https://orcid.org/0000-0003-2570-0217
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ABSTRACT
Although trademarks are the most widely used form of Intellectual Property Rights (IPRs) by firms across all economic sectors worldwide, this indicator is a much less exploited information resource in empirical analysis compared with patents. Our work addresses this gap by investigating the relationship between trademark registration and entrepreneurial activity using data for 33 European countries. Our empirical results show a positive and significant relationship between the share of the self-employed workforce in a given country that can be considered ‘entrepreneurial’ – which we associate with the share of Kirznerian entrepreneurs – and trademark registration at the country level. These results have important implications for scholars, practitioners and policymakers, which are discussed in this work.
Acknowledgments
The authors would like to thank the guest editor, Jörn Block, and two anonymous reviewers for their insightful comments that contributed substantially to the development of this paper. This paper is part of Serhiy Lyalkov's doctoral dissertation, which has been written under the framework of the PhD Program in Economics, Business, Finance and Computer Science at the University of Huelva and the International University of Andalusia, Spain.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The observed differences in favour of trademark registration figures can be explained (at least) by (i) their comparatively lower cost and (ii) the absence of novelty requirement for trademark registration.
2 Some notable exceptions are presented in subsection 2.1 below.
3 There is also a growing body of empirical literature exploring the different impacts of entrepreneurship on regional economic growth depending on whether regional entrepreneurship is based on technological innovations (Schumpeterian type) or opportunity discoveries (Kirznerian type) (see e.g. Sundqvist et al. Citation2012; Aparicio, Urbano, and Audretsch Citation2016; Ferreira et al. Citation2017 as recent examples).
4 Most entrepreneurs don’t employ personnel, are home-based, and earn low incomes (Shane Citation2009).
5 Linder’s (Citation1961) hypothesis suggests the quality of products as being the main determining factor of the closeness of exporter supply structures and importer preferences, which in turn explains why richer countries trade more among themselves than with poorer countries.
6 Brand equity investment series data were obtained from studies that have followed the Corrado-Hulten-Sichel (CHS) methodology (Corrado, Hulten, and Sichel Citation2005).
7 See Dahlqvist and Wiklund (Citation2012) for an interesting discussion of this issue.
8 Srinivasan, Lilien, and Rangaswamy (Citation2008) find patents and trademarks to be complementary assets in the high-tech industry. Llenera and Millot (Citation2013) find evidence of this complementarity in sectors such as pharmaceutical or chemical products.
9 GEM data suffers from severe drawbacks, such as the limited numbers of covariates or the impossibility of comparing its figures with data from official international statistics such as Eurostat or the OECD. Fortunately, other cross-country datasets for the European area also allow to obtain accurate proxies for opportunity and necessity entrepreneurs. We refer here to the European Community Household Panel (ECHP), the European Union Statistics on Income and Living Conditions (EU-SILC) or the European Working Conditions Survey (EWCS), the last being the one we use in the present study.
10 Detailed definitions of all our country-level and individual variables are presented, respectively, in and in the Appendix.
11 This set includes the EU-28 together, five candidate countries (Albania, the Former Yugoslav Republic of Macedonia, Montenegro, Serbia and Turkey) and two EFTA countries (Norway and Switzerland).
12 Albania and Montenegro are excluded during this process.
13 The EWCS did not collect information for Serbia and Switzerland in 2010.
14 GERD has been widely used within entrepreneurship literature as a measure of technological commitment in a particular economy (see e.g. Van Stel, J.M. Millán, and Román Citation2014).
15 PPS is the technical term used by Eurostat for the common (artificial) currency in which national accounts aggregates are expressed when adjusted for price-level differences using PPPs. Thus, PPPs can be interpreted as the exchange rate of the PPS against the €.
16 Eurostat aggregations based on ‘Statistics on high-tech industry and knowledge-intensive services’ (sometimes referred to as simply ‘high-tech statistics’) can be found at https://ec.europa.eu/eurostat/cache/metadata/Annexes/htec_esms_an3.pdf.
17 The reference categories for occupational status and start-up motivation are, respectively, dependent self-employed workers and necessity entrepreneurs. Therefore, each unitary increase in the share of self-employed with employees and opportunity entrepreneurs lead, respectively, to a unitary decrease in the share of dependent self-employed workers and necessity entrepreneurs.
18 This more restrictive definition excludes those industries classified by ‘Eurostat high-tech statistics’ as only medium-high (and not strictly high) technology manufacturing industries. We refer here to the following NACE rev. 2 codes: 20 = Manufacture of chemicals and chemical products; 27 = Manufacture of electrical equipment; 28 = Manufacture of machinery and equipment not elsewhere classified; 29 = Manufacture of motor vehicles, trailers and semi-trailers; and 30 = Manufacture of other transport equipment.
19 The EU-28, two candidate countries (the Former Yugoslav Republic of Macedonia and Turkey) and three EFTA countries (Iceland, Norway and Switzerland).