INNOVATION ACTIVITIES IN GREECE: A STATISTICAL AND EMPIRICAL APPROACH

Transcript

1 Ελληνικό Στατιστικό Ινστιτούτο Πρακτικά 18 ου Πανελληνίου Συνεδρίου Στατιστικής (2005) σελ INNOVATION ACTIVITIES IN GREECE: A STATISTICAL AND EMPIRICAL APPROACH Christos Kitsos, George M. Korres, and Yannis C. Hatzikian TEI of Athens University of Aegean TEI of Athens xkitsos@teiath.gr gkorres@hol.gr hatzikia@otenet.gr ABSTRACT Much of the recent work on economic growth can be viewed as refining the basic economic insights of classical economists. The statistical analysis is therefore very important. Nowadays there are well-organized databases, and the researcher can easily decide about the sample, rather than some years ago. So the determinant statistical factors in economical applications are: design and analysis. Usually is adopted that design which minimizes the variance of the estimators under investigation. This paper attempts to examine the role of entrepreneurship, and those of innovation activities and the effects of output growth, using both a theoretical and empirical approach in a Greek case study. We are performing a survey to the Greek through ARCHIMEDES program. We would like to thank the Ministry of Education for their support. The research project entitled: «Women and Innovation: Determinant factors and obstacles of innovative activity of Greek firms: » was co-funded by 75 from the European Union and 25 from the Greek Government under the framework of the «Education and Initial Vocational Training Program-Archimedes». 1. INTRODUCTION The growing importance of technological change in world production and employment is one of the characteristics of the last four decades. Technological change is not only a determinant of growth, but also affects the international competition and the modernisation of a country. This paper attempts to analyse the framework of technological policy, the role and implications of national system of innovations and particular on regional growth and sustainable development. The innovation index can be considered as the parameter of proportion to a bionomial distribution needed to be estimated. Therefore various statistical approaches can be adopted. The sequential approach is considered in this paper: as the data set is augmented the innovation is reestimated sequentially from a statistical point of view. 2. THE TECHNOLOGICAL FRAMEWORK The analysis of system of innovations helps us to understand and to explain, why the development of technology is necessary in a certain direction and at a certain rate. We should be very careful in the definition of the «national systems» according to which

2 sub-systems should be included and which process should be studied in the different countries. In setting the different priorities we should take into account the different conditions of each country and each regions, the different elements and objectives of other sectors. We should try to establish some close linkages among different priorities and the policy's objectives from other sectors. Technological policy only recently has been distinguished from the science policy. The «science policy» is concerned with education and the stock of knowledge. «Technology policy» is concerned with the adoption and use of techniques, innovation, and diffusion of techniques. Usually, the technological policy should aim to create a favourable «psychological climate» for the development of research and innovations. Figure 1 illustrates the network of regional innovation systems, with the inter-flows between human resources, transfer organisations and companies. Figure 1: Regional Innovation Network-System Firms: R&D Departments Innovation Financing: Banks, Technology Transfer Organisations: Parks, Networks, Chambers Universities and Public R&D Human-Resources, Technology Inf. Systems, Patens, Data-Bases. 3. EMPIRICAL MODEL: TEST OF THREE HYPOTHESES AND THE RESULTS A general function is by definition a relationship between output and inputs. For a single country, say ith, the production function may be written as: Y it = f i (X i1t,x i2t,...,x imt,t ), where Y it is the quantity of output produced per producer unit and X ijt is the quantity of the jth input employed per producer unit (j=1,2,...m) in the ith country for the period. It is often faced the problem that a test of two alternative hypotheses it is not an informative procedure, especially when the H o is rejected. Therefore when a study of innovation indexes is performed the problem of discriminating among three hypotheses is proposed. This problem has been faced by Kitsos and Hatzikian (2005) and in Kitsos et. al. (2005). This multiple-decision procedure is suitable for the

3 innovation indexes which are faced in this paper, as the proportions from a bionomial distribution, Sobel and Wald (1949), Choi (1968). In this paper the fixed size problem the three hypotheses is discussed, while for the sequential one see Kitsos et.al (2005). Let us consider x i i= 1,2,3 n i.i.d (independent identical distributed) observations coming from a Bernoulli distribution. For the parameter of interest 0<p<1 three hypotheses are assumed: H o : p = p o H 1 : p = p 1 H 2 : p = p 2 (3.1) With 0< p o < p 1 < p 2 <1.The optimum solution of the three hypotheses problem as in (3.1) is, in principle, of the form, considering the statistic T = Σt(x i ), is to accept H o : if T A H 1 : if A T B (3.2) H 2 : if T>B The constants A, B are functions of the significance levels δ i for testing H i, i= 0,1,2. Therefore for the cumulative distribution function (cdf). F(t; p)=p[t t /p] holds: F(A; p 0 ) 1-δ ο, F(Α; p 1 ) F(B; p 1 ) 1-δ 1, F(B;p 2 ) δ 2 For the Bernoulli observations the cdf F is calculated as: t n x n x F(t;p)= p ( 1 p) x= 0 x With n being the sample size. If n is assumed large, as it happens in surveys concerning innovation indexes then T is normally distributed of the form: T=N(np, np( 1 p) ) (3.3) An approximation of p in (3.6) can be the average p values of the assumed values for the innovation index. Thus if we let Φ to be the cdf of the normal distribution N(0, 1), we can solve for n by trial and error the equation P21 s2 P10 s0 Φ=( n + tδ )+Φ ( + t 2 δ ) = 2-δ 0 1 With P ij = p i -p j, S i = pi ( 1 pi ) i=0,1,2 j=0,1 Therefore the optimal sample size n = n(δ ο, δ 1, δ 2 ) for testing a fixed sample size three hypotheses test is evaluated. Here we provide a part of the calculations we have performed with the significance level equal to 0.01.Table 1 illustrates the sample size for the non-sequential test. Table 1: Sample size for the non-sequential test H o : p = p o H 1 : p = p 1 H 2 : p = p 2 P o P 1 P 2 n

4 We have derived the main data-set from the research project entitled «Women and Innovation: Determinant factors and obstacles of innovative activity of Greek firms: » that was co-funded by 75 from the European Union and by 25 from the Greek Government under the framework of the «Education and Initial Vocational Training Program-Archimedes». In order to collect some primary data, we have applied a questionnaire to several sectors in Greek. In particular, the dataset was collected from 129 Greek from various sectors and several areasprefectures of the country. Using our findings from collected data, we can notice that the main part for research and development activities usually related with big and also directed towards to obtain the machinery and equipments accounting around to 58. Whereas, education is the main source for innovation activities in Greek accounts around to 48, and the rest of main sources are following from introduction of innovation in the market accounts around to 27 and the planning for productiondistribution and other activities accounts around 25, respectively. Indexes Table 2: Innovation in manufacturing : Greece * * ** innovative Enterprises with 26, , ,3 100 innovation activity Product innovators 22,5 85,1 25,2 83,3 18,4 67,3 Process innovators 18,5 70,2 23,7 78,1 17,5 64,1 Intramural R&D 20,6 77,9 21,2 69,8 21,8 79,8 Research and 15,8 59,7 18,9 62,3 17,3 64,7 experimental development - R&D Continuous R&D 5,1 19,3 7,1 23,3 7,1 26,1 Occasional R&D 10,7 40,3 11,8 39,1 na na Enterprises with Cooperation arrangements on innovation activities 4,7 17,7 6,5 21,4 5,1 19,

5 Product innovators that introduced new or improved products to the market Enterprises receiving public funding 10,4 39,2 14,0 46,0 10,3 37,8 11,4 43,1 10,9 35,8 17,0*** 16,4 Sources: GSRT (2001), European Commission, (2004). Note:*>20 employees;**>10 employees;***central government Table 3: Innovation in service with 10 or more employees: Greece Indexes innovative Enterprises with 11, , ,9 100 innovation activity Intramural R&D 4,2 37,5 6,2 40,0 6,6 20,3 o experimental - R&D 8,3 75,0 13,0 84,0 16,1 72,0 Continuous R&D 5,6 50,0 5,6 36,0 10,5 32,0 Occasional R&D 2,8 25,0 7,5 48,0 na na Enterprises with Cooperation arrangements on innovation activities Enterprises receiving public funding 6,3 56,3 5,6 36,0 12,8 39,1 2,1 18,8 3,1 20,0 15,5* 15,1 Sources : GSRT (2001), European Commission, (2004). Note : *Central Government In addition we have derived some relevant information and measures regarding the implications of innovation activities, in relation to both of the products and process. According to these results, the main implications for both small and medium firms for the products is related to increase the range of goods and services, and less to improve the quality of the products of goods and services. Regarding the financing of innovation activities, only a small part of Greek firms has received financial support either from local-regional authorities or from central government and European Union. In particular, a part accounting 18 of Greek firms have received financial support for innovation activities through European Union, while the other sources of financial support, namely the central government and the local-regional authorities, account around to 6 and 3, respectively. Furthermore, we can illustrate some valuable information regarding the main obstacles for the development of innovation activities. According to our data, the main obstacles in relation to «economic factors» are the lack of appropriate financial sources for innovation activities that accounts 70 for small firms and only 10 for big firms, while the high risk activities accounts around to 74 for small firms and only 5 for big firms, respectively. Finally, the main obstacles in relation to the «internal factors» are the lack of information of new technologies and for the markets accounting 75 for small firms and 25 for medium firms, while the lack of specialized staff accounts 60 for small firms and 40 for medium firms, and the

6 managerial inflexibilities that accounts 40 for the small firms and 40 for the medium firms, respectively. Our empirical results are compatible and we can compare with those findings from GSRT, General Secretary for Research. Table 2 illustrates the results for innovation activities in manufacturing sector in Greece, while Table 3 illustrates the results for innovation activities in service sector in Greece. 4. POLICY IMPLICATIONS AND CONCLUSIONS This article attempts to identify the R&D activities and also to investigate the estimation-methods, the techniques of scientific and technological activities and the measurement problems at a regional level. The term of «Research and Development Statistics» covers a wide range of statistical series measuring the resources devoted to R&D stages, R&D activities and R&D results. It is important for science policy advisors to know who finances R&D and who performs it. Small countries, like Greece, is likely to need a more comprehensive and oriented policy of co-operative innovative effort, in order to develop their future capabilities and to make the necessary choice for technological priorities. The various most important factors which might influence the incidence of innovation and the speed of its diffusion are the following: (a) the technical applicability; (b) profitability; (c) finance, (lack of financial resources might delay the diffusion of new processes); (d) size, structure and organisation, (large companies may for a number of economic and technological reasons which behave differently from the SMEs; (e) management attitudes, (which is the most difficult to assess or to quantify, but nevertheless they may be as important as economic factors in influencing the rate of adoption of new methods); (f) other factors, such as research and development activities, access to information, the labour market availability of certain skills, licensing policy, the market situation and more precisely the growth of demand for the product as well as the competitive position with special regard to the import competition. All these illustrate the wide range of factors which could contribute to explain the differences in the speed of diffusion. ΠΕΡΙΛΗΨΗ Οι περισσότερες εργασίες στην οικονομική μεγέθυνση μπορούν να θεωρηθούν ότι εξετάζουν τους βασικούς εσωτερικούς παράγοντες και τα χαρακτηριστικά εκείνα που αφορούν τους κλασσικούς οικονομολόγους. Επομένως, η στατιστική ανάλυση είναι ιδιαίτερα σημαντική. Αναμφισβήτητα υπάρχουν αρκετές οργανωμένες βάσεις δεδομένων, και οι ερευνητές, περισσότερο απ ότι πριν μερικά χρόνια, μπορούν εύκολα να αποφασίσουν σχετικά με το δείγμα. Συνήθως, χρησιμοποιούνται εκείνες οι μέθοδοι που στοχεύουν να ελαχιστοποιήσουν την διακύμανση των εκτιμητών που είναι υπό διερεύνηση. Το άρθρο αυτό στοχεύει να εξετάσει τον ρόλο της επιχειρηματικότητας, και των καινοτομικών δραστηριοτήτων και των αποτελεσμάτων στην αύξηση του προϊόντος, χρησιμοποιώντας από κοινού τόσο την θεωρητική όσο και την εμπειρική

7 προσέγγιση, σε μια μελέτη περίπτωση για την Ελλάδα. Έχουμε αναπτύξει ένα ερωτηματολόγιο για τις Ελληνικές επιχειρήσεις διαμέσου του προγράμματος «Αρχιμήδης». Το ερευνητικό πρόγραμμα έχει τίτλο «Γυναίκα και Καινοτομικότητα: Οι προσδιοριστικοί παράγοντες και τα εμπόδια των καινοτομικών δραστηριοτήτων στις Ελληνικές επιχειρήσεις: » που συγχρηματοδοτήθηκαν 75 από την Ευρωπαϊκή Ένωση κατά 25 από την Ελληνική Kυβέρνηση κάτω από το πλαίσιο «Εκπαίδευση και Αρχική Επαγγελματική Εκπαίδευση του Προγράμματος Αρχιμήδης». Το συγκεκριμένο άρθρο ξεκινάει από την παραδοχή ότι υπάρχουν δύο βασικά υποδείγματα καινοτομιών: το πρώτο σχετίζεται με το υπόδειγμα της δημιουργικής καινοτομικότητας και το δεύτερο αφορά το συνολικό δημιουργικό υπόδειγμα καινοτομικότητας. Συνεπώς, είναι απαραίτητη μια στατιστική μελέτη που θα μας βοηθήσει να καταλήξουμε σε ορισμένα ασφαλή συμπεράσματα και πολιτικές. REFERENCES Abramovitz M., (1986). Catching-up, foreign ahead and falling behind, Journal of Economic History, vol. 46. Choi.S.C (1968). Truncated sequential designs for clinical trials based on Markov Chains. Biometrics, 24, Commission of the European Communities, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, World Bank (1993). System of National Accounts -SNA 1993, Brussels/Luxembourg, New York, Paris, Washington D.C. Dosi G., Pavitt, K. and Soete L. (1990).The economics of technical change and international trade, Harvester publishers, Wheatsheaf, London. Eurostat, and OECD (1995). Manual on the Measurement of Human Resources Devoted to S&T, Canberra Manual, Paris European Commission., (2004), Innovation Results for the EU, Iceland and Norway, Data (CIS III). Luxembourg: Office for Official Publications of the European Communities. GSRT - General Secretariat for Research and Technology, (2001), National Innovation Survey (CIS II & III) in Greek Enterprises Athens : GSRT - General Secretariat for Research and Technology - Ministry of Development, Kitsos C. P., Hatzikian J. (2005). Sequential techniques for innovation indexes, 16 th ISPIM Annual Conference, Porto, Portugal, June Kitsos C. P. et.al (2005). Women and Innovation: Determinant factors and obstacles of innovative activity of Greek firms: : ARCHIMEDES research project, Athens, Greece Korres G. (1995). Greek technological growth and the prospects for modernisation, working paper presented on an international congress at London School of Economics. Lundvall B. (1997). National Systems of innovations: towards a theory of innovation and interactive learning Harvester publishers, Wheatsheaf, London