The Research And Development Performance Of Various EU Social Regimes

Puskarova, Paula; Gurnikova, Jana Article The Research And Development Performance Of Various EU Social Regimes Comparative Economic Research. Central and Eastern Europe Provided in Cooperation with: Institute of Economics, University of Łódź Suggested Citation: Puskarova, Paula; Gurnikova, Jana (2013) : The Research And Development Performance Of Various EU Social Regimes, Comparative Economic Research. Central and Eastern Europe, ISSN 2082-6737, Łodz University Press, Łodz, Vol. 16, Iss. 4, pp. 23-38, https://doi.org/10.2478/cer-2013-0026 This Version is available at: https://hdl.handle.net/10419/259161 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0 10.2478/cer-2013-0026 PAULA PUŠKÁROVÁ*, JANA GURNÍKOVÁ** The Research And Development Performance Of Various EU Social Regimes1 Abstract The paper provides a new approach to the classification of EU countries into innovation-performance groups, taking into account their social regime. In the Introduction, it draws on some empirical evidence of synchronised research and development (R&D) performance within a social regime. In the second and third parts it reviews the literature on measuring R&D performance, and in the fourth part it summarizes social regime classifications. The fifth and longest part of the paper proceeds to a comparative analysis of the empirical data, pointing out disparities, both respects to numbers and members, in the composition of innovation-performance groups. In the final part, the paper summarizes key findings. Keywords: R&D expenditures, patent application, total factor productivity, social regime *Full Researcher at the Institute for Economics and Management, University of Economics in Bratislava ** University of Economics in Bratislava, Faculty of National Economy 1 This article has been supported by the VEGA project No 1/0340/11, entitled Bulding of a knowledge economy in the Slovak Republic in the Post-Lisbon period (Formovanie znalostnej ekonomiky v Slovenskej republike v post-lisabonskom období). 24 Paula Puškárová, Jana Gurníková 1. Introduction The current state of literature demonstrates, based on a reasonably sizable amount of evidence, that EU countries may be divided into innovation leaders and innovation laggards. However, we have not so far witnessed any attempts to distinguish EU social regimes by research and development (R&D) performance, although there is empirical evidence that social regimes and innovation performance do interact (Puškárová 2012). Innovation is of an intangible nature, which makes its quantification particularly tricky. The scientific literature agrees on three basic approaches: 1. Input, 2. Output, 3. Indirect effects. The input and output methods draw on a model of the innovation process, while the indirect method is implied by the Cobb-Douglas production function. In addition, the literature exposes (although maybe not explicitly) parameters derived from input and output ones, e.g. elasticity of R&D expenditures, productivity of R&D workforce, profitability of R&D inputs, etc. We decided to draw on this current state of the literature and summarize key findings, using comparative analysis of selected parameters. Based on the data available, we decided to anchor our analysis in the data derived mainly from EUROSTAT, as explained further on in the paper. 2. Parameterization of innovation through inputs/output The innovation process can be visualized by the following Figure 1. Figure1. Innovation process Source: authors‘ own visualisation of EUROSTAT methodology. PATENT • registration application The Research And Development Performance... 25 The model demonstrates that the two standard production inputs – labour and capital - are processed in order to deliver the desired outcome, i.e. a patent application leading hopefully to a patent. The labour input in the innovation process is required to demonstrate, however, a certain additional quality compared to the usual labour input. The nature of the innovation process implies that the labour must be both highly skilled and highly creative, i.e. that it possesses a certain level of human capital which can be transformed into a tangible product known as intellectual capital. The creativity of labour input and its expertise in utilizing this creativity determine the quality and quantity of the innovation process outcome. In the common scientific consensus, the innovation process results in a patent or a patent application. However, we need to bear in mind that a lot of creative work remains unprotected by a patent, and thus the outcome of creative labour and capital inputs cannot be measured by the limited terms of a patent alone. Pros and cons of parameterization through input/output One of the most often articulated defects of measuring innovation through R&D expenditures (capital input into the innovation process) is that it ignores the stochastic nature of the innovation process (Keller 2010, p. 804). Pertinent results of the R&D expenditures come mostly with some time delay, and thus often do not appear in the same year that the R&D expenditures were incurred. In other words, the results of R&D expenditures from a certain time unit (year) may emerge in the same or in several consecutive years. Some researchers reduce the significance of this flaw in the analysis by applying only business R&D expenditures. It is arguable that returns on business R&D is empirically higher than public (and thus more or less also total) R&D. In our opinion however, this approach does not eliminate the impact of this defect completely (many business projects are split into several years), and further, it undermines the multiplication effect of public R&D, which accounts for the major R&D source in the “new” member countries. The output method of innovation parameterization concentrates on innovation output, most commonly measured by patent volumes, i.e. the number of patent applications. Various patent offices maintain their own records on patent data, as well as providing information to the other statistical offices. The advantage of patent data analysis is simple – a patent is an indisputable form of concrete evidence that the R&D is about to generate profits in the form of licene fees, fees for patent usage, etc. The output parameterization of innovation has, nonetheless, its flaws too. Many patents are just upgrades of already existing patents, i.e. their value should 26 Paula Puškárová, Jana Gurníková be partially attributed to former R&D (e.g. Jaffe and Trajtenberg 2002). Besides, the decision whether to apply for a patent relates to the particular company, and the literature exhibits a fair amount of evidence that a lot of innovation is not protected by an official patent, even though it is in use (e.g. Griliches 1990). One important practice can be observed in the current state of literature: in analysing R&D performance, the number of patent applications is preferred to the number of registered patents. The reason, in our assumption, lays in time delays attributable to the administration of a patent application – granting a patent may last up till several years and thus, the time coherence between R&D and its results deteriorates more than when a patent application is used for measurement of an output. 3. Parameterization of innovation through TFP The most common technology parameterization is based on the thesis of its effect – i.e. increased productivity of production inputs. This effect is, in the literature, made equal to the parameter A = TFP (total factor productivity) from the Cobb-Douglas production function. Equation 1 below gives the calculation of TFP: (1) Contrary to the input and output parameters of technology, TFP is a derived indicator, i.e. its value is calculated from the primary data on production inputs and output. Pros and cons of parameterization through TFP The advantage of the idea of the Solow residual (how TFP is often addressed) is as follows: as a residual of the inputs productivity, it embodies all the innovation (even unregistered patents) that is effectively used and contributes directly to growth. Thus, it is the truest measure of innovation’s effect on growth. However, there has been a rather fierce discussion in the literature throughout the years regarding both the calculation of TFP nominal values, and its real impact on the growth. The calculation of the TFP is rather ambiguous, due to the risk of measurement error and the risk of selected variable bias, whereby manipulation of primary data exerts a significant effect on the calculated TFP data, and as The Research And Development Performance... 27 a result some part of TFP value can be neglected (Katayama et al 2009). Due to these difficulties, the literature also provides certain solutions. One of them suggests working with TFP growth rates instead of TFP nominal values. This approach is recommended especially by ceteris paribus. Another resolution might be derived from the application of nominal TFP values together with input parameters of technology, mostly R&D expenditures (Griliches 1984, Keller 2010). However, the application of TFP growth terms may also not show the impact of innovation on GDP growth so purely. The concern is that when extracting human capital (parameterized by the education received) from TFP and considering it as a part of labour input (which is also commonly viewed as the most characteristic feature of the R&D workforce), the importance of TFP growth for economic growth drops rapidly (Manuelli and Seshadri 2010). In the light of all the discussions over TFP mentioned above, and inasmuch as TFP is also a matter of technology spillovers (Eaton and Kortum 1999), we decided not to use TFP as the suitable indicator for analysis of a social regime’s R&D performance. 4. Determination of social regimes Although some empirical evidence shows that R&D performance and social regimes do interact (the EU’s most innovative countries maintain the most generous social protection nets), the scientific literature seems reluctant to analyse this nexus. This phenomenon challenges the traditional theoretical assumption that individualism (and not solidarity) motivates a nation to innovate (this assumption is the key notion of liberalism). In our analysis, we decided to distinguish EU countries by social regimes according to the analysis established by Esping-Andersen (1990, pp. 28-54), upgraded by Bohle and Greskovits (2007) and Ferrera (1996) and Bonoli (1997), as follows: a) liberal (UK, Ireland) b) social-democratic (Scandinavian countries – Denmark, Finland, Sweden) c) conservative (Germany, France, Austria, Belgium, Netherlands, Italy) d) southern (Italy, Spain, Malta, Portugal, Cyprus, Greece) e) neoliberal (Estonia, Latvia, Lithuania) f) embedded neoliberal (V4 countries) g) neocorporativist (Slovenia). Esping-Andersen defined the three models (a-c) based on decommodification indices and the implicit level of stratification, whereby the social-democratic 28 Paula Puškárová, Jana Gurníková regime demonstrates the strongest redistribution of income throughout the social groups, and the liberal regime demonstrates the lowest level of solidarity with the low-income and handicapped groups. Despite numerous critiques of Esping-Andersen’s categorization, based primarily on the limited number of countries in question, the over-estimation of the impact of social security benefits, and the neglect of gender implications, this classification is nonetheless widely preferred due to its easy and wide applicability. Ferrera and Bonoli argued that the southern EU states cannot be defined by either of Esping-Andersen’s social regimes because they are characterized by: • high fragmentation and polarization of society, where pension system is generous but nonetheless many groups are excluded from the social protection; • deviation from traditional corporate provision of health insurance; • high degree of collision between the systems of private and public social institutions; • unresolved nepotism in the distribution of social benefits. Bohle and Greskovits further argue that the “new EU countries“ cannot be classified by Esping-Andersen’s social regimes since they still lack the tradition (i.e. experience) in terms of public social institutions, a market economy, social inclusiveness and macroeconomic stability. 5. Profiling EU social regimes based on selected innovation parameters In this part, we proceed to visualisation of various innovation parameters on the level of EU social regimes, and in doing so detect disparities. 5.1. Performance of EU social regimes in terms of their average R&D expenditures In terms of real average R&D expenditures, we register continuous growth in all social regimes. The most successful are the Scandinavian countries, which stand out against other social regimes (Figure 2). By considering national deviations from the social regime average level, we can detect a few significant countries, although not among the countries and regimes with the highest R&D spending (Table 1). The Research And Development Performance... 29 Figure 2. Average R&D expenditures in EU social regimes, constant 2000 prices, PPS, p.c. 0 200 400 600 800 1000 1200 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 liberal conservative social-democratic Slovenia Italy southern neoliberal embedded neoliberal Denmark Source: author’s own elaboration, calculation based on total social protection expenditures derived from EUROSTAT 2013 databases. In the group of the six EU-founders, we can identify two development trajectories: Germany’s strong convergence towards the Scandinavian countries in terms of R&D expenditures levels, and the trajectory of French-speaking countries – BENELUX 2 and France, which rank among the EU’s average R&D spending countries. The lowest R&D spending country from this group is indisputably Italy, which converges in absolute R&D expenditures terms towards their more culture-similar countries of southern social regimes. Throughout the observed period, Austria has had the role of most R&D accumulating country among all the EU countries, and not only in terms of R&D expenditures in PPS and in % GDP. Since 2002, it exceeds, in terms of inflationfree PPS, even its strong strategic partner – Germany. In terms of R&D expenditures as a share of GDP share, it is however still catching up to the level of Germany. 2 Please note that in terms of R&D expenditures as a GDP share, Luxembourg does not demonstrate even the EU average level. 30 Paula Puškárová, Jana Gurníková The liberal social regime shows a strong convergence between the R&D expenditures of Ireland and Great Britain. The southern regime is also characterized by convergence. The belowaverage levels of R&D expenditures in this category put the countries of the southern regime into the position of innovation laggards, although still spending more on R&D than the countries of neoliberal or embedded neoliberal regimes. However, it is important to pay attention to Portugal, which managed to double its R&D spending between 2005 and 2009 from 0.75 % to 1.5 % GDP. The “new“ EU member states (i.e., those acceding to the EU in 2004 and 2007) are the lowest R&D spending group. The only exception is Slovenia, which has accelerated its R&D in 2009, despite its low starting R&D volume in 1995, up to the level of the liberal economies. In this group, the highest R&D spending countries are the Czech Republic, Estonia and Hungary. Slovakia is the only country from this group that has experienced severe cuts in R&D expenditures, which were only partially compensated for by the increase in R&D expenditures in the last two years of the observed period. Table 1 clearly shows that among the highest R&D spending social regimes a strong convergence exists, while among the lowest R&D spending social regimes a strong divergence takes place throughout the observed period. This may imply a more general thesis that in the earlier stages of economic development, the R&D spending policies vary by country. However, more developed countries attain the R&D spending limits, and other countries do catch up. Further, we assume that this R&D spending limit is determined by its culturally-shaped (biased) social regime. Table 1. Convergence within EU social regimes, measured by standard deviation of national R&D expenditures SOCDEM CONS - German speaking CONS - French speaking LIB SOUTH NEOLIB EMB. NEOLIB 1995 153.29 191.48 50.31 116.39 41.77 20.18 30.49 1996 152.52 189.38 39.32 91.50 45.04 17.02 34.92 1997 151.21 181.34 27.90 77.36 45.54 13.51 42.58 1998 154.06 175.65 17.92 77.50 53.58 9.90 42.78 1999 152.82 160.22 22.02 88.95 55.85 14.81 43.66 2000 196.77 151.81 13.57 92.42 62.37 10.53 50.08 2001 209.00 142.81 6.73 92.35 64.60 17.47 52.57 2002 182.81 143.27 13.11 87.68 68.55 19.36 55.11 The Research And Development Performance... 37 general thesis that in the earlier stages of economic development, R&D spending policies vary by country. In other words, more developed countries manage to attain the R&D spending limits and other countries have to catch up. This paper can also shed some light on the thesis that R&D spending levels and limits are determined by the relevant culturally-shaped social regime. References Bohle D., Greskovits B. (2007), Neoliberalism, embedded neoliberalism and neocorporatism: Towards transnational capitalism in central-eastern Europe,‘West European Politics’, Routledge, Vol. 30, No. 3 Bonoli G. (1997), Classifying Welfare States: a two-dimension approach, ‘Journal of Social Policy’, Cambridge University Press, Vol. 26, No. 3 Eaton J., Kortum S. (1999), International technology diffusion: Theory and measurement., ‘International Economic Review’, Wiley Blackwell, Vol. 40 Esping-Andersen G. (1990), The Three Worlds of Welfare Capitalism, Princeton University Press, New Jersey Eurostat(2013), EUROSTAT Database Available at: <http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database> Ferrera M. (1996), The southern model of welfare in social Europe, ‘Journal of European Social Policy’, Sage Publications, Vol. 6, No. 1 Griliches Z. (1984), Introduction to R&D, Patents and Productivity, University of Chicago Press, Chicago Griliches Z. (1990), Patent Statistics as Economic Indicators: A Survey, ‘Journal of Economic Literature’,American Economic Association Publishing, Vol. 28 Jaffe A., Trajtenberg M. (2002), Patents, citations and innovations: A window on the knowledge economy, MIT Press, Cambridge (MA) Katayama H., Lu S., Tybout J. R. (2009), Firm-level productivity studies: Illusions and a solution, ‘International Journal of Industrial Organization’, Elsevier, Vol. 27, No. 3 Keller W. (2010), International trade, foreign direct investment, and technology spillovers, [in:] Hall B., Rosenberg N. (Eds) Handbook of the Economics of Innovation – Volume 2, Elsevier, North-Holland Manuelli R. E., Seshadri A. (2010), Human Capital and the Wealth of Nations. Available at: http://www.econ.wisc.edu/~aseshadr/working_pdf/humancapital.pdf 38 Paula Puškárová, Jana Gurníková Puškárová P. (2012), Inovačná výkonnosť krajín V4, [in:] Machová M, Petianová A (eds) EDAMBA 2012: proceedings of international scientific conference for doctoral students and young researchers: 22nd November, 2012 Bratislava. Publishing House Ekonom, Bratislava Rastogi P. (2009), Management of technology and innovation: Competing through technological excellence. SAGE Publications, New Delhi Streszczenie OSIĄGNIĘCIA W OBSZARZE DZIAŁALNOŚCI BADAWCZOROZWOJOWEJ W RÓŻNYCH SYSTEMACH SPOŁECZNYCH KRAJÓW UNII EUROPEJSKIEJ W artykule przedstawiono nowy sposób klasyfikacji krajów UE z punktu widzenia stopnia ich osiągnięć innowacyjnych z uwzględnieniem systemu społecznego. Wstęp nawiązuje do wybranych danych empirycznych, dotyczących powiązania osiągnięć badawczo-rozwojowych (B&R) z typem systemu społecznego. Część druga i trzecia zawiera przegląd literatury dotyczącej pomiaru osiągnięć badawczo-rozwojowych. Część czwarta to podsumowanie klasyfikacji systemów społecznych. Piąta i najdłuższa część artykułu zawiera analizę porównawczą danych empirycznych, wskazującą różnice dotyczące zarówno danych liczbowych jak i systemów społecznych wchodzących w skład poszczególnych grup. W końcowej części pracy przedstawiono streszczenie najważniejszych wniosków.