-29

ential and its components. Effects are somewhat smaller, and with a more marked difference
between host and non-host effects, than generally found above, but remain quite strongly
significant.

5.4

Assessment

These empirics lead to several reasonably clear-cut conclusions. One is that the I-M ET R,
while theoretically appealing, does not serve well as a sufficient statistic for cross-border tax
effects on tangible investment, at least as it has been operationalized here. This reflectsvery different impacts from its main components: the cross-border rate differential, in turndecomposed into host and non-host effects, and the host KF -M ET R.
The former, reflecting the difference between the statutory rate in the host country and aweighted average of those available elsewhere to the parent investor, has a highly significant
and very large effect, especially but not only where markets are more highly integrated.
While the point estimates above vary quite widely, between the extremes ofaround 1.6 to
10.1, they tend to be notably higher than the 2.5 or so reported as benchmark in De Mooij
and Ederveen (2003) and as median estimate in Feld and Heckemeyer (2011). Our preferred
estimate would be around the 3.6 in the baseline results in column (2) of Table 2. We return
shortly to the question of why our results point to stronger tax responsiveness than does thebulk of previous work.
Importantly, the semi-elasticities with respect to host and non-host statutory rates thatemerge are much the same as those for the difference between them. There are thus strong
signs of spillover effects at work through this route. (The meta-analyses, recall, look only atthe effects of the host country rate.)
The impact of the host country KF -M ET R, in contrast, appears close to zero andinsignificant. This is somewhat more in line with, for example, the conclusion that Feld andHeckemeyer (2011) draw from their analysis, though we are not aware of any other study
comparable to that here in relating the KF -M ET R to a measure approximating cross-bordertangible investment.
There also emerges a significant interaction between the rate differential and the host
KF -M ET R, tending to diminish the impact of the former while making more likely a30negative impact of the latter. The puzzle, however, is that the sign of the interaction runs
counter to that suggested by theory.
Finally, but importantly, we find no spillover effects from the KF -M ET R in either
the parent or non-host countries—an issue that, so far as we aware, no previous study has
addressed. And this is so on both the intensive and, perhaps more surprisingly, the extensive
margin of FAI. Rankings of countries by KF -M ET Rs have little real bearing on cross-borderdecisions on tangible foreign investments.

5.5

Comparing Tax Effects in FAI and FDI Data

Before applying these results to the analysis of the global minimum tax, it remains to explain
why the semi-elasticities emerging from the analysis here are noticeably higher than thosein much of the previous literature. The natural suspicion is of course that this difference
somehow arises from the use of data on FAI rather than, as in much of literature, on FDI.
There are indeed hints from some previous work (albeit mainly on U.S. data) that effects
are stronger when looking specifically at investment in plants, closer to the concept of real
investment underlying FAI data, rather than at FDI.47
As a first step towards establishing and explaining whether one should expect any systematic difference in apparent tax responsiveness emerging using FAI and FDI data, Table
11 reports results taking each in turn as dependent variables in an estimating equation of
the form of equation (8). The sample period, dictated by the availability of bilateral FDI
data, is 2003-2016, and so somewhat different from that of the baseline results of Table 2.48
Columns (1) and (3) include the composite tax differential, columns (2) and (4) its distinct
components.
For FAI, the semi-elasticity on the differential term in column (1) is much as in thebaseline, and so too (though somewhat smaller) are the the coefficients on the host49 and47

Again, see De Mooij and Ederveen (2003) and Feld and Heckemeyer (2011), though note too that theevidence has not been overwhelming: the former finds (p.688) “...no strong evidence that studies using[Property, Plant and Equipment] data yield higher semi-elasticities than those using FDI.”48

In effect, we lose several years of observations for FAI parented in the United States (for which the data
starts in 1997).
49

The term ‘host’ is less apt in the FDI than in the FAI context, since there is no presumption that inward

31

non-host components in column (3). All, moreover, are highly significant. In the FDI data,in contrast, while the coefficients on the statutory rate terms take the expected sign they are
in all cases not only much lower but also insignificant. In all columns the host KF -M ET R
enters with the expected negative sign, and is again insignificant in the FAI data. It proves
significant, however, in the FDI data—though only moderately so.
While this result—that tax effects are stronger using FAI data than FDI data—is notwholly unexpected given some previous work, it may nonetheless seem counter-intuitive:
Might one not, to the contrary, expect the tax-motivated use of conduit arrangements,
picked up directly in FDI statistics but not in FAI, to generate tax effects that are moremarked, not less, in FDI data?
Some explanation can be found, however, in the narrative around Table 1 in Section 3
and Figure 1. Suppose, for instance, that the statutory rate in host country H is reduced, sothat real investment there by the parent in U P increases; and suppose moreover that this isfinanced by increasing both direct investment from U P to H and conduit investment from
U P through IP to H. In the FAI data the aggregate of these two flows, FAI from U P to H,
is indeed identified as responding to the tilting of the tax differential in favor of investmentin H. In the FDI data, the increased FDI flows from both U P and IP to H are also in linewith the shift in tax differentials. But the FDI data also picks up the increased flow from U P
to IP : that is, the conduit country IP experiences an increase in inward FDI even though
in tax terms it has become less attractive. In this way, FDI flows through conduit countries
can dilute the apparent impact of taxation on investment. And, given the significance ofconduit activities, the downward bias this implies in measured tax effects may be sizable.

6

The Global Minimum Tax and Tangible Investment

A centerpiece of the G20/OECD-led agreement of October 2021, and now in the process of
implementation (‘Pillar Two’), is the proposed adoption of a minimum effective corporate tax
rate of 15 percent. The primary motivation behind this is to limit the shifting of paper profits
and tax competition for both investment and shifted profits. The impact on productive
flows remain as real investment.

32

investment, however, which one might expect to be a central consideration, has received
surprisingly analytical attention,50 though some have expressed concern that there will bewill be significant collateral damage in the form of reduced investments.51 These are issues
that the analysis and results above are well-suited to illuminate.
To this end, as providing some sense of the pattern, direction and magnitude of effects
on tangible investment, we compare, for each host-parent country pair, fitted FAI in 2016 to
predicted FAI in the presence of a minimum tax set at varying levels, using for this purposethe coefficients corresponding to the baseline specification in column (5) of Table 2, whichallows for effects through both the rate differential term (fairly moderate in size, by thestandard of the full set of results above) and the KF -M ET R in the host country.52 In doing
so, we model the minimum tax as simply a restriction on the permissible rate of corporation
tax although, in practice, the Pillar 2 rules are more complex than this.53 Nor do we takeaccount of the possible strategic implications of adopting a minimum tax: it might be that
this will lead countries that it does not directly affect to change their own tax rates.54 In
50

Exceptions are UNCTAD (2022) and OECD (2020c), which focus primarily on quantifying the impact
on effective rates; the former then applies semi-elasticities reflecting the existing literature, the latter allows
only for investment effects through the KF -M ET R. Some attention has also been paid to the question ofwhether, by narrowing rate differentials, the minimum tax will generate improvements in the cross-country
allocation of capital: we do not address this issue.
51

As, for example, Bunn (2021).

52

For simplicity we ignore the interaction term. Note too that we set aside the possibility of an impact
through the traditional profit shifting route Ωh , which was found to be insignificant in the results above.
53

Broadly, their essence is the application to each entity within any multinational group with global
turnover of more than e750 million of a ‘top up’ tax, at a rate equal to the excess of 15 percent over theeffective (not statutory) rate of domestic corporate taxation, to a base comprising accounting profit lessa ‘carve out’ calculated as a fixed proportion of payroll and tangible capitals: see Devereux, Vella andWardell-Burrus (2022) and UNCTAD (2022) for more detail. An important implication of the feature thatthe minimum is applied at the level of the entity, not that of the country, is that an entity operating ina country that has a statutory rate above the minimum may nonetheless be subject to a top-up tax if it
benefits from some form of tax incentive that brings its effective rate below the minimum. The inability
to deal with this is a clear disadvantage to our use of country-level data. Similarly, we cannot restrict theanalysis to multinational groups above the e750 threshold; the analysis that follows, however, is in termsof percentage changes in the I/K ratio, and so can be interpreted as applying to in-scope multinationals,
though with the proviso that there are some signs that large and (as many in this group will be) highly
profitable multinationals are somewhat less tax-responsive than others (Millot et al. (2020)).
54

There are now several papers on this issue, including Hebous and Keen (2021), Johannesen (2022) andJaneba and Schjelderup (2022); an empirical assessment of the revenue implications of induced changes intax rates is in International Monetary Fund (2022).

33

abstracting from such considerations,55 the results are far from being a full assessment of theinvestment implications of Pillar 2, but we believe are potentially instructive nonetheless.
The focus is thus on the impact of the minimum rate through two channels: the rate
differential term ∆h in the host country—which, importantly, will capture not only any ratechange induced by the minimum in the host country itself but also those induced in themuch larger number of other potential host countries— and the host country KF -M ET R.
Computing the effect of a minimum tax on the rate differential is simply a matter of replacing
each Tj by max(m, Tj ). This is likely a reasonable approximation, notwithstanding theadditional complexities noted above.56 The post-minimum KF -M ET R is computed bysimply recalculating it the minimum rate whenever it binds, which will generally imply an
increase. This is somewhat problematic, because the structural base changes introduced by
the minimum matter a good deal, and imply that the impact may be strongly circumstancespecific: In some cases, the KF -M ET R might even fall. The common expectation, however,appears to be that KF -M ET Rs will generally increase, and this directional effect at leastis captured by the recalculation just described.57
Conceptually, the effect on FAI through the KF -M ET R in the host country is straightforward: any minimum-induced increase will reduce inward FAI. With KF -M ET Rs seen
above to have no role outside the host country, there are through this route no spillover
effects on countries that are not themselves constrained by the minimum.
The effects through the statutory rate differentials ∆h , both at country level and (especially) in aggregate, are more subtle. They are easiest to see by supposing that all countries
carry the same weight δhj , so that the weight attached to each tax rate is the proportionof countries setting that rate. Assuming further, for simplicity, a continuous distribution oftax rates prior to implementation of the minimum, with density f (T ) and distribution F (T )55

One issue that need not concern us, however, is the vexed question of which country collects the revenue
from any top-up tax: from the perspective of the investor, that is immaterial.
56

The operation of the carve out means that effective rates will generally be lower than the minimum
rate itself—and for this reason we at some points below consider rates somewhat below the proposed 15
percent—but to the extent that the carve out covers financing costs, the minimum rate applies to rents andso is analogous to the statutory rate in the formalization above.
57

The impact of the minimum tax on KF -M ET R remains to be fully characterized, though see for example
International Monetary Fund (2023), Mintz (2022) and UNCTAD (2022).

34and taking αh to be constant at unity, with a minimum tax rate of m the rate differential in
host h is
R1
mF (m) + m T f (T )dT − max(m, Th )∆h (m) =.
1 − max(m, Th )(13)

Differentiating with respect to m, for countries that are obliged by the minimum to raise
their rate (Th < m), gives, after some simplification,(1 − m)2 ∂∆h (M )

∂m

Z 1
=−

(1 − T )f (T )dT ≤ 0,

(14)

m

An increase in the minimum rate thus reduces inward investment in all countries that it constrains: they never recover from the adverse investment effect of the minimum even though,with further increases, they come to benefit from spillover effects as the minimum comes toconstrain more countries. More straightforwardly, for countries that are not constrained by
the minimum (Th > m)∂∆h (M )
F (m)
=
> 0,
∂m
1 − Th(15)

so that they always gain investment as the minimum rate increases, since that makes them
unambiguously more tax-attractive.
Country-specific results58 are shown in Figure 5. Importantly, most of the host countries
in the sample have relatively high statutory tax rates: in only six is it fifteen percent or
lower. Reflecting this, at a minimum rate of 15 percent all countries other than Bulgaria,
Bosnia and Herzegovina, Ireland, and Cyprus would see a small increase in their inward
FAI.59 Higher minima, of course, bind more countries, and in several cases the investment
effects become very pronounced: at a minimum rate of 20 percent, for example, Bulgaria
and Ireland see reductions in inward FAI of around 18 and 16 percent respectively, while
FAI into Spain increases by nearly 7 percent.
Figure 6 shows the numbers of host countries that gain/lose FAI at each level of the58

Note that in all calculations we continually update levels of FAI as the minimum is increased: the effects
of increasing the rate from 20 to 21 percent, for example, reflect the effects of the increases up to 21 percent.
59

Two countries have initial rates of 15 percent (Latvia and Lithuania), so are unaffected by a minimum
rate of 15 percent.

35

minimum rate: only at a minimum rate of 24 percent does the number of countries losing
inward FAI rise to equal that of those gaining. As the minimum rate increases, so too does thedisparity between those who receiving more inward FAI and those receiving less: as Figure
6 shows, the ‘winners’ gain increasingly more FAI (both on average and in aggregate), while
the growing number of ‘losers’ lose increasingly more FAI (also on average and in aggregate).
Turning to effects on total inward FAI across all 32 host countries, Figure 7 shows thepredicted changes at various levels of the the minimum rate. The effect through the statutory
rate differential, shown by the light bars, while seen above to be ambiguous in principle
proves to be both positive and increasing (though at a decreasing rate) throughout therange shown—the explanation of which is taken up below. The dark bars show the change
due to the increased KF -M ET Rs in the constrained countries. Through this channel, as
noted above, aggregate FAI unambiguously decreases.
The effect through the statutory rate differential is evidently the stronger of the twofor the wide range of minimum rates considered. The increase in aggregate FAI is initiallymodest: less than a one percent at a minimum of 15 percent. The effect on aggregate FAI
peaks at a minimum of around 21 percent, with a 3 percent increase in aggregate FAI. The
increase (relative to the absence of a minimum) declines thereafter, but only vanishes at aminimum rate of 29 percent. Collectively, far from suffering a reduction in real investment
from adoption of the global minimum, the host countries in our sample stand to gain, in
terms of inward FAI, from adoption of even a minimum rate far higher than ever envisaged
in the discussions leading to Pillar Two.
With inward real investment rising in some countries and falling in others, the findingthat the impact of the minimum rate is to increase aggregate FAI over such a wide rangeof minima is an empirical finding, not a theoretical one. It reflects a general feature of thejoint distribution of tax rates and levels of FAI in the sample. To see this, note from (8)that the impact of FAI into any country j from the minimum-induced change in the rate
differential60 term is dF AIj = F AIj β∆ d∆j . Imagine then that the minimum simply requires
the lowest-tax country, 1, which initially has a rate of zero,61 to raise its rate by some amount
60

Here assuming for simplicity a single parent country.

61

This serves to abstract from effects operating through the denominator of ∆1 , which would dampen the36

A. Then FAI into country 1 itself falls by β∆ F AI1 .A. while in every other country j, it rises
Pby β∆ F AIj δj1 A, so that fall in total investment elsewhere is β∆ j6=1 F AIj1 δj1 A. Assuming
further that in all j the same weight is attached to tax rates in all countries, the condition for
aggregate FAI to increase when a binding minimum rate is applied to the lowest tax countryP
becomes that (1/(N − 1) j6=1 F AIj > F AI1 : that is, the level of FAI in that lowest-tax
country be below the average level elsewhere.
Generalizing this, it is shown in Appendix B that (in the continuous case as above) it issufficient for an increase in the minimum rate to increase aggregate FAI that E[F AI|T <m] < E[F AI], so that the average level of FAI in countries constrained by the minimum
is below that across all countries. To the extent that smaller countries tend to have lower
statutory tax rates (as many models of tax competition predict, and as is the case in ourdataset),62 and given too that most of the host countries in our sample are, viewed in a
global setting, relatively high tax ones, there are thus systematic reasons to suppose thata minimum tax rate, at least at moderate levels, will increase aggregate FAI into thesecountries.
This finding of an increase in total FAI as a consequence of adopting a global minimum
tax is in stark contrast to the slight reduction found by others: of around 2 percent, forinstance, in the baseline estimates of UNCTAD (2022).63 This reflects two differences inapproach. First, that here builds on fully articulated and empirically estimated cross-borderrelationships rather than applying presumed elasticities to constructed effective tax rates.
Second, the result here is not an estimate of an overall global effect but of the effect on 32 hostcountries that, in global terms, are relatively high tax ones. Those countries, nonetheless,account for more than half of global FDI—and they are the ones that have been driving theinternational tax reform agenda.
reduction in F AIh : more generally, the condition that follows is thus sufficient for an increase in aggregate
FAI.
62

On the theory point, see for instance Keen and Konrad (2013); on the data point, Supplementary
Appendix Figure C.3 confirms that size, whether measured by GDP or population, is positively correlatedwith the statutory tax rate.
63

Devereux et al. (2020), Hanappi and Cabral (2022) reach broadly similar conclusions.

37

7Conclusions

Using a newly assembled dataset that does not suffer from fundamental limitations of FDI
statistics, and guided by a simple analytical framework that highlights the possibility of
implicit profit shifting through real investment decisions, the purpose here has been to revisit
the questions of the extent to which, and through precisely which channels, international
corporate tax arrangements affect aggregate cross-border tangible investment. Addressingthat has also enabled an analysis of what the implications for tangible investment might
be of what is likely the most profound of the fundamental reforms of the international tax
architecture now underway: the adoption of a global minimum corporate tax.
There emerge effects of statutory tax rates on tangible investment that, across a wide
range of specifications, are strongly significant and large relative to prior consensus values.
Moreover, spillover effects from tax rates elsewhere appear to be about as large as theinvestment effects of the host country’s own tax rate. Our preferred estimated semi-elasticity,with respect to both the host statutory rate and the weighted average of rates elsewhere, if
pressed to provide one, would be around 3.6. This is noticeably higher than many previous
estimates. The reason, we have argued, is that a dampening of apparent tax effects is
an inherent consequence of the conduit arrangements that are reflected in the FDI datathat have commonly been used, but which are not present in FAI statistics. These effects
from statutory rates, moreover, are consistent with the relatively little recognized channelof implicit profit shifting through tax-induced changes in real investment decisions. Indeed
effects through this route appear empirically to dominate any associated with profit shiftingby means of artificial arrangements lacking real substance, which have received much more
attention both in the literature and from policy makers. Spillover effects from statutory
rates appear, as one might expect, to be stronger within the relatively integrated market of
the EU, but they are nonetheless also marked outside.
The impact of the traditional closed economy King-Fullerton marginal effective tax rate,
in contrast, is weak at best. This is more in line with previous findings, though it also tends
to refute the idea that the composite effective rate derived in the theoretical part above(the I-M ET R) can serve as a sufficient statistic for tax effects on inward FAI. Interaction38

effects—–often neglected in trying to understand ‘which’ tax rate matters for cross-borderinvestment—require some qualification of the results for both the cross-border differential instatutory rates and the host marginal effective rate, but do not overturn the broad empiricalconclusions on each. Requiring much less qualification is the finding that closed economy
marginal effective tax rates outside the host country have little if any impact on inward FAI,
and this is so even when some impact might be expected given fixed location costs impacting
the extensive margin of investment. The implication is that league tables of marginal effective
tax rates cast very little light on the relative tax attractions for multinationals of investingin alternative countries.
Applying these results to the analysis of the global minimum tax, due for implementationfrom 2024, the strength of the spillover effects through statutory tax rates mean that FAI
into the (relatively high rate) host countries in our sample would be expected to increase
with the imposition of a minimum rate at the proposed level of 15 percent. This is in sharp
contrast to a view that increases in marginal effective rates as a consequence of the minimumtax (leaving aside the question of whether and when that will indeed be the consequence)
will tend to depress real investment. Indeed while inward tangible investment into some hostcountries in the sample would fall at higher levels of the minimum, the total across all ispredicted to increase up to a minimum of close to 30 percent.
There remain, of course, many open issues. These include, for example, understanding and testing the implications of the potentially very different commercial structures ofmultinational enterprises for the appropriate weighting of statutory rates in evaluating crossborder spillovers. The direction of the interaction effects noted above also remain conceptually puzzling; and, on the policy side, the characterization of the minimum tax reformtaken here is highly simplistic. Nonetheless, the analysis casts some light on the surprisingly
under-studied questions of how, and how much, the international tax system and the fundamental changes to it with which policymakers now need to grapple affect aggregate tangible
cross-border investment.

39

Figure 1. FDI and FAI: An example

Notes: UP is the country of the ultimate parent, IP is the country of the immediate parent, and H is thehost country of investment. If the entity in H has a minority foreign ownership, it is not recorded as a‘foreign’ affiliate and so F AIU P,H is equivalent to zero.

40

Figure 2. FAI vs. FDI
Total Hosts in Sample vs. EU Host Sample

Note: This figure compares the FAI and FDI series for the sample period of 2003-2012. Four trends areshown: for the time series of FAI and FDI in aggregate for the full sample, and for the time series of FAI
and FDI in aggregate across the EU countries in our sample.

41

Figure 3. FAI vs. FDI: By Parent Economy Characteristics(a) Low-Tax(b) Investment Hubs

Notes: This figure compares the share of FAI or FDI relative to total FAI or FDI by parent economy characteristics. These characteristics include: low-tax parents, defined as economies with a statutory Corporate
Income Tax (CIT) rate less than 20 percent (panel (a)) and investment hubs (panel (b)).

42

Figure 4. Non-linear Effects of Corporate Tax Rates(a) Marginal Effects of CIT Differentials(b) Semi-elasticities of Host METR

Notes: The top panel shows marginal effects of the corporate income tax (CIT) differential along the distribution of Host KF-METR values; the bottom panel shows semi-elasticities (which are equivalent to marginal
effects) of the Host KF-METR along the distribution of CIT differential values. The dots correspond topoint estimates, and the lines extending from each dot are 95-percent confidence bands. The range on thex-axis in panel (a) is -25 to 25. The range on the x-axis in panel (b) is -0.3 to 0.3. The de-meaning prior tointeraction means that the coefficient on the main KF-METR term is to be interpreted as the effect of the
KF-METR at its mean value (that is, when the demeaned differential = 0). The same applies to the maindifferential term: the coefficient there is the effect of the differential when the KF-METR is at its averagevalue.

43

Figure 5. Distribution of Investment Impact and Minimum Taxation

Notes: This figure plots the percentage change in inward FAI across the 32 host countries in our sample, atminimum tax rates of 15, 20 and 25 percent. The simulation uses FAI for the year 2016. The statutory
CIT rate in each country is shown in parentheses on the vertical axis. For the Host KF-METR and rate
differential specific effects, see Supplementary Figure C.4.

44

Figure 6. Numbers of ‘Winners’ and ‘Losers’ at Various Minimum Rates

Notes: This figure shows the number of countries that would gain or lose inward FAI at minimum tax rates
between 13 and 30 percent. The number of ‘winning’ countries (in terms of gaining FAI), on the left handscale, is indicated by the black bars, while the number of ‘losing’ countries is indicated by the grey bars.
On the right-hand scale, the blue diamonds indicate the percentage gain in FAI for an average winningcountry, while the pink +’s denote the aggregate percentage gain in FAI across all winning countries; thegreen triangles denote the percentage loss in FAI for the average losing country, while the red x’s denote theaggregate percentage loss in FAI across all losing countries.

45

Figure 7. Aggregate FAI and Minimum Tax Rates

Notes: This figure plots the percentage change in aggregate FAI into the 32 host countries at minimum tax
rates ranging from 13 to 30 percent. These results correspond to corporate tax rates and FAI for the year
2016. The light grey bars show the impact of the minimum rate through the statutory rate differential, thedark grey bars show the impact through the KF-METR, and the red lines show the total impact.

46

Table 1. Summary Statistics
Panel A: Tax Variables
Rate Differential
Rate Differential, EU Hosts
Rate Differential, Non-EU Hosts
Non-Host Component
Non-Host Component, EU
Non-Host Component, Non-EU
Host Component
Statutory CIT Rate
Host KF-METR

Mean
0.043
0.015
0.000
0.289
0.228
0.004
0.246
24.587
13.535

SD
0.108
0.087
0.014
0.168
0.153
0.039
0.157
7.301
8.838

Panel B: Investment Variables
I/K (FAI)
I/K (FDI)
FAI (mil. USD)
Lag K (mil. USD)
Log(FAI)

0.416
0.421
469.338
1835.101
2.355
0.027
0.036
0.060

Panel C: Weights
Sales Share (Lagged)
Capital Share (Lagged)
Export Share (lagged)
Panel D: Zeroes
I/K (FAI)
I/K (FDI)

P10
P50-0.091 0.030-0.091 0.000
0.000 0.000
0.000 0.337
0.000 0.282
0.000 0.000
0.000 0.249
16.000 25.000
2.833 13.080

P90
0.183
0.124
0.000
0.474
0.391
0.000
0.435
35.00024.6610.862
1.592
2730.990
17882.450
2.797

0.000
0.000
0.000
0.000
0.000

0.191
0.054
4.385
13.134
1.478

0.954
0.790
666.2022054.840
6.502

0.127
0.159
0.176

0.000
0.000
0.000

0.000
0.000
0.001

0.023
0.024
0.142

Share
33.79 %
44.98 %

Notes: There are 12,630 observations in our main sample. Panel A shows summary statistics for taxrelated variables. The (statutory corporate tax) rate differential term is defined as the weighted difference
scaled by (100-host country statutory rate) and multiplied by α, as in equation (4). The statutory rate and
KF-METR are shown in percentages. Panel B shows summary statistics for investment variables. I/K ratios
are investment flow divided by lagged capital stock, for the type of investment indicated (foreign affiliate
investment and foreign direct investment, respectively). The summary statistics for I/K in the FDI dataset
are associated with a different sample (N=17,442). Panel C summarizes the data used to construct the rate
differential: weights by sales, capital and exports. Panel D shows the proportions of observations for FAI
and FDI in our main sample that are zero.

47

48

-0.004(0.003)-0.071**(0.029)-0.101(1.477)
12,6300.129
Host, Parent
Bilateral
Year
Parent1.630***(0.571)

12,6300.165
Parent,Bilateral
Host-Year,
Parent

2.667***(0.605)(2)

12,6300.164
Parent,Bilateral
Host-Year,
Parent

2.764***(0.659)-2.607***(0.631)(3)

12,6300.165
Parent,Bilateral
Host-Year,
Parent-1.495***(0.414)2.080***(0.530)1.344**(0.599)(4)

12,6300.128
Host, Parent
Bilateral
Year
Parent-0.003(0.005)1.745***(0.508)(5)

Host, Parent
Bilateral
Year
Parent12,630-0.004(0.003)-0.070***-0.025

1.637***-0.523(6)

Notes: All specifications are estimated in PPML using an unbalanced panel of all available host-parent-year observations. The
dependent variable is the It /Kt−1 ratio for inward FAI. Specifications (1), (5) and (6) include host country, year, and parent
country fixed effects, and the following characteristics of the host and parent countries (as indicated): GDP (in log), capital
account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Specifications (2) to(4) include host country-year fixed effects and parent country fixed effects, and the following characteristics of the multinational
firm’s parent country: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure
share, and exchange rates. Standard errors, in parentheses, are clustered by host country. ***, ** and * indicate significanceat the 1%, 5% and 10% levels respectively. All specifications control for bilateral host-parent characteristics including distance(in log) and an indicator for common official language.

FEs:

Observations
Pseudo-R2
Controls:(Rate Differential)2Rate Differential × Host KF-METR

Host KF-METR

Non-Host Component (Non-EU)

Non-Host Component (EU)

Host Component

Non-Host Component

Rate Differential(1)

Table 2. Baseline ResultsTable 3. Spillovers from KF-METRs outside the Host Country?

Rate Differential
Host KF-METR

(1)

(2)

(3)

(4)

1.501***(0.514)-0.002(0.004)

1.497***(0.513)-0.002(0.004)-0.005(0.006)1.487***(0.510)0.001(0.009)-0.002(0.009)0.137(0.342)

1.384**(0.543)-0.006(0.006)-0.005(0.006)

Parent KF-METR
Mean Non-host KF-METR
Lowest Non-host KF-METR
Constant-31.598**(12.503)-32.426**(12.679)-34.436**(13.496)-0.018(0.013)-32.072**(12.673)

Observations
Pseudo-R2

8,008
0.08168,008
0.08178,008
0.08178,008
0.0819Controls:

Host, Parent
Bilateral
Year, Parent

Host, Parent
Bilateral
Year, Parent

Host, Parent
Bilateral
Year, Parent

Host, Parent
Bilateral
Year, Parent

FEs:

Notes: All specifications are estimated in PPML regression using an unbalanced panel of allavailable host-parent-year observations for which the parent-country KF-METR is available.
All specifications include parent country and year fixed effects, and the following characteristics of the host and parent countries: GDP (in log), capital account openness index,
trade openness, inflation rate, government expenditure share, and exchange rates. Standard
errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%,
5% and 10% levels respectively.

49

Table 4. ‘Pure’ Profit Shifting(1)
Rate Differential
1.832***(0.480)
Host KF-METR-0.003(0.005)
Pure profit shifting (Ω) -0.043(0.033)Observations
Pseudo R2

12,6300.129

Notes: The dependent variable is It /Kt−1 , and estimation is by PPML. Year and parent country fixed effects
are included, as well as controls for host, parent and gravity variables. Standard errors, in parentheses, areclustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

Table 5. Taxation in the Parent Country: Worldwide vs. Territorial(1)
Rate Differential × Territorial Parent
Rate Differential × Worldwide Parent

3.208***(0.524)2.707**(1.158)

Non-Host Component × Territorial Parent
Non-Host Component × Worldwide Parent
Host Component × Territorial Parent
Host Component × Worldwide Parent
Observations
Pseudo-R2
Controls:

11,2080.180
ParentBilateral
Host-Year
ParentFEs:(2)

3.076***(0.592)2.982**(1.163)-3.212***(0.525)-2.457**(1.072)
11,2080.180
ParentBilateral
Host-Year
ParentNotes: All specifications are estimated in PPML. The dependent variable is the It /Kt−1 ratio for FAI. All
specifications include parent country and host country-year fixed effects, and the following characteristicsof the multinational firm’s parent country: GDP (in log), capital account openness index, trade openness,
inflation rate, government expenditure share, and exchange rates. Distance between the host and parent,
and a dummy variable indicating whether they share a common language are also included. Standard errors,in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels
respectively.

50

Table 6. Heckman Selection Model

Rate Differential(1)
FAI> 0First (extensive) stage(2)(3)
FAI> 0
FAI> 01.792***(0.526)0.006(0.007)-0.001(0.001)

2.107***(0.648)0.005(0.006)-0.001(0.001)(4)
FAI> 04.427***(1.594)-0.022**(0.010)5.621***(1.418)-0.025**(0.010)-0.001(0.001)-0.001*(0.001)(1)
LnFAI

Second (intensive) stage
(2)
(3)
LnFAI
LnFAI

(4)
LnFAI

1.490***(0.405)0.013***(0.004)
1.831***(0.161)1.457***(0.420)0.013***(0.003)
1.955***(0.203)

1.839***(0.394)0.009**(0.004)
0.676***(0.113)

1.977***(0.420)0.009**(0.004)0.742***(0.130)Observations

4,666

4,006

4,322

3,731

Exclusion
restrictions:

Weighted METR,
by capital

Weighted METR,
by capital
VAT and PIT Rates
Tax Revenue / GDP
Parent, Year
Host, Parent
Bilateral

Weighted METR,
by salesWeighted METR,
by salesVAT and PIT Rates
Tax Revenue / GDP
Parent, Year
Host, Parent
Bilateral

Host KF-METR
Weighted Non-Host
KF-METR(using capital)
Weighted Non-Host
KF-METR(using sales)

Rate Differential
Host METR
Inverse Mills

FEs at both stages:
Control variables
at both stages:

Parent, Year
Host, Parent
Bilateral

Parent, Year
Host, Parent
Bilateral

Notes: These are Heckman selection results using an unbalanced panel of all available host-parent-

year observations. By the argument set out in the text, the weighted average of KF-METRs outside
the host country is included at the first stage but excluded at the second. This non-host country
KF-METR term, is constructed in two ways: in columns (1) and (2) using capital-based weights,
and in columns (3) and (4) using sales-based weights. Columns (2) and (4) include additional
exclusion restrictions as indicated. Second stage results are from OLS specification using the log of
the FAI ratio as a dependent variable on the restricted sample of positive FAI only, with an inverse
Mills ratio. Standard errors are clustered at the host country level. In both stages we include thefollowing controls for each of host and parent country: GDP (in log), capital account openness
index, trade openness, inflation rate, government expenditure share, and exchange rates. Gravity
variables are also controlled for. Parent and year fixed effects are included. ***, ** and * indicate
significance at the 1, 5, and 10 percent levels respectively.

51

Table 7. More Complex Dynamic of Investment and Timing of Tax Response(1)
Rate Differential
Lagged I/K

2.811***(0.726)
0.286***(0.0335)

Non-Host part
Host part

Observations
Pseudo R2

9,637
0.173

(2)

(3)

(4)

(5)

4.348***(0.811)2.139**(0.868)0.457(1.303)2.666***(0.676)

9,612
0.163

7,430
0.152

Rate Differential
0.286***(0.0335)
2.790***(0.823)-2.825***(0.671)

Rate Differential (t-1)

1.920**(0.867)

Rate Differential (t+1)

9,637
0.173

9,637
0.158

Notes: The dependent variable is It /Kt−1 , where I is FAI flow and K is FAI stock. Estimation is
by PPML. In columns (1) and (2) the baseline specification is augmented with the first-lag of theI/K ratio (that is, FAI flow at time t − 1 scaled by FAI stock at time t − 2). In columns (3)-(5) we
augment the baseline specification with leads and lags of the CIT differential term. Host-year fixed
effects and parent fixed effects are included, and parent country macroeconomic variables as wellas bilateral gravity variables are included as control variables. Standard errors, in parentheses, areclustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

52

Table 8. Alternative Weighting Schemes in Constructing the Rate Differential Term
Weights:
Dep Var:CapitalI/K (FAI)(1)

Rate Differential

Exports
I/K (FAI)(2)

3.853***(1.060)Non Host Component

(3)

(4)

0.508
(1.642)

Constant-38.441***(8.829)5.015***(1.392)
1.684(2.915)-29.396***(6.768)Observations
Pseudo-R2

12,6300.169

12,6300.229

12,6300.161

12,6300.161

Controls:

ParentBilateral
Host-Year,
ParentParentBilateral
Host-Year,
ParentParentBilateral
Host-Year,
ParentParentBilateral
Host-Year,
ParentHost Component

FEs:-42.616***(10.400)

0.487(1.674)-0.405(1.819)-42.576***(10.344)

Notes: All specifications are estimated in PPML using an unbalanced panel of all available
host-parent-year observations. Specifications (1)-(2) use the rate differential weighted using
shares of fixed capital (lagged). Specifications (3)-(4) use the rate differential weighted using
shares of exports (lagged). Fixed effects included in each column are indicated in the table;
also included are gravity variables and the following characteristics of the parent country:
GDP (in log), capital account openness index, trade openness, inflation rate, government
expenditure share, and exchange rates. Standard errors, in parentheses, are clustered by
host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

53

Table 9. Endogeneity: A Control Function Approach(1)
Rate Differential
Host-KF METR

(2)

(3)

2.930**(1.483)-4.924***(1.223)1.866(1.375)-4.271***(1.363)-0.486(1.376)3.140***(1.100)0.513(1.268)2.512**(1.231)

12,7650.169
Parent, Host-Year
Host Comp. (2)Non-Host Comp. (2)12,0770.186
Parent, Host-Year
Host Comp. (2)Non-Host Comp. (2)Host METR (2)VAT and PIT Rev. (1)Host Pop. (0)1.271(1.164)-0.003(0.004)

Non-Host Component
Host Component
Residual (Rate Diff.)
Residual(Host METR)
Residual (Host Comp.)(Non-Host Comp.)
Residual(Host Comp.)
Observations
Pseudo R2
Fixed effects
IVs (no. of lags):

1.490(1.226)-0.001(0.017)

13,483
0.153
Parent, Year
CIT Differential (2)Host METR (2)Notes: The dependent variable is the I/K ratio for FAI. The specification is our baseline PPML,
augmented with residuals from first stage regressions of tax variables. The residuals are predictions
from first-stage regressions, with the instruments included as indicated in the table. We: 1) regressendogenous (tax) variables on instruments and controls, to retrieve predicted residuals, then 2)
estimate PPML regressions with the first stage predicted residuals as controls. With this approachstandard errors tend to be underestimated. Fixed effects are included as indicated in the table.
Macroeconomic variables as well as bilateral gravity variables are included as control variables.
Standard errors in parentheses are clustered by host. ***, ** and * indicate significance at the 1%,
5% and 10% levels respectively.

54

Table 10. Country-Pair Fixed Effects(1)
Rate differential

1.212***(0.307)

Non-host Component

1.805***(0.357)-0.767**(0.391)

Host Component

Observations
Pseudo R2
Fixed Effects

(2)

8,744
0.166
Host-Parent
Year

8,744
0.168
Host-Parent
Year

Notes: All specifications are estimated in PPML. The dependent variable is the It /Kt−1
ratio for FAI. All specifications include host-parent and year fixed effects, and the following
characteristics of the multinational firm’s parent country: GDP (in log), capital accountopenness index, trade openness, inflation rate, government expenditure share, and exchange
rates. Distance between the host and parent, and a dummy variable indicating whether theyshare a common language is also included. Standard errors, in parentheses, are clustered by
host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

55

Table 11. Cross-border Tax Effects: FAI vs. FDI

Dep. Var. is I/K corresponding to:Rate Differential

(1)
FAI

(2)
FDI

1.738***(0.508)0.354(0.647)

Non-Host Component

(3)
FAI

(4)
FDI

0.178
(0.587)-0.814(0.623)-0.017*(0.009)-28.415**(14.487)-0.003(0.005)-48.299***(10.644)-0.018*(0.009)-26.619*(14.407)2.010***(0.619)-1.648***(0.520)-0.003(0.005)-47.749***(10.575)

Observations
Pseudo-R2

12,5320.129

17,442
0.157

12,5320.130

17,4420.159

Controls:

Host, Parent
Bilateral
Year
ParentHost, Parent
Bilateral
Year
ParentHost, Parent
Bilateral
Year
ParentParent,Bilateral
Host-Year,
ParentHost Component
Host KF-METR
Constant

FEs:

Notes: All specifications are estimated by PPML and include parent country and year
fixed effects along with the following characteristics of the multinational firm’s host andparent country: GDP (in log), capital account openness index, trade openness, inflation
rate, government expenditure share, and exchange rates. Standard errors, in parentheses,
are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels
respectively.

56

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60

AppendicesA

Proof of Proposition 1

Attaching multiplier µ to the constraint that
is
Π=

N
X

P

j=1 Sj = 0, the Lagrangean for the multinational’s problem





(1 − Tj ) Rj F1 (K1 ), ..., FN (KN ) + Sj − ρ(1 + Mj )Kj − C(Sj , Kj )

j=1

+µ

N
X

(A.1)
Sj ,

j=1

and so the necessary condition on Kh is that


N
X
∂Rj 0
∂C
∂Rh 0
Fh − ρ(1 + Mh ) −+(1 − Tj )F ≤ 0.
(1 − Th )
∂Fh
∂Kh
∂Fh h

(A.2)

j6=h

Collecting terms in Fh‘ , (A.2) implies


 X


X
N
∂Rj
∂R
∂Rj
∂Rh X
∂Rj
0
+
(1 − Tj )Fh = (1 − Th )
+ Th
−
Tj
Fh0
(1 − Th )
∂Fh
∂Fh
∂Fh
∂Fh
Fh
j6=h
j6=h
j6=h

X

∂R 0
= 1 − Th + αhF
Tj δhj − Th
∂Fh h

(A.3)

j6=h

P
where the first equality follows on using ∂Rh /∂Fh = ∂R/∂Fh − j6=h ∂Rj /∂Fh and simplifying, and thesecond from rearrangement and recalling the definition of αh in the text. Substituting (A.3) into (A.2) andrearranging gives
1 − Th + αhXTj δhj − Th

j6=h

∂C
∂R 0
F ≤ (1 − Th )(1 + Mh ) +
∂Fh h
∂Kh

(A.4)

Turning to the term ∂C/∂Kh on the right of (A.4), with shifting costs assumed to be of the form



1
Si2 ,
2φρKi

(A.5)

(1 − Th )φρKi − Si + µρKi .

(A.6)

C(Si , Ki ) =the necessary condition on Si requires that

P
P
P
Summing (A.6) over i, using i Si = 0 and defining ωi ≡ Ki / j Kj , gives µ = − i (1 − Ti )ωi ; substitutedPback into (A.6), this implies that Sh = φρKh ( i Ti ωi − Tj ). Using this in the implication of (A.5) that


2
∂C
1
Sh
=−
,
∂Kh
2φρ
Kh

(A.7)

P
gives ∂C/∂Kh = −(φρ/2)( j6=h Tj ωj − Th )2 , where ωj is as defined in the text. Using this in (A.4) and61

rearranging:P(1 − Th )(1 + Mh ) − (φ/2)( j6=h Tj ωj − Th )2
∂R 0
P
F ≤ρ
.
∂Fh h
1 − Th + αh ( j6=h Tj δhj − Th )(A.8)

Dividing numerator and denominator on the right of (A.8) by 1 − Th and rearranging gives the formulationin Proposition 1 but with the I-M ET R defined as in (10). Setting φ = 0 (corresponding to infinite shifting
costs), Proposition 1 follows.

B

Sufficient condition for a minimum tax to increase
aggregate FAI

Recalling (8), the expected impact on FAI into host country h of an increase in the minimum rate is givenhby F AIh β∆ (∂∆h /∂m); the effect on aggregate FAI thus has the same sign as E[F AIh ∂∆∂m ]. To sign this(maintaining the same assumptions as in Section 6), define i(T, m) = 1 if T > m and zero otherwise andcombine (14)-(15) to give

∂∆h
F (m)
=
i(Th , m) −∂m
1 − ThR1m(1 − T )f (T )dT(1 − i(Th , m)).
(1 − m)2(B.1)

Noting in the first term that T > m when i(T, m) = 1 and in the numerator of the second adding and
R1
subtracting m mf (T )dt, some rearrangement gives∂∆h≥ F (m) − (1 − i(Th , m)) +(1 − m)
∂m

R 1

m


(T − m)f (T )dT(1 − i(Th , m)).
1−m

(B.2)

Since the final term in (B.2) is non-negative, multiplying by F AIh and taking the expectation across all
hosts gives


Z m
∂∆h
(1 − m)E F AI
≥ F (m)E[F AI] −F AIf (T )dt∂m0
= F (m) E[F AI] − E[F AI|T < m] ,from which the result follows whenever the minimum binds some countries.

62

(B.3)

C

Supplementary Tables and Figures
Figure C.1. Worldwide vs. Territorial Parent CIT Systems and the Rate Differential

Notes: The figure shows the marginal effect of the CIT differential for three subsets of the sample: the fullsample, restricted to observations with a positive differential (that is, bilateral pairs for which the differential
term is greater than zero), and restricted to observations with a negative differential (that is, bilateral pairs
for which the differential term is less than zero).

63

Figure C.2. Worldwide vs. Territorial Parent CIT Systems and the Differential Term Components(a) Response of the Host Component(b) Response of the Non-Host Component

Notes: The top panel shows the marginal effect of the host component of the CIT differential forthree sets of the sample: the full sample, restricted to observations with a positive differential (thatis, bilateral pairs for which the differential term is greater than zero), and restricted to observations
with a negative differential (that is, bilateral pairs for which the differential term is less than zero).
The lower panel shows a similar set of results for those three samples in terms of the response of
the non-host component of the differential.

64

Figure C.3. Size and Corporate Tax Rates in 2016(a) Population vs. Stat. Rate (All)(b) Population vs. Stat. Rate (Ex-USA)(c) GDP vs. Stat. Rate (All)(d) GDP vs. Stat. Rate (Ex-USA)

Notes: These figures show that our data is consistent with models of tax competition, which predictthat smaller countries have lower tax rates. Each panel shows a scatter plot overlaid with a best-fit
line. Either all countries or all countries excluding the USA are included, as the USA tends to be
an outlier here.

65

Figure C.4. Heterogeneity by Corporate Income Tax Variables(a) Effect of the CIT Differential(b) Effect of Host KF-METR

Notes: The top panel shows the effect of the CIT differential on the percentage change in hostcountry FAI, for three minimum tax scenarios (15, 20 and 25 percent as indicated). The lower
panel shows the effect of host country KF-METR on the percentage change in host country FAI,
for three minimum tax scenarios (15, 20 and 25 percent as indicated). For the latter figure, notethat only host countries with KF-METRs initially below the minimum statuory rate are directly
affected through this channel.

66

Table C.1. Summary of Main Tax Variables by Country
Variable:
Statistic:
Austria*
Belgium*
Bosnia and Herzegovina
Bulgaria
Canada*Croatia
CyprusCzechia*
Denmark*
Estonia*
Finland*France*Germany*
Greece*Hungary*
Ireland
Italy*
LatviaLithuania
Luxembourg*
Malta
Netherlands*
Norway*Poland*Portugal*
Romania
Slovakia*
Slovenia*
Spain*
Sweden*United Kingdom*
United States

Statutory Rate
Mean
26.15
34.01
10.00
12.12
27.01
20.00
11.20
21.73
25.70
21.90
24.93
35.63
33.17
24.43
18.66
12.50
33.38
15.25
15.39
29.02
35.00
27.10
27.51
19.49
28.76
17.15
20.29
20.76
31.02
25.79
26.26
39.20

Host KF-METR
Mean
13.61
13.72
13.29
3.29
14.96
7.29
12.05
9.88
16.76
33.42
15.98
18.01
18.33
10.93
10.14
7.51
4.85
17.65
1.88
11.31
56.17
11.69
21.06
10.50
12.04
6.63
6.44
9.91
26.33
15.65
20.34
23.39

Rate Diff.
Mean
0.01
-0.05
0.03
0.08
0.01
0.03
0.07
0.03
0.01
0.04
0.02
-0.06
-0.02
-0.01
0.05
0.06
-0.04
0.06
0.06
-0.02
-0.08
0.00
-0.01
0.03
-0.02
0.07
0.04
0.03
-0.03
0.01
0.01
-0.06

Rate Diff.
SD
3.00
0.22
0.00
4.00
1.12
0.00
1.52
3.73
2.16
1.86
2.77
1.38
4.24
2.73
1.25
0.00
2.88
0.96
1.34
0.34
0.00
3.38
0.90
1.92
2.17
3.00
1.90
3.13
2.79
2.47
3.72
0.13

Notes: Host countries that we treat as having territorial corporate income tax (CIT) system are indicatedby *. “Rate Diff.” refers to the rate differential term ∆hpt .

67

Table C.2. Number of Parent Countries by Host Country and Years in the Data
Host Country
Austria
Belgium
Bosnia and Herzegovina
Bulgaria
CanadaCroatia
CyprusCzechia
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
IrelandItaly
LatviaLithuania
Luxembourg
Malta
Netherlands
NorwayPolandPortugal
Romania
Slovakia
Slovenia
Spain
SwedenUnited Kingdom
United States

Mean
101
127
126
92
29
102
69
77
90
92
99
83
102
129
97
118
97
80
98
99
141
89
113
107
91
72
89
62
85
74
65
50

Minimum
5
107
110
51
22
7
2
2
24
16
17
25
69
99
19
94
52
22
40
74
117
7
80
32
14
25
28
12
41
7
3
20

Maximum
148
145
145
136
35
133
159
119
147
162
167
139
138
140
136
142
143
157
153
137
158
146
126
153
155
112
153
147
153
148
123
66

First Year
2003
2010
2013
2003
2011
2008
2004
2003
2006
2003
2003
2003
2007
2012
2004
2008
2003
2003
2003
2009
2008
2003
2008
2007
2003
2004
2004
2003
2003
2003
2006
1998

Last Year
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016

Notes: This table shows the number of parent countries with non-missing FAI data, for each host country inthe dataset. Mean, minimum, and maximum refer to the average number of non-missing parent observations
per host country-year, and the number of non-missing parent observations for the host country-year withthe least and most observations, respectively. The last two columns show the range of dates for which FAI
data is available for the host country.

68

Table C.3. All Parents in the Dataset: Worldwide and Territorial Classification
Worldwide:
Afghanistan
Albania
Algeria
AngolaAntigua and Barb
Argentina
Armenia
Australia
Azerbaijan
BahamasBahrain
Bangladesh
Barbados
Belarus
BelizeBenin
BhutanBolivia
Bosnia and Herze
Botswana
BrazilBrunei
Bulgaria
Burkina Faso
Burma
BurundiCabo VerdeCambodia
Cameroon
Central African
Chad
Chile
China
Colombia
Comoros (the)Congo (the)Costa Rica
Croatia
CyprusCôte d’Ivoire
Djibouti
Dominica
Dominican Republ
EcuadorEgypt
El Salvador
Equatorial Guine
EritreaCôte d’Ivoire
Djibouti
Dominica
Dominican Republ
EcuadorEgypt
El Salvador
Equatorial Guine
Eritrea
Ethiopia
Fiji
Gabon
GambiaGeorgia
Ghana
GrenadaGuatemalaGuineaGuinea-Bissau
GuyanaHaiti
HondurasHong Kong
India
Indonesia
Iran
Iraq
IrelandIsraelJamaicaJapan
JordanKazakhstan
Kenya
Kiribati
KuwaitKyrgyzstan
Lao People’s Dem
LatviaLebanonLesothoLiberia
Libya
Lithuania
Macao
Macedonia
Madagascar
MalawiMalaysia
Maldives
Mali
Malta
Marshall Islands
Mauritania
Mauritius
MexicoMicronesia (FedeMoldova
Mongolia
Montenegro
MoroccoMozambique
Namibia
Nepal
New Zealand
Nicaragua
Niger
Nigeria
Oman
Pakistan
PanamaPapua New Guinea
Paraguay
Peru
Philippines
Puerto Rico
Qatar
Romania
RussiaRwandaSamoa
San Marino
Sao Tome and Pri
Saudi Arabia
SenegalSerbiaSeychelles
Sierra Leone
Singapore
Solomon Islands
South Africa
South Korea
South Sudan
Sri Lanka
St. Kitts and Ne
St. Lucia

St. Vincent and
Sudan
Suriname
Syria
TaiwanTajikistan
Tanzania, UnitedThailand
Timor-Leste
Togo
Tonga
Trinidad and Tob
Tunisia
TurkeyTurkmenistan
UgandaUkraineUnited Arab Emir
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
Yemen
ZambiaZimbabwe

Territorial:
Austria
Belgium
CanadaCzechia
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
Iceland
Italy
Luxembourg
Netherlands
NorwayPolandPortugal
Slovakia
Slovenia
Spain
SwedenSwitzerland
United Kingdom

Notes: There is some time variation in our sample for countries with territorial CIT systems. Those include
the following countries, which are designated as having worldwide CIT systems prior to the year indicatedhere: Czechia (2004), Estonia (2005), Finland (2005), Greece (2011), Poland (2004), Slovakia (2004), Slovenia(2004), and United Kingdom (2009). Note that the final year in our sample is 2016. The territorial andworlwide designation are obtained from PWC Tax Summary Reports, in particular a report titled “Evolutionof Territorial Tax Systems in the OECD” from 2013.

69

Table C.4. Tax Rates in 2016
Host Country
Austria
Belgium
Bosnia and Herzegovina
Bulgaria
CanadaCroatia
CyprusCzechia
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
IrelandItaly
LatviaLithuania
Luxembourg
Malta
Netherlands
NorwayPolandPortugal
Romania
Slovakia
Slovenia
Spain
SwedenUnited Kingdom
United States
Total

Host KF-METR
13
14
14
3
14
8
12
8
14
31
13
19
18
17
12
8
-16
16
2
11
56
8
19
11
12
6
13
9
25
13
18
23
14

Host Statutory Rate
25
34
10
10
27
20
13
19
22
20
20
34
30
29
19
13
31
15
15
29
35
25
25
19
30
16
22
17
25
22
20
39
23

Rate Differential
0.000-0.111
0.121
0.100
0.018
0.036
0.086
0.052
0.022
0.044
0.040
-0.070-0.052-0.0440.055
0.086-0.0580.070
0.086-0.032-0.1160.002-0.0060.049-0.047
0.088
0.022
0.053
-0.0040.023
0.049-0.1040.015

Notes: This table shows for each host country, for 2016, the KF-METR, statutory CIT rate and averageover all parent countries p of the rate differential ∆hp2016 . KF-METR and the statutory CIT rate are inpercent.

70

Table C.5. Distribution of the Statutory Rate Differential Term in 2016
Host country
Austria
Belgium
Bosnia and Herzegovina
Bulgaria
CanadaCroatia
CyprusCzechia
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
IrelandItaly
LatviaLithuania
Luxembourg
Malta
Netherlands
NorwayPolandPortugal
Romania
Slovakia
Slovenia
Spain
SwedenUnited Kingdom
United States
Total

Mean
0.000
-0.111
0.121
0.100
0.018
0.036
0.086
0.052
0.022
0.044
0.040
-0.070-0.052-0.0440.055
0.086-0.0580.070
0.086-0.032-0.1160.002-0.0060.049-0.047
0.088
0.022
0.053
-0.0040.023
0.049-0.104
0.015

S.D.
0.074
0.110
0.100
0.096
0.040
0.072
0.090
0.076
0.065
0.078
0.075
0.102
0.079
0.084
0.078
0.091
0.089
0.082
0.086
0.079
0.118
0.063
0.064
0.075
0.087
0.083
0.070
0.074
0.076
0.072
0.080
0.115
0.104

P10
-0.108-0.2680.000
0.000-0.004-0.0390.000-0.018-0.050-0.046-0.047-0.241-0.181-0.178-0.0280.000-0.2080.000
0.000-0.175-0.288-0.093-0.095-0.026-0.1790.000-0.0640.000-0.115-0.062-0.047-0.278-0.117P50
0.000-0.091
0.111
0.091
0.000
0.000
0.063
0.006
0.000
0.000
0.000
0.000
-0.006
0.000
0.014
0.060
-0.001
0.035
0.059
0.000
-0.091
0.000
0.000
0.003
0.000
0.065
0.000
0.010
0.000
0.000
0.016
-0.068
0.000

P90
0.098
0.000
0.266
0.242
0.076
0.149
0.222
0.166
0.125
0.175
0.154
0.000
0.023
0.046
0.166
0.222
0.009
0.197
0.205
0.038
0.000
0.091
0.086
0.164
0.042
0.214
0.127
0.172
0.099
0.127
0.175
0.000
0.156

Notes: This table shows the distribution by host country of the rate differential ∆hp2016 .

71