Intellectual property rights and firm
performance in the European Union
Firm-level analysis report, February 2021

2 Foreword
Imprint

Foreword
By effectively protecting their intellectual property, innovative companies can secure financing,grow, collaborate and create value. But how does owning intellectual property rights (IPRs)
impact their performance? In 2015, an EUIPO study revealed that EU firms with at least one patent,
trade mark or registered design record higher revenues per employee and pay higher wages thancompanies with no IPRs.
This joint follow-up report by the EUIPO and the EPO covering the period from 2007-2019 delves
deeper into the role of IPRs by analysing a representative sample of over 127 000 European firms
from all 27 EU member states and the UK. According to this analysis, firms that own IPRs generate
20% higher revenues per employee than their counterparts without an IP portfolio.
The highest revenue-per-employee gains are linked to bundles of trademarks, with performance
premiums of 63% for trade mark and design owners, and 60% for combined patent, trade mark anddesign owners. Firms that own IPRs also pay on average 19% higher wages than firms that do not.
These figures are undoubtedly impressive, but a more detailed analysis of the overall picture reveals
significant potential, especially when it comes to small and medium-sized enterprises (SMEs).
Although fewer than 9% of European SMEs rely on IPRs, this subset of companies appears to generate
68% higher revenues per employee than SMEs without IPR portfolios.
This, in our view, is a critical message for European businesses and policymakers. Our latest report
not only outlines the virtuous cycle between IP and economic performance, but also highlights
considerable scope for value gains by making IP more accessible to SMEs.
Looking to the future, IPR-intensive industries in general, and SMEs innovating with new technologies
in particular, can be expected to play a crucial role in pulling Europe’s economy out of post-pandemic
recession.
This report highlights the need to provide enhanced support for innovative SMEs echoed in thevision set out in the EPO Strategic Plan 2023 and the EUIPO Strategic Plan 2025, through its SME
Programme, as well as the SME strategy outlined in the European Commission’s recent IP Action
Plan. By contributing to a wider understanding of the role played by IPRs in the EU’s society andeconomy, this report helps bring home the fundamental message that IPRs are for everyone.

Christian Archambeau			
Executive Director, EUIPO			

António Campinos
President, EPO

Foreword 3

Joint project team
EPO project team			

EUIPO project teamYann Ménière, Chief Economist			Ilja Rudyk, Economist			
Muzio Grilli, Economist			
				
			

Nathan Wajsman, Chief Economist
Michał Kazimierczak, Economist
Carolina Arias Burgos, Economist

Acknowledgements
The authors are grateful for comments on an earlier version of this report from Katrin Hussingerof the University of Luxembourg, Federico Munari of the University of Bologna and Hansueli
Stamm of the Swiss Federal Institute of Intellectual Property.

4 Table
Imprintof contents

Table of contents

1.

2.

List of tables

6

List of figures

7

List of abbreviations

8

List of countries

9

Executive summary

10

1.1

Methodology

11

1.2

Key findings

12

1.3

Discussion and conclusions

15

Introduction
2.1

3.

16

Setting the scene: the increasing importance of intellectual property rights
for firms

17

2.2

Contribution of the study

19

2.3

Outline of the report

20

Data and methodology

21

3.1

23

Data
3.1.1

Firm data

23

3.1.2

IPR data

25

3.1.3

Matching process

26

3.2

Variables included in the dataset

27

3.3

Summary and limitations of the data

28

Table ofForeword
contents 5

4.

5.

Descriptive statistics

29

4.1

Key financial and company variables

30

4.2

IPR ownership by firm characteristic

32

4.2.1

IPR ownership by firm size

32

4.2.2

IPR ownership by economic sector

36

4.2.3

IPR ownership by EU Member State

40

IPRs and firm performance: econometric analysis

43

5.1

Selection of variables

43

5.1.1

Dependent variable

43

5.1.2

Explanatory variables

44

5.2

5.3

5.4

6.

Econometric methodology

45

5.2.1

Panel data

45

5.2.2

Logarithmic transformation

46

Results

46

5.3.1

Interpreting regression results

46

5.3.2

IPR ownership and firm performance

47

Discussion of the results

52

Conclusions

54

Annex / Supplementary econometric analysis

56

Impact of macroeconomic conditions

56

Impact of innovation status of home country		

61

References686 List
Imprintof tables

List of tables
TablesTable E1:
Table E2:
Table E3:
Table 1:
Table 2:
Table 3:
Table 4:
Table 5:
Table 6:
Table 7:
Table 8:
Table 9:
Table 10:
Table 11:
Table 12:
Table A1:
Table A2:
Table A3:
Table A4:
Table A5:

Average values of selected variables by IPR ownership, 2015-201812

Top 10 NACE categories for IPR ownership

13

Main results of the econometric analysis

14

Key characteristics of the methodological approach

21

Distribution of firms in the sample

24

Average values of performance variables by IPR ownership31

IPR ownership by firm size

32

Ownership of European and national IPRs by firm size (IPR owners only)

33

Type of IPR ownership by size (IPR owners only)

34

Average stock (counts) of IPRs by firm size (IPR owners only), 201835

IPR ownership by NACE category

36

Share of IPR owners by EU Member state (overall)

41

Share of IPR owners by EU Member State (SMEs)

42

Results of models with IPR ownership dummy

48

Results of models with IPR ownership type dummies by firm size

50

Results of models with control for stagnation period

57

Results of models with control for GDP growth59

Results of models with IPR ownership dummy by innovation rank of home country

62

Results of models with IPR ownership types dummies by innovation rank of home country

64

Results of the tests of a single linear combination of parameters for full sample model shownin Table 12

66

Table A6:

Results of the tests of a single linear combination of parameters for SMEs sample model shownin Table 12

66

Table A7:

Results of the tests of a single linear combination of parameters for large firm sample model
shown in Table 1267

List Forewordof figures 7

List of figures
FiguresFigure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure A1:
Figure A2:

NACE divisions with the highest share of patent owners

38

NACE divisions with the highest share of trade mark owners

39

NACE divisions with the highest share of design owners

40

Differences in IPR ownership premium by firm size

49

Differences in the various types of IPR ownership premiums by firm size

51

GDP growth rate (EU28), 2008-2019

56

Differences in marginal effects of IPR ownership during periods of stagnation and economic
growth58

Figure A3:

Marginal effects of IPR ownership at various levels of GDP growth60

Figure A4:2020 European Innovation Scoreboard results

61

Figure A5:

Differences in IPR ownership premium by innovation rank of home country

63

Figure A6:

Differences in the various types of IPR ownership premiums by innovation rank of home country65

8 List
Imprint
of abbreviations

List of abbreviations
EC

European Commission

EPO

European Patent OfficeEU

European Union

EUIPO

European Union Intellectual Property Office

EUTM

European Union trade markGDP

Gross domestic product

IPIntellectual property

IPRsIntellectual property rights

IQR

Interquartile range

NACE

Nomenclature générale des activités économiques dans les CommunautésEuropéennes (Statistical classification of economic activities in the European
Community)

OHIM

Office for Harmonization in the Internal Market (since 23 March 2016: EUIPO)

PATSTAT

EPO’s Worldwide Patent Statistical Database

R&D

Research and development

RCD

Registered Community design

SMEs

Small and medium-sized enterprises

List of Foreword
countries 9

List of countries
AT

AustriaBE

Belgium

BG

Bulgaria

CY

CyprusCZ

Czech Republic

DE

Germany

DK

Denmark

EE

Estonia

ES

Spain

FI

Finland

FR

FranceGR

GreeceHR

Croatia

HU

Hungary

IE

Ireland

IT

Italy

LT

Lithuania

LU

Luxembourg

LV

Latvia

MT

Malta

NL

Netherlands

PL

PolandPT

PortugalRO

RomaniaSE

SwedenSI

SloveniaSK

Slovakia

UK

United Kingdom

10 Executive summary

1. Executive summary
One of the mandates of the European Observatory on Infringements of Intellectual Property
Rights, which is part of the European Union Intellectual Property Office (EUIPO), is to provide
evidence-based data on the impact, role and public perception of intellectual property in theeconomy of the European Union (EU). In order to meet that objective, the Observatory isundertaking a programme of socio-economic studies.
Similarly, the Strategic Plan 2023 of the European Patent Office (EPO) prioritises the conduct of
economic studies to meet the increasing demand among stakeholders for greater awareness of the
impact of the European patent system and its development.
In 2013, the two offices published a joint study which assessed the combined contribution ofindustries that make intensive use of the various types of intellectual property right (IPR) to theeconomies of the EU as a whole and to the individual EU Member States.1 The study was updated in2016 and again in 2019.2 Among the main findings of the 2019 edition was that industries that makeabove-average use of IPRs contributed 29% of employment and 45% of GDP in the EU, with these
proportions having risen since the previous study in 2016.
The present report is a follow-up study that delves deeper into the role of IPRs by analysing alarge representative sample of over 127 000 European firms in order to compare the economic
performance of firms that own IPRs with those that do not. In 2015, the EUIPO (then known as
OHIM) published a similar study 3 which showed that companies that own IPRs have higher revenue
per employee and pay higher wages than companies that do not. The present joint EPO/EUIPO
study is an update of the 2015 EUIPO study, with improved data and methodology. In contrast withthe earlier study, which was based on data from 12 Member States, this new study includes datafrom companies in all 28 Member States.4
The IPRs included in the study are patents, trade marks and designs (or any combination of thethree). Because of their nature, copyright, plant variety rights and geographical indications, whichwere part of the industry-level studies, are not included here. 5 On the other hand, the present study
includes both European and national IPRs, an important enhancement to the data, and provides acomplete view of each company’s IPR portfolio, both European and national.

1
2

3
4
5

Intellectual property rights intensive industries: Contribution to the economic performance and employment in the European Union.
Industry-Level Analysis Report, September 2013.
Intellectual property rights intensive industries and economic performance in the European Union. Industry-Level Analysis Report,
October 2016. Second edition; IPR-intensive industries and economic performance in the European Union. Industry-Level Analysis
Report, September 2019. Third edition.
Intellectual property rights and firm performance in Europe: an economic analysis. Firm-Level Analysis Report, June 2015.
The UK left the EU on 31 January 2020. However, the period covered by this study is 2007-2019, during which the UK was a member
state, so data on UK companies is included.
C opyright is not always registered, while geographical indications are not registered by individual companies, so data on theownership of those IPRs at company level does not exist. Plant variety rights are the subject of a separate research project at
the EUIPO.

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Executive summary 11

The data on each company’s IPR portfolio was matched with information contained in thecommercial database ORBIS. This database provides financial and other information on millionsof European companies, collected from the filings and accounting reports made by companies in
the commercial registers of the EU Member States. The study uses financial and other information
about companies that are registered as formal owners of patents, trade marks and/or designs. Somecompanies that are part of a larger group structure may not be the formal owners of IPRs (their
headquarters may have the formal ownership), but they may still use IPRs in their commercial
activities.
There are various ways to measure the economic performance of a company. Because of data
constraints and the need for like-for-like comparisons (eliminating the effect of firm size on thestatistical results), “revenue per employee” was chosen as the main indicator of firm performance.
The dataset was constructed in such a way that the sample is representative of the general
populations of firms in the EU. This allows a comparison of the performance of companies that own
IPRs with companies that do not, while controlling for relevant factors such as country, sector or sizeof company. To our knowledge, the coverage of the dataset is significantly larger than that of any
other data source of this type currently available, ensuring a sample sufficiently large to draw robust
and representative conclusions.
The study makes no policy recommendations, as this is not within its scope. Instead, it provides
evidence that can be used by policymakers in their work, and serves as a basis for raisingawareness of IP among Europe’s citizens in general, and small and medium-sized enterprises(SMEs) in particular.

1.1

Methodology

The data was analysed using two types of methodology.
First, descriptive statistics were compiled to illustrate the differences between owners andnon-owners of IPRs in terms of economic characteristics. Differences were tested for statisticalsignificance. Chapter 4 presents the results of this analysis.
Chapter 5 reports the findings of an econometric analysis of the data. It allows for an in-depth
examination of the relationship between firms’ ownership of IPRs and their economic performance.
While causality cannot be proven in the strict sense of the word, given the available data, econometricanalysis allows researchers to control for several additional factors that affect economic
performance and to “isolate” the relationship between IPR ownership and firm performance. The
results of the analysis strongly suggest that there is a systematic, positive relationship between
ownership of IPRs and economic performance at individual firm level.

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12 Executive summary

1.2

Key findings

Table E1 summarises the main financial and firm variables for the most recent years in the sample.

Table E1:
Average values of selected variables by IPR ownership, 2015-2018

Non-IPR owners

IPR owners

Any IPRs% difference compared with
non-owners
Patent owners% difference compared with
non-owners
Trade mark owners% difference compared with
non-owners
Design owners% difference compared with
non-owners

Number of
employees

Revenue per
employee(EUR ‘000/year)

Wages peremployee(EUR '000/year)

5.1

148.6

29.8

13.5

178.6

35.6

163.8%

20.2%

19.3%

28.7

202.4

45.5

460.1%

36.3%

52.6%

13.5

179.6

35.0

164.3%

20.9%

17.4%

29.1

196.3

38.7

467.9%

32.2%

29.7%

Note: The figures are based on available observations of 127 199 firms. All differences are statistically significant at the 1% level.
The “Any IPR owners” group is defined as firms that own at least one patent, trade mark or design, or any combination thereof.
The “Patent owners”, “Trade mark owners” and “Design owners” groups are defined as firms that own at least one of theseparticular IPRs. Since many firms own combinations of the three, the owners of the various IPRs overlap.

As Table E1 shows, firms that own IPRs tend to be larger than firms that do not, as measured by the
number of employees (13.5 vs. 5.1 employees on average). For this reason, economic performance
metrics such as revenue, profits and wages are expressed on a per-employee basis.
Thus, firms that own IPRs have on average 20% higher revenue per employee than firms that do not.
In terms of individual IPRs, the average performance premium experienced by IPR-owning firms is
36% for patents, 21% for trade marks and 32% for designs.
Table E1 also indicates that firms that own IPRs pay on average 19% higher wages than firms that donot. Here, the strongest effect is associated with owning patents (53%), followed by designs (30%)
and trade marks (17%). Both in terms of revenue per employee and wages paid, patents, comparedwith trade marks and designs, are the IPR type that on average generates the highest rewards forfirms and their employees. This is also consistent with the results of the industry-level IP Contribution
Study (EPO/EUIPO, 2019), which looked into the contribution of IPR-intensive industries to the EU
economy in terms of gross domestic product, employment, wages and international trade. In thatstudy, patent-intensive industries were found to have the highest wage premium as well.

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Executive summary 13

Table E2 shows that the most IPR-intensive sectors are information and communication, with 18% offirms in that sector owning IPR, followed by manufacturing, with 14% of such companies being IPRowners, and other service activities (14%). Professional services firms (“professional, scientific andtechnical activities”) are also relatively IPR-heavy (13% of such companies own IPR).
Given that SMEs account for the vast majority of companies in the sample (as is the case in the
population of European firms), the overall distribution of IPR owners between countries is similar
to the results for SMEs. Firms from Malta, Portugal, Cyprus, Germany, Austria, Spain, France, Poland
and the UK are most likely to be IPR owners. In those countries, more than 10% of all SMEs own atleast one of the three IPRs.

Table E2:
Top 10 NACE categories 6 for IPR ownership
NACE section

IPR ownership(%)

J: Information and communication

17.67

C: Manufacturing

14.42

S: Other service activities

14.40

M: Professional, scientific and technical activities

12.97

N: Administrative and support service activities

10.66

E: Water supply; sewerage, waste management and remediation activities

9.60

G: Wholesale and retail trade, repair of motor vehicles and motorcycles

8.95

D: Electricity, gas, steam and air conditioning supply

5.90

L: Real estate activities

5.75

I: Accommodation and food service activities

5.51

Note: The table illustrates the share of IPR owners within the total population of firms representing each NACE section.
Only NACE sections with 100 or more firms in the sample are shown.

6

Established in 1970, NACE (“Nomenclature statistique des activités économiques dans la Communauté européenne”)
is the classification of economic activities used by the European Commission. Its current legal basis is Regulation (EC)No 1893/2006 of the European Parliament and of the Council of 20 December 2006 establishing the statistical classificationof economic activities NACE Revision 2.

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14 Executive summary

The econometric analysis presented in chapter 5 makes it possible to isolate the effect of IPR
ownership from other factors such as the size of a firm or the countries and sectors in which it
operates. The results, which are summarised in Table E3, confirm the positive association between
IPR ownership and economic performance, with revenue per employee 55% higher for IPR owners
than for non-owners. This can be regarded as one of the central results of this study.
In addition, the analysis shows that this relationship is particularly pronounced for SMEs.7 SMEs
that own IPRs have 68% higher revenue per employee than SMEs that do not own any IPRs at
all. Thus, while the majority of SMEs in Europe do not own IPRs, those that do have significantly
higher revenue per employee. In the case of large companies, revenue per employee is 18%
higher for IP owners than for non-owners. Here the analysis shows that almost six out of ten
large firms in Europe own IPRs, although the association with higher revenue per employee
is less pronounced than in the case of SMEs.

Table E3:
Main results of the econometric analysis
Difference in revenue per employee between
IPR owners and non-IPR owners
Large companies

+18%

SMEs

+68%

Total

+55%

Note: Based on observations of a total of 120 983 firms. Differences are statistically significant at the 99% confidence level.

The econometric analysis in chapter 5 further shows that increases in firm performance dependon the type and combination of IPRs. The highest revenue-per-employee increases are linked to
combined trade mark and design owners and combined patent, trade mark and design owners,
with performance premiums of 63% and 60% respectively. Patent-only owners have 43% higher
revenue per employee, trade mark-only owners 56%, design-only owners 31%, patent and trade markowners 58%, and patent and design owners 39%.

7

Article 2 of the annex to the Commission Recommendation of 6 May 2003 concerning the definition of micro, small andmedium-sized enterprises (2003/361/EC) defines an SME as a company with fewer than 250 employees and a turnover not 		
exceeding EUR 50 million and/or an annual balance sheet total not exceeding EUR 43 million.

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Executive summary 15

1.3

Discussion and conclusions

The analysis presented in this study confirms that there is a strong positive relationship between
the ownership of different types of IPR and firm performance as measured by revenue per employee
and average wages paid. This finding is consistent with the earlier 2015 study (OHIM, 2015), as
well as with the industry-level IP contribution studies and the 2019 study of high-growth firms,
which found a positive relationship between a company’s IPR activity and the likelihood of achieving
high growth in subsequent years (EPO/EUIPO, 2019).
As with every statistical analysis, these results must be interpreted with care. They do not constituteconclusive proof that encouraging firms to make greater use of IPRs will cause their performance
to increase. The study shows a positive relationship between firms that own IPRs and theirperformance (as measured by revenue per employee). Indeed, there may be several mechanisms
through which the link between the ownership of IPRs and firm performance may work. However,given the available data, it is not possible to disentangle these in the analysis.
The positive association between IPR ownership and economic performance is particularly
strong for SMEs. At the same time, less than 9% of SMEs in the sample own one of the three IP
rights included in the study. The reasons for the low uptake are explored in the EUIPO survey ofEuropean SMEs (EUIPO, 2019). This study (as well its earlier edition from 2015) indicated thatbarriers faced by SMEs include lack of knowledge about IPRs, a perception that registration
procedures are complex and costly, and the high cost of enforcement of those rights, a particularburden for SMEs (EUIPO, 2017). Given this, and the importance of SMEs in the European economy,
the EPO and the EUIPO are taking steps as IP offices to address those concerns so as to enable
European SMEs to take full advantage of their innovation and intellectual property, in the context
of the EPO’s Strategic Plan 2023, the EUIPO Strategic Plan 2025 and the European Commission’s
SME strategy formulated in early 2020 (EC, 2020).

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16 Introduction

2. Introduction
What types of company own more IPRs in Europe: SMEs or large companies? What proportion oflarge companies and SMEs owns IPRs? Do firms that own IPRs exhibit better performance thanthose that do not? Do companies register patents, trade marks and designs as single IPRs or as
bundles?
This report presents the findings of an empirical economic analysis of the relationship between
IPRs and the performance of firms in Europe. The study was carried out jointly by the economic
teams of the EPO and the EUIPO, acting through the European Observatory on Infringementsof Intellectual Property Rights. It is a follow-up to an earlier study linking IPR ownership with firm
performance published by the EUIPO (then known as OHIM) in 2015 (OHIM, 2015).
In 2013, the EPO and the EUIPO published a joint study on the contribution of IPR-intensive
industries to the EU economy (EPO/OHIM 2013), in terms of gross domestic product, employment,
wages and international trade. This study was updated in 2016 and 2019 (EPO/EUIPO, 2016;
EPO/EUIPO, 2019). The 2019 edition showed that IPR-intensive industries account for 39% of(direct and indirect) employment and 45% of GDP in the EU. Those earlier studies focused on
industries, the present study is aimed at firm level. While the earlier studies quantified
the impact of IPR-intensive industries on main macro-economic variables, the present study
quantifies the ownership of IPRs in terms of firm performance indicators. Finally, the industry-levelstudies were of a purely descriptive nature: they examined the structure of the EU economy
to identify sectors that use IPRs intensively, and quantified their “weight” in the overall economy.
In contrast, this firm-level study is analytical in nature: the unit of analysis is the individual
firm, and a very large sample of more than 127 000 companies is analysed using statisticaltechniques to discern whether there is an association between IPR ownership and economic
performance. While the studies – both industry-based and firm-based - concluded that IPRs
are important in a modern economy, the analysis in the current study provides a much stronger
indication of this importance for the performance of firms, especially SMEs.

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Introduction 17

2.1

Setting the scene: the increasing importance of intellectual
property rights for firms

Judging by the strong growth in usage, in recent decades IPRs have become increasingly importantfor firms. They are no longer perceived merely as tools that reward creativity and inventiveness,deter imitation and secure the reputation of a company’s products and services. Beyond theirtraditional function, IPRs provide companies with new opportunities to reap the benefits of their
original works and adequately monetise their intellectual property. IPRs have become flexible
instruments that provide firms with an array of strategic options. Firms can for example decide toopen up IPRs for use by others through licensing programmes or through R&D joint ventures, thus
creating valuable economic activity.
Patent portfolios, technology licensing programmes, brand equity and goodwill determine muchof the value of many modern companies, and have become central to their financial performance.
Indeed, the corporate value of many large firms in advanced economies is now mostly accounted
for by intangible rather than tangible assets (Brand Finance, 2019). Ideas and creativity are often
the most valuable source of inputs, replacing the more traditional factors of production such as
land, labour and capital.
Within this changing environment, businesses are adapting their models accordingly, and seek toappropriate returns on their investments by exploiting their intangible assets. A range of options
from formal to informal tools of protection of their intellectual assets is at their disposal. The choiceof specific tools depends on a number of factors, including the size of the firm and the sector inwhich it operates.
Additionally, instead of focusing on one IPR at a time, companies increasingly look at theirintellectual assets collectively and take the combination and interaction of various IPRs into
account in their decisions. In fact the use of IPRs as a bundle displays significant potential for firms
to strengthen their competitive position in the market, and research has shown that IPRs can beused in a complementary way in order to generate additional streams of revenue and to improve afirm’s financial performance (EUIPO, 2020).

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18 Introduction

Box 1:
Patents, trade marks and designs
The methods of protecting intellectual property can be divided into two broad categories: informal methods of intellectual
property protection (e.g. speed to market and trade secrecy) and formal IPRs. Formal IPRs include patents, trade marks,
designs, copyright, geographical indications and plant variety rights. This study focuses on patents, trade marks anddesigns, because firm-level data for the other formal IPRs was not available. Box 1 summarises the main aspects of eachof these three IPRs.

Patents

Trade marks

Designs

Subject-matterIndustrially applicableinvention in all fields oftechnology, newandinvolves aninventive stepDistinctive signs that
distinguish a company’s
goods or services fromothersOrnamental andnon-functional featuresof an article or productConferred rights

Exclusive right to make,use and sell the patented
invention

Exclusive right to use thetrade mark in trade andprevent its use by othersfor similar or identicalgoods or services

Exclusive right to use thedesign and prevent its use
by others

Benefits

Incentive for innovation;
protection of knowledge;
full technical disclosureof invention

Promotes quality andcompetition between
brands; provides the
public with brandinformation and use incommerce

Ornamental andnon-functional featuresof an article or product;
provides a means for
product differentiationand promotes competition
between brands

Duration

Typically maximum 20
years from filing

Commonly 10 years fromfiling, but can be renewed
indefinitely for successive
periods

The usual maximum termis 25 years

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Introduction 19

2.2

Contribution of the study

Several attempts have been made in the academic and management literature to show how
firms use IPRs and how such use is linked to their economic and financial performance. Whilemost of these studies focus on patents, some also document the use of trade marks, designs, or
combinations of different IPRs (e.g., Hall et al., 2014; EUIPO, 2017b, Thoma, 2020). Studies
assessing the direct link between IPR use and various measures of firm performance remain
scarce (Arora et al., 2008; Munari, 2012; Greenhalgh and Rogers 2012; Crass and Peters, 2014,
Crass, 2014; Schautschick and Greenhalgh, 2016; Llerena and Millot, 2020), due in particular to
the methodological constraints dictated by the nature of the available data. While those studies
by and large found a positive link between IPR protection and performance of firms, they differ as
regards the size of such a premium and the sectoral scope thereof.
The EPO and the EUIPO have been carrying out such research since 2015, when the EUIPO 8
published the first edition of the present study, based on a sample of 130 000 firms from12 EU Member States (OHIM, 2015). The main contribution of that study was in the construction
and analysis of a unique and large dataset with financial variables and IPR data at firm level for
European enterprises. The dataset contained company-level information for multiple years on
patents, trade marks and designs (at both national and European level), together with data on
the financial performance and structural data of the firms for the period 2002-2010. Using this data,
the relationship between IPR ownership and economic performance was analysed, and a positive
relationship was found.
In 2019, the EPO and the EUIPO published a joint study in which they examined the link between
the IPR activity of SMEs and their subsequent growth (EPO/EUIPO, 2019). The main finding of thestudy was that SMEs that had filed at least one patent, trade mark or registered design were more
likely to experience growth in turnover and employment in subsequent years than SMEs that were
not active in the IPR space.
The present study is a continuation of the EUIPO’s 2015 study, incorporating several enhancements:➔	The study is based on data for all 28 EU Member States as of 1 January 2020.
➔	The sampling methodology has been improved to ensure that the results are representative➔

for the population of European firms.
Additional factors and controls have been taken into account in the econometric analysis.

As was the case in the previous studies, an in-depth analysis of the data in this study has foundclear (statistically significant) and positive relationships between the ownership of IPRs andfirm performance as measured by revenue per employee. Moreover, this relationship is more
pronounced for SMEs than for large firms.

8

Prior to March 2016, the EUIPO was known as the Office for Harmonization in the Internal Market (OHIM).

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20 Introduction

Due to data and methodological limitations, caution is needed in interpreting such findings. The
findings indeed bear out strong correlations between IPR and economic performance but do notallow for confirmation of causal links. Causality can potentially work in two different directions:the ownership of IPRs could lead to better financial performance, or it could be the case thatfirms own more IPRs because they are performing better. Also there could be other variables
confounding the relationship between IPR usage and economic performance. Nevertheless,economic theory on IPRs sets out various possible ways in which IPRs can lead to superior
economic performance. IPRs create incentives for firms to engage in innovative activity byraising appropriability (Romer, 1990; Schotchmer, 2006,) that allows them to reduce costs (mainly
process innovation) or/and raise profitability and sales (mainly product innovation). The ownershipof IPRs is therefore likely to be a proxy for a firm’s general ability to generate intangible
assets. The positive relationship between IPR ownership and economic performance might
partially be due to the positive influence that intangible assets can have on productivity.
Licensing activity enabled by IPRs brings additional revenue to innovative firms without themhaving to engage directly in the corresponding production activity. Finally, the exclusive rights
conferred by patents, trade marks and designs can lead to reduced competitive pressure on theirholders, and thus to higher profits.
Conversely, strong economic performance can also impact a firm’s decision to apply for formal
IPRs. Companies that have already been able to generate high financial returns in the past mightturn to using tradable and enforceable IPRs to protect their revenue streams in the future. Finally,
it is important to bear in mind that the statistical association found between IPRs and performance
may be driven primarily by unobserved firm-specific features (e.g. management practices, qualityof management). However, the available data and the econometric methodology used meant that it
was possible to reduce this risk considerably.

2.3

Outline of the report

The structure of this report is as follows:➔	Chapter 3 describes the data sources, the data-matching methodology applied, the resulting➔➔➔➔

dataset and the types of analysis carried out in this study.
Chapter 4 provides a first look at the evidence, in the form of descriptive statistics of thedataset.
Chapter 5 sets out the main findings of the econometric analysis.
Chapter 6 summarises and discusses the findings of the study.
The annex contains supplementary information and analyses.

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Data and methodology 21

3. Data andmethodology
The main purpose of the present study is to examine the relationship between firms’ ownershipof IPRs as registered in the official national and European IPR registers and their economic
performance. Productivity (revenue per employee) was chosen as the main measure of economic
performance. Other metrics, such as profitability, could have been chosen, but the coverage ofsuch metrics in the ORBIS database (see below) is less complete than for revenue. Furthermore,
revenue is less subject to accounting considerations than profit.
The overall approach taken in this study is summarised in Table 1, which specifies the types of IPR
included in the research, the level at which the analysis was conducted, the IPR metrics used andthe performance measures employed.

Table 1:
Key characteristics of the methodological approach
Characteristics of the analysis
Type of IPR– National patents– European patents 7– National trade marks– European Union trade marks– National designs– Registered Community designs
9

Level of analysis

Firm level

IPR metrics– Whether or not the firm owns IPRs
– Stocks of IPR per employee

Performance metrics

Revenue per employee

Methodological approach– Descriptive statistics– Econometric panel analysis

With regard to the methods of IP protection considered, the analysis is based on three maincategories of IPRs: patents, trade marks and designs. A distinction is made between IPRs granted/
registered at European level and those granted/registered at national level, because thecharacteristics of such rights are different and it would be incorrect to treat a single nationalright and a European right (a bundle of national rights in the case of patents) equally. The IPR data
was obtained from the EPO’s PATSTAT database, the EUIPO’s trade marks and designs registerand the databases of the national IP offices. A description of these databases is provided in section
3.1.1 below.
9“European patents” are patents granted by the EPO and are actually bundles of national patents.

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22 Data and methodology

The relationship between economic performance and IPR ownership is assessed in this study
at firm level. Information on the financial performance and basic characteristics of firms
was obtained from the ORBIS database and was matched with the IPR data describedabove. A more detailed discussion of these issues is provided in section 3.1.2.
This study employs two methodological approaches to identify links between IPR status andperformance: descriptive statistics and econometric analysis.
These methodological approaches are complementary. The descriptive statistics provide a basic
overview of the characteristics of firms included in the dataset and identify the extent to which thelevel of IPR ownership differs between firms with different characteristics (e.g. sector, country,
size, etc.). The econometric analysis moves beyond pure description and seeks to identify the
relationship between IPR ownership and the performance of a firm, controlling for other factorsthat affect performance. The results of these two types of analysis are presented in chapters 4 and5 respectively.
Many of the analyses presented in this report are modelled on the previous firm-level study (OHIM,
2015). However, the present study is based on a broader range of firms with their principal placeof business in all 28 Member States of the European Union as at 1 January 2020 (compared with
12 Member States in 2015). There are also other methodological improvements that make thefindings of the present report more robust. In particular, the descriptive statistics have been
conducted on a post-stratified sample with weights adjusted to account for missing observations
and to match the firms’ distribution among the EU Member States. In the econometric models,
additional controls, such as number of employees, have been added to account for factors thatmight have an impact on revenue per employee, so as to strengthen the statistical validity of theanalysis.

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Data and methodology 23

3.1

Data

The results presented in this report are based on a newly produced dataset which consists of apanel of 13 years (2007-2019) that includes information on more than 127 000 companies from all
28 EU Member States as of 1 January 2020.
The dataset used for this study combines financial information reported by a large number ofEuropean firms (ORBIS database, see below) and details of intellectual property rights obtained bythese firms from national and European IP offices (see the following section for a description of thedata sources). To the best of the authors’ knowledge, the coverage of the dataset is significantly
larger than that of any other data source of this type currently available, ensuring a sufficiently large
sample to draw robust and representative conclusions.

3.1.1 Firm data
The source of data on firms’ demographic and financial variables was the ORBIS dataset. Thisdataset, compiled by Bureau van Dijk, contains information on millions of firms in Europe. The
advantage of ORBIS compared with other commercial firm-level datasets is the breadth and scopeof the data available. The data is collected from publicly available business registers and includes
all types of firms obliged to disclose annual financial statements in the relevant national businessregisters. ORBIS covers firms of all types and sizes, although financial data availability may belower for some firm categories, e.g. small firms for which the reporting regime in some Member
States is less stringent than for larger companies.
The selection of a random sample of firms from ORBIS was the first step in the data preparation
process. The sample selection process was conducted separately in four size strata: micro, small,medium and large companies.10 This ensured a sufficient number of observations in each stratum toobtain statistically significant results of the various analyses.
The number of observations in each stratum and country was pre-determined on the basis of the
population of each country and the number of firms with their principal place of business in eachcountry as shown in Eurostat statistics. Firms were also pre-selected on the basis of the availabilityof turnover and employment data in ORBIS (at least three consecutive years with employment or
turnover data available between 2007 and 2016).11 In the case of some Member States for which
the number of eligible firms in some strata was lower than pre-determined, all the eligible firms
were added to the sample. Otherwise, the sample was selected randomly from among eligible firms
representing each stratum and country.12

10
11

12

Ideally the strata are based on seat country, size and industry affiliation to account for the different industry compositionsof each MS. However, there are too many missing data points in Eurostat statistics for this strategy to be feasible.
For many firms there are breaks in the time series for particular years. There are only 5 242 firms with either employment
or turnover data available for the entire 13 year period. Depending on the specific analysis, the number of firms in the 		subsamples may be lower than the overall number of firms in the entire sample.
There are substantial differences in data availability for specific country and strata. It affects mostly firms of smaller size
for which reporting requirements are less burdensome than for larger firms. To account for possible bias stemming fromdata availability, a specific binary control for country effects is included in the econometric specifications.

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24 Data and methodology

The final sample resulted in a dataset which consists of a panel of 13 years (2007-2019) andincludes information on 127 199 firms from the 28 EU Member States as of 1 January 2020 as shownin Table 2.

Table 2:Distribution of firms in the sample
Memberstate

Large(> 250employees)

Medium(50-249
employees)

Small(10-49employees)

Micro
(<10)

Overall per MS

AT

300

500

750

1 500

3 050

BE

350

1 000

1 000

2 000

4 350

BG

300

500

750

1 500

3 050

CY

30

50

100

150

330

CZ

350

1 000

1 000

2 000

4 350

DE

1 000

2 000

3 000

5 000

11 000

DK

300

500

750

1 500

3 050

EE

100

200

500

1 000

1 800

ES

1 000

2 000

3 000

5 000

11 000

FI

300

500

750

1 500

3 050

FR

1 000

2 000

3 000

5 000

11 000

GR

350

1 000

1 000

2 000

4 350

HR

100

200

500

1 000

1 800

HU

350

1 000

1 000

2 000

4 350

IE

300

501

750

1 500

3 051

IT

1 000

2 000

3 000

5 000

11 000

LT

100

200

500

1 000

1 800

LU

30

50

100

150

330

LV

100

200

500

1 000

1 800

MT

30

50

100

126

306

NL

349

999

745

388

2 481

PL

1 000

2 000

3 000

5 000

11 000

PT

350

1 000

1 000

2 000

4 350

RO

350

1 000

1 000

2 000

4 350

SE

350

1 000

1 000

2 000

4 350

SI

100

200

500

1 000

1 800

SK

300

500

750

1 500

3 050

UK

1 001

2 000

2 999

5 001

11 001

Overall
per size11 19024 15033 04458 815

127 199

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Data and methodology 25

As the size of each stratum in the final dataset does not completely reflect the distribution of the
population of European firms between Member States and size categories, subsequent analyses
were conducted using weights specific to each Member State and stratum (descriptive statistics) or
using relevant size, 13 Member State and sector controls (econometric analysis) that make it possible
to draw conclusions that are valid for the population of European firms in general.

3.1.2 IPR data
Once the sample of firms had been selected, the following IPR data repositories were queried for
information on IPRs registered by firms in the sample:➔

PATSTAT, the EPO’s Worldwide Patent Statistical Database. This database contains allrecords of published patents filed at the EPO and the vast majority of national patentoffices around the world. It includes information such as filing and grant dates, legal
events, citations and classification data. The dataset used in this report includes
information extracted from PATSTAT’s April 2020 version.14➔

The EUIPO Register. The Register contains data on filing, registration and expiry ofEU trade marks and registered Community designs registered at the EUIPO.

➔

TMView and DesignView. Maintained by the EUIPO, these databases provided
information on national trade marks and designs.

Part of the statistical analysis in this study relies on the construction of stocks of IPRs.15 It wastherefore necessary to account for the application and expiry dates of each individual IPR. This
information is well covered in the European-level IPR repositories. For some countries, however,
although information on the current status was available, the exact expiry date of specific IPRs
that are no longer valid was not included in the data. In such cases, the expiry year was imputed
based on the average validity period of the same type of national IPR with similar status in the same
Member State or within the entire dataset of national rights.
In a few cases,16 information on expired IPRs was not available at all, so stock variables include only
partial information, comprising rights which are still valid.

13
14
15
16

The size control in the econometric analysis was a binary SME/large company variable. No distinction was made between
micro/small/medium companies within the SME category.
During the patent data extraction process only those national patent applications have been considered that did not containa European patent application in the DOCDB patent family.
Total number of valid IPRs in each year of the analysis.
This was the case for national trade marks of Spain and national designs for Italy.

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26 Data and methodology

3.1.3 Matching process
As there was no common identifier available in all the data sources, several fields available
in all the datasets, i.e. name of firm, legal form and address, served to identify the firms acrossmultiple databases. Before the final matching was executed, the relevant fields in all the data
sources were cleaned of non-distinctive information, such as legal forms, and standardisedusing the same pre-processing algorithms. The data cleaning and standardisation processes
were modelled on the algorithms prepared by the Catholic University Leuven (Magerman et al.,2006) and were further refined by the project team.17
The join between various data sources was performed using the standardised and harmonisedname fields. The matching process included out-of-sample ORBIS records to verify whether amatch between firms included in the sample and IPR records was the best possible one.
The exact match process was used to match ORBIS with the IPR data. In cases where several
ORBIS records linked to one IPR record, various disambiguation procedures based on comparisonsof legal forms and addresses were employed to choose the best available match. In cases where
there was not enough information in the data, or if the out-of-sample firms provided a better match
than the firms included in the sample, the match between the sample firm and the IPR records wasdeemed to have failed and was rejected.
Because in the IPR repositories the same company may be associated with different identifiers,
after the matching, the stock of IPR information was consolidated on the level of the ORBIS
identifier which denotes the individual firm establishment in the final panel dataset. Since theauthors were interested in comparing the performance of firms that are indicated as owners of IPRs
in IPR registers with those that are not, neither IPR ownership data nor financial and employment
variables were consolidated on the level of economic group. This may introduce some bias, as thebenefits of IPRs may be distributed among a wider array of firms within the group, and not only
those that are formal owners of IPRs. Consequently, some of the firms with non-IPR owner status in
the data may in fact benefit from IPRs held within the economic groups of which they are members.
As the final sample consists of firms with sufficient key data availability and the sample is reweighted
to reflect the composition of the population of European firms, all the analyses in the present reportwere conducted on the entire sample with the necessary post-stratification adjustments.18

17
18

A detailed description of the matching process, including the matching algorithm, is available on request.
Post-stratification consisted in assigning an individual weight to each observation, such that weighted data resembled
the distribution of firms between Member States and size classes in the EU.

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Data and methodology 27

3.2

Variables included in the dataset

Firm variables➔	ORBIS identity number➔
Member State where company is located➔Year of incorporation➔
NACE Rev. 2 code - original NACE class industry (4-digit) code as shown in ORBIS. On
the basis of this code, the more aggregated NACE division (2-digit) code is used in somedescriptive statistics.
➔
Revenue➔
Number of employees➔
Size: micro, small, medium or large according to the European Commission definition. For
the assignation of a firm to a specific size category, both employment and turnover wereconsidered. Whenever the size indicators calculated on the basis of employment andrevenue did not coincide, the firm was assigned to the larger size category.
➔Wages (cost of employees).
IPR variables➔	
Binary indicators of IPR ownership
Based on the sample data, several variables were constructed to capture the IPR ownershipstatus of each firm. The main focus of the study is to assess systematic performancedifferences between two groups of firms: those that are IPR-active and those which are
not using registered IPR. IPR ownership status was therefore defined as a time-invariant
variable with IPR owner status assigned to a firm if it was the owner of any IPR during anyof the years covered by the panel.19 In the descriptive statistics and econometric models,
several combinations of binary IPR variables were used to reflect the entire set of possible
ownership of individual IPRs and their bundles at both national and European level.
➔

Stocks of IP rights
The application and expiry dates were used to construct for each firm in each of the years
covered in the panel:– European patent stock including applications with filing date starting from 1981;– national patent stock including applications with filing date starting from 1981;– European Union trade mark stock including applications with filing date starting from 1996;– national trade mark stock including applications with filing date starting from 1876;– Registered Community design stock including applications with filing date startingfrom 2003;– national design stock including applications with filing date starting from 1891;

Due to the complexity of the data cleaning, standardisation and matching processes and the qualityof information on national IPRs, the matching to national IPRs was restricted only to rights assignedin the seat country of each firm in the sample.
19

The time frame between the IPR aquistion and possible effects on economic performance may be different for various IPRs andcombinations thereof included in the study. The effects may also depend on the industry in which the firm is active. Given the
broad scope of the analysis and lack of consensus in the economic literature about the proper time lags for each IPRs and their
combinations in different industries, the IPR status is defined as time-invariant. This choice is also justified by a study design which
focuses on performance differences between IP owners and non-owners, rather than analysis of the causal relationship between
IPR and firm performance.

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28 Data and methodology

3.3

Summary and limitations of the data

The present study is built on the foundations of a carefully constructed dataset. To the best of theauthors’ knowledge, the coverage of the dataset is significantly larger than any other data source of
this type currently available, ensuring a sufficiently large sample to draw robust and representative
conclusions.
Nevertheless, there are limitations as regards both the data and the methodology that must be
noted. Some of these limitations are inherent in the methodology or the data, while others canhopefully be overcome in future studies.
One of the limitations of this study is the lack of data on intangible assets (some of which can beprotected by IPRs, as investment in R&D and design and marketing activities) and on the qualityof those assets. The dataset at hand contains no information on a firm’s investments in intangible
assets, and it is therefore impossible to control for this characteristic in the analysis. If, for example,
firms that have many IPRs are also those that invest more in intangible assets in general, then theapparent relationship between IPR ownership and economic performance found in this study could
be explained by the higher investment in intangibles. Without data on intangible investments, thishypothesis cannot be ruled out, nor can its potential impact on the results be assessed.
The quality of the economic and financial data in ORBIS largely depends on the reporting
requirements in the various Member States, and on the degree to which firms comply with theserequirements. Significant efforts were made in this study to correct for this problem. Nevertheless,the fact remains that to the extent that data is not supplied by firms to the relevant national authorities,
there is no alternative source of such data — it simply does not exist in an accessible form.
The econometric analysis was conducted in a manner so as to correct for potential problems inthe data, such as unobserved heterogeneity. However, the fact remains that the observed
relationships in the data could be a result of some underlying factors that are difficult to measure,
such as quality of the management or features of invention that may both be correlated with thepropensity to use IPR and boost economic performance of a firm. This is an inherent potential
risk in every econometric analysis.

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Descriptive statistics 29

4. Descriptive statistics
This chapter presents descriptive statistics which explore patterns of IPR ownership by firms in
Europe. It starts by focussing on whether on average IPR-owning firms exhibit significant differencescompared with non-IPR-owning firms, in terms of key financial and company variables, including
revenue and number of employees. Next, it investigates how IPR ownership is associated withfirms characteristics. The analysis relies on cross-tabulations to study how IPR ownership varies
across large firms and SMEs and firms in different sectors or different Member States. In addition,
this chapter will also analyse the average stocks of IPRs and compare them across firm types.
In the tables in this chapter, the number of firms (indicated for each variable in the tables) may varythroughout the tables since not all variables are available for all firms. In computing the statistics,
the outlier observations, defined as those in the top and bottom 2.5% of the distribution of theapplicable variable, were omitted from the calculations.
All the calculations in this section have been performed on the post-stratified sample. Eachobservation in the sample has been assigned a specific individual weight reflecting the numberof firms with similar characteristics in the overall population of the EU firms. This procedure allows
for calculation of the statistics representative for the EU. As a result, the descriptive statistics
in the overall sample are driven mainly by results of the SME subgroup. As SMEs constitute a large
majority of firms in the overall population of the firms in the EU Member States, the weight of thosefirms in the overall result is much higher than the weight of large firms.

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30 Descriptive statistics

4.1

Key financial and company variables

Table 3 summarises the key financial and company variables in the sample (revenue per employee,
number of employees, wages), broken down by owners and non-owners of IPRs and also by
type of IPR. Significance tests were conducted to determine whether the mean values of interest
between the group of owners and the group of non-owners were significantly different for each typeof IPR. All the differences shown in the table are statistically significant at the 1% level.20
The results of these statistical tests show that:➔

Revenue per employee is significantly greater for owners than for non-owners of IPRs.
When considering all IPRs, revenue per employee is 20.2% higher for IPR owners thanfor non-owners. The average premium in the case of patents, trade marks and designs is
36.3%, 20.9% and 32.2% respectively. The premiums for European-level rights are higher
than for national rights in the case of patents and trade marks, and equal to nationalrights in the case of designs.

➔

Owners of patents, trade marks and/or designs have a greater number of employees thannon-owners do (employing 2.6 times the number of workers compared with non-owners).
These differences are particularly large for patent and design owners, especially thosefirms that own European-level rights.

20

Significance tests are used to calculate the probability that the observed difference could have come about merely by chance(and therefore does not reflect a real difference between owners and non-owners). If the calculated probability is less than 1%,
then the result (that is, the observed difference) is said to be significant at the 1% significance level.

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Descriptive statistics 31

Table 3:
Average values of performance variables by IPR ownership
Employment

Revenue per
employee(EUR ‘000/
year)

Wages peremployee(EUR ‘000/
year)

5.13

148.55

29.83

N = 64 416

N = 66 581

N = 47 585

13.52

178.61

35.57% difference compared with non-owners

163.76***20.23***

19.27***

Number of observations

N = 21 751

N = 22 034

N = 18 551

28.71

202.4345.53% difference compared with non-owners

460.09***36.27***

52.64***

Number of observations

N = 3 896

N = 4 164

N = 3 548

European patent owner

42.89

235.0851.93% difference compared with non-owners

736.75***58.25***

74.12***

Number of observations

N = 2 311

N = 2 561

N = 2 188

National patent owner

27.71

187.3944.82% difference compared with non-owners

440.64***26.14***

50.28***

Number of observations

N = 2 784

N = 2 973

N = 2 530

Any trade mark owner

13.54

179.5935.02% difference compared with non-owners

164.25***20.89***

17.42***

Number of observations

N = 20 520

N = 20 813

N = 17 548

EU trade mark owner

23.69

231.5242.98% difference compared with non-owners

362.16***55.85***

44.09***

Number of observations

N = 7 630

N = 7 984

N = 6 964

13.5

173.6534.19% difference compared with non-owners

163.39***16.9***

14.63***

Number of observations

N = 18 580

N = 18 854

N = 15 900

Non-IPRownersNumber of observations

IPR owners

Any IPR

PatentownersTrade markownersPatent owner  National trade mark owner

Designowners29.11

196.3338.7% difference compared with non-owners

Any design owner467.91***32.16***

29.74***

Number of observations

N = 3 021

N = 3 293

N = 2 840

Registered Community design owner

37.68

199.7446.24% difference compared with non-owners

635.18***34.46***

55.02***

Number of observations

N = 1 897

N = 2 116

N = 1 826

National design owner

29.15

200.5437.54% difference compared with non-owners

468.63***35***

25.86***

Number of observations  

N = 1 752

N = 1 923

N = 1 651

Note: The performance indicators (revenue per employee and wages per employee) are calculated as the mean value ofthe per-firm average of variables over the period 2015-2018. The asterisks denote that the null hypothesis that the groupmeans are equal is rejected at the 10% (*), 5% (**) or 1% (***) significance level. The “Non-owners” group is defined as
firms with no stock of any registered IPR (patent, trade mark or design). The “Any IPR owners” group is defined as firmsthat owned at least one patent, trade mark or design, or any combination thereof. The “Patent owners”, “Trade markowners’ and “Design owners” groups are defined as firms that owned at least one of the particular IPRs. Since many
firms own bundles of IPRs, the various groups of IPR owners overlap.

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32 Descriptive statistics

Employees of companies that own patents, trade marks and/or designs earn higher wages (19.3%
on average) than those that work for companies that do not own these types of IPR. Here, thestrongest effect is associated with owning patents (52.6%), followed by designs (29.7%) andtrade marks (17.4%). European-level rights are associated with higher wage premiums thannational rights for all three types of IPR.

4.2

IPR ownership by firm characteristic

4.2.1 IPR ownership by firm size
Table 4 shows the distribution of IPR ownership by firm size. The main finding is that IPR ownershipis considerably lower among SMEs than among large firms. Less than 9% of the SMEs in the sample
own any of the three types of IPR or a combination thereof, compared with more than 55% of large firms.
A similar picture emerges when considering the individual IPRs. Less than 1% of SMEs own apatent, compared with almost 18% of large firms. For trade marks, the corresponding percentages
are 8.2% and 52.5%, and for designs they are 0.7% for SMEs and 13.2% for large firms.
It should once again be stressed that firms that are not the formal owners of IPRs can still use IPRs.
For example, a firm that is part of a company group may not be the entity within the group thatformally owns the IPRs, but it can still use them.

Table 4:
IPR ownership by firm size
Large (%)

SME (%)Overall (%)

IPR non-owners

44.4

91.29

91.19

IPR owners

55.6

8.71

8.81

Patent non-owners

100
82.24

100
99.1

100
99.07

National patents only

4.1

0.46

0.47

European patents only

4.92

0.27

0.28

National and European patents

8.75

0.17

0.18

Trade mark non-owners

100
47.54

100
91.84

100
91.76

National trade marks only

23.66

6.4

6.43

EU trade marks only

4.26

0.7

0.71

National and EU trade marks

24.54

1.06

1.11

Design non-owners

100
86.82

100
99.35

100
99.32

National designs only

4.08

0.36

0.36

Registered Community Designs only

4.97

0.23

0.24

National and Registered Community Designs

4.13

0.07

0.08

100

100

100

N = 106 958

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Descriptive statistics 33

Tables 5, 6 and 7 explore aspects of IPR ownership among the firms that own IPR. In Table 5,ownership of national and European-level rights is compared across size categories of firms.
In general, SMEs tend to own national rights while large firms have a higher propensity to own
European-level rights. Thus, among patent owners, 52% of SMEs own only national patents, while
19% of them own both national and European patents. In the case of large firms, these proportions
are almost reversed: 23% own national patents only, while 49% own both national and European
patents.
Among trade mark owners, 78% of SMEs and 45% of large firms own national trade marks only,
while 47% of large firms compared with 13% of SMEs own European Union trade marks (EUTMs).
Only about 8% of both SMEs and large firms own exclusively EUTMs.
In the case of designs, the differences between large and small owners are somewhat lesspronounced. 55% of SME design owners own national designs only, compared with 31% of large
firms. Almost equal shares of each type of owner own exclusively Registered Community Designs(RCDs) (38% for large firms and 35% for SMEs). Large design owners are more likely to combine
RCDs with national designs (31%) than SMEs (10%).

Table 5:
Ownership of European and national IPRs by firm size (IPR
owners only)
Type of IPR

Geography of IPRs

Patents(N = 4 596)

Trade marks(N = 23 339)

Designs(N = 3 619)

Large (%)

SME (%)Overall (%)

National only

23.09

51.72

50.66

European only

27.67

29.66

29.59

National and European

49.24

18.61

19.76

100

100

100

National only

45.09

78.42

78.01

European only

8.12

8.58

8.58

National and European

46.79

13

13.42

100

100

100

National only

30.95

54.9

53.99

European only

37.69

34.71

34.82

National and European

31.36

10.39

11.19

100

100

100

N = 24 739
Note: “National only” indicates that the firm owns a national patent but no European patents. It may nevertheless own othertypes of IPR. If a firm owns a bundle of IPRs it may appear in more than one IPR owner category.

Back to contents

34 Descriptive statistics

Table 6 examines the extent to which firms own individual IPRs or bundles of IPRs. Roughly equal
proportions of SMEs and large firms own exclusively patents (about 4%), but SMEs are much more
likely than large firms to be exclusively trade mark owners (84% for SMEs compared with 58% forlarge companies). Very few firms own only designs - less than 2% of SMEs and less than 1% oflarge firms.
Large firms are more likely to own combinations (bundles) of IP rights than SMEs. Almost 13% oflarge IPR owners own a combination of patents, trade marks and designs, compared with 1% of
SMEs. For the other possible combinations — patents and trade marks and patents and designs —the proportion of large companies owning such bundles is roughly twice that of SMEs: 9% versus
4% for the trade mark/design combination and 0.6% versus 0.3% for the patent/design bundle.

Table 6:
Type of IPR ownership by size (IPR owners only)
Type of IPR

Large (%)
(N = 8 043)

SME (%)(N = 16 696)

Overall (%)(N = 24 739)

Patents only

4.18

4.33

4.33

Trade marks only

57.81

83.83

83.51

Designs only

0.84

1.74

1.73

Patents and trade marks

14.31

4.37

4.49

Patents and designs

0.63

0.33

0.33

Trade marks and designs

9.41

4.12

4.18

Patents, trade marks and designs

12.83

1.28

1.42

100

100

100

Back to contents

Descriptive statistics 35

Table 7 explores the stock (number) of IPRs owned by the different types of firms. Large firms own
more IP rights than SMEs. On average, a large EUTM owner owns 12.3 EUTMs compared with 2.6
EUTMs owned by the average SME EUTM owner. For national trade marks, the difference is evenmore pronounced, with large trade mark owners owning approximately nine times as many national
trade marks as SME owners.
In the case of design rights, large firms own on average almost 30 RCDs compared with 8.4 RCDs
for SME design owners. In the case of national designs, the large owner average is double that for
SMEs (22 versus 11).
Finally, for European patents, large patent owners own 10 times as many patents as SME patent
owners (29.4 compared with 2.8), while for national patents the ratio is about 5 to 1 (11.5 patents
on average owned by large firms, 2.6 by SMEs).

Table 7:
Average stock (counts) of IPRs by firm size (IPR owners only), 2018
EUTMs
(N = 8 088)

National
trade marks(N = 20 868)

RCDs
(N = 1 769)

National
designs(N = 1 274)

Europeanpatents(N = 2 558)

National
patents(N = 2 576)

Large

12.29

26.1

29.65

22.14

29.41

11.46

SME

2.57

2.95

8.43

11.31

2.81

2.58

Overall

2.91

3.24

10.1

11.81

4.46

3.05

N = 23 929

Back to contents

36 Descriptive statistics

4.2.2 IPR ownership by economic sector
Table 8 presents differences in the ownership of IPRs depending on the main activity of the firms
concerned, as defined by the NACE section classification. 21

Table 8:IPR ownership by NACE category
NACE section

Size

B: Mining

Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall
Large
SME
Overall

C: Manufacturing

D: Electricity, gas, steam and air
conditioning supply
E: Water supply; sewerage, waste
management and remediation
activities
F: Construction

G: Wholesale and retail trade, repairof motor vehicles and motorcycles
H: Transporting and storage

I: Accommodation and food service
activities
J: Information and communication

K: Financial and insurance activities

L: Real estate activities

M: Professional, scientific andtechnical activities
N: Administrative and support service
activities
S: Other service activities

N

Any IPR (%)

Patents (%)

TMs (%)

Designs (%)

130
343
473
4 760
17 17021 930
269
516
785
250
859
1 109
813
11 65812 471
3 384
24 18627 570
971
5 249
6 220
353
5 113
5 466
777
4 742
5 519
497
215
712
376
5 416
5 792
1 420
9 863
11 283
1 376
5 794
7 170
20
307
327

42.59
1.22
1.28
68.79
14.1
14.42
58.1
5.44
5.9
42.13
9.36
9.6
47.27
3.48
3.51
52.58
8.89
8.95
45.39
3.64
3.76
51.89
5.48
5.51
63.38
17.58
17.67
28.2
0.38
0.4
41.02
5.74
5.75
51.38
12.91
12.97
38.6
10.57
10.66
22.49
14.39
14.4

20.55
0.38
0.41
36.33
3.05
3.24
13.47
0.58
0.69
9.58
1.55
1.61
14.23
0.52
0.53
8.33
0.63
0.64
4.18
0.08
0.09
0.98
0.06
0.06
9.68
0,.87
0.88
4
0.02
0.02
3.37
0.27
0.27
14.48
1.54
1.56
3.48
0.57
0.58
1.69
0.01
0.01

39.61
0.89
0.95
63.01
12.24
12.53
56.77
4.89
5.34
39.37
8.28
8.51
42.65
3.27
3.29
51.01
8.35
8.42
44.05
3.58
3.69
51.89
5.42
5.45
62.3
17.25
17.34
27
0.37
0.4
40.05
5.54
5.56
48.07
12.11
12.17
37.95
10.18
10.27
22.49
14.39
14.4

6.31
0
0.01
24.96
2.04
2.17
9.33
0.06
0.14
5.98
0.06
0.1
5.65
0.19
0.19
10.79
0.57
0.59
4.73
0.03
0.05
3.16
0.07
0,.07
8.24
0.71
0.73
2.8
0.01
0.01
5.62
0.27
0.27
7.78
1.04
1.05
3.41
0.81
0.82
1.69
0.01
0.01

Note: The table illustrates the share of specific IPR owners within the total population of firms representing each NACE section within a specific size bracket.
Only NACE sections with 100 or more firms in the sample are shown.

21

Established in 1970, NACE (“Nomenclature statistique des activités économiques dans la Communauté européenne”) is theclassification of economic activities used by the European Commission. Its current legal basis is Regulation (EC) No 1893/2006of the European Parliament and of the Council of 20 December 2006 establishing the statistical classification of economic
activities NACE Revision 2.

Back to contents

Descriptive statistics 37

The four largest sectors in the sample are manufacturing, construction, wholesale and retail,and professional, scientific and technical services. There are significant differences between
the sectors in respect of IPR use. This is particularly visible when looking at large firms. The most
IPR-intensive sectors are manufacturing, with 69% of large companies owning IPRs, andinformation and communication, with 63% of such companies being IPR owners.
The results show that manufacturing firms have the highest shares of patent owners (36% of large
companies) and design owners (25% of large firms). Construction and the wholesale and retailtrade sectors own a relatively large number of trade marks, with 43% and 51% of large firms inthose sectors being trade mark owners. Professional services firms (“professional, scientific andtechnical activities”) are relatively patent-heavy (14.5% of large firms own patents) but they also
own many trade marks (48% of large firms).
A similar pattern is seen among SMEs in the various sectors. For example, 3.2% of SMEs inmanufacturing own patents, a far higher proportion than in any other sector.

Back to contents

38 Descriptive statistics

Figures 1, 2 and 3 show the NACE divisions with the highest share of ownership of patents,
trade marks and designs respectively. The figures next to each bar indicate the percentage
of firms in the sector that own the IP right in question. Thus, 21% of firms in the sector Scientificresearch and development own patents, the highest share among the sectors by a large margin(in second place is Manufacture of machinery and equipment, with 12% of firms owning patents).
When it comes to trade mark ownership, the sector with the highest proportion of trade mark owners
is the pharmaceutical industry (more than 39% of firms in that sector own trade marks), followed by
Manufacture of beverages (38%) and Scientific research and development (33%).
In general, fewer firms own registered designs than is the case for patents and trade marks. Even in
the most design-intensive sector, Manufacture of electrical equipment, the share of design-owningfirms is only 6.2%. Other sectors with relatively high proportions of design ownership are Manufactureof rubber and plastic products (5.8%) and the automobile industry (4.9%).

Figure 1:
NACE divisions with the highest share of patent owners
72: Scientific research and development (N=478)

21.07%

28: Manufacture of machinery and equipment
n.e.c. (N = 1 907)

11.96%

29: Manufacture of motor vehicles, trailers andsemi-trailers (N = 645)

8.84%

20: Manufacture of chemicals and chemical
products (N = 854)

8.48%

26: Manufacture of computer, electronic andoptical products (N = 848)

7.97%

27: Manufacture of electrical equipment(N = 850)

7.45%

22: Manufacture of rubber and plastic products(N = 1 285)

7.24%

24: Manufacture of basic metals (N = 508)

7.11%

21: Manufacture of basic pharmaceutical products
and pharmaceutical preparations (N = 345)

5.98%

30: Manufacture of other transport equipment(N = 246)

4.96%
0%

Back to contents

5%

10%

15%

20%

Descriptive statistics 39

Figure 2:
NACE divisions with the highest share of trade mark owners
21: Manufacture of basic pharmaceuticalproducts and pharmaceutical preparations(N = 345)

39.51%

11: Manufacture of beverages (N = 377)

38.09%

72: Scientific research and development(N = 478)

32.69%

20: Manufacture of chemicals and chemical
products (N = 854)

30.74%

58: Publishing activities (N = 853)

26.79%

29: Manufacture of motor vehicles, trailers andsemi-trailers (N = 645)

22.92%

26: Manufacture of computer, electronic andoptical products (N = 848)

20.82%

60: Programming and broadcasting activities(N = 172)

19.4%

73: Advertising and market research (N = 1 222)

18.11%

59: Motion picture, video and television programme
production, sound recording and music publishingactivities (N = 453)

17.43%
0%

10%

20%

30%

40%

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40 Descriptive statistics

Figure 3:
NACE divisions with the highest share of design owners
27: Manufacture of electrical equipment(N = 850)

6.23%

22: Manufacture of rubber and plastic products(N = 1 285)

5.77%

29: Manufacture of motor vehicles, trailers andsemi-trailers (N = 645)

4.89%

20: Manufacture of chemicals and chemical
products (N = 854)

4.59%

11: Manufacture of beverages (N = 377)

4.15%

26: Manufacture of computer, electronic andoptical products (N = 848)

3.88%

72: Scientific research and development(N = 478)

3.49%

28: Manufacture of machinery and equipment
n.e.c. (N = 1 907)

3.49%

13: Manufacture of textiles (N = 644)

3.11%

32: Other manufacturing (N = 821)

2.71%
0%

2%

4%

6%

8%

4.2.3 IPR ownership by EU Member State
Tables 9 and 10 show the pattern of ownership of the different types of IPRs by firms from the
28 EU Member States. Table 9 shows the results for all firms, while Table 10 focuses on SMEs.
Given that SMEs account for the vast majority of firms in the sample (as is the case in the populationof European companies), the overall results are similar to the results for SMEs.
SMEs from Malta, Portugal, Cyprus, Germany, Austria, Spain, France, Poland and the UK are most
likely to be IPR owners. In those countries, more than 10% of all SMEs own at least one of the three
IPRs. Looking at the individual IP rights, SMEs from Belgium, the Netherlands, the UK, Sweden,
Germany, Denmark, Austria and Finland have the highest proportions of European patent
ownership. By and large, SMEs from those countries also have the highest propensity to own
national patents, with some exceptions: Belgian and Danish SMEs own relatively few national
patents, while Polish SMEs tend to own national but not European patents.
For trade marks, SMEs from Malta, Portugal, Cyprus, Germany, Spain and France have the highest
propensity to own national trade marks, while SMEs from Luxembourg, Belgium, the UK, Germany,
Denmark, the Netherlands and Sweden have the highest proportions of EUTM ownership. It
appears that with the exception of Germany, SMEs from various Member States tend to have highproportions of either national or European-level trade mark ownership but seldom both.
Back to contents

Descriptive statistics 41

As was shown in the previous sub-sections, few SMEs own design rights. In no Member State does
the percentage of design owners among SMEs reach 1%. SMEs from Poland, Austria, Denmark,the UK and Belgium are relatively more likely to own RCDs, while SMEs from Croatia, Malta, theUK, Slovenia, Germany, France, Poland and Austria have the highest propensity to own national
design rights.

Table 9:
Share of IPR owners by EU Member State (overall)
Share of owners (%)
MemberState

Number offirms

Europeanpatents

NAT
patents

EUTM

NAT TMRCD

NAT DESAny IPR

AT

2 685

0.84

1.46

2.9

8.95

0.66

0.67

11.74

BE

3 695

1.13

0.16

3.77

6.37

0.53

0.13

9.11

BG

2 474

0.01

0.21

0.47

3.74

0.07

0.26

4.21

CY

200

n/a

n/a

2.61

12.93

0.01

0.02

13.14

CZ

3 825

0.01

0.65

0.59

7.27

0.22

0.22

7.88

DE

9 640

1.03

1.74

3.07

10.88

0.54

0.76

12.67

DK

2 488

0.88

0.34

2.6

8.88

0.59

0.38

10.17

EE

1 694

0.03

0.2

1.52

8.02

0.2

0.24

8.79

ES

8 517

0.19

0.75

2.03

10.54

0.24

0.47

11.71

FI

2 807

0.72

1.31

1.65

4.01

0.28

0.12

5.1

FR

7 816

0.33

0.39

1.21

10.44

0.17

0.68

11.15

GR

2 952

0.01

0.22

0.84

8.66

0.01

0.13

9.45

HR

1 462

n/a

0.03

0.07

2.55

n/a

0.9

3.18

HU

3 920

0.03

0.1

0.4

3.16

0.08

0

3.44

IE

2 615

0.28

0.05

1.53

1.74

0.07

0.03

2.92

IT

10 040

0.36

0.42

1.28

2.73

0.3

0.2

3.99

LT

1 530

0.13

0.18

0.78

9.5

0.15

0.31

9.84

LU

272

0.41

0.12

6.34

4.5

0.04

0.04

9.36

LV

1 466

0.04

0.22

0.17

3.55

0.06

0.02

3.76

MT

284

0.02

n/a

2.02

15.18

0.02

0.85

16.95

NL

2 480

1.1

1.09

2.46

2.69

0.06

0.01

4.96

PL

9 599

0.09

0.76

1.62

8.51

0.69

0.66

10.22

PT

3 454

0.01

0.08

0.8

13.52

0.17

0.39

13.83

RO

4 014

0

0.07

0.23

3.96

0.03

0.19

4.13

SE

4 052

0.88

0.67

2.06

7.17

0.3

0.47

8.52

SI

1 586

0.29

0.59

1.13

4.77

0.33

0.77

6.09

SK

2 648

0.01

0.2

0.4

4.56

0.02

0.19

5.07

UK

8 743

1.04

0.76

3.58

8.39

0.54

0.81

10.19

Back to contents

42 Descriptive statistics

Table 10:
Share of IPR owners by EU Member State (SMEs)
Share of owners (%)
MemberState

Number offirms

Europeanpatents

NAT
patents

EUTM

NAT TMRCD

NAT DESAny IPR

AT

2 282

0.76

1.38

2.78

8.82

0.63

0.65

11.58

BE

2 973

1.12

0.15

3.74

6.33

0.52

0.13

9.06

BG

2 160

0.01

0.2

0.44

3.66

0.06

0.25

4.13

CY

168

n/a

n/a

2.56

12.89

n/a

0.02

13.07

CZ

3 440

0.01

0.63

0.56

7.2

0.22

0.21

7.8

DE

8 452

0.9

1.64

2.89

10.65

0.47

0.7

12.42

DK

1 877

0.83

0.31

2.49

8.72

0.55

0.37

9.99

EE

1 572

0.03

0.19

1,.5

7.92

0.2

0.23

8.68

ES

7 329

0.18

0.74

1.99

10.48

0.23

0.47

11.64

FI

2 467

0.67

1.23

1.54

3.86

0.23

0.09

4.93

FR

6 518

0.31

0.38

1.17

10.38

0.16

0.67

11.08

GR

2 557

0

0.21

0.84

8.64

0.01

0.13

9.43

HR

1 365

n/a

0.02

0.05

2.46

n/a

0.88

3.09

HU

3 513

0.02

0,1

0.38

3.11

0.08

0

3.39

IE

2 153

0.28

0.05

1.51

1.72

0.07

0.03

2.87

IT

8 785

0.34

0.41

1.25

2.7

0.29

0.19

3.95

LT

1 424

0.13

0.17

0.75

9.41

0.14

0.3

9.75

LU

208

0.38

0.11

6.23

4.42

0.04

0.04

9.26

LV

1 364

0.03

0.21

0,.14

3.46

0.06

0.01

3.68

MT

249

0.02

n/a

1.98

15.15

0.02

0.85

16.9

NL

856

1.1

1.08

2.43

2.66

0.06

0

4.92

PL

8 687

0.08

0.73

1.58

8.44

0.66

0.64

10.14

PT

3 041

0.01

0.08

0.77

13.47

0.16

0.39

13.78

RO

3 609

0

0.06

0.19

3.83

0.02

0.15

3.99

SE

3 495

0.87

0.66

2.02

7.11

0.29

0.47

8.44

SI

1 470

0.27

0.56

1.09

4.69

0.32

0.76

5.99

SK

2 317

0.01

0.2

0.39

4.53

0.02

0.18

5.02

UK

7 166

1.03

0.74

3.52

8.29

0.53

0.79

10.07

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IPRs and firm performance: econometric analysis 43

5.

5. IPRs and firm
performance:
econometric analysis
This chapter examines the link between IPRs and firm performance in more depth. In particular,
econometric modelling is applied to estimate the relationship between firms’ performance and ownershipof IPRs overall, but also the ownership of patents, trade marks and registered designs separately.
Applying econometric techniques to the dataset makes it possible to control for external influences
on firm performance to a great extent. Compared with previous studies, the data used in this study
offers not only a wide geographical scope but also information on several registered IPR types andon the performance of a large number of firms over several years.

5.1

Selection of variables

Econometric analysis consists in using statistical techniques to examine the relationship between avariable whose movements the researcher seeks to explain (called the dependent variable) using
a set of explanatory or independent variables.
This section describes the selection of the dependent variable and the independent variables.

5.1.1 Dependent variable
The purpose of this study is to analyse the relationship between IPR ownership and firm
performance. Therefore, the dependent variable of the models needs to be an indicator ofcompany performance. The ORBIS database described in chapter 3 contains several potential
candidates, including various measures of revenue, sales and profit or loss. Given that thisstudy relies on data from all the EU Member States, it is important that the basis on which thedependent variable is constructed is identical between countries over time as this will minimise
the risk that weaknesses in the data lead to biased results. For this reason, various measures of
profitability, which are often affected by accounting and tax considerations, were discarded infavour of revenue as the basis of the performance measure in the models. Given the substantial
variation in revenue between firms of different sizes, the decision was made to transform theperformance measure by dividing it by the number of the firm’s employees.
Thus, revenue per employee (operating revenue per employee in a given year) is the measure of
performance in the econometric models.
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44 IPRs and firm performance: econometric analysis

5.1.2 Explanatory variables
The differences between firms in their performance, as represented by revenue per employee, arethen sought to be explained by a set of explanatory variables, which fall into two main categories.
The first category consists of binary or dummy variables 22 that indicate whether the firm owns IPRs
(or whether it owns a particular type of IPR):➔	
IPR owner: takes the value 1 if the firm owned any type of IPR in any of the years, and 0➔

otherwise.
A set of dummy variables which indicates whether or not a firm owned a specificcombination of IPRs in any of the years and which divides the sample into eightcorresponding groups:– Patents only: takes the value 1 if the firm owned patents but no other type of IPR,
		 and 0 otherwise.
– Trade marks only: takes the value 1 if the firm owned trade marks but no other type of		 IPR, and 0 otherwise.
– Designs only: takes the value 1 if the firm owned design rights but no other type of IPR,
		 and 0 otherwise.
– Patents and trade marks: takes the value 1 if the firm owned patents and trade marks
		 but not designs, and 0 otherwise.
– Patents and designs: takes the value 1 if the firm owned patents and designs but not		 trade marks, and 0 otherwise.
– Trade marks and designs: takes the value 1 if the firm owned trade marks and designs
		 but not patents, and 0 otherwise.
– Patents, trade marks and designs: takes the value 1 if the firm owned all three types of		 IPR, and 0 otherwise.
– No IPRs: takes the value 1 if the firm did not own any type of IPR, and 0 otherwise.
In addition, so-called control variables were also used. These are variables which indicate, or
control for, non-IPR factors which might affect company performance and which therefore need tobe taken into account when analysing the relationship between performance and IPR ownership:
➔
➔
➔
➔

➔
➔

22
23

Country dummies: the country in which the firm is domiciled.
Sector dummies: the sector in which the firm is active (NACE sections).
Year dummies: the year (to control for the effects of the economic cycle).
SME: a dummy variable which takes the value 1 if the firm is an SME, and 0 if the firmis large (in cases where the company moved between categories during the panel
period, it is assigned to the larger size category).
Age of company: a variable that indicates the age of the firm.
Employment: a variable indicating the number of employees in the firm.23

Dummy or binary variables are variables which take a value of either zero or one. For example, a dummy variable indicating 		
whether or not a firm is domiciled in Spain would take a value of one for all Spanish firms and zero for all other firms.
While the SME dummy is already an indication of the size of the firm, the category ranges from firms with one employee to firms with
249 employees. It can thus be expected that companies of different sizes within the SME category behave differently in respect of their
IPR and business strategy. This is even more true of large firms, which can range in size from 250 employees to tens of thousands.

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IPRs and firm performance: econometric analysis 45

5.2

Econometric methodology

5.2.1 Panel data
The dataset used for this study consists of data for 127 199 firms, with up to 13 (annual)
observations per firm (2007-2019). However, not all firms have data for the entire period. On
average, there are data for 7.4 years per firm, yielding more than 940 000 observations in
total. However, in practice the number of observations is somewhat lower, around 890 000,
because outlier observations 24 were eliminated from the analysis.
The dataset has a so-called panel format as it contains both a cross-sectional and a time-series
dimension. Panel data makes it possible to exploit not only cross-sectional variation (differences
between firms at one point in time) but also time-series variation (changes for one firm over time).
Since both dimensions of variation within the data can be exploited simultaneously, panel data
estimates are based on a larger sample size, and are thus potentially more accurate than thoseobtained through simple cross-section or time-series regressions.
Furthermore, a particular type of econometric modelling can be applied, namely panel regression
techniques. This method is able to control for unobserved heterogeneity (i.e. the effect of variables
that are not observable in the data but which might have an impact on the dependent variableof interest). This feature is particularly important since it minimises the risk of obtaining spurious
results driven primarily by firms characteristics that have an effect on their performance but which
are not observed and are not related to the ownership of IPRs. For instance, a company’s specificorganisational structure, its business model, and its management quality are all factors which may
have a significant impact on the relationship between IPR ownership and performance but for
which data is lacking. Failure to deal with unobserved characteristics could produce biased results.
However, unobserved heterogeneity in a panel framework can be dealt with through the use ofparticular panel estimation techniques, which are designed with the precise goal of controlling forspecific company features that are not directly observable in the data.
This study focuses on systematic differences in performance between the group of IPR active firms
and firms that do not use IPR protection over a period of 13 years. The main variable of interest IPR activity - has been defined as a binary and time-invariant variable In this context, a fixed-effects
estimator would ignore all the variations in the explanatory variables across individual firms, suchas their sector and country of activity. Therefore, the so-called random effects model has been
selected as the panel regression method best suited to this research design. Random effects models
combine the cross sectional (between firms) and time series (within firms) features of the data formodel estimation. This allows for estimation of time-invariant variables (Wooldridge 2013). This
technique relies, however, on the assumption that firm-specific unobservable characteristics havea non-systematic (i.e. random) influence on performance and are uncorrelated with the explanatory
variables.

24

Those observations for which the value of revenue per employee exceeded the range of 1.5* IQR (interquartile range).
The exclusion of extreme observations from an analysis is standard practice in econometrics.

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46 IPRs and firm performance: econometric analysis

5.2.2 Logarithmic transformation
In regression analysis, it is common to logarithmically transform variables where a non-linear
relationship exists between the independent and dependent variables. This approach allows
for inclusion of non-linear focal relationship, while still preserving the linear regression model.
Logarithmic transformations are also a convenient means of transforming a highly skewed variable
into one that is more approximately normal. In this study, the distributions of the performancevariable as well as of the stock variables are highly skewed. Finally, it is appropriate to make alogarithmic transformation when it is suspected that a given percentage change in an explanatory
variable will lead to a constant percentage change in the dependent variable.
Consequently, in the regressions presented below, the dependent variable (revenue per employee)
and the continuous explanatory variable (employment) are expressed in logarithms.

5.3

Results

5.3.1 Interpreting regression results
To interpret the regression results presented in the tables below, a little background knowledge of
econometrics and statistics is required. In this section, the necessary knowledge to understand thediscussion that follows is provided.
The tables containing the regression results consist of the following columns:
Variable

Coef

The “Variable” column contains the explanatory variables of the regression model. Explanatory
variables are those factors that are believed to have an impact on firm performance.
The “Coef” column shows the impact of each explanatory variable on firm performance. A positive
value for the coefficient means that an increase in the value of the focal variable is associated with
better performance, whilst a negative coefficient means that an increase in the value of the variable
is related to weaker performance. The greater the magnitude of the coefficient (either positive or
negative), the greater the association between the control variable and the performance of the firm.
It is important to note that not all variables have a statistically significant influence on the dependent
variable. Statisticians and econometricians use significance tests to determine whether or not aparticular explanatory variable has a statistically significant relationship to the dependent variable.
The significance level indicates the probability of a false rejection of the null hypothesis, forexample that the coefficient is different from zero, in a statistical test. In other words, it indicatesthe probability that the coefficient appears to be non-zero solely as a result of chance. In the tablesof results, a single asterisk next to an entry in the “Coef ” column indicates that the coefficient issignificantly different from zero at the 10% significance level, whilst a double asterisk indicates that
it is significant at the 5% level and a triple asterisk indicates that it is significant at the 1% level. The

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IPRs and firm performance: econometric analysis 47

confidence that the independent variable truly has an impact on the dependent variable is greater
if the coefficient is significant at the 1% level than if it is significant at the 5% or even 10% level.
Some of the variables included in the regressions are “dummy variables”, which take a value ofeither zero or one. For example, some of the models include a dummy variable that takes a valueof one if the company owns IPRs (or, in some models, a specific type of IPR), and zero otherwise.
Interpreting the coefficients on dummy variables is slightly more complex than interpreting thecoefficients on standard logarithmic variables. The coefficient on a dummy variable indicates thechange in firm performance, given a change in the value of the dummy variable from zero toone. For example, a positive and significant coefficient on the “IPR owner” dummy variable in aregression examining whether IPR owners exhibit superior performance to non-owners would beinterpreted as follows:
IPR owners have higher revenue per employee compared with non-owners of IPRs, all else being
equal.
It is also important to note that the dummy variables enter into the models without a logarithmictransformation. All the models with IPR dummies have the log-linear form. In such models, for asmall change in log (y), the coefficients, when multiplied by 100, approximate well the percentagechange in the dependent variable. However, as the change in log (y) becomes larger, theapproximation % ∆ y ≈ 100 * ∆log(y) becomes inaccurate. To calculate the exact percentage
difference in the revenue per employee between IPR owners and non-owners, the coefficients mustbe transformed using the following formula:%change = 100 * (exp(coef) – 1)
In the results tables below, the entry in the “Coef ” column for all dummy variables presents theestimated coefficient. The calculated marginal effects,25 according to the formula above, are showngraphically immediately following the regression results.

5.3.2 IPR ownership and firm performance
The purpose of this first set of models is to analyse whether those firms that own IPRs exhibit
superior economic performance to those that do not. The variables of interest in these models aredummy variables which take a value of either zero or one, indicating:➔➔whether or not a firm owns any form of IPR (“IPR owner”), andwhether or not a firm owns a specific combination of IPRs (“patents only”, “trade marks
only”, “designs only”, “patents and trade marks”, “patents and designs”, “trade marks
and designs”, “patents, trade marks and designs”).

All the models include controls for country, sector, year, age of firm, SME dummy (in model 1), anda proxy for firm size based on the number of employees.
25

Predicted change in a dependent variable when a specific independent variable changes, while all the other control variables
remain constant.

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48 IPRs and firm performance: econometric analysis

The first set of results is shown in Table 11. In these regressions, carried out for the sample as awhole (column 1 in the table) and separately for SMEs (column 2) and large firms (column 3), asingle dummy variable “IPR owner” is added to the set of control variables to identify whether or nota firm has ever owned any type of formal IPR protection. The SME dummy variable is only applicablein the model in column 1, since the other two columns contain models with only one type of firm.
Looking at the results, there is a strong positive relationship between IPR ownership and revenue
per employee, as shown by the positive and statistically significant coefficients of the “IPR owner”
dummy variable. Furthermore, this relationship is particularly strong in the case of SMEs, where therelevant coefficient is 0.517, compared with 0.166 in the case of large firms.

Table 11:
Results of models with IPR ownership dummy
Dependent variable
Revenue per employee (log)
Full sample
(1)

SMEs
(2)

Large
(3)

0.440***

0.517***

0.166***(0.007)(0.007)(0.012)0.005***

0.006***

0.001***(0.0002)(0.0002)(0.0002)-0.265***-0.266***-0.263***(0.001)(0.001)(0.002)

6.566***

6.340***

6.469***(0.230)(0.427)(0.256)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Year?

Yes

Yes

Yes

882 249718 791

163 458

R2

0.258

0.252

0.337

Adjusted R2

0.258

0.252

0.336

IPR owner
SME-1.168***(0.008)Age
Employment (log)Constant

Observations

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level.

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IPRs and firm performance: econometric analysis 49

As noted above, when the dependent variable is log-transformed, as is the case here, the dummy
variable coefficients must be transformed to calculate the impact on revenue per employee using
the following formula:%change = 100 * (exp(coef) – 1)
Figure 4 presents recalculated estimations of differences in revenue per employee between IPRowners and non-owners depending on firm size. The results of model 1 thus indicate that:
The revenue per employee of IPR owners is 55% higher that of non-IPR owners.
In the case of SMEs, the revenue per employee of IPR owners is 68% higher thanthat of non-owners.
In the case of large firms, the IPR ownership premium is 18%.

➔➔➔

These results can be considered as the central conclusions of the present study.

Figure 4:
Differences in IPR ownership premium by firm size 26
80
68%
60

55%

40
n = 102 674
n = 120 983

N = 718 791

N = 882 249
2018%
n = 18 309

N = 163 458
0Full sample

SMEs

Large

Note: n indicates number of individual firms and N indicates total number of observations.

In subsequent regressions, the “IPR owner” dummy is replaced by a set of dummy variables that
identify the specific combinations of IPRs that were owned by the firm during the period
under consideration. Table 12 shows the results of these regressions. As in Table 11, results
are shown for all firms (column 1), and for SMEs and large companies separately (columns 2 and 3).

26

As shown in Table 11, the value of the IPR coefficient is statistically significant at a 5% significance level in all models
illustrated in the figure.

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50 IPRs and firm performance: econometric analysis

The analysis of the results in Table 12 suggests that some IPR bundles have a stronger effect onfirm performance than others. Looking at the full sample results in column 1, the coefficients ofthe patent/trade mark, design/trade mark and patent/trade mark/design combinations are higher
than the coefficients of the individual IPRs.27 This pattern also holds when looking at theresults for SMEs and large firms separately. In fact, the coefficient corresponding to the bundle of
all three IP rights for SMEs is the largest of all the IPR coefficients in these regressions.

Table 12:Results of models with IPR ownership type dummies by firm size
Dependent variable
Revenue per employee (log)
Full sample
(1)

SMEs
(2)

Large
(3)

0.355***

0.403***

0.169***(0.027)(0.033)(0.042)0.442***

0.511***

0.147***(0.007)(0.008)(0.014)0.270***

0.323***

0.050(0.044)(0.050)(0.086)0.454***

0.562***

0.232***(0.019)(0.026)(0.027)0.327***

0.433***

0.106(0.078)(0.102)(0.111)

0.488***

0.612***

0.185***(0.020)(0.026)(0.030)Patents, trade marks and designs

0.469***

0.681***

0.248***(0.024)(0.038)(0.030)SME-1.166***0.006***

0.001***

Patents only
Trade marks only
Designs only
Patents and trade marks
Patents and designs
Trade marks and designs(0.008)Age

0.004***(0.0002)(0.0002)(0.0002)

Employment (log)-0.265***-0.266***-0.264***(0.001)(0.001)(0.002)

6.559***

6.344***

6.460***(0.230)(0.427)(0.256)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Constant

Year?
Observations
R2
Adjusted R

2

Yes

Yes

Yes

882 249718 791

163 458

0.258

0.252

0.337

0.258

0.252

0.336

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level.

27

Tables A5 to A7 in the Annex show the results of the series of linear hypotheses tests, which allow to verify whether
the differences between individual IPR coefficients are statistically significant.

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IPRs and firm performance: econometric analysis 51

Figure 5 “translates” the cofficients in Table 12 into percentage differences in revenue per employee
associated with the various IPR combinations. The positive effects of IPR bundling can be seen
when looking at patents, for example. SMEs that have a patent but no other IPR generate revenue
per employee 50% above non-IPR owning SMEs. When this patent is combined with a trade mark,the premium rises to 75%, and when the firm owns all three IPRs, its revenue per employee isalmost double that of non-owners (98% higher). Similarly, the premium associated with owning adesign rises significantly when the design is combined with a patent or a trade mark, and even more
(to the same 98% level) when it is combined with both a patent and a trade mark.
One possible explanation of the positive impact of including a trade mark in combination with theother two IP rights is that while owning a patent or a design indicates that a firm has successfully
engaged in some type of innovation, it does not mean that the firm has derived any revenue fromthis innovation. However, when a trade mark is added to the mix, it could indicate that the firm hasentered or is about to enter the marketplace with the resulting product or service, thus generating
additional turnover, reflected in the main performance measure in this study, revenue per employee.

Figure 5:
Differences in the various types of IPR ownership premiums byfirm size
Patents only
Full sample

SMEs

Large

Trade marks only

43%

56%

31%

***

***

***

50%

67%

38%

***

***

***

18%

16%

***

***

Patents and trade marks
Full sample

5%

Patents and designs

Trade marks and designs

58%

39%

63%

***

***

***

SMEs

Large

Designs only

75%

54%

84%

***

***

***

26%

11%

***

20%

***

Patents, trade marks and designs
60%

Full sample

***
98%

SMEs

Large

***
28%

***

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52 IPRs and firm performance: econometric analysis

5.4

Discussion of the results

Overall, the econometric analysis has led to the following key findings:➔

IPR owners perform better than non-owners. Overall, revenue per employee isapproximately 55% higher for IPR owners than for firms that do not own IPRs. This
relationship is particularly pronounced for SMEs. SMEs that own IPRs have 68% higher
revenue per employee than SMEs that do not own any IPRs at all. In the case of large
firms, revenue per employee is 18% higher for IPR owners than for non-owners.

➔

Increases in firm performance depend on the type and combination of IPRs.
The highest revenue-per-employee increases are linked to combined trade mark/designand combined patent/trade mark/design owners: 63% and 60% respectively. Trade
mark-only owners have 56% higher revenue per employee, patent-only owners 43%,and design-only owners 31%.

Although due care was applied in all phases of the research, some methodological choices,
necessary in all applied research, may potentially bias the econometric results, as noted above.
Furthermore, it is important to note that although the econometric analysis was able to show astrong and significant correlation between the variables of interest, given the available dataa causal link between IPR ownership and firm performance cannot be established. Previous
studies (O’Mahony and Vecchi, 2009; Marrocu et al., 2011) have shown that there is a positive
influence of intangible assets on firm performance. Intangible assets can be protected by formal,
registered IPRs or by other informal forms of protection that did not manifest themselves in the IPR
registries. It is likely that firms that use formal IPR protection also invest more in intangible assets andinnovate more than firms that do not use formal IPR protection.
In addition, it cannot be ruled out that the IPR measures in the models are correlated with someomitted variable representing the propensity to invest in intangible assets, which cannot be
observed in the data. As a result of this possible omitted variable bias, IPR coefficients estimatedin econometric models capture not only the effect of the use of formal IPRs for intangible assets
protection, but also the positive effect of those intangible assets themselves. The result that therelationship is found to be stronger for SMEs than for large firms is consistent with this hypothesis.
Most large firms rely in some way or another on intangible assets, whereas many smaller firms
often build their business on tangible assets alone. Trade marks, designs and patents provide legal
protection for specific, but not all, types of intangible asset. Hence, the group of large firms that do
not own these particular forms of IPR may consist of firms that generate other types of intangible
asset (e.g. copyright or trade secrets) and therefore exhibit high economic performance. In contrast,
the group of SMEs that do not own trade marks, patents or designs is likely to consist of a large
number of firms that do not own any intangible assets at all.

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IPRs and firm performance: econometric analysis 53

An alternative reasoning suggesting a different causal relationship cannot be ruled out. The
positive relationship between IPR ownership and performance could also stem from a possible
impact of higher revenue per employee on the ownership of registered IPRs. Firms that alreadyenjoy high revenue without using formal IPRs could decide to start using trade marks, patents ordesigns to protect their future returns and to secure income with enforceable and tradable rights.
Nonetheless, economic theory provides a number of arguments that can support the claimof the positive impact of IPRs on firm performance. IPRs provide incentives that increase a firm’s
propensity to engage in innovative activity. By providing legal certainty, IPRs promote licensing
activity and enable firms to increase their revenue without having to expand their production
capabilities. Patents, trade marks and designs provide exclusive rights and thus reduce competitive pressure on their owners, potentially leading to higher revenue and profits. These hypotheses
cannot, however, be exhaustively tested with the data at hand.
In addition to the models presented in this section, the Annex presents two additional sets ofregressions that have been used to test the robustness of the results across different time periods
and geographical areas. The first set divides the sample of firms according to the status of thecountry in which they have their principal place of business in the European Commission’s Innovation
Scoreboard, in order to examine the influence of the home country’s innovation performance on
the economic performance of its firms. It finds that the IPR ownership premium is particularly
pronounced in countries which are classified as modest or moderate innovators, suggesting thatfirms which do innovate and protect their innovation with IPR have a particularly strong competitive
advantage in such countries.
The second set of supplementary regressions attempts to disentangle the impact ofmacroeconomic conditions on the relationship between IPR ownership and company
performance. This analysis finds that while the IPR ownership premium is somewhat affectedby recessions, the effect is modest and does not change the main conclusions.

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54 Conclusions

6. Conclusions
The main contribution of this study lies in the construction and subsequent analysis of a unique andvery large dataset with financial information and IPR data at firm level.
The overarching result of this analysis is that ownership of IPRs, specifically patents, trade marks
and designs, is strongly associated with improved economic performance at individual
firm level. This association is especially strong in the case of SMEs.
In particular, the descriptive statistics in chapter 4 show that:➔First, revenue per employee for owners of IPRs (patents, trade marks and/or designs,
national and/or European) is on average 20% higher than for non-owners of IPRs. This
IPR ownership “revenue premium” is largest for patent owners at 36.3%, followed
by design owners at 32.2%, and trade mark owners at 20.9% All the differences
found are statistically significant, meaning that they are very unlikely to have comeabout purely by chance.

➔

Second, wages paid by IPR owners are on average 19.3% higher than those paid byfirms that do not own IPRs. In this case, the “wage premium” is highest for patents(52.6%), followed by designs (29.7%) and trade marks (17.4%). Again, these differences
are statistically significant.

➔Third, a considerable number of firms (91.2%) do not own any of the three IPRs.
Moreover, a greater proportion of SMEs than large firms are non-owners (91.3% of
SMEs versus 44.4% of large firms). In other words, only 8.7% of SMEs own any of thethree IPRs included in this study, but 55.6% of large firms do. This does not imply that
44.4% of large firms do not use IPRs in their commercial activities. They may use
IPRs, but not be the formal owners. If they are part of a larger group structure, IPR
ownership may be with other entities within the group.

➔

Fourth, there are noticeable differences among IPR-owning firms. On average, large
firms own more rights than SMEs, independent of the type of right. Compared withlarge firms, SMEs tend to use national rights more often than European rights, andthey have a relatively greater reliance on trade marks.

The econometric analysis in chapter 5 confirms and indeed strengthens the link between
ownership of IPRs and the economic performance of firms. In particular, companies that own IPRs
were found to have 55% higher revenue per employee than firms that do not own IPRs, supporting
the indicative result from the descriptive statistics. Thus, while a cause-and-effect relationship has
not been proven, there is a strong indication that such a relationship might exist.

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Conclusions 55

Furthermore, this positive relationship between IPR ownership and revenue per employee
is particularly strong for SMEs. As shown by the results reported in Figure 4, SMEs that own
IPRs have 68% higher revenue per employee than SMEs that do not. In addition, these results
hold for all three IPRs under study and for combinations of those IPRs. Across all individual IPRs
and all combinations of IPRs, these performance premiums are larger in the case of SMEs than in
the case of all firms (see Figure 5). Moreover, the fact that firms that own a combination of IPRs
often perform even better than those that own just one of these rights is an indication that IPRs
complement each other. This “combination bonus” is again particularly large for SMEs.
These results confirm the evidence concerning the importance of IP rights for economic
performance found in previous studies published by the EPO and the EUIPO. The 2013, 2016
and 2019 studies (EPO/OHIM, 2013; EPO/EUIPO, 2016; EPO/EUIPO, 2019) consistently showedthat IPR-intensive industries make a substantial contribution to the economy of the EU, while
the first firm-level study published by the EUIPO (OHIM) in 2015 (OHIM, 2015) indicatedthe positive relationship between firm performance and ownership of IPRs, based on a sample
from 12 EU Member States. The EPO/EUIPO study of high-growth firms published in 2019
showed that SMEs that apply for a trade mark, patent or registered design are more likely thanother SMEs to experience high growth in subsequent years.
The present study improves on the 2015 study by including data from all the EU Member States
and by revising the econometric methodology to take into account additional relevant factors.
The findings that emerge make an even stronger case for the positive relationship between IPR
activity and the performance of firms, particularly the smaller firms that are the backbone ofEurope’s economy.

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56 Annex / Supplementary econometric analysis

Annex /
Supplementary
econometric analysis
Impact of macroeconomic conditions
The panel dataset used for this study covers the period 2007-2019, which was characterised by aturbulent macroeconomic situation in Europe. Figure A1 illustrates the economic growth rate in the
28 EU Member States (EU28) between 2008 and 2019.

Figure A1:
GDP growth rate (EU28), 2008-2019

2.5

0.0

-2.5

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Note: Based on Eurostat table tec00115. Real GDP growth rate, percentage change on previous year.

The marginal effects of IPR ownership may differ depending on the overall economic situation. In
order to check this hypothesis, the main models were extended by incorporating specific variables
allowing for examining differences in marginal effects due to macroeconomic conditions.

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Annex / Supplementary econometric analysis 57

Table A1 shows the same model as in Table 11 in chapter 5, but now incorporating a binary variable(dummy) for the period 2007-2013, in which GDP in the EU stagnated, as shown in Figure A1. The
model also includes an interaction term IPR ownership x stagnation. Since the stagnation dummy
depends directly on the year of the observations, the models do not include separate year dummies.
Column 1 is the main model with the SME dummy, analogous to column 1 in Table 11. Model 2
introduces a dummy for the period 2007-2013 (stagnation dummy). Columns 2 and 3 similarly
correspond to the same columns in Table 11 and show the separate regressions for SMEs andlarge firms respectively.

Table A1:
Results of models with control for stagnation period
Dependent variable
Revenue per employee (log)All firms
(1)

SMEs
(2)

Large
(3)

0.458***

0.539***

0.136***(0.007)(0.008)(0.013)-0.040***-0.029***-0.108***(0.001)(0.002)(0.003)0.004***

0.004***

0.004***(0.0001)(0.0002)(0.0002)-0.263***-0.261***-0.262***(0.001)(0.001)(0.002)-0.028***-0.028***0.022***(0.002)(0.003)(0.004)6.513***

6.226***

6.519***(0.230)(0.427)(0.256)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Year?

No

No

No

882 249718 791

163 458

R2

0.254

0.247

0.331

Adjusted R2

0.254

0.247

0.331

IPR owner
Stagnation
SME-1.167***(0.008)Age
Employment (log)IPR ownership/stagnation
Constant

Observations

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level. Stagnation period
defined as period between 2007-2013.

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58 Annex 1/ Supplementary econometric analysis

Figure A2 presents recalculated estimations of differences in revenue per employee between IPRowners and non-owners depending on firm size, analogously to Figure 4 in chapter 5.
As can be seen, during periods of stagnation, the marginal effects of IPR ownership are slightly
lower for SMEs and slightly higher for large firms, compared with the results in Figure 4. On the other
hand, during periods of economic growth, IPR-owning SMEs benefit slightly more, while large IPR
owners benefit slightly less. However, in all cases, the differences are modest, and thus indicatethat the revenue premium of IPR-owning firms is resilient to macroeconomic fluctuations.

Figure A2:
Differences in marginal effects of IP ownership during periodsof stagnation and economic growth
80
71%
67%
58%

60

54%

40

20

17%

15%

0
Full sample
Growth

SMEs

Large

Stagnation

Note: The graph shows the marginal effects of IP ownership recalculated from coefficients shown in Table A1 using theformula: % change = 100 * (exp(coef) – 1). Stagnation period defined as years of 2007-2013. Growth defined as years of
2014-2019.

An alternative method of accounting for macroeconomic conditions is to include GDP growth as an
explanatory variable, instead of a stagnation dummy as was done above. The results are shown in
Table A2, where the impact of the macroeconomic situation on marginal effects of IPR ownership isanalysed by adding control of GDP growth in the firm’s home country.28 In that sense, this is a more
complete model than the preceding one, which used a dummy variable for the stagnation period
for the EU as a whole.

28

Based on Eurostat table tec00115, real GDP growth rate, percentage change on previous year. The data in Eurostat is available
as of 2008. Additionally, data for GDP growth for Ireland in 2015 has been inflated to over 25% due to the relocation to Ireland of
a limited number of big economic operators. To limit the possible bias from this revision, the observations associated with Irelandand the year 2015 were removed from the dataset. See the Eurostat clarifications:
https://ec.europa.eu/eurostat/documents/24987/6390465/Irish_GDP_communication.pdf

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Annex 1/ Supplementary econometric analysis 59

The results are very similar to those in Table 11. The principal coefficient of interest is thecoefficient of the IPR owner dummy. In the model in Table 11, that coefficient is 0.440 for all
firms, 0.517 for SMEs and 0.166 for large firms. The differences between those coefficients andthe corresponding coefficients in Table A2 are only visible in the third digit after the decimal
point. This indicates that the IPR ownership premium is relatively stable over the economic
cycle and periods of economic recession or expansion do not alter the differences in
performance between IPR owners and non-owners in a major way.

Table A2:
Results of models with control for GDP growth
Dependent variable
Revenue per employee (log)IPR owner
GDP growth

All firms
(1)

SMEs
(2)

Large
(3)

0.439***

0.514***

0.172***(0.007)(0.008)(0.013)0.005***

0.005***

0.007***(0.0003)(0.0003)(0.001)0.004***

0.006***

0.001***(0.0002)(0.0002)(0.0002)-0.267***-0.265***-0.269***(0.001)(0.001)(0.002)-1.178***SME(0.008)Age
Employment (log)0.004***

0.005***-0.004***(0.0004)(0.001)(0.001)

6.545***

6.295***

6.492***(0.231)(0.429)(0.256)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Year?

Yes

Yes

Yes

823 879672 036

151 843

0.267

0.260

0.346

0.267

0.260

0.345

Interaction IP/growth
Constant

Observations
R

2

Adjusted R

2

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level.

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60 Annex / Supplementary econometric analysis

Figure A3 illustrates changes in marginal effects of IPR ownership depending on the GDP growthrate. As was the case with the previous set of models (using a stagnation dummy), the IPR
ownership premium for SMEs increases with increasing economic growth, while for large firms
it decreases slightly.

Figure A3:
Marginal effects of IPR ownership at various levels of GDP
growth
80

60

40

20

-15
SMEs

-10

-5

Full sample

Large

0

5

Note: The graph shows the marginal effects of IP ownership recalculated from models’ coefficients (Table 4) using theformula: %change = 100 * (exp(coef) - 1). Dashed lines present the 95% confidence intervals.

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10Annex / Supplementary econometric analysis 61

Impact of innovation status of home country
A second supplementary investigation carried out in this study attempts to discern whether the
IPR ownership premium generated by companies based in different EU Member States depends
on how innovative the home Member State of the company is assessed to be in the European
Commission’s annual European Innovation Scoreboard.29
The Innovation Scoreboard “grades” each Member State as either a modest, moderate, strong or
leading innovator. The results of the 2020 exercise are shown in Figure A4.

Figure A4:2020 European Innovation Scoreboard results

Source: European Commission.

29

See https://ec.europa.eu/growth/industry/policy/innovation/scoreboards_en for a description of the 2020 edition of theInnovation Scoreboard.

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62 Annex / Supplementary econometric analysis

In order to examine the impact of the innovation rank of a Member State on the IPR ownershippremium generated by firms from that Member State, separate regressions were run for firms
based in Member States classified as modest/moderate innovators and those classified as strong/
leader innovators. The results are shown in Table A3.
The first column reproduces the basic model in Table 11. Column 2 shows the results from a
regression using data for companies from modest/moderate innovators, while column 3 shows
the results for companies from strong/leader innovators.

Table A3:
Results of models with IPR ownership dummy by innovation
rank of home country
Dependent variable
Revenue per employee (log)
Full sample(1)

Modest/moderate(2)Strong/leader(3)0.440***

0.533***

0.334***(0.007)(0.009)(0.009)-1.168***-1.249***-1.042***(0.008)(0.012)(0.011)0.005***

0.006***

0.004***(0.0002)(0.0003)(0.0002)-0.265***-0.305***-0.209***(0.001)(0.002)(0.002)

6.566***

5.136***

6.270***(0.230)(0.212)(0.231)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Year?

Yes

Yes

Yes

882 249462 057

420 192

R2

0.258

0.235

0.258

Adjusted R2

0.258

0.235

0.258

IPR owner
SME
Age
Employment (log)Constant

Observations

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level.

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Annex / Supplementary econometric analysis 63

Figure A5 shows the calculated IPR ownership premiums that result from the models in columns
2 and 3 of Table A3. Companies based in Member States that are classified as modest or
moderate innovators generate significantly higher IPR ownership premium of 70% comparedwith 55% for all companies. Conversely, firms from Member States that are strong or leader
innovators generate an IPR ownership premium of 40%.

Figure A5:
Differences in IPR ownership premium by innovation rank ofhome country
80
70%

60

55%

40%

40

n = 61 361

N = 462 057
n = 120 983

N = 882 249
20

n = 59 622

N = 420 192

0Modest/moderate
Full sample
Note: n indicates number of individual firms and N indicates total number of observations.

Strong/leader

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64 Annex / Supplementary econometric analysis

Table A4 shows the results of regressions with separate IPR ownership dummies for the different
IP rights, analogous to Table 12 in chapter 5. Column 1 reproduces the corresponding column inTable 12, while columns 2 and 3 show the results from models run separately for firms from modest/
moderate and strong/leader innovator countries. The resulting IPR ownership premiums are shownin Figure A6.

Table A4:
Results of models with IPR ownership types dummies byinnovation rank of home country
Dependent variable
Revenue per employee (log)
Full sample(1)

Modest/moderate(2)Strong/leader(3)0.355***

0.412***

0.280***(0.027)(0.040)(0.036)0.442***

0.539***

0.332***(0.007)(0.010)(0.010)0.270***

0.313***

0.210***(0.044)(0.057)(0.069)0.454***

0.573***

0.339***(0.019)(0.030)(0.025)0.327***

0.458***

0.201*(0.078)(0.113)(0.107)0.488***

0.554***

0.401***(0.020)(0.030)(0.028)0.469***

0.558***

0.354***(0.024)(0.038)(0.030)-1.166***-1.247***-1.041***(0.008)(0.012)(0.011)0.004***

0.006***

0.004***(0.0002)(0.0003)(0.0002)-0.265***-0.305***-0.209***(0.001)(0.002)(0.002)

6.559***

5.128***

6.264***(0.230)(0.212)(0.231)Country control?

Yes

Yes

Yes

Sector control?

Yes

Yes

Yes

Year?

Yes

Yes

Yes

882 249462 057

420 192

0.258

0.235

0.258

0.258

0.235

0.258

Patents only
Trade marks only
Designs only
Patents and trade marks
Patents and designs
Trade marks and designs
Patents, trade marks and designs
SME
Age
Employment (log)Constant

Observations
R

2

Adjusted R

2

Note: Standard errors in parantheses. * denotes statistical significance at 10%, ** at 5% and *** at 1% level.

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Annex / Supplementary econometric analysis 65

Figure A6:
Differences in the various types of IPR ownership premiums byinnovation rank of home country
Patents only
Full sample

56%

31%

***

***

***

51%

71%

37%

***

***

***

32%

39%

23%

***

***

***

Patents and trade marks
Full sample

Patents and designs

Trade marks and designs

58%

39%

63%

***

***

***

Modest/moderate
Strong/leaderDesigns only

43%

Modest/moderate
Strong/leaderTrade marks only

77%

58%

74%

***

***

***

40%

22%

***

49%

***

Patents, trade marks and designs
60%

Full sample

***
75%

Modest/moderate
Strong/leader

***
42%

***

As was the case in the basic model, companies based in modest/moderate innovator countries
generate higher IPR ownership premiums than firms based in strong/leader innovator countries.
This is the case for all individual IP rights and all combinations of IP rights.
One possible reason for this result could be that innovative firms (as evidenced by their ownershipof IPR) in countries where most firms are not particularly innovative enjoy a stronger competitive
advantage and thus a higher IPR ownership premium than firms based in countries where theircompetitors are also innovative. The relationship between the degree to which a country is aninnovator and the performance of individual firms based in that country is an interesting subject for
further research.

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66 Annex
Imprint / Supplementary econometric analysis

Table A5:
Results of the tests of a single linear combination of parameters
for full sample model shown in Table 12
1
1. Patents only

2

3

4

5

6

7

0.001

0.096

0.002

0.735

0.000

0.001

0.000

0.512

0.142

0.025

0.254

0.000

0.517

0.000

0.000

0.110

0.209

0.614

0.044

0.079

2. Trade marks only

0.001

3. Designs only

0.096

0.000

4. Patents and trade marks

0.002

0.512

0.000

5. Patents and designs

0.735

0.142

0.517

0.110

6. Trade marks and designs

0.000

0.025

0.000

0.209

0.044

7. Patents, trade marks and designs

0.001

0.254

0.000

0.614

0.079

0.523
0.523

Note: The table presents p values of the tests of the hypotheses regarding equality of the IPR coefficients: H0 : ᵝ1= ᵝ2 . P values
below 0.05 (shaded in grey) indicate that the difference between coefficients is statistically significant at the 5% significance level.

Table A6:
Results of the tests of a single linear combination of parameters
for SMEs sample model shown in Table 12
1
1. Patents only

2

3

4

5

6

7

0.001

0.176

0.000

0.777

0.000

0.000

0.000

0.055

0.444

0.000

0.000

0.000

0.328

0.000

0.000

0.219

0.165

0.008

0.088

0.022

2. Trade marks only

0.001

3. Designs only

0.176

0.000

4. Patents and trade marks

0.000

0.055

0.000

5. Patents and designs

0.777

0.444

0.328

0.219

6. Trade marks and designs

0.000

0.000

0.000

0.165

0.088

7. Patents, trade marks and designs

0.000

0.000

0.000

0.008

0.022

0.130
0.130

Note: The table presents p values of the tests of the hypotheses regarding equality of the IPR coefficients: H0 : ᵝ1= ᵝ2 . P values
below 0.05 (shaded in grey) indicate that the difference between coefficients is statistically significant at the 5% significance level.

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Annex / Supplementary econometric
Foreword
analysis 67

Table A7:
Results of the tests of a single linear combination of parameters
for a large firm sample model shown in Table 12
1
1. Patents only

2

3

4

5

6

7

0.616

0.210

0.178

0.594

0.747

0.106

0.259

0.002

0.712

0.208

0.001

0.040

0.687

0.133

0.027

0.262

0.207

0.668

0.489

0.211

2. Trade marks only

0.616

3. Designs only

0.210

0.259

4. Patents and trade marks

0.178

0.002

0.040

5. Patents and designs

0.594

0.712

0.687

0.262

6. Trade marks and designs

0.747

0.208

0.133

0.207

0.489

7. Patents, trade marks and designs

0.106

0.001

0.027

0.668

0.211

0.109
0.109

Note: The table presents p values of the tests of the hypotheses regarding equality of the IPR coefficients: H0 : ᵝ1= ᵝ2 . P values
below 0.05 (shaded in grey) indicate that the difference between coefficients is statistically significant at the 5% significance level.

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68 References
Imprint

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Published and edited by the EPO and the EUIPO
Munich, Germany, and Alicante, Spain© EPO and EUIPO, 2021
www.epo.org
www.euipo.europa.eu

The full report can be downloaded at:www.epo.org/ipr-performancewww.euipo.europa.eu/ipr-performance

ISBN: 978-3-89605-263-6