Patent boxes design, patents location, and local R&D

Abstract

Patent boxes have been heavily debated for their role in corporate tax competition. This paper uses firm-level data for the period 2000-12 for the top 2,000 corporate research and development investors worldwide to consider the determinants of patent registration across a large sample of countries. Importantly, we disentangle the effects of corporate income taxation from the tax advantage of patent boxes and exploit a new and original dataset on patent box features such as the conditionality on performing research in the country or their coverage. We find that patent boxes have a considerable effect on attracting patents, mostly because of their favourable tax treatment. Patents with high earnings potential are particularly sensitive. Patent boxes with a large coverage also have stronger effects on the location of patents. We also analyse the impact of patent boxes and their tax advantages on local R&D activities and find that R&D development conditions tend to attenuate the dominant fiscal effect of patent boxes.

Full text

SUMMARY
Patent boxes have been heavily debated for their role in corporate tax competition.
This paper uses firm-level data for the period 2000–12 for the top 2,000 corporate
research and development investors worldwide to consider the determinants of patent
registration across a large sample of countries. Importantly, we disentangle the effects
of corporate income taxation from the tax advantage of patent boxes and exploit a
new and original dataset on patent box features such as the conditionality on per-
forming research in the country or their coverage. We find that patent boxes have a
considerable effect on attracting patents, mostly because of their favourable tax treat-
ment. Patents with high earnings potential are particularly sensitive. Patent boxes
with a large coverage also have stronger effects on the location of patents. We also
analyse the impact of patent boxes and their tax advantages on local R&D activities
and find that R&D development conditions tend to attenuate the dominant fiscal
effect of patent boxes.
JEL codes: F21, F23, H25, H73, O31, O34
—Annette Alstadsæter, Salvador Barrios, Gaetan Nicodeme,
Agnieszka Maria Skonieczna, and Antonio Vezzani
Patentb
oxes
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Patent boxes design, patents
location, and local R&D
Annette Alstadsæter, Salvador Barrios, Gaetan Nicodeme,
Agnieszka Maria Skonieczna, and Antonio Vezzani*
School of Economics and Business and Norwegian University of Life Science; European
Commission and Joint Research Centre; European Commission, ULB, CESifo, and CEPR;
European Commission; European Commission and Joint Research Centre
1. INTRODUCTION
A growing number of developed economies have recently implemented patent box
regimes. Patent boxes [also called intellectual property (IP) boxes] are output-related tax
incentives that apply reduced rates to income earned from exploiting IP (CPB, 2015).
* We are thankful to Nicola Fuchs-Schu¨ndeln, to four anonymous referees and to our two discussants
Giacomo Calzolari and Gabriel Felbermayr for their insightful comments and suggestions. We are
thankful as well as for the comments received at the 65th Economic Policy Panel, Central Bank of
Malta. We also acknowledge useful comments from Maarten Buis, Irem Guceri, David Hannigan,
Shafik Hebous, Andrea Ichino, Henrik Paulander, Eric Strobl, Sara Riscado,Isabel Alvarez, Lourdes
Moreno, Carlos Garcimartin, as well as the participants at the 2015 PSE CESifo conference, the 9th
annual tax symposium of the Oxford University Centre for Business Taxation and the 71st Annual
Congress of the International Institute of Public Finance and the ICEI seminar at the Universidad
Complutense Madrid for valuable comments and suggestions. The findings, interpretations, and con-
clusions expressed in this paper are entirely those of the authors and should not be attributed to the
European Commission. Possible errors and omissions are those of the authors and theirs only.
The Managing Editor in charge of this paper was Nicola Fuchs-Schu¨ndeln.
PATENT BOXES 133
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It is called a box in reference to the box to be ticked on the tax form to benefit from the
regime. In other words, a patent box is a special tax regime that grants preferential tax
treatment to corporate revenues from IP.
The use of such schemes has raised suspicion about yet another tax competition
device. In July 2013, German finance minister Wolfgang Scha¨uble publicly criticized
patent box regimes as ‘going against the European spirit’, suggesting that they should
simply be banned.
1
Such concerns appear justified by anecdotal evidence. For
instance, Pfizer’s widely discussed and failed attempt to takeover Astra Zeneca
appeared to be essentially tax motivated.
2
The company resulting from this merger
would have been incorporated in the United Kingdom taking advantage of a reduced
corporate tax rate of 10% (instead of a standard rate of 21%) over future profits gen-
erated from patents. Similarly, the UK company GlaxoSmithKline has recently cen-
tralized all its vaccine-related IP in Belgium mainly for fiscal reasons while carrying its
physical capital investment at home.
3
In another notable case, the hotel reservation
company Booking.com was expected to reduce its tax rate by around 4 percentage
points thanks to the Dutch patent box regime.
4
These examples seem to suggest that
the decisions on patent registration by firms may have little to do with developing
research and innovation but a lot to do with tax planning, echoing Minister
Scha¨uble’s worries that patent boxes are simply there ‘to attract companies’. Such con-
cerns were also voiced in the context of the Organisation for Economic Cooperation
and Development (OECD) Base Erosion and Profit Shifting (BEPS) discussions and in
the European Union (EU) code of conduct on business taxation.
5
The need to align
taxation with ‘substantial’ research activity being developed by companies is now
indeed seen as a key factor to ensure that such preferential regimes reach their goal of
fostering innovation and economic growth.
6
In this paper, we provide novel empirical evidence on the determinants of the geo-
graphical distribution of patent applications made by the 2,000 top corporate R&D
worldwide investors. We focus on both tax and non-tax features of patent box regimes
that might affect patent registration and local R&D activity. Our sample covers patents
registered in 33 host countries
7
for three sectors of activity (the pharmaceutical industry,
the car industry, and the Information and Communications Technology, ICT) that
have been particularly active in global patenting in the past decades, by parent
1Breidthardt (2013).
2Houlder (2014).
3 See Powley (2014) and L’Echo (2015).
4Breidthardt (2013).
5OECD (2014, pp. 27–53) and Council of the European Union (2014).
6Van der Made (2014,2015).
7 The EU28 (except Bulgaria, Latvia, and Malta), Canada, China, Japan, the Republic of Korea,
Lichtenstein, Norway, Switzerland, and the United States.
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companies located in 39 home countries
8
during the period 2000–12. We disentangle
the general effects of the corporate income tax (CIT) rate from tax and non-tax charac-
teristics of patent boxes such as their coverage and eligibility conditions, and investigate
whether or not these characteristics influence local research activity. Importantly, our
firm-level data include 12 countries with patent boxes, of which 10 have introduced a
patent box within the period 2000–12.
To the best of our knowledge, this is the first attempt to analyse the various specific
designs of patent boxes and to test their impacts on patent location and local inventor-
ship. Our results suggest that patent boxes have a strong effect on attracting high-value
patents (those with high earnings potential), mainly owing to the favourable tax treat-
ment they offer. Patents are also found to be more sensitive to the tax advantages offered
by patent boxes when these have a large coverage in terms of the types of IP covered
and when they grant their benefit to pre-existing patents, acquired patents, and/or
embedded royalties. Importantly, our results suggest that the tax advantages of patent
boxes do not stimulate local innovative activities, given our finding that they fail to
incentivize companies to develop local research. Nevertheless, our results show that the
imposition of local R&D development conditions in the patent box regime has the
potential to attenuate the fiscal effect of patent boxes.
There is to date little empirical evidence on the impacts of patent boxes on R&D and
patent location,
9
albeit the field is growing. A negative relationship between the level of
the CIT rate and the amounts of both a firm’s intangible assets and its patents has been
documented by Dischinger and Riedel (2011),Ernst and Spengel (2011),Karkinsky and
Riedel (2012),Bo¨hm et al. (2014),Ernst et al. (2014),Griffith et al. (2014),andBo¨senberg
and Egger (2017). For example, Karkinsky and Riedel (2012) estimate that a percentage
point increase in the corporate tax rate reduces patent applications filed at the location
by around 3.5%. Bo¨hm et al. (2014) and Griffith et al. (2014) show in addition that the
quality of an intangible asset and the anti-avoidance framework (e.g., controlled foreign
company rules) play a role in the location decisions. Bo¨hm et al. (2014) and Ernst et al.
(2014) suggest that low income tax rates particularly attract patents with high earnings
potential. However, these papers use older data that do not cover the introduction of
the many recent patent boxes and they often mainly analyse the effect of the (effective)
CIT rate on the patent location choices. For instance, Griffith et al. (2014) use data
extending till 2005 to simulate the impact of recent preferential tax regimes for patent
income and conclude that they are likely to result in substantial revenue losses for all
8 The EU28 (except Bulgaria, Cyprus, Czech Republic, Estonia, Greece, Croatia, Lithuania, Latvia,
Malta, Poland, Romania, Slovak Republic), Australia, Bermuda, Brazil, Canada, Cayman Island,
China, Curacao, Hong Kong, India, Israel, Japan, Republic of Korea, Lichtenstein, Mexico, Norway,
Russia, Saudi Arabia, Switzerland, Singapore, Taiwan, Thailand, Turkey, and the United States.
9 In terms of macroeconomic effects, a recent study by Chen et al. (2016) finds some evidence that the
implementation of a patent box in high-tax countries both reduces the outward profit-shifting and
increases employment by multinationals in these countries.
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countries. More recently, Bradley et al. (2015) use data extending from 1990 to 2012.
Using OLS, for the log of total patents per country and year, they find that a one-
percentage point reduction in the patent box tax rate increases patent applications by
3%. They however find no effect of patent box regimes on attracting foreign patents,
indicating that the increase derives from domestic owners and inventors. Bo¨senberg and
Egger (2017) equally extend their data till 2012 and find in contrast that whereas larger
front-end R&D tax incentives (e.g., deductions and allowances for R&D costs) raise the
propensity to file patents, back-end R&D tax incentives (e.g., patent boxes) have their
biggest impact on patent trading.
The rising concerns surrounding patent boxes are part of a long-standing discussion
on tax competition. This literature usually advocates for an increased global coordina-
tion of corporate tax policies. Countries around the world have always been eager to be
attractive to foreign portfolio and physical investment, thus triggering a race to the bot-
tom in corporate taxation, realizing the theoretical predictions of Zodrow and
Mieszkowski (1986) and Wilson (1986).
10
In the OECD, the average CIT rates fell from
48.5% in 1985 to 28.7% in 2007, while in the EU (EU-15) the fall was from 48.7% in
1985 to 28.8% in 2007. Recently, however, this race to the bottom seems to have lev-
elled off. The EU-15 average moved from 27.5% in 2008 to 26.3% in 2015 and the
OECD average changed from 27.6% to 26.4% over the same period.
11
At the same
time, however, many EU Member States narrowed their corporate tax base with a view
to stimulating and attracting investment.
12
Tax competition thus seems to have changed
its nature, moving from a focus on statutory rates to one on tax bases.
13
Patent boxes
are an important driver of these recent developments, with EU countries being espe-
cially active. Patent boxes first appeared in France and Ireland as early as the 1970s.
Interestingly, Ireland is, to date, the only country that has abolished its patent box for
budgetary reasons (2010), but has reintroduced such regime as from 2016.
14
Figure 1
shows that the number of patent boxes in the EU has grown from 2 in 1995 to 11 in
2015, with a clear acceleration in recent years. Bra¨utigam et al. (2017) argue that the rea-
son for this acceleration is the Cadbury-Schweppes ruling of the European Court of
10 See Devereux et al. (2008) for an empirical analysis. Data on corporate tax rates can be found in, inter
alia, European Commission (2017) and OECD (2015a).
11 The EU-28 average moved from 22.7% in 2008 to 22.1% in 2015. The OECD data are for those
that were members in 1985.
12 See Garnier et al. (2014) for a recent review on policy measures at EU level. See also Atkinson and
Andes (2011) for a discussion of patent boxes into a US setting.
13 The literature on the economic effects of harmful tax practices is summarized in Nicodeme (2009).
Two strands are opposed with, on the one hand, authors that consider that these practices are para-
sitic and increase tax competition (e.g., Slemrod and Wilson, 2009) and, on the other hand, authors
who argue that such practices increase economic efficiency by allowing states to offer preferential
regimes to mobile activities (e.g., Keen, 2001).
14 At a rate of 6.25%, that is half of the standard 12.5% corporate income tax rate.
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Justice from 2006, which limits the applicability of Controlled Foreign Corporations
(CFC) rules within the EU. The tax reduction offered by patent boxes varies across
countries but the average advantage over the period has been as high as 75% reduction
in the CIT rate (i.e., 17.9 percentage points).
Patent box schemes came under the scrutiny of the EU and OECD because of the
apparent lack of linkage between the tax advantage offered and the presence of research
or innovation activity. Discussions at both the OECD and the EU have led to an agree-
ment on the requirement to establish a nexus between the income derived from IP and
the expenditure incurred to develop this asset, for the income to qualify for the patent
box preferential regime (OECD, 2014,2015b).
15
The existence of development condi-
tions in some patent boxes may shed light on the potential effect of the nexus condition
developed by the OECD and the EU, notably with regard to its effect on patent loca-
tion, tax revenues, and local R&D. Our finding that the tax-sensitivity of patent location
Figure 1. Average corporate tax rate and patent boxes in the EU-28
Sources: Taxes in Europe Database and own computations. The columns indicate the number of patent box
regimes in the EU-28 and the crosses indicate the arithmetic average of the percentage reduction in corporate
income taxes offered by the patent boxes. The straight line represents the arithmetic average statutory tax rate in
the EU-28, including local taxes and surcharges.
15 In the EU, an agreement on a modified nexus approach requires that Member States with patent
boxes that do not meet this condition close them to new entrants by 30 June 2016 and abolish them
by the 30 June 2021 (Van der Made, 2015).
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is reduced when such specific conditionality is imposed would suggest that the nexus
approach could (at least partly) inhibit the still dominant tax competition dimension of
patent boxes.
Theoretically, there are a number of reasons for suggesting that patent boxes do not
necessarily serve the goal of boosting local R&D activity. First, unlike expense-based tax
incentives for R&D, such schemes do not reward firms for the social benefits that they
cannot appropriate. Instead, they award additional tax benefits to a successful innova-
tion that already enjoys IP protection. Un-patentable research efforts with potentially
higher social spillovers are less attractive and thus become indirectly discriminated
against. Second, patent boxes also rank very low in terms of good tax incentive practices
such as their coverage (determining the size of the tax base), their targeting, and their
organizational practices (CPB, 2015).
16
In our regressions, we provide evidence that the presence of a patent box has a dis-
tinctive effect on patent location and that the tax advantage offered through patent
boxes is effective in attracting patent registrations and high value patents in particu-
lar. Our results suggest that a distinction between countries that have a low tax rate
under the general regime and countries that have a low rate because of a patent box
is useful. In our regressions we test whether the tax advantage offered by patent
boxes has a different effect than the standard CIT rate and we test whether its effect
is affected by the characteristics of patent boxes. The remainder of the paper is
organized as follows. Section 2 describes patent box regimes and their characteristics
and details the nexus approach chosen by developed economies. Section 3 explains
our patent data and Section 4 discusses our empirical strategy. Next, Section 5
describes our identification strategy. Section 6 presents our empirical results before
concluding in Section 7.
2. PATENTS, PATENT BOX DESIGN, AND LOCAL R&D
2.1. Who patents and why?
A patent is a ‘legal title that gives inventors the right, for a limited period (usually
20 years), to prevent others from making, using or selling their invention without their
permission in the countries for which the patent has been granted’.
17
Before moving
into the analysis on the location of patents, it is useful to understand why companies
16 CPB (2015) reviews the economic literature on the determinants of R&D activity to benchmark the
tax schemes. Patent boxes are found to have several non-recommended practices such as being
related to output or having weak targeting.
17 Definition according to the European Patent Office: http://www.epo.org/service-support/glossary.
html.
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patent their inventions in the first place and why it is strategically important to locate
patent for fiscal reason, in particular for large multinationals. The patent system is terri-
torial, and a patent is valid for the geographical area for which it is granted. This has
the effect of dividing world markets into protected trade areas (Greenhalgh and Rogers,
2010).
18
Holders of a patent issued by a patent office have a given period of time
(12 months) to file a patent application abroad and still claim priority for the existing
application.
Large R&D-intensive firms tend to patent more, whereas process-oriented innovators
patent less than product-oriented innovators (Peeters and van Pottelsberghe, 2006).
Many sectors are not patent-active, and patenting firms represent a small part of the
population of firms, for example, only between 1.6% in Ireland and 8.8% in Germany
(OECD, 2013). Hall et al. (2013) find that even among firms that conduct R&D in the
United Kingdom, only 4% patent. The share of patenting firms is much lower than one
might expect given that around 20% of firms that invest in R&D report product innova-
tions. Findings are similar for the United States as only 5.5% of US manufacturing firms
own a patent (Balasubramanian and Sivadasan, 2011). Regressing by sector is hence jus-
tified by the heterogeneity of the determinants of patent registration across sectors. This
derives from sectorial differences in the economic, tax, and patenting perspectives.
Computers, electronics, machinery, chemicals, and pharmaceuticals are the sectors with
the highest patenting activities (OECD, 2013). ICT, pharma, and car sectors are the
most patent- and R&D-intensive companies in our sample. Empirical evidence suggests
that for many sectors patents are an ineffective way to appropriate returns, and secrecy
(e.g., the Coca-Cola formula is a closely held trade secret, hence not patented) and lead
times are used extensively (Arundel, 2001;Hanel 2008;Hall et al.,2013). This does not
necessarily mean that different means of appropriation are substitutes, as for non-
patentable inventions such as software in Europe. Hall et al. (2014) review the vast eco-
nomic literature with a focus on the trade-offs between using patents (and hence disclos-
ing) versus secrecy. They conclude that the most robust finding is heterogeneity in the
use of patents across industries. The nature of innovation and degree of competition are
key factors that will shape a firm’s propensity to use secrecy rather than patents.
Companies that regard patents as important appropriation means and that are more
likely to opt for patents are the larger firms, those that already have patents and R&D-
performing firms, which typically jointly form our sample. However, Hall et al.’s review
highlights that the theoretical literature concentrates on the binary choice between pat-
ents and secrecy, while the available survey data suggest that patents and secrets are
used as complements. Firms hence appear to combine formal (patents, copyrights,
18 This means, for instance, that a US company holding a US patent (granted by the United States
Patent and Trademark Office) would need to file for patent/register with the EPO or a national pat-
ent office to obtain a patent that also covers European countries.
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trademarks) and informal (secrecy, lead times) means of appropriation to protect differ-
ent elements of their innovation (Hall et al.,2013,2014). This is important for our work,
as the evidence presented in this paper suggests that many patent boxes apply to IP,
which is much broader than patents.
Furthermore, even for large, R&D-intensive firms coming from sectors where patents
are used intensively, differences in strategy remain (Dernis et al.,2015). We are interested
in these differences as we expect that responses to patent boxes will vary across sector.
Griffith et al. (2014) already show higher sensitivity to tax in certain broad categories of
industries. Indeed, patent value, R&D intensity, and organizational structure of MNEs
will vary across sectors. Intensity in intangible assets will vary per industry and will be an
important element in firm decision-making over how to organize tax planning activities.
Beer and Loeperick (2015) show that intangible asset endowment of subsidiaries and the
supply-chain complexity of multinationals explain aggregate profit-shifting trends. Their
paper reveals noticeable differences in both intangible endowments of affiliates across
different sectors as well as a major variance in the complexity of the MNE groups these
affiliates belong to. According to their classification, pharmaceuticals and ICT are top
or above the median in terms of intangible endowment, while motor vehicles have
much smaller share of intangibles in total assets but the complexity of their supply chain
is high.
Another difference relates to the motives for patenting, which can differ across sectors.
For example, they may depend on whether an industry mainly produces ‘discrete’ or
‘complex’ products (Cohen et al.,2000). The most important objective behind patenting
is to prevent third parties from exploiting the protected invention. However, strategic
patenting seems increasingly important and may also provide signals to rivals, potential
negotiation leverage, and boost to reputation, but also incentives for R&D employees
and the measurement of performance (Blind et al.,2006). Such strategic motives can
affect the sensitivity of patents to taxation. For example, there is limited incentive to
exploit a patent which is deployed for blocking a competitor. There is an interest to
keep a patent at a location it was invented if it is used as a tool for motivating employees
or measuring performance.
2.2. Patent boxes: a European story
The European patent system, more specifically considered in this paper, is rather com-
plex. The patent applicant have a choice between following the national procedure in
each state for which (s)he seeks protection or taking the European route via the
European Patent Office (EPO), which in a single procedure confers protection in all the
designated contracting states. However, the EPO applicant will still need to validate the
European patent in the designated states within a short time limit after the EPO grants
the patent (usually 3 months). This could entail a substantial cost due to a number of
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requirements, such as payment of the fees and translations.
19
The patent can also be
owned by someone outside Europe (home country) or developed by someone residing
outside Europe (host country). It should be noted that patent protection is a separate
issue from patent boxes (even though the former is a condition for benefiting from the
latter). Even if a company registers its patent in a country, it cannot benefit from its pat-
ent box when it is not tax resident because its IP income is not taxable income in this
country. Registration of a patent does indeed not create a permanent establishment that
would give rise to a taxable income in the country of registration (and hence to a tax
rebate).
Patent boxes are very heterogeneous in their design. These differences are shown in
more detail in Table 1 where we focus on five design characteristics that are expected to
make the tax advantage more or less pronounced: (a) which IP rights qualify for the pat-
ent box (the coverage); (b) the treatment of existing patents; (c) the treatment of acquired
patents; (d) the treatment of embedded royalties; and (e) the existence of development
conditions.
20
The name ‘patent boxes’ can be deceptive, as many patent boxes have a much larger
coverage than just patentable rights, as summarized in Table 2. All patent boxes cover
patents and often rights equivalent to patents such as supplementary protection certifi-
cates. Besides patents, patent boxes can also cover designs and, to a lesser extent, trade-
marks. In addition, they often consider copyrights, sometimes with a restriction to
software, probably to compensate for the fact that software is not patentable in Europe
unlike in the United States. Firms often combine different forms of IP, even for the same
invention (Hall et al., 2014). This implies that the advantage conferred by patent boxes
with a wide IP coverage could be more generous than intended by policymakers and
would over-subsidize the same invention.
Second, the effects of a patent box on tax revenues depend on its provisions. Existing
(i.e., prior) patents may in some cases also benefit from the lower tax rates of patent
boxes, as in the systems put in place in Cyprus, France, Hungary, Malta, Spain, the
United Kingdom, Ireland (up to 2010), Liechtenstein, and the Nidwalden canton in
Switzerland. This represents a windfall gain to firms with existing patents, as after-tax
19 Patenting in the EU is expected to become less complex and costly thanks to the introduction of the
European patent with unitary effect, the so-called ‘unitary patent’ (European Commission, 2011).
Such patent will be yet another option for users besides already-existing national and ‘classical’
European patents. It will enable a unitary effect in 25 EU states without the need for subsequent vali-
dation. However, the system is not yet in force. The unitary patent may be requested from the date of
the entry into force of the Agreement on a Unified Patent Court. Twenty-five EU Member States
signed the agreement on 19 February 2013. It will need to be ratified by at least 13 states, including
France, Germany, and the United Kingdom to enter into force.
20 In our analysis, we do not include Israel and Turkey that offer some tax advantages with an IP-
related component, but these tax schemes are much broader and apply in special economic zones
only. Turkey and Israel are also not in our sample. Italy also introduced a patent box regime in 2015
that offers a 50% exemption since 2017 (30% and 40% in 2015 and 2016, respectively) but is outside
our sample.
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Table 1. Patent box characteristics (2000–14)
NL BE LU FR PT CN UK ES HU MT CH (NW) CY LI IE
Top corporate income tax
rate in % (2014)
25 33.99 39.2 38 31.5 25 21 30 20.6 35 14.5 12.5 12.5 12.5
ETR on patent income
within the patent box
(calculated on top CIT
rate with surcharges)
5 6.798 5.84 15.5
(16.245
2010–15)
15.75 15 10 12 10.3 0 8.8 2.5 2.5 0
Year of introduction
(changed)
2007
(2010)
2007 2008 1971 2014 2007 2013
(h)
2008
(2013)
2003
(2012)
2010 2011 2012 2011 1973
(2008)
Only patents and rights
associated with patents
Y* Y* Y* Y
Applicable to existing IP Y Y Y Y Y Y Y Y Y Y
Applicable to acquired IP (b) Y (e) Y (i) Y Y Y Y Y Y
Authority granting the IP
right
Y (c) Y (c) (c) (c) (j) Y Y Y Y Y Y Y
Development condition Y Y Y Y Y Y Y
Capital gains included Y Y Y Y Y (l) (n) Y Y Y
Income from the sales of
innovative products
(embedded royalties)
Y Y Y Y Y n.a. Y
R&D can be performed
abroad (or within a
group)
(a) (d) Y Y (f) (g) (k) (m) Y Y Y Y Y (o)
Cap YY
Notes: Y, Yes; *See specificity in Table 2. (a) Covers patents developed within a group when managed and coordinated in the NL; (b) if fully or partially improved; (c) has to be regis-
tered at the national IP office; (d) if in a qualified R&D centre; (e) must be held for at least 2 years. Anti-avoidance rules for intragroup exploitation of IP rights; (f) double tax relief lim-
ited to 50%; (g) at least 60% done in China; (h) phased in till 2017; (i) if further developed and actively managed; (j) if granted by EPO or UKIO; (k) if active ownership and self-
developed; (l) if between unrelated parties; (m) if self-developed; (n) exempted if held for at least 1 year or used to buy other IP; and (o) limited to EEA since 2008.
Sources: Various sources such as Deloitte, EY, KPMG, PWC, International Bureau of Fiscal Documentation, and National websites sources, ACCA (2013), European Commission
(2014),Evers et al. (2015), and Cao (2011).
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income from their existing patents in that jurisdiction increases with no further action
required.
Third, the treatment of acquired patents differs across patent boxes. A majority of
patent boxes allow patents acquired from related or third parties, whereas only a small
number of countries allow the use of acquired patents on condition that the acquirer fur-
ther develops these patents.
Fourth, patent boxes also vary in the treatment of embedded royalties. The three
‘narrowest’ patent boxes in terms of coverage (in the United Kingdom, Belgium, and
The Netherlands) include only income from patents under their IP tax rules (as shown
in Table 1). However, at the same time, these patent boxes also include the embedded
royalties in the calculation of eligible income.
21
This means that the income from the
sale of products that include patented items and the notional royalty from using
patented industrial processes, fall under the patent box, implicitly increasing the cov-
erage (and cost in terms of tax expenditures) of the IP boxes. For instance, Evers et al.
(2015) find that the treatment of expenses relating to IP income is generally more
decisive for the effective tax burden than the nominal IP Box tax rate. The treatment
of expenses can be so generous that IP Boxes provide negative effective tax rates
(ETRs). In these cases, unprofitable investment projects are subsidized by the patent
box regime. It is also important to note that other elements of the tax system need to
be in place to make such schemes beneficial for tax-planning purposes, namely an
extensive network of bilateral treaties, weak CFC legislation, flexible transfer pricing
rules, and flexibility of the tax administration (e.g., advance rulings). In addition,
some countries offer standard corporate tax rates below the tax advantage offered by
Table 2. Coverage of patent boxes by country in 2014
Coverage NL BE LU FR PT CN UK ES HU MT CH
(NW)
CY LI
Patents and
associated
patent rights
YYYYYYYYYYY YY
Trademarks Y Y Y Y Y Y
Designs and models (a) Y Y Y Y Y Y Y
Copyrights (a) (c) (c) (c) Y (d) Y Y Y
Domain names Y Y Y
Know-how (a) (b) (b) Y Y Y Y
Y, Yes; (a) only if R&D declaration; (b) know-how closely associated with patents; (c) only software; (d) only
artistic.
Sources: European Commission.
21 Embedded royalties also exist in broader patent boxes such as in Luxembourg, Liechtenstein, and
Nidwalden canton in Switzerland.
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a patent box and could be more attractive for companies that prefer to book their
full profits in such jurisdictions.
In the next section, we examine the fifth important characteristic of patents, the possi-
ble imposition of development conditions.
2.3. Patent boxes and the link with local R&D
Current patent boxes approach the question of the link with underlying research
activity – thanks to which an IP right originated – in different ways. In half of the
cases considered in this paper, the patent boxes do not require any development
work by the taxpaying company in question. Patent boxes in The Netherlands,
Belgium, the United Kingdom, Ireland (up to 2010), Spain, Portugal, and China
contain(ed) provisions specifying the link with the underlying research activity.
22
In
the EU, this is usually done in the form of a development condition that requires at
least part of the patent to be developed by the beneficiary corporate group within
the Single Market. However, these conditions differ in their definition and strength.
For instance, the Belgian patent box requires that the qualifying patent shall have
been developed fully or partially by the taxpaying company in a Belgian R&D cen-
ter that qualifies as a branch of activity. In the Netherlands, the patent box applies
to intangible assets that the company has developed itself. It also covers intangible
assets that are in large part the result of R&D work, conditional on the taxpaying
company receiving a declaration from the Dutch Research Agency (Schellekens,
2013). This declaration in turn links the R&D activity with the use of the Dutch pay-
roll deduction scheme for researchers. Under the UK patent box a company or
group must have performed qualifying development in relation to the IP right
in the United Kingdom, and the rules include provisions against full
outsourcing (HRMC, 2010). Nevertheless, an additional ‘active ownership condi-
tion’ potentially limits the constraining aspect of the development condition. In such
case, another company within a group could have fully developed the IP right, while
the company that pays tax in the United Kingdom actively manages the IP
portfolio.
Generally, development conditions often contain qualitative terms such as ‘sub-
stantial’ or ‘significant’ work that are open to interpretation and have to be assessed
on a case-by-case basis.
23
It is also worth mentioning that in the specific case of the
EU, its Member States cannot restrict the benefits of R&D tax incentives to activ-
ities performed in their territory as this would infringe upon the freedom of
22 China has a preferential rate for new high-technology enterprises, which need to meet a number of
requirements to qualify to profit from the rate (e.g., level of R&D expenses).
23 In our sample, only China applies the territorial restrictions so that most of the related R&D must be
done in China.
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establishment and prevent companies from conducting their R&D elsewhere in the
EU.
24
3. PATENT DATA
This paper uses the patent applications to the EPO
25
of world corporate R&D investors
from 39 home countries
26
in 33 different host countries
27
over the period 2000–12. The
analysis is based on the top 2,000 worldwide corporate R&D investors as reported by
the EU Industrial R&D Scoreboard (European Commission, 2013), which ranks the
companies that invested the largest amounts of R&D in 2012.
28
Altogether, these com-
panies accounted for about 90% of global business R&D spending.
29
The Scoreboard data are drawn from the latest available company accounts reported
in the ORBIS database as provided by Bureau Van Dijk Electronic Publishing. ORBIS
24 See Baxter and Fournier European Court of Justice cases, C-254/97 and C-39/04.
25 We have chosen to use patents from EPO because this set of patents seems the best to study the
impact of taxes on the location of the legal ownership of patents. Pooling patents from different offices
may at first sight seem a good approach, but presents a series of serious shortcomings. First, when
using different patent offices, one shall be prudent with the concept of patent counts as the same
invention (patent) can be filed in different patent offices to seek protection in different legislations/
markets (see the Apple vs Samsung case). This is particularly true when considering large R&D invest-
ors operating on a global scale. In this case, using patents is possibly leading to multiple counting and
the concept of INPADOC families should be preferred (the INPADOC family concept connect all
the documents directly or indirectly linked to one specific priority patent document). Second, patent
boxes impose some restrictions on the authority granting the IP right limiting it to patent registered at
EPO or national patent offices. Third, there are also ownership requirements and it is reasonable to
expect that third parties would only accept to pay for the use of IP rights that are effectively protected
in the territory where they are used. The focus on EPO patents seems the most aligned with the
MNEs patenting strategies as these companies would apply for a patent at EPO and then validate it
in the designated states. The EPO (2012) annual report shows a record number of patent filings at
257,700 and 65,700 patent grants. 24.6% of filings originated from the United States, followed by
Japan (20.1%) and Germany (13.3%). With 2,289 applications, Samsung topped Siemens (2,193) and
BASF (1,713). There seems, however, to be no reliable data source to identify patents that are filed
both at the EPO and at national patent offices.
26 Home countries: Australia, Austria, Belgium, Bermuda, Brazil, Canada, Cayman Island, China,
Curacao, Denmark, Finland, France, Germany, Hong Kong, Hungary, India, Israel, Italy, Ireland,
Japan, Republic of Korea, Lichtenstein, Luxembourg, Mexico, The Netherlands, Norway, Portugal,
Russia, Saudi Arabia, Slovenia, Spain, Switzerland, Singapore, Sweden, Taiwan, Thailand, Turkey,
United Kingdom, and the United States.
27 Host countries: Austria, Belgium, Canada, China, Croatia, Cyprus, Czech Republic, Denmark,
Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, The Republic of Korea,
Lichtenstein, Lithuania, Luxembourg, The Netherlands, Norway, Poland, Portugal, Romania,
Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, and the United States.
28 This has implications for the interpretation of our results as we de facto exclude companies not engag-
ing in R&D activity. Hence, our results shall be interpreted as the various effects of patent boxes on
patent location of companies engaging in R&D activities rather than their effect on companies start-
ing research ex-nihilo.
29 The EU Industrial R&D Investment Scoreboard sample is assembled by the Joint Research Centre of
the European Commission. For more information on the sample of firms included in the R&D
Scoreboard, see http://iri.jrc.ec.europa.eu/scoreboard.html and European Commission (2014).
PATENT BOXES 145
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contains ownership, balance-sheet accounting, and financial information about firms
located worldwide. The patents filed by these companies at the EPO are from the
Patstat
30
database in the framework of a JRC-OECD joint project (see Dernis et al.,
2015). This project has carried out a matching on a by-country basis using a series of
string-matching algorithms contained in the Imalinker system (Idener Multi Algorithm
Linker) developed for the OECD by IDENER, Seville (2013).
31
To ensure a high qual-
ity of the matching, threshold values for string matching have been set in order to mini-
mize both false positives and false negatives. After the matching procedure, results for
10% of companies were inspected manually. In particular, matches for the 2.5% of
companies with the highest (and lowest) patent/R&D ratios were manually adjusted.
Moreover, another 5% of companies were randomly checked; random checks con-
firmed the goodness of matching. Overall, 97% of the top-performing companies could
be matched to at least one patent applicant.
The characteristics of innovations vary across sectors and so does the influence of tax-
ation on the patent location choices, as discussed in Section 2.1. Therefore, to account
for this heterogeneity we adopt a sectorial approach to our regressions. Our regressions
in Section 6 confirm this heterogeneity. We identify three sectors of interest: the car
industry (ICB code 3350), the ICT industry (ICB code 9500), and the pharmaceutical
industry (ICB code 4570), together with their subsectors. Focusing on these three sectors
allows covering 60% of total patents and those sectors are also the top R&D investors in
the 2012 scoreboard.
Patent applications pertain to different technological fields.
32
Globally, in our full
sample, chemistry counts for close to a quarter of all applications, very closely followed
by electrical engineering; about a fifth of all applications are related to mechanical engi-
neering and instruments. The remainder, ‘other fields’ counts for the remaining 9% of
patent applications. Their distribution, however, differs widely across sectors. As shown
in Table A1 of the Appendix, each sector focuses mainly on one specific technology
field, but not exclusively. In the car industry, 64% of the 88,826 patent applications are
related to mechanical engineering technology. In the ICT sector, electrical engineering
accounts for 81% of all applications and in the pharmaceutical sector, chemistry has the
lion’s share with 79.5%. These differences justify a sectorial approach.
33
30 PATSTAT is the European Patent Office’s Worldwide Patent Statistical Database, which contains
data about 70 million applications from more than 80 countries. See more details at http://www.
epo.org.
31 Overall, in 2012 the top R&D investors controlled more than 500,000 subsidiaries (defined as firms
more than 50% owned by the parent), including ‘branches’, which account for about 34% of all sub-
sidiaries. Patent applications have been aggregated at the group level. A more extensive description of
the approach used to perform the matching between Orbis and PATSTAT can be found in Dernis
et al. (2015). For a description of Imalinker, see http://www.idener.es/?portfolio¼imalinker.
32 The Industry Classification Benchmark (ICB) is a classification widely used by stock exchanges such
as the NASDAQ and the NYSE.
33 Moreover, to ensure consistency, our econometric estimations are run considering only the patents
registered under the most frequent technology by sector.
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An important characteristic of the patent box data is that it is highly skewed.
Companies in many instances do not register their patent just in a given year and country.
In addition, when they do register, they usually do it for one single patent only. However,
a few companies sometimes register a very large number of patents in a given year and in
a given country. Figure 2 shows the very skewed distribution of patents across companies
considered in our empirical analysis. The large multinationals included in our sample
have a patenting behaviour that differs from other companies with a large number of pat-
ent registrations. We find a positive and significant correlation between company size
(measured by total employment) and patent registrations. Hence, the nature of our data
on patent application is likely to have a bearing on the econometric strategy used for esti-
mating the impact of taxation and patent boxes on patent registrations. These issues are
discussed in the next section dealing with our econometric approach.
4. EMPIRICAL STRATEGY
We follow the structural model proposed by Griffith et al. (2014) and consider the payoff
pi;j;tof a firm ifrom registering a patent in a specific location jat time tas being deter-
mined by industry and country-specific characteristics such that:
pi;j;t¼fðETRj;t;Xj;t;
i;j;tÞ;(1)
0.00 1.00 2.00 3
.00 4
.00 5
Density
01000 2000 3000 4000 5000
N umber of patents
kernel = epanechnikov, bandwidth = 17.7643
Pharma
0.002 .004 .006 .008
Dens ity
0100 0 2000 3000 4000 5000
Num ber of patents
kerne l = epan echnikov , ban dwidt h = 15 .334 1
ICT
0.00 05
.00 1.0015.00 2.0 0 25
Dens ity
01000 2000 3000 4000
N umber of patents
kernel = epanechnikov, bandwidth = 96.3774
Car
Figure 2. Distribution of patents across companies
Note: Kernel densities are calculated for companies included in the estimation sample with less than 5,000 patents
in order to improve visualization.
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where the company iwill register its patent in country jif its expected payoff is higher
than the expected payoff in any other alternative location k,thatis,pj;t>pk;t.This
probability is a function fof the ETR in country jat time t– that is, which is defined as:
ETRj;t¼CITj;ttax advantagej;t:(2)
The ETR therefore accounts for the possible tax rebate granted via the patent box to
income-related patents. The variable X
j,t
stands for country-specific and time-specific
characteristics that can influence patent registration and which are described in the
sequel. We also assume that payoffs are uncorrelated such that the error term i;j;tfol-
lows a normal distribution with zero mean and unitary standard deviation N0;1ðÞ.
Griffith et al. (2014) suggest however that the expected payoffs of registering patents in
two different locations can be correlated, hence invalidating the previous assumption
such that: covðpi;t;pk;tÞ 6¼ 0. The possibility for a non-zero correlation in expected pat-
ents payoffs is dealt with by Griffith et al. (2014) by estimating a mixed logit model where
the effects of taxation are assumed to vary across ideas, which the authors define as
industry/firm size categories. Using such a mixed logit approach allows estimating the
degree of heterogeneity in the effect of a specific variable, including the tax rate, along
the industry/firm size dimensions. It can be used when patent registrations made by a
given company ioccur only once in a given year and a given country. To compare our
results with those of Griffith et al. (2014) and the rest of the literature, we run some
regressions with a logit model.
However, we have seen from our sample data that some companies often register
more than one patent in a given country and year. Hence, it could be preferable to
take advantage of this additional information and to use the total number of patents
registered as dependent variable instead of zero–one dummy to estimate the proba-
bility of registering a patent (Hausman et al., 1984). The model to be estimated
becomes:
ni;j;t¼kETRj;tþbXj;tþ/tþujþei;j;t;(3)
where the dependent variable is the number nof patents registered by a company iin a
given country jin year t./
t
and u
j
are, respectively, time- and country-fixed effects.
In this model, the parameters kand bcould in principle be estimated via a standard
OLS as in Bradley et al. (2015).
34
It is however known that OLS models assumptions do
not hold in cases where the dependent variable is discrete outcome, as in our case. As
put by Karkinsky and Riedel (2012), the ‘OLS approach however does not account for
the fact that the patent variable is restricted to positive values’.
34 Although these authors use the log of the total number of patent applications per (owner or inventor)
country and per year.
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An alternative solution is then to use a Poisson model. However, the problem of over-
dispersion arises in that case. This problem typically characterises patent registration
data from large companies as in some industries only a handful of them generate a very
large number of patents. To overcome these problems, a negative binomial model pro-
vides a better alternative than the Poisson model. This is also the preferred option of
Karkinsky and Riedel (2012).
The negative binomial model preserves the conditional mean assumption of the
Poisson model but it allows for a larger variation of outcomes than a Poisson and one
can specify a so-called over-dispersion parameter that represents an objective proxy of
the cause of this over-dispersion (Long and Freese, 2014). In our negative binomial
model and in line with the observation of our data sample, we assume that the latent
heterogeneity inducing an over-dispersion in patent registrations is the firm size (repre-
sented by the number of employees).
35
We incorporate this parameter as exposure vari-
able in the marginal negative binomial distribution and the model can be easily
estimated using maximum likelihood (Greene, 2008).
We next also consider an alternative mixed negative binomial model in order to
account for unobserved heterogeneity and to estimate fixed and random effects on the
effective tax variable, which reflects the fact that firms do not behave similarly to fiscal
conditions. This mixed model allows us checking whether our main results hold when
using a specification including random effects comparable to Griffith et al. (2014).
Next, as in Griffith et al. (2014) we must consider the influence of additional non-tax
factors on the location choice for patents registration. These controls are embodied in
the set of variables Xof Equation (3). We first control for the size of the market meas-
ured by the log of GDP (in Euros) of the country of potential application by the variable
GDP level. We also control for the innovation potential of the country, captured by pri-
vate business R&D expenditures (BERD) in percentage of GDP, Business R&D/GDP.
We moreover include a control for the degree of IP protection in the potential countries
of location of patents, with the index variable IP protection. For this variable, we take a
widely used index developed by Ginarte and Park (1997) and subsequently updated by
Park (2008). Finally, we also control for research activities related to the patents consid-
ered in our data. The Real Research Activity variable measures whether or not any of the
inventors of a given patent reside in the country where the patent is registered according
to the Patstat database. Since we use a count model, the Real Research Activity is measured
by the number of patents where at least one of the inventors resides in the country where
the patent was registered, as a percentage of the total number of patents registered in
that country by a given firm. All four controls variables also used by Griffith et al. (2014)
are expected to exert a positive effect on patent applications. It should be noted that our
equation includes only alternative choices-level control variables as usually done in this
kind of model. The only company-level control variable is the size of the company
35 See also Cohen et al. (2000) for evidence of this relationship.
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(measured by its level of employment) which is however only used to correct for over-
dispersion in patent registration as discussed earlier. As our explanatory variables are
defined at company-level basis and that patent registration under the EPO is exclusive
(despite the fact that patents can later be registered in multiple countries), it is likely that
our residuals are correlated across parent companies, thus biasing our estimated stand-
ard errors. In order to correct for this, we cluster our observations at the level of the
parent company, following Moulton (1990).
5. IDENTIFICATION STRATEGY
Before discussing the estimation results, we should note that our identification strategy
hinges on the assumption that governments’ decisions to set-up a patent box regime or
to change the characteristics of existing one are exogenous to the conditions of R&D
activities in their country. However, despite the fact that these tax policy decisions are
unlikely to be frequent (which lends some support to the exogeneity assumption), we
cannot fully rule out the possibility for these changes to be endogenous. Indeed, the pres-
ence of patent boxes or of some of their features (such as the existence of a development
condition) may be chosen by governments because of existing business R&D activities.
The causation can go in both directions of attracting patents or retaining existing ones.
Our approach looks at annual microeconomic decisions of multinationals to register
patents through different subsidiaries potentially located in 33 countries as a response to
macroeconomic decisions on patent boxes and tax rates set by governments. These latter
are however not immediately adjusted on the basis of current firms’ decisions. Our data
on patent boxes indeed suggest that – at least for the period considered – patent box
regimes seldom change once in force in a given country (with a few exceptions such as
the recent change in tax rebate in the Spanish scheme). However, in order to test more
thoroughly whether our identification strategy is valid we have run a set of logistic
regressions at the country level to check whether local business R&D activities had a
bearing on the presence of patent boxes and features. In particular, we have estimated
regressions where the presence of a patent box (and the presence of development condi-
tions) represented by a dummy variable is used as dependent variables and the BERD
as share of GDP was used as explanatory variable. In addition, we have also used as
dependent variable a dummy variable indicating the presence of development condi-
tions (conditional of course on the existence of a patent box regime). We also include
time- and country-fixed effects.
The results of these regressions are shown in Table A2 of the Appendix. In these
regressions, we define different lag structures for the effect of BERD including lags and
leads. The coefficients attached to BERD never come out as statistically significant,
except only in the regression with the development condition as dependent variable.
Even in this case, the lag and lead of BERD are only significant at 10%. These results
lend some support to the exogeneity of patent boxes to the BERD activity. One must
admit however that our identification strategy is limited in the absence of a valid
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instrumental variable controlling for the fact that patent boxes might be introduced in
anticipation of rising R&D.
6. ECONOMETRIC RESULTS
Table 3 provides the summary statistics on the dependent and exogenous variables for
the estimation samples used to run our base regressions. The average value of the patent
count is more than 10 times lower than its standard deviation in all sectors, illustrating
the skewness of the dependent variable. The control variables display almost identical
means and standard deviations across sectors as these statistics are country-specific. The
level of IP protection, GDP, and business R&D also display low variability compared
with the tax advantage and the patent box dummy variables, reflecting the fact that
over the period considered countries have increasingly used patent boxes, thereby
reducing their effective CIT rate to attract patenting activities. Table 3 also provides
information on the degree of foreign ownership of the companies in the three sectors.
All companies in the sample are multinationals, that is, they have at least one affiliate
located in a different country. The three sectors are however not homogeneous regard-
ing their foreign presence. The car industry is clearly over-represented as companies
covered in our sample have on average 537.2 foreign affiliates in the 33 host countries
considered, against 94.2 in the ICT sector and 14.6 in the pharmaceutical industry.
Companies in the ICT sector are much more heterogeneous in relation to their foreign
presence, with a large ratio of their standard deviation to the average number of foreign
affiliates of 2.6 against 1.4 for the car industry and 0.6 for the pharmaceutical sector.
ICT companies are also those that register fewer patents abroad (9.2% of the total pat-
ents registered in this sector), while the pharmaceutical sector register a quarter of its
patents (26.3%) in a different country against 14.3% in the car industry. Therefore,
there is no clear correlation between the extent of foreign presence and the registration
of patents abroad in the sample of sectors considered here.
6.1. Patent boxes and the fiscal advantage of patent box regimes
We first run our basic regression separately for the three sectors of interest using two dif-
ferent specifications: we use a logit like in Griffith et al. (2014) and a negative binomial
model. The results of these estimations are reported in Table 4. All regressions contain
our variable of interest (the ETR), country- and time-fixed effects, and our four control
variables (GDP level,Business R&D/GDP,IP protection,andReal Research Activity). As
expected, the level of IP protection and Real Research Activity has both a large, positive, and
significant effect on patent location. Interestingly, the level of business R&D to GDP
seems to have no strong effect on patent location and is only significant for the ICT sec-
tors in the logit regression and for the pharmaceutical sector in the negative binomial
specification, respectively. Finally, the log of GDP has contrasted effects. It appears to
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Table 3. Summary statistics – base model
Car ICT Pharmaceuticals
(Number of observation: 30,881) (Number of observation: 78,139) (Number of observation: 57,193)
Min Max Mean Standard
deviation
Min Max Mean Standard
deviation
Min Max Mean Standard
deviation
Patent count 0 869 1.833 20.305 0 1.249 1.662 25.555 0 598 1.061 12.445
Effective corporate income tax 0 51.612 24.963 10.273 0 51.612 24.929 10.258 0 51.612 24.901 10.247
Statutory corporate income tax 12.5 51.612 27.808 7.387 12.5 51.612 27.763 7.385 12.5 51.612 27.734 7.381
Tax advantage in patent box 0 32.850 2.845 7.022 0 32.850 2.834 7.001 0 32.850 2.833 6.999
Patent box dummy 0 1 0.156 0.363 0 1 0.156 0.363 0 1 0.157 0.363
Business R&D/GDP 0.010 4.088 1.239 0.699 0.010 4.088 1.236 0.698 0.010 4.088 1.231 0.696
IP protection 0 1 0.498 0.500 0 1 0.499 0.500 0 1 0.499 0.500
Real research activity 0 1 0.095 0.294 0 1 0.082 0.274 0 1 0.092 0.288
GDP level (log) 7.898 16.353 12.420 1.792 7.898 16.353 12.424 1.793 7.898 16.353 12.423 1.791
Number of foreign affiliates 1 2,187 537.2 734.9 1 1,074 94.2 247.1 1 41 14.6 8.4
Sources: Patstat for patent count; Patstat and OECD for Real research activity; OECD for Business R&D; OECD for GDP; the Taxes in Europe Database, the OECD tax database,
and the International Bureau of Fiscal Documentation database, as well as national ministries of finance websites, for the statutory rates and the patent characteristics; Ginarte and
Park (1997), and Park (2008) for IP protection. Except for the number of foreign affiliates, the sample statistics are for the regressions in Tables 4 and 5.
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exert a strong and significant positive effect for the pharmaceutical sector in both specifi-
cations. The effect is negative for the car industry, although only significant for the nega-
tive binomial model. This may be due to the absence of US leadership in this sector and
a high geographical concentration of patents in a few countries, despite the presence of
country dummies.
36
The ETR provides contrasting results depending on the sector and specification con-
sidered. It is negative and significant at the 1% or 5% levels in the pharmaceutical and
car industries but statistically insignificant for the ICT sector. All in all, the results in
Table 4 suggest that a lower ETR tends to attract patent registrations, a result in line
with Griffith et al. (2014).
We can compare our results with previous results in the literature. We first compare
our results with those of Griffith et al. (2014) who use a logit model as in our first set of
Table 4. Basic regressions: the impact of the effective corporate tax rate on pat-
ent registrations
(1) Logit (2) Negative binomial
Pharma ICT Car Pharma ICT Car
ETR 0.018*** 0.006 0.019** 0.042*** 0.010 0.056***
(0.006) (0.005) (0.009) (0.011) (0.029) (0.020)
Business R&D/GDP 0.212 0.356*** 0.093 0.561** 0.205 0.247
(0.153) (0.134) (0.234) (0.267) (0.755) (0.476)
Intellectual property
protection
2.258*** 1.686*** 1.817*** 4.962*** 4.772*** 4.919***
(0.106) (0.088) (0.112) (0.196) (0.381) (0.412)
Real research activity 4.369*** 4.076*** 5.210*** 9.571*** 13.037*** 10.602***
(0.074) (0.076) (0.110) (0.277) (0.532) (0.717)
GDP level (log) 0.886*** 0.263 0.246 2.074*** 0.277 2.935***
(0.323) (0.294) (0.374) (0.726) (0.992) (1.016)
Country-fixed effects Yes Yes Yes Yes Yes Yes
Time-fixed effects Yes Yes Yes Yes Yes Yes
Observations 57,193 78,139 30,881 57,193 78,139 30,881
Wald test (Chi-square) 11,088 13,749 8,491 38,499 20,699 10,980
Prob. >Chi-square [0, 000] [0, 000] [0, 000] [0, 000] [0, 000] [0, 000]
Alpha (overdispersion) – – – 24.22 60.79 22.31
Alpha std. error – – – (0.928) (2.330) (1.333)
Notes: We use the number of patents registered by one company in a specific country during a specific year as
dependent variable (count of patents) for the Negative Binomial model. For the logit model, we use a dummy
indicating the presence of (at least) one patent. Standard errors, clustered at company and year level, are reported
in parentheses. The levels of significance are reported as ***p<0.01, **p<0.05, *p<0.1. The model is estimated
via a logit model in regressions (1) and via a negative binomial model in regressions (2). In this latter, we use as
exposure variable the total number of employees of a company (including its subsidiaries). The unit of observation
is parent company – country of the subsidiary company (-ies) – year. The Wald test informs about the joint signifi-
cance of the parameter estimates, the null hypothesis being that all of the regression coefficients are simultane-
ously equal to zero. The alpha parameter informs about the degree of dispersion, if alpha is significantly greater
than zero then the data are over dispersed and are better estimated using a negative binomial model than a pois-
son model.
36 In our sample, the top three patent locations for the car industry (DE, JP, and US) represent 83.0%
of cases, compared with 69.7% in the pharmaceutical sector and 66.7% in the ICT sector.
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regressions (Columns 1–3) and find negative semi-elasticities between 0.5 and 3.9
depending on the selected countries.
37
Using the same specification, our results show sig-
nificant and negative semi-elasticities of 1.8 and 1.9 for the pharmaceutical and cars sec-
tors, respectively (all taken at mean values of the regressors). The semi-elasticity for the
ICT sector is estimated at 0.6 but fails to be statistically significant. Next, using the nega-
tive binomial model (Columns 4–6) our semi-elasticities for the Pharmaceutical sector
and the Car sector are both negative and equal to 4.2 and 5.6, respectively. In contrast,
the semi-elasticity for the ICT industry would be positive (1.0) but it is not statistically
significant. In a previous study, Karkinsky and Riedel (2012) find semi-elasticities of
about three for the negative binomial model.
38
Importantly, our results suggest that the
negative binomial model specification is preferable to a logit specification according to
the LR test reported at the bottom of Table 4. They also point that over-dispersion is an
issue to consider given the high statistical significance of the Alpha parameter. Hence,
we use the negative binomial model from there on.
The finding that higher ETRs lower the number of patents registered in a given
country makes us expect that the tax advantage offered through a patent box should
exert a positive influence on patent registration. To test this, Table 5 reports negative
binomial model regressions with a separate impact of the statutory corporate tax rate
(CIT) from the tax advantage related to the patent box together. The regressions also
include a dummy variable indicating whether a patent box is in place in a given coun-
try/year. The tax advantage offered by the patent box regime comes as suspected with
a positive effect in all regressions, which is significant at the 1% level in the
Pharmaceutical and Car industries and at the 10% level in the ICT sector. Calculating
the marginal effects, we find that for each percentage point increase in the tax advantage
offered by the patent box, the number of patents in the concerned country will rise by as
much as 11.8%, 8.6% and 17.0% for the pharmaceutical, ICT, and car industries,
respectively. These results therefore confirm that the tax advantage of patent box
regimes explains their positive and significant impact on patent registration. Our regres-
sions also tend to confirm large difference in coefficients across sectors. They can be
explained by the interplay of the tax and strategic factors. We find ICT to be on average
the least sensitive sector to the tax advantage offered through patent boxes. This can be
due to the ‘complex’ nature of the industry (Cohen et al. 2000), but also to the fact that
R&D and product cycles in this sector can be much shorter. Bilir (2014) indeed finds
that firms with short life-cycle technologies are insensitive to the strength of IP rights at a
37 Please note that Griffith et al. (2014) estimated a mixed effect logit while here we use a simple logit
specification in order to be able to compare the results with other papers as well. The elasticities
obtained using a mixed logit did not differ significantly from the ones reported here. We estimated a
mixed effect model for the negative binomial model which is our preferred specification.
38 Using OLS, Bradley et al. (2015) find a semi-elasticity of the tax rate on the number of patents of
about 3. In their OLS estimates, Karkinsky and Riedel (2012) find semi-elasticities ranging between
2.3 and 7.7.
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location, because offshore imitation is less likely to succeed before obsolescence. There
may be less interest in tax gains from patents which protect short-lived technology if a
complex tax planning need to be organized first. On the other hand, R&D cycles in new
drugs and cars can be rather long and they are more of a ‘discrete industry’ (Cohen,
2000). In addition, motor vehicles and chemicals (subsector of pharma) also seem to
have more complex supply chains. This suggests higher sensitivity to tax (Beer and
Loeperick, 2015).
39
Our estimations can also be used to analyse the extent to which the size of the tax
advantage offered through patent box regimes matter for attracting patent registration.
Table 5. Estimating the effect of the tax advantage on patent registrations
Negative binomial Mixed negative binomial
(1) (2) (3) (4) (5) (6)
Pharma ICT Car Pharma ICT Car
Statutory CIT 0.171*** 0.018 0.070* 0.130*** 0.069** 0.057**
(0.034) (0.056) (0.038) (0.022) (0.033) (0.025)
Tax advantage in
patent box
0.112*** 0.083* 0.157*** 0.085*** 0.138*** 0.227***
(0.021) (0.044) (0.027) (0.015) (0.023) (0.036)
Tax advantage in patent
box (random effects)
– – – 0.000 0.012*** 0.014**
– – – (0.000) (0.004) (0.006)
Patent box dummy 2.000*** 2.495*** 2.794*** 1.652*** 3.081*** 3.856***
(0.303) (0.411) (0.252) (0.168) (0.252) (0.322)
Business R&D/GDP 0.674** 0.468 0.307 0.197 0.469 0.009
(0.285) (0.653) (0.446) (0.263) (0.316) (0.404)
IP protection 4.895*** 4.947*** 5.048*** 4.584*** 4.774*** 4.683***
(0.179) (0.383) (0.378) (0.168) (0.195) (0.303)
Real research activity 8.897*** 11.543*** 10.398*** 7.429*** 9.551*** 10.424***
(0.250) (0.473) (0.665) (0.154) (0.222) (0.266)
GDP level (log) 1.928** 0.476 2.425** 0.376 0.764 0.705
(0.751) (0.941) (1.014) (0.507) (0.593) (0.888)
Country-fixed effects Yes Yes Yes Yes Yes Yes
Time-fixed effects Yes Yes Yes Yes Yes Yes
Observations 57,193 78,139 30,881 57,193 78,139 30,881
Chi-square 41,905 20,144 12,404 2,866 2,272 1,657
Prob. >Chi-square [0, 000] [0, 000] [0, 000] [0, 000] [0, 000] [0, 000]
Alpha (overdispersion) 22.46 57.08 20.86 – – –
Alpha std. error (0.878) (2.369) (1.244) – – –
Notes: We use the number of patents registered by one company in a specific country during a specific year as
dependent variable (count of patents). Standard errors, clustered at company and year level for the negative bino-
mial regressions, are reported in parentheses. The levels of significance are reported as ***p<0.01, **p<0.05,
*p<0.1. The model is estimated via a negative binomial model in regressions (1)–(3) and via a mixed negative
binomial model in regressions (4)–(6). In this latter, we use as exposure variable the total number of employees of
a company (including its subsidiaries). The unit of observation is parent company – country of the subsidiary com-
pany (-ies) – year. The Wald test informs about the joint significance of the parameter estimates, the null hypothe-
sis being that all of the regression coefficients are simultaneously equal to zero. The alpha parameter informs
about the degree of dispersion, if alpha is significantly greater than zero then the data are over dispersed and are
better estimated using a negative binomial model than a poisson model.
39 Their research on the link between supply chain and tax sensitivity is described in Section 2.1.
PATENT BOXES 155
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This question is particularly relevant from a fiscal perspective given that government
may aim at minimizing the tax revenues losses from patent boxes while maximizing their
expected positive impact on patenting activities. The effect of a patent box regime
depends on the tax rebate offered – itself often a percentage of the CIT rate – and on
the conditions under which this tax rebate applies, that is, the patent box dummy. In
addition, a company may choose to set up a subsidiary in a given country primarily to
reduce its overall tax bill by shifting patent registration there, but it might also consider
the level of the CIT rate applying to revenues other than patents. To account for the
full effect of patent box regimes we need to consider all components together.
To investigate the global effect of patent boxes and their tax advantage on patents
location, it is important to recall that in non-linear models – such as in the negative
binomial used here – marginal effects are sensitive to the baseline values chosen for all
independent variables. However, the baseline value of a control for a specific category
of observations (e.g., all observations with a patent box) differs from the baseline value
for the entire sample (e.g., all observations with or without a patent box). In our sample,
the average tax advantage given by patent boxes is about 17 percentage points but this
average drops to 2.7 percentage points when we consider the whole sample, including
observations without a patent box, for which this advantage is therefore zero. This aver-
age value of 2.7 percentage points for the full sample is even well below the lowest
observed tax advantage in our sample (bar the zeros), that is 8.8 percentage points. A
specific concern about the estimation of marginal effects of interaction effects in non-
linear model lies in the fact that the marginal effect cannot be directly determined by
the first derivative of the expected value of the dependent variable with respect to the
interaction term. The marginal effect should be instead calculated as the cross partial
derivative of the dependent variable with respect to each interacted variable separately
in order to interpret it correctly. A very practical solution is to calculate the incidence
ratio. The marginal effect of the interaction term between the tax rebate and the patent
box dummy variable can be interpreted directly as a measure of the differential impact
of the tax rebate due to the presence of a patent box regime. Calculating the incidence
ratio, one can infer the marginal effects of multiplicative terms directly.
40
Figure 3 shows
the predicted percentage change in the number of patents at levels of corporate tax
rebate conditional on the existence of a patent box regime. Given the non-linearity of
40 In particular the marginal effects of an interaction term provided by the statistical software will be the
marginal effect of the interaction term calculated at the average sample value for both elements of the
interaction on the expected value of the number of patents @EðpatentsÞ
@ðdj;tTÞ;where dj,tstands for the patent
box dummy variable and Tis the tax advantage in the patent box regime. In reality, the average tax
advantage conditional on having a patent box is higher than for the total sample (which includes the
cases for which there is no patent box). Hence, the marginal effect is not calculated at the right refer-
ence point. We are instead interested in the marginal effect of the patent box on the marginal effect
of the tax advantage on the expected number of patents, that is, @@EðpatentsÞ
@T
ðÞ
.@dj;t. We are particularly
thankful to Marteen Buis for very helpful discussion on this point.
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the model, this effect varies with the level of tax rebate but we see that the effect of pat-
ent boxes is economically significant.
Back to our regressions, we see in Columns (1)–(3) of Table 5 that the coefficient of
the patent box dummy variable is negative and significant. This variable captures all
patent-box-related features which are not represented by the tax advantage for a given
level of the tax advantage which is set at the average sample value. Our previous discus-
sion shows that this sample value is arguably very low since the sample average includes
countries with and without patent boxes and in the latter the tax advantage is zero.
Therefore, strictly speaking, the negative coefficient on the patent box dummy could
reflect the fact that, for a given tax advantage, that is, given at its average sample value,
other patent box features such as the compliance to conditions that must be met in
order to grant the tax rebate exert a negative impact on patent registration.
As a robustness check, we also estimate our model by analysing whether or not firms
could respond heterogeneously to the tax advantage offered by patent boxes. As Griffith
et al. (2014) point out, there is little reason to consider that patents payoffs are uncorre-
lated across countries, such that Covðpj;t;pk;tÞ 6¼ 0. They suggest using a mixed model
with random coefficients in order to control for the possible correlation in location
choices. Such an approach is particularly relevant when large multinational companies
– as the ones considered in our data – develop a high number of patents and arbitrate
across different locations. In such cases, patents payoffs are more likely to be correlated.
0%
10%
20%
30%
40%
50%
60%
70%
80%
1% 3% 6% 9% 12% 15% 18% 21% 24% 27% 30%
Predicted %-change in number
of patents registered
Corporate tax rebate under the patent box regime
CAR - Average CIT ICT - Average CIT PHARMA - High CIT
CAR - High CIT ICT - High CIT PHARMA - Average CIT
Figure 3. Predicted percentage change in the number of patents at levels of
corporate tax rebate conditional on the existence of a patent box regime
Source: From regressions (1)–(3) in Table (5).
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We hence estimate such mixed models using our negative binomial model, estimating
the random coefficient on the tax advantage offered through patent boxes. Our results
are reported in the last three columns of Table 5. We find significant random effects on
this variable for the ICT sector (significant at 1%) and the Car industry (at 5%) but no
significant random effects for the Pharmaceutical sector. Griffith et al. (2014) find large
and significant random effects for the effect of the corporate tax rate on patent registra-
tion. This difference with our results might be due to the fact that we run our estimation
by sector of activity that are less broadly defined than the ones considered by the afore-
mentioned authors. In addition, we measure the impact of the tax advantage and not
the impact of the corporate tax rate and we control for the existence of a patent box,
which is not considered in Griffith et al. (2014). While heterogeneity still matters in our
case as shown by the significance of the random effects in two out of three sectors, our
different specification and the fact that we observe actual tax rebate under a patent box
regime can explain our differences in results. In the extensions of our empirical analysis
presented in the following sections, we use the negative binomial model without the ran-
dom term, which is computationally less demanding, since the model with the random
effect does not appear to modify our results significantly.
41
We have finally run a number of robustness check of our results reported in Table 4
in order to check whether our estimates could be biased by the fact that we do not con-
trol for the fact that countries compete to attract patent registration and that the number
of patents registered in one countries might be negatively affected by the number of pat-
ents registered in third countries. As the previous discussion shows this competition is
done globally and the geographical mobility of patents is relatively weightless, that is,
companies can decide to move such intangible assets from one country to another one
at very low cost, unlike tangible assets. In addition, our results could also be biased if
large companies, in particular companies with large number of registered patents, are
able to influence the tax policy of countries, notably small countries that strive to attract
foreign investment and R&D activities. We have tested these assumptions by first run-
ning additional regressions controlling for the occurrence of new patent boxes in the
sample of countries considered.
42
We have included a dummy variable equal to 1 when
a new patent box regime was created in at least one of the countries in a given year.
Our results indicate that this variable exerts a negative and highly significant effect on
patent registration, thus suggesting that countries are effectively competing to attract for-
eign patents in their own constituency. The results of all our main variables of interest
41 As additional robustness check we also verified whether the effect of patent boxes could come with a
lag given that the effect of patent box may take time before becoming tangible in order for firms to
adapt to such policy change. The results of these regressions showed no significant differences with
the results reported here.
42 Results of these additional regressions are available from the authors upon request.
158 ANNETTE ALSTADSÆTER ET AL.
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remained virtually unchanged. Second, we also removed the top 5% patent registering
companies from the sample in order to check whether not including these large compa-
nies could potentially alter our results. As before, our results remained broadly in line
with the ones reported in Table 5; in some cases, we even observe an increased signifi-
cance of the coefficients. Likewise, if we in addition exclude small countries (which we
define as the bottom 20% countries in terms of GDP size) from our sample, the results
remain in line with the one reported in Table 5.
6.2. Patent value
Innovation outcome distributions are highly skewed with major innovations capturing
the lion’s share of value creation (Scherer and Harhoff, 2000). Patent value can serve as
a proxy for innovations with high earning potential, the holy grail of innovation policy.
The role played by ideas and patent value is therefore quite fundamental in the analysis
of patent boxes. The motives for different patent registration choices are likely to be cor-
related within ideas, and so is the potential influence of tax determinants, since firms are
likely to decide on the geographical registration of their patent portfolio strategically,
depending on the market potential of new ideas embedded in patents. Griffith et al.
(2014) use a group variable based on the simultaneity between industry and the network
of inventors of patents registered by a single firm to identify idea membership. Such a
measure could nevertheless be regarded as somewhat restrictive, since it excludes patents
registered by different firms but relating to the same idea or invention, as well as patents
relating to the same idea or invention but registered at different times. There are also
two reasons for using an alternative measure of patent value. First, competing firms are
also likely to compete for similar ideas. Second, firms may attempt to protect ideas or to
generate revenues from a given idea by registering patents at successive times. To
account for these possibilities, we use instead an indicator variable based on the informa-
tion provided by the International Patent Documentation, that is, the so-called
INPADOC family group, produced by the EPO. The INPADOC family groups indi-
cate if a given patent registration corresponds to the same priority and invention. Using
information based on INPADOC membership is likely to provide an accurate measure
of the value of the patent given that it is not exclusive in terms of the time of registration
and firm ownership of the patent. We defined high-value patents as those belonging to
the top quartile by sector in terms of INPADOC family size. In line with our approach,
patent’s family size is also a preferred value measure of Bo¨hm et al. (2014).InFigure 4,
we report the weighted average of the statutory and ETRs (i.e., including the patent box
rebate whenever in place), using as weight the total number of patents registered. As one
can see, high-value patents tend to be located in countries with lower corporate taxation
and with a larger gap between the standard CIT rate and the ETR. This descriptive evi-
dence thus suggests that firms have exploited the tax advantage offered by patent boxes
especially for high-value patents.
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To confirm these results, we have run regressions separately for high-value pat-
ents, defined as patents belonging to the top quartile in terms of patent family size as
defined above, and compared the results with the regressions covering the remaining
patents. The results of these additional regressions are reported in Table 6.The
effects of both the statutory CIT rate and the tax advantage in the patent box regime
are different between the two groups of regression. The negative coefficients
obtained for the statutory CIT rate are larger in absolute terms for high-value pat-
ents, and the tax advantage coefficients are always larger. Since these additional
regressions are run over different sample sizes, we have tested the significance of the
difference in the coefficients estimated using a Wald test. The results of these tests
are reported in the last row of Table 6 showing that the null hypothesis of equal coef-
ficients can be rejected. These results therefore suggest that high-value patents tend
to be significantly more sensitive to taxes.
Figure 4. Average effective corporate tax paid on patent revenues: high- vs. low-
value patents
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6.3. Patent box characteristics
Next, we are interested in whether or not the specific characteristics of patent boxes
have an effect on patent location and whether these effects vary across sectors. Given
the high multicollinearity in some of the patent box characteristics,
43
not all these
Table 6. Estimating the effect of the tax advantage on patent registration: high
vs. low value patents
Model estimated Negative binomial –
high value patents
Negative binomial –
other patents
(1) (2) (3) (4) (5) (6)
Pharma ICT Car Pharma ICT Car
Statutory CIT 0.256*** 0.090 0.229*** 0.112*** 0.032 0.014
(0.054) (0.080) (0.086) (0.037) (0.055) (0.038)
Tax advantage in
patent box
0.172*** 0.075 0.224*** 0.093*** 0.068* 0.191***
(0.044) (0.071) (0.037) (0.023) (0.039) (0.047)
Patent box dummy 2.912*** 2.397*** 3.216*** 1.591*** 2.070*** 2.969***
(0.600) (0.804) (0.406) (0.347) (0.344) (0.272)
Business R&D/GDP 1.410** 2.212 1.695*** 1.129*** 1.611*** 0.381
(0.637) (1.406) (0.610) (0.295) (0.339) (0.701)
IP protection 6.103*** 4.230*** 4.570*** 4.321*** 4.650*** 5.608***
(0.456) (0.646) (0.628) (0.189) (0.263) (1.014)
Real research activity 8.472*** 17.623*** 7.963*** 8.650*** 9.775*** 11.999***
(0.431) (1.525) (0.621) (0.260) (0.365) (0.756)
GDP level (log) 2.168 0.167 0.007 0.958 1.221 0.122
(1.332) (1.132) (1.873) (0.936) (0.744) (1.312)
Country-fixed effects Yes Yes Yes Yes Yes Yes
Time-fixed effects Yes Yes Yes Yes Yes Yes
Observations 15,215 21,037 8,253 41,978 57,102 22,628
Chi-square 9,160 9,316 8,100 42,419 19,211 15,651
Prob. >Chi-square [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Alpha (overdispersion) 26.71 82.14 18.04 19.27 44.44 20.31
Alpha std. error (1.980) (7.835) (2.523) (0.941) (2.012) (1.261)
Chi-square equality of
coefficients between
high value patents
and other patents
(tax advantage
in patent box)
22.08 47.13 5.29 – – –
Prob. >Chi-square [0.000] [0.000] [0.071] – – –
Notes: We use the number of patents registered by one company in a specific country during a specific year as
dependent variable (count of patents). High value patents are defined as patents that belong to the top quartile in
terms of INPADOC family size. Standard errors, clustered at company and year level, are reported in parenthe-
ses. The levels of significance are reported as ***p<0.01, **p<0.05, *p<0.1. The model is estimated via a nega-
tive binomial model. The unit of observation is parent company – country of the subsidiary company (-ies) – year.
The Wald test informs about the joint significance of the parameter estimates, the null hypothesis being that all of
the regression coefficients are simultaneously equal to zero. The alpha parameter informs about the degree of dis-
persion, if alpha is significantly greater than zero then the data are over dispersed and are better estimated using
a negative binomial model than a poisson model.
43 An unreported correlation matrix shows a degree of correlation between the various characteristics.
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characteristics were included in the regression. To test the effects of characteristics, we
have identified five dimensions of patent boxes and we add dummy variables reflecting
these specific features of the patents as described in Tables 1 and 2. These regressions
are run conditionally to the existence of the patent box regime, that is, they are run for
countries/years in which a patent box regime was in place. In doing, so we can interpret
our estimates in terms of marginal effect of a given patent box characteristic for a base-
line average effect of the patent box. The coefficients estimated for the patent box char-
acteristics, the tax advantage, and the interaction between these two variables are
reported for the three sectors in Table 7.
44
The first set of characteristics considered is dummy variables, respectively, for
whether or not acquired patents, embedded royalties, and existing patents (i.e., patents
prior to the creation of the patent box) qualify for the tax advantages of patent boxes,
see Columns (1)–(3) of Table 7. We focus on the coefficient obtained on the tax advant-
age interacted with the specific patent box characteristic without making any inference
on the separate dummy variables since, as discussed earlier, such discussion is best made
for other than average values of the control variables. We find the tax advantage in the
acquired patents characteristics to be positive and significant for the ICT and Car sec-
tors but insignificant for the Pharmaceutical industry.
45
A similar finding is shown for
the embedded royalties characteristic. For the existing patents condition, the results are
contrasted. The interaction between this condition and the tax advantage turns out to
be insignificant for the pharmaceutical and ICT sectors and negative and significant for
the Car industry. The result on the existing patents condition for the car industry might
reflect the dominant role played by large car producers with high patenting activity,
such as Germany and Japan, where strategic market considerations might prevail over
tax advantage when deciding about the location of a patent registration. The results for
the acquired and existing patents characteristics can also be explained by the fact that
patents are not developed in isolation but are usually part of a patent portfolio strategy
by firms. Acquired patents can raise the value of other (including) future patents in a
portfolio. To achieve this multinationals build up higher-value and better-matching pat-
ent portfolio (see Bo¨senberg and Egger, 2017).
Next, Column (4) of Table 7, we look at the effect of having patent boxes offering a
tax advantage to a larger range of rights than just patents (see Table 2 for details). In all
three sectors, the coefficient is positive and significant, indicating that the broader the
tax base concerned by the patent box the more attractive the country to patent registra-
tion. Finally, in Column 5 of Table 7, we consider the role played by development con-
ditions whereby countries grant tax rebate conditional on R&D activities being
44 In order to save space the results for all other control variables are omitted. The full results are avail-
able from the authors upon request.
45 Note that as we observe patent applications, we cannot see changes in legal ownership of patents in
our data.
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developed within the country. Controlling for the development conditions, dummy vari-
able makes the tax advantage to be insignificant in the car sector, but it makes it nega-
tive and significant for the Pharmaceutical and ICT sectors. The effect of development
condition thus appears to be rather heterogeneous across sectors. It indicates that the
imposition of development conditions can potentially decrease the tax sensitivity of pat-
ents registration.
Table 7. Estimating the effect of the tax advantage on patent registration: pat-
ent box characteristics
Model estimated Negative binomial
(1) (2) (3) (4) (5)
Acquired
patents
Embedded
royalties
Existing
patents
Coverage
of patents
Development
conditions
Panel 1 – Pharma
Patent box charac-
teristic (dummy)
2.738*** 3.739* 2.910 1.820*** 3.336***
(0.508) (2.134) (1.891) (0.687) (0.620)
Patent box charac-
teristic
(dummy) * Tax
advantage in pat-
ent box
0.111 0.171 0.009 0.288*** 0.247***
(0.070) (0.169) (0.146) (0.060) (0.064)
Tax advantage in
patent box
0.036 0.040 0.092 0.097*** 0.014
(0.028) (0.035) (0.135) (0.019) (0.077)
Panel 2 – ICT
Patent box charac-
teristic (dummy)
0.891* 9.419** 4.310 9.188*** 1.971***
(0.490) (3.880) (3.323) (1.469) (0.694)
Patent box charac-
teristic
(dummy) * Tax
advantage in pat-
ent box
0.403*** 0.975*** 0.355 0.866*** 0.282***
(0.099) (0.310) (0.326) (0.161) (0.089)
Tax advantage in
patent box
0.037 0.277*** 0.290 0.108** 0.272***
(0.056) (0.062) (0.239) (0.050) (0.098)
Panel 3 – Car
Patent box charac-
teristic (dummy)
6.064*** 16.138*** 26.435*** 5.938*** 4.732***
(0.970) (3.481) (5.937) (1.449) (1.634)
Patent box charac-
teristic
(dummy) * Tax
advantage in pat-
ent box
0.708*** 1.300*** 1.960*** 0.782*** 0.136
(0.092) (0.243) (0.528) (0.125) (0.198)
Tax advantage in
patent box
0.337*** 0.384*** 1.721*** 0.339*** 0.042
(0.047) (0.068) (0.522) (0.062) (0.122)
Additional controls
included in all
specifications:
Statutory corporate income tax, Business R&D/GDP, Intellectual
property protection, Real research activity, GDP level (log),
country-fixed effects, time-fixed effects.
Notes: We use the number of patents registered by one company in a specific country during a specific year as
dependent variable (count of patents). Standard errors, clustered at company and year level, are reported in
parentheses. The levels of significance are reported as ***p<0.01, **p<0.05, *p<0.1. The model is estimated
via a negative binomial model. The unit of observation is parent company – country of the subsidiary company
(-ies) – year. Observations: 8,957 for Pharma, 12,226 for ICT, and 4,832 for Car. Chi-squares and overdispersion
are high and significant in all the specifications.
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6.4. Effects of patent boxes on research activity
We now consider the interaction between patent box regimes and local R&D activities
as this is an often advocated justification for granting preferential tax treatment. We
define a measure of local R&D activity based on information for the total number of
inventors (of patents) registered by each multinationals in each country and year. This is
measured at company level to allow us linking the presence of patent boxes with patent
registrations and local innovative activity with precision. We are interested in testing two
arguments put forward in the patent box policy debate: (i) to what extent the tax rebate
granted by a patent box is effectively promoting local inventorship in the foreign affiliate
of the multinationals, and (ii) how effective are development conditions included in some
patent box regimes in ensuring that the tax rebate is effectively fostering R&D in the
country where the patent is being registered. A first option for measuring the impact of
patent boxes on local R&D activities by foreign affiliates could be to simply consider the
change in the total number of inventors associated to patents registered by a multina-
tional in a given country. However, in doing so we could possibly capture cases where
the innovative activity of a given multinational is globally increasing (or decreasing) and
wrongly attribute the change in foreign affiliate R&D activities to the existence of a pat-
ent box regime. In our estimations, we therefore use a control variable represented by
the growth in R&D activities in the home country. A second option is to build a depend-
ent variable that distinguishes the changes in R&D activities both at the multinational
group level and in the host countries where patents are registered. To validate the argu-
ment of fostering local research activities, our dependent variable should therefore cap-
ture a positive change in local R&D in the country of the patent measured as an
increase in the number of inventors in the country of registration (i.e., the host country)
and a decreasing or stable number of inventors in the multinational group globally. This
indicator can be transformed into a dummy variable taking the value 1 if those two con-
ditions hold (i.e., increase in the number of host country researchers and a decrease or
stabilization in the total number of researchers within the corporate group). Indeed,
although we do not observe whether the inventors actually move from one country to
another, we can reasonably assume that such simultaneous rise and fall in the number
of inventors in two different parts of the (company) group indicates an inventor shift.
This binary variable is used as dependent variable to assess its determinants through
logistic regressions. These regressions are performed at the company-level. As control
variables, we use the same as the previous specifications with two exceptions. First, we
need to remove the Real Research Activity variable used in previous regressions as this
variable could be endogenous in this specification. Secondly, we include a binary varia-
ble indicating the presence of development conditions in the patent box regime. As
before all our regressions are performed by clustering observation at the level of parent
companies.
The estimations reported in Table 8 provide the results of running OLS regressions
where the dependent variable is the annual change in the (log) number of inventors
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(Columns 1–3) and logistic regressions for the probability of performing an inventor shift
(Columns 4–6). Our results suggest that the tax advantage linked to the patent box is
negatively correlated with both the annual growth in the number of inventors and the
probability of moving inventors to the patent box country. These results could indicate
that countries offering generous tax rebates through the patent box have also more diffi-
culty in fostering local R&D even with a patent box. However, little can be said regard-
ing a possible causality link between the size of the patent box tax advantage and the
local R&D activity. The result obtained for the dummy variable indicating the presence
of a development condition seems to be more straightforward. This variable displays a
positive and significant effect (at 1%) in all specifications. Countries including a specific
local development condition therefore have a higher probability of fostering local R&D
activities or in experiencing an inventor shift and are perhaps more likely to promote
Table 8. Impact of patent boxes on real research activity
Model estimated Linear regression on
local inventors growth
Logit on inventors shift
(1) (2) (3) (4) (5) (6)
Pharma ICT Car Pharma ICT Car
Statutory CIT 0.008 0.004 0.004 0.045** 0.023 0.065***
(0.010) (0.012) (0.009) (0.020) (0.027) (0.023)
Tax advantage in
patent box
0.032** 0.046*** 0.065** 0.099*** 0.103*** 0.189***
(0.012) (0.016) (0.026) (0.015) (0.016) (0.044)
Development conditions
are required in the
patent box
0.651*** 1.089*** 1.232*** 2.139*** 2.006*** 3.797***
(0.153) (0.242) (0.422) (0.256) (0.257) (0.659)
Business R&D/GDP 0.154 0.023 0.228 0.316 0.295 0.259
(0.182) (0.179) (0.238) (0.255) (0.259) (0.300)
Intellectual property
protection
0.684*** 0.471*** 0.473*** 0.628*** 0.307* 0.050
(0.209) (0.159) (0.172) (0.230) (0.173) (0.214)
GDP level (log) 0.514 0.667* 0.255 0.342 0.745 0.205
(0.413) (0.371) (0.544) (0.566) (0.525) (0.794)
Inventor growth
at the MNE level
0.365*** 0.288*** 0.250***
(0.059) (0.050) (0.077)
Time-fixed effects Yes Yes Yes Yes Yes Yes
Constant 0.957*** 0.763*** 0.867*** 1.609*** 1.502*** 0.951***
(0.183) (0.151) (0.157) (0.276) (0.224) (0.267)
Observations 3,327 3,727 2,029 3,327 3,727 2,029
R-squared kPseudo-R
2
0.062 0.078 0.067 0.044 0.041 0.053
F-test kChi-square 10.49 15.04 7.66 112.4 149.7 79.62
Prob. >Chi-square (F-test) [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Notes: In the logistic specification we use as dependent variable a binary variable taking the value 1 if the number
of researchers of the company registered in the host country increases while the number of researchers of the com-
pany registered at the multinational group level decreases or is stable, and takes the value 0 otherwise. In the lin-
ear regression, we use as dependent variable the growth rate of researchers of a company registered in the host
country. We use observations with the presence of a patent box only. Standard errors, clustered at company and
year level, are reported in parentheses. The levels of significance are reported as ***p<0.01, **p<0.05, *p<0.1.
The model is estimated via ordinary least squares in regressions (1)–(3) and via logistic regressions in (4)–(6). The
models are estimated only for country/year with a patent box. The unit of observation is parent company – coun-
try of the subsidiary company (-ies) – year. The Wald (F-) test informs about the joint significance of the parame-
ter estimates, the null hypothesis being that all of the regression coefficients are simultaneously equal to zero.
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local R&D activities in their favour, as reflected by the positive and significant coefficient
attached to the development condition variable. This result also holds independently of
controlling for the change in R&D activities at the multinational group level as shown
by the results on the regressions on local inventors growth.
7. CONCLUSIONS
This paper analyses how the implementation of patent boxes affects the patent-filing
strategies of top corporate R&D investors across countries. For this, we use a recent and
rich firm-level dataset for the 2000–12 period on the top 2,000 corporate R&D investors
from 39 countries, considering their ownership structure, and analyse the determinants
of patent registration across a large sample of 33 host countries.
For the first time, we disentangle the effect of three key characteristics of patent boxes:
the corporate income taxation, the tax advantage of registering patents in a patent box
country, and other characteristics of the patent boxes that define both their coverage
(i.e., the tax base) and non-fiscal characteristics such as local R&D development
conditionality.
Patent boxes exert a strong effect on attracting patents, mostly due to the specific
favourable tax treatment that they bring about. However, this effect varies across sectors
and with the specific characteristics of the patents. High-value patents are shown to be
more influenced in their location choices by the tax advantage offered by patent boxes
than patents of lower value. The possibility to grant the patent box tax regime to patents
that have been acquired existed previously or contain embedded royalties seems to
make patent location even more sensitive to the tax advantages offered by patent boxes.
The same can be said of patent boxes broadening their coverage to other rights such as
trademarks, design, and models, copyrights, or domain names. Our results also suggest
that in the majority of cases, the existence of a patent box regime incentivizes multina-
tionals to shift the location of their patents without spurring local R&D activities or with-
out favouring a shift of inventors. This suggests that the effects of patent boxes are
mainly of a tax nature.
An interesting development of patent boxes concerns the possibility of imposing
development conditions for the patent to qualify for the advantageous tax regime. This
is the case in several countries. These conditions provide a proxy for the possible effect
of conditionality clause agreed at the EU and OECD, that is, the so-called nexus
approach. Our results show that such specific condition appears to dampen the domi-
nant effects of the tax advantage of the patent box regime on patent locations while
encouraging local inventorship.
Patent boxes are a relatively recent development among the tools offered to compa-
nies to boost R&D activities. They have been criticized for offering additional tax
advantages to income already profiting from an IP protection and having potentially lit-
tle effect on the level of R&D. Their development has raised concerns over the fact that
they could exert a significant effect on patent location without any change in the real
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research activity, targeting only the tax benefits. Our results confirm these fears, with the
tax attractiveness of patent boxes being greater the broader their coverage. Recent
debates on the potentially harmful consequences of patent boxes have addressed the
possibility of linking the advantages of patent to the requirement of a real research activ-
ity by the taxpayer that receives tax benefits. Our results suggest that it has the potential
to decrease the still dominant tax effects of patent boxes on patent location and to raise
the level of local inventorship. The nexus approach therefore offers some potential to
mitigate the role of patent boxes as new tax competition tools.
Discussion
Giacomo Calzolari
University of Bologna
What is in an (IP) box: patents or secrets?
Patent boxes, or IP boxes, are a favourable tax regime for income generated with
Intellectual Property that spread mainly in developed countries since 2000. (The term
‘box’ refers to a box to tick in the tax form that refers to the special IP regime.)
Governments justify IP boxes as an incentive to R&D, innovation, and ultimately growth.
However, the ensuing international tax competition creates doubts about the effectiveness
of this policy which may just lead to innovation ‘diversion’ across countries rather than
innovation ‘creation’. Are IP boxes yet another dimension of the international tax war
that forced countries to reduce corporate tax rates by 50% in the last 20 years and also
eroded the taxable corporate income base? Are governments using IP boxes able to spur
local innovation even if this comes with a reduction of R&D in other countries?
This interesting paper tackles the very important issue of empirically and precisely
identifying the ultimate effects of these IP box policies. The empirical analysis is based
on the geographical distribution of patent applications and focuses on both tax and non-
tax features of IP box regimes using a uniquely detailed analysis. Notably, the authors
are able to identify which IP rights qualify for the patent box (i.e., the ‘scope’ of the IP
box), the tax treatment of already existing patents, the treatment of acquired patents,
the treatment of embedded royalties, and whether a country’s IP box contemplates
‘local development conditions’ that restrict the favourable regimes to IP developed in
the country. The dataset is novel and relatively large, encompassing more than 30 coun-
tries for three sectors of activity (pharma, car, and ICT), and firm-level data for patent
filing by 2,000 parent companies of large (often multinational) corporations in the years
2000–12. This rich dataset is a significant improvement with respect to the limited exist-
ing empirical literature and allows the authors to move forward and provide interesting
results.
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The paper delivers two sets of results. The presence of an IP box in a country reduces
the negative effect of the general tax rate on the number of patents filed in that country.
More interestingly, being able to take apart the effect of the level of corporate tax rates
from the tax rate reduction due to the IP box, the authors can show a positive and signif-
icant effect of the tax incentive offered by IP boxes. They also identify significant hetero-
geneity of effects: across sectors, with a much stronger effect for ICT; across values of
the inventions, with a stronger effect attracting high-value patents; and about scope,
with a stronger effect when the IP box tax benefit extends to pre-existing or acquired
patents or other intellectual property rights. A second set of results refers to the effects of
IP boxes on promoting local inventorship and the effectiveness of specific ‘local develop-
ment conditions’ that are sometimes imposed with an IP box. Unexpectedly, the tax
benefit of an IP box reduces the probability of moving inventors towards a country offer-
ing the IP-related benefit. However, imposing a condition of having developed the IP
locally instead increases this probability, as expected.
The nature and type of the firms and sectors used in the empirical analysis is relevant
for the results. The firms in the dataset are very large corporations that account for 90%
of global R&D globally. On the one hand, this is good news, because the paper delivers
very important results that may affect policies and firms’ behaviour on a significant scale
and breadth. On the other hand, the size and relevance of the firms may well introduce
an endogeneity issue: these firms certainly have strong bargaining power towards local
governments and they are known to significantly lobby and affect national policies. For
example, the 10 corporations in Fortune 100 that lobbied most in 2010 paid an average
effective tax rate of 17%, while the others paid an average of 26%.
46
The authors are
aware of the potential endogeneity issue and discuss it referring to their use of firm-level
data against a policy that is instead macro and general. They also present regressions
where the dependent variable is the IP box country-date dummy and the main explana-
tory variable is firms’ R&D expenditures, showing that its coefficient is not statistically
significant. Still, given the sectors used in the analysis and the selection of firms, some of
them are clearly ‘giants’ with strong bargaining power also with large and rich countries.
Concerning the sectors of activity, the authors account for possible differences by sep-
arately replicating their regressions for each of the three sectors, ICT, car, pharmaceuti-
cal. A more parsimonious approach could have been using sector dummies, as it is often
the case for works addressing sectoral differences. The industrial organization literature
has also shown that the structure and organization of R&D activities greatly varies not
only across these sectors but also across countries. Indeed, very large differences emerge
in the results, some of which seem statistically significant. A thorough discussion of the
characteristics of these very different sectors could be useful to explain these differences.
For example, Muller et al. (2008) show that in the German car industry, most of the
46 The Economist, ‘Lobbying: Grey eminences. How companies try to influence governments’,
February 22, 2014.
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R&D activities are performed by upstream suppliers, that may not qualify as firms oper-
ating in that sector. This is different for US car manufacturers which instead tend to
internalize their innovation activities. Clearly, these different approaches and organiza-
tions of R&D may exist afortioriacross the sectors that produce very different outputs,
such as those investigated in the paper.
The unexpected result of IP boxes significantly reducing the number of researchers,
especially for car and pharmaceuticals, is based on a different dependent variable, that
is, a dummy taking value 1 if the number of researchers employed by the firm in the
country increases year-to-year and at the same time the number of researchers employed
by the same firm in other countries reduces or is stable year-to-year (it takes value 0 oth-
erwise). The identification and use of this dummy is a clever idea because it directly
allows to account for researcher shifts or diversions, rather than creation, as discussed
above, and should also account for possible global trends in R&D activities. However,
the dummy is also non-standard and it should be ‘handled with care’, especially given
the unexpected significantly negative coefficient of the tax advantage induced by the pat-
ent box. A result showing a coefficient not significantly different from zero would have
been ‘welcome’ and understandable. However, the negative statistically significant effect
requires instead a careful interpretation. What are the employment policies of these
large corporations? How mobile are their researchers across research centres? Are these
changes permanent or transitory? These and other questions are important dimensions
of the analysis of the effects of IP boxes that should be addressed probably with alterna-
tive and competing approaches of analysis. It is instead a ‘reassuring’ result that, when a
country imposes a local content development of the IP activities to qualify for the tax
advantage, then the number of researchers employed in the country increases.
On the one hand, the empirical analysis shows that the effective tax advantage of IP
boxes induces more patents. On the other hand, all over the regressions the dummy of
an IP box has negative effects on the number of patents filed in the country. The authors
interpret this unexpected systematic result as the consequence of other non-tax effects of
patent boxes, possibly related to the administrative burden to qualify for the tax rebate.
Although this is a possibility, further investigations are needed in the future to sort out
the ambiguous picture and identify the overall effect of IP boxes.
It is now useful to briefly recall the economics of patenting decisions. A firm discover-
ing an innovative product or process has essentially two options to protect its IP, either
patent it or keep it secret (see, among others, Hall et al. 2014). In the former case, the
firm protects its innovation in all the countries in which the patent has been filed and
granted, but at the same time, the innovation becomes common knowledge, to competi-
tors as well. With a trade secret instead, the firm keeps the innovation private in all
national markets and possibly indefinitely. However, it runs the risk of information leak-
age and re-invention by other firms which would very much limit if not destroy the value
of the innovation, again in all countries of potential activity. Hence, firms face a trade-
off between patenting in all relevant markets and opting for a trade secret. Note that,
obviously, if the solution of the trade-off induces the firm to patent in one country, it
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should also induce to patent in other countries as well, if these are relevant markets for
the firm. In fact, the patent in country x would allow competitors in country y to inspect
the innovation from the patent filed for country x and adopt the innovation for the mar-
ket of country y, unless the firm has not patented it in country y too.
Trade secrets are not just a curiosity. Linton (2016) reports that in the United States
‘government surveys consistently show that firms are more likely to identify trade secrets
as “very important” to their operations than other types of IP’, including patents. The
US International Trade Commission 2014 survey shows that trade secrets are ‘very
important’ for 56% of firms operating internationally, compared with 37% for patents,
and this is true specifically also in the ICT sector, one of the sectors studied in the
present paper. These observations are consistently identified in other countries as well.
Trade secrets and patents are generally considered as substitutes because, obviously, a
given innovation can be protected either by a patent or by a secrecy but not both. For
example, Png (2015) empirically showed that the adoption of the Uniform Trade
Secrets Act in the United States, which strengthened the protection of trade secrets,
reduced the number of filed patents by 15.3% in complex technology industries, and
even more so for large companies and major inventions.
Given this substitutability between trade secrets and patents for a given innovation (not in
general), we may expect that a more favourable tax treatment of patents (or more gener-
ally of explicit IP, when compared with trade secrets, as in the case of wide scope IP
boxes) may shift the balance between trade secrets and patents and ultimately induce
firms to patent more and rely less on trade secrets. This simple observation brings novel
and possibly interesting interpretations of the present paper. Indeed, trade secrets and
their use are clearly difficult to measure and empirical analysis either relies on surveys or
on indirect observations (as in Png, 2015). Exploiting cross-country variations of the
trade-off between trade secrets and patents, as in the present paper, seems a very effec-
tive environment for such analysis.
As a final step of this reasoning, it is also worth mentioning that, since national patents
protect inventions only in the very same market of filing, firms may want to patent abroad
and in other countries to avoid imitation by local firms or exporters into those markets.
Archontakis and Varsakelis (2016) discuss and document the dramatic increase of patenting
abroad in the last 30 years. A possible explanation for this trend is that with increasing glob-
alization and international competition, if a firm decides to patent in a country, it may be
inducedtopatentalsoinothercountries.Infact, relying on trade secrets shields the firm
from imitation globally (as long as the secret is kept), while disclosing the innovation through
a patent quickly leaks the information globally. Note that the data used in the present paper
refer to countries that do not rely on international patents (also in the EU, the European
Patent requires validations in each country where a firm wants the patent to be active), and
thusapatentinonecountrydoesnotgrantIPprotectioninothercountries.Clearly,since
patenting is costly, firms do not patent their inventions in every single country, even if this
implies that imitators will exploit the innovation and compete in countries where the firm
did not file the patent. Still, we may expect that an IP box in a given country may induce
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firms to patent there, and at the same time this shift from trade secret to patent may also
induce the firm to patent elsewhere, especially for high-valued innovations. The IP box
environment and the data set used by the authors may be used in the future to further
advance our understanding of how firms decide to develop and protect their IP.
Gabriel Felbermayr
LMU Munich and Ifo Institute
According to Lipsey (2010), intangible assets such as intellectual property ‘have no clear
geographical location, but only a nominal location determined by the parent company’s
tax or legal strategies’. Therefore, it is natural to expect that MNEs shift income related
to these assets to countries offering the most favourable conditions, both in terms of
applicable tax rates and in terms of the definition of the tax basis. Countries, therefore,
have incentives to set tax rates and define tax bases strategically. Indeed, one observes,
both casually and in rigorous econometric analysis, the existence of cross-country varia-
tion in tax regimes and firms’ practices to exploit this variation with the objective of low-
ering corporate income tax liabilities.
Observers of these practices, most often from large high-tax countries, regularly
criticize the existence of low-tax regimes as unfair and disloyal to the common
European cause and denounce MNEs for taking advantage of these. Famously,
President Emmanuel Macron has asked for greater tax harmonization, knowing that his
Berlin counterparts take similar views.
This paper asks how influential corporate income taxes and specific further exemptions
for patents are in determining where firms choose to legally register ownership of an
important form of intangible assets, namely patents. It uses matched patent-investor data
provided by a recent JRC-OECD project which covers the period 2000–12. The analysis
is based on the top 2,000 global corporate R&D investors. These investors are based in
39 home countries, and they may file their patents in 33 different host countries. The
authors exploit changes in the effective tax rates on patent-related corporate income over
time to explain the number of patents registered by some firm in some country at some
point in time. This is a promising approach, because there has been substantial variation
inthelevelofcorporateincometaxesand,even more so, in the availability of special tax
provisions for patent-related income (the so-called patent boxes). At the same time, in the
period under study, global value-added networks operated by MNEs have expanded rap-
idly thanks to falling communication and transportation costs.
The paper documents three main results. First, it confirms other findings, for exam-
ple, by Griffith et al. (2014), that patent boxes indeed have a strong effect on patent fil-
ings, in particular of high-value patents. Second, the granting of special tax advantages
to patent-related income does not per se attract companies’ R&D investment into local
markets. Third, making the granting of tax preferences conditional on R&D investment
reduces the fiscal effect of patent boxes.
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These results are important, because they provide convincing evidence that backs up
the criticism described above. Granting special tax privileges to patents seems particu-
larly problematic from an ethical point of view, because the very nature of patents
already grants firms temporary monopoly power. It may also lead to inefficient substitu-
tion effects when patent boxes affect firms’ decisions over whether to protect new tech-
nologies by applying for patents or by relying on secrecy.
The results can therefore underpin recent initiatives to harmonize corporate income
taxation in the EU. They can also be used to rationalize a move from production-based
to consumption-based taxation, that is, a shift from the origin principle to the destination
principle, as partly coded in the proposed new US tax laws. These are important devel-
opments, and the question arises whether the empirical analysis can be fully trusted.
In my view, this is a solid paper. There are, however, concerns that need to be
addressed in further research. One issue of the econometric analysis, not surprisingly in
the absence of natural experiments, relates to identification. The model used by the
authors is rather parsimonious – this is an advantage per se – but the analysis may well
suffer from omitted variable bias. For example, a positive business cycle shock in country
jmay lead firms to locate activities there and protect them with patents. At the same
time, the same shock may allow the government to relax tax rates as tax bases expand
more quickly than government expenditures. Such shocks endanger correct identifica-
tion by biasing estimates away from zero. Also, firms are more likely to locate patent
ownership in countries where they have associated real innovative activity. This may
reflect co-location externalities, or the influence of tax rules which seek to limit the extent
to which income and real innovative activity can be geographically separated. This
would call for a more comprehensive list of controls, possibly for a more strongly satu-
rated fixed-effects specification. Finally, another concern lies in the fact that firms are
heterogeneous: only very few of them patent and even fewer are MNE. So, firms of dif-
ferent sizes have very different capabilities to benefit from patent boxes. To capture this,
Griffith et al. (2014) include location–industry–firm size-fixed effects; these control for all
country characteristics that affect a firm’s payoff and that do not vary through time, but
that potentially do vary across firms in different industries and different parent firm sizes.
While the paper provides arguments to fight against tax competition on empirical
grounds, it does not clarify whether and to what extent tax competition in general and
in the form of patent boxes in particular is detrimental to overall European welfare. In
fact, small peripheral countries in Europe rely on low taxes as a means to weather com-
petition from the more central countries whose firms benefit from geographic locational
advantages. Harmonization would push peripheral countries into seeking other means
to attract international firms, for example by investing into infrastructure. In the same
way that their taxes could be inefficiently low from a European perspective, their invest-
ment efforts could equally be excessively high. And even if this could be avoided, harmo-
nization could imply that central countries in the EU need to stand ready to finance
transfers to peripheral ones in order to maintain living standards there.
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Another open question relates to the policy instruments available to fight unfair tax
competition. This is crucial, since harmonization of tax rates and tax bases are likely to
be opposed by countries such as Ireland, Cyprus, or Luxembourg. The EU commission
is increasingly using state aid regulation to address tax competition issues in member
states, as favourable tax regimes are assumed to be equivalent to subsidies. But for this
argument selectivity – that the benefits are available only to certain firms – is crucial.
For example, in 2016, its DG competition ruled that the Republic of Ireland’s tax bene-
fits system was a form of state aid as it allowed companies such as Apple Inc. to reduce
taxes paid on profits made within the EU. It postulates that this treatment is selective, as
other companies are not treated equally, and thus prohibited. The Commission
required Apple to pay e13 billion in back taxes to the Irish government. Ireland denies
that the tax scheme available to Apple was not available to other companies, and claims
that, as a consequence, this is not a case of illegal state aid. In any case, while selectivity
would be illegal in Europe, tax policy is a national prerogative, and state aid regulation
would be only a very blunt instrument to address tax competition.
Panel discussion
Fabian Waldinger suggested the inclusion of firm-fixed effects in the regressions to better
ascertain how certain companies switch around their patents across locations. Roberto
Galbiati asked if the magnitude of the effects is comparable to those of other subsidies to
R&D shown in previous studies. He also reinforced the importance of trade secrets and
suggested to look at survey information on the relative importance of patents versus
trade secrets.
Beata Javorcik observed that the paper is focused on large players in the R&D space
and raised doubts that a large pharmaceutical company would choose to register a pat-
ent in a country and not in others given the risk of imitation in countries where the pat-
ent was not registered. She also asked if the results are indeed driven by the patenting
choice or, instead, the decision of where to locate a subsidiary. Banu Demir argued that
these large firms may be able to put pressure on the government to adopt specific poli-
cies and suggested to use the revenue generated in a particular country as a share of the
GDP of that country as a control in the regressions.
Replying to comments and questions, Salvador Barrios acknowledged that the paper
should provide more information on the structure of the firms and agreed that more
robustness tests examining the role of the largest companies in small countries are
needed. He also clarified they do not have reliable data on trade secrets and argued that
while patent boxes can indeed be part of strategies to attract FDI, they are not the pri-
mary determinant. Regarding the issues raised about patent registration such as double
counting, Agnieszka Skonieczna said that is precisely why they focus on patents regis-
tered via the EPO.
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APPENDIX: ADDITIONAL DESCRIPTIVE STATISTICS AND REGRESSION
RESULTS
Table A1. Technology field of patent applications by sector 2000–12
Chemistry Electrical
engineering
Instruments Mechanical
engineering
Other fields Total
Cars 9% 17% 8% 64% 2% 88,826
ICT 4% 81% 10% 5% 0% 165,187
Pharma 79% 2% 17% 2% 0% 75,859
Total applications 74,982 150,380 36,737 65,920 1,853 329,872
Sources: European Commission, Patstat, and OECD.
Table A2. Tests for the identification strategy: patent boxes versus business
R&D
Panel (A) : Patent box Panel (B): Development condition
Business
R&D/GDP
0.221 1.240 1.382 0.227 0.437 0.282
(0.171) (2.473) (2.845) (0.223) (3.021) (3.451)
Lead business
R&D/GDP (tþ1)
0.458 0.981 0.092 2.070* 2.199 0.716
(0.958) (1.538) (1.734) (1.179) (1.826) (2.087)
Lag Business
R&D/GDP (t1)
0.727 0.020 1.129 2.390* 2.091 0.675
(0.982) (1.597) (1.825) (1.238) (2.053) (2.285)
CIT 5.823 13.546
(10.893) (12.032)
Lag CIT (t1) 8.773 6.096
(10.821) (11.794)
Time-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Country-fixed effect Yes Yes Yes Yes Yes Yes Yes Yes
Constant 2.569***2.488***2.440***1.0993.289***3.234***3.210*** 0.766
(0.744) (0.748) (0.751) (1.086) (1.034) (1.038) (1.041) (1.473)
Observations 533 448 436 356 533 448 436 356
v
2
21.55 16.79 16.87 17.01 18.67 18.86 18.56 16.91
Pseudo-R
2
0.0515 0.0476 0.0483 0.0561 0.0669 0.0800 0.0794 0.0884
Log-likelihood 198.4 168 166.2 143 130.2 108.4 107.6 87.13
Notes: Standard errors in parentheses:
***p<0.01,
**p<0.05,
*p<0.1. The dependent variable is a dummy equal to 1 when a patent box regime (panel A)/a development con-
dition (panel B) exists in a given country/year. The unit of observation is country-year.
174 ANNETTE ALSTADSÆTER ET AL.
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