The Impact of Innovative Activity in Economic Performance: An ...

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600 International Conference on Applied Economics – ICOAE 2008 a The cut value is ,500 Observed Classification Table(a) Predicted DE96_05 Percentage Correct ,00 1,00 Step 1 DE96_05 ,00 23 8 74,2 1,00 7 13 65,0 Overall Percentage 70,6 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1(a) Innovation 2,575 1,150 5,011 1 ,025 13,125 Size ,736 ,357 4,253 1 ,039 2,087 Ownership -,599 ,924 ,420 1 ,517 ,550 DF96_05 ,000 ,000 ,064 1 ,800 1,000 Constant -6,341 2,557 6,149 1 ,013 ,002 a Variable(s) entered on step 1: Innovation, Size, Ownership, DF96_05. In order to avoid any possible bias in the analysis by considering both IPRs and R&D activity as innovative activity for the firms, and attribute to them as one variable, we chose to proceed further to the analysis, segregating and include them in the model as separate variables. The reason was that the latter is real in-house R&D process, while the former includes the acquisitions of intellectual Property Rights or the ‘third parties production’ the so-called as «FASON». As we can see from the results apposed in the following summarised table, only R&D is really a critical factor concerning higher rates of growth, not IPRs. That is a truly interesting finding, confirming that in-house innovative process is what firms (especially smaller and independent) should focus on, in order to survive and grow. 5. Conclusions Table 10: Summarised Process’ Results Dependent: Sales Dependent: Employment Nagelkerke ,339 Nagelkerke ,283 R Square R Square Percentage 76,5 (0: 82,1 Percentage 76,5 (0: 80,6 Correct 1: 69,6) Correct 1: 70,0) DF96_05 ,083 (Exp(B):1,003) DF96_05 ,861 (Exp(B):1,000) Ownership ,331 (Exp(B):2,747) Ownership ,352 (Exp(B): ,399 Size ,476 (Exp(B): ,773) Size ,086 (Exp(B): 1,804) RandD ,033 (Exp(B): 4,577) RandD ,019 (Exp(B):5,254) IPRs ,773 (Exp(B): 1,245) IPRs ,909 (Exp(B): ,918) In the present survey, we tried to approach a very important sector of the economy, the pharmaceutical industry, which affects not only the overall economic indices, but also the quality of life. Through the proper transformation of our data set and the development of an econometric model (logistic regression), we manage to include economic and non-economic factors in the same analysis, trying to cover a broader research field. The use of such methodology, although not common in economic analysis, proved to be very useful for data sets consisting of both economic and non-economic data, with dichotomous dependent variables. Our scope was to examine the role of innovative activity in firms’ growth (in terms of sales and employment), among other factors such as investment in tangible assets, size of the firms and ownership status (subsidiary company or independent). Our findings seem to confirm that innovative activity proves to be (in pharmaceutical sector), the most important factor for firms’ better performance. Furthermore, segregating R&D and IPRs and attributing them as two different explanatory variables, we isolated the effect of active inhouse innovative process from the acquisition of foreign know-how, and find that only the former can interpret the achievement of high rates of growth. As subsidiary companies control the major part of the market-share in Greek pharmaceutical industry and achieve higher net profits’ ratio, the only factor that seems to be able to help the other firms in the sector to survive, is active innovative process. The latter should be considered thoroughly, from Greek firms which seem to present low rates in that kind of expenses. References Autio, E., and Parhankangas, A., (1998), ‘Employment Generation Potential of New, Technology-Based Firms During a Reccessionary Period: The Case of Finland’, Small Business Economics, 11:113-123.
International Conference on Applied Economics – ICOAE 2008 601 Bartjokas, A., (2001), Financial Markets and Technological Change: Patterns of Technological and Financial Decisions by Manufacturing Firms in Southern Europe, The United Nations University, Institute for New Technologies (INTECH): Discussion Paper Series, No. 2001-4, August. Chirinko, S. R., (1993), ‘Business Fixed Investment Spending: Modeling Strategies, Empirical Results, and Policy Implications’, Journal of Economic Literature, XXXI (December):1875-1911. Delapierre, M., Madeuf, B., and Savoy, A., (1998), ‘NTBFs-The French case’, Research Policy, 26:989-1003. European Commission (2007), EU Industrial R&D Investment Scoreboard (2007): http://iri.jrc.ec.europa.eu European Federation of Pharmaceutical Industries and Associations-EFPIA (2007), The Pharmaceutical Industry in Figures, www.efpia.org. Ferguson, R., and Olofsson, Ch., (2004), ‘Science Parks and the Development of NTBFs-Location, Survival and Growth’, Journal of Technology Transfer, 29:5-17. Giudici, G., and Paleari, S., (2000), ‘The Provision of Finance to Innovation: A Survey Conducted among Italian Technology - based Small Firms’, Small Business Economics, 14:37-53. Hahn, J, P., and Shaver, M, J., (2005), ‘Does International Research and Development Increase Patent Output? An Analysis Of Japanese Pharmaceutical Firms’, Strategic Management Journal, 26:121-140. ICAP (2008), Greek Financial Directory, ICAP, Greek economic database, www.icap.gr Jefferson, G., Huamao, B., Xiaojing, G., and Xiaoyun, Y., (2006), ‘R&D Performance in Chinese Industry’, Economics of Innovation and New Technology, 15(4/5) June/July:345–366. Kafouros, M., (2005), ‘R&D and Productivity Growth: Evidence from the UK’, Economics of Innovation and New Technology, 14(6) (September):479-497. Kousoulakou, X., and Fragkoylakis, B., (2003), The market of Medicine, 2003: Foundation for Economic and Industrial Research, (FEIR- IOBE), published survey. LIFT (1998), Financing Innovation, A Guide (Innovation and participation of SMEs’ programme, European Commissions Fifth Research Framework Programme), Luxembourg. Makris, I., (2007), ‘Innovative process and Firm’s Performance: A survey in traditional and high-tech sectors of Greek Economy’, Global Business and Economics Anthology, II, December:117-124. Makris, I., (2006), ‘How the Greek Banks manage risky projects’, Operational Research-An International Journal (ORIJ), Special issue “Modelling and Decision Support in Financial Markets”, 6 (August):183-195. Lowe. J.D., and Parvar, J., (2004), ‘A logistic regression approach to modeling the contractor’s decision to bid.’, Construction Management and Economics, 22:643-653. Reed, J., and Moreno, G., (1986), ‘The Role of Large Banks in Financing Innovation’. In Landau, R. and Rosenberg, N.(eds), The positive sum strategy: Harnessing technology for economic growth, Washington Academy Press: Washington:453-465 Statbank (2004), A survey for Medical and Cosmetics, Statbank: Greek economic database, www.statbank.gr, Storey, D.J., and Tether, B.S., (1998), ‘New technology-based firms in the European Union: an introduction’, Research Policy, 26:933- 946. Tucker, J., (2006), Neural Networks Versus Logistic Regression In Financial Modelling: A Methodological Comparison, Discussion Paper, http://citeseer.ist.psu.edu/. Voulgaris, F., Papadogonas, T., and Agiomirgianakis, G., (2005), ‘Job Creation and Job Destruction in Greek Manufacturing’, Review of Development Economics, 9(2):289–301.
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