Prof. Dr. Andreas Stephan

1. Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level Data from East Germany and Poland. Prof. Dr. Andreas Stephan European University Viadrina Frankfurt/Oder and German Institute for Economic Research (DIW Berlin) Anna Lejpras European University Viadrina Frankfurt/Oder and German Institute for Economic Research (DIW Berlin) 16th January 2007

2. Location Factors, Innovativeness and Firm Performance. Background of the Research Project • German Institute for Economic Research (DIW Berlin) has carried out large-scale surveys for the German Ministry of Economic Affairs on the situation and perspectives of East German firms over years 1995 to 2004. • For example, in year 2000 about 8000 firms across all branches of the economy responded, and in 2004 about 6000 firms responded (response rates approximately 20%) • Reports for the ministry were prepared and descriptive analyses of the data were performed, but no ambitious research on these unique firm panel data so far. • Approval of an application to the German Science Foundation (DFG) on a research project based on this data and conducting a survey of Polish firms.

3. Location Factors, Innovativeness and Firm Performance. Some selected results on the importance and assessment of locational factors in East Germany from the survey 2004 • Qualified labour supply: • 19.8 % of the firms said it is of some importance, • 3.3 % said it is very important, • but 76.9 % responded it is not important: • Assessment of conditions: • very good: 2.7 %, • very poor: 9.4% • Closeness to universities • 75.3 % of the firms said it is of some importance, • 2.9 % said it is very important, • Assessment of conditions: • very good: 11.3 %, • very poor: 3.3%

4. Location Factors, Innovativeness and Firm Performance. Some selected results on the importance and assessment of locational factors in East Germany from the survey 2004 • Transportation infrastructure: • 37.8 % of the firms said it is of some importance, • 3.8 % said it is very important, • but 58.7 % responded it is not important: • Assessment of conditions: • very good: 6.6 %, • very poor: 4.3% • Further investigation: what is the difference in assessment of locational conditions between non-innovative vs. innovative firms?

5. Location Factors, Innovativeness and Firm Performance. Purpose of the seminar/meeting • Presentation and discussion of research project • Presentation and discussion of the questionaire for the Polish firms • Agreement on conducting the survey in behalf of both European University Viadrina and the Polish Academy of Sciences (PAN) • Further research cooperation

6. Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level Data from East Germany and Poland. Prof. Dr. Andreas Stephan European University Viadrina Frankfurt/Oder and German Institute for Economic Research (DIW Berlin) Anna Lejpras European University Viadrina Frankfurt/Oder and German Institute for Economic Research (DIW Berlin) 16th January 2007

7. Location Factors, Innovativeness and Firm Performance. Overview Project aims Cluster approach and Porter‘s Diamond model Conceptual design of the model Model estimation for East-German firms Survey of Polish firms

8. Location Factors, Innovativeness and Firm Performance. Project Aims • Analysis of the impact of location factors on the innovativeness and firm performance in East Germany and Poland • To this end a structural equation model is developed that: • derives from Porter‘s Cluster Approach • is estimated with the Partial Least Squares method • is based on the data that was collected in 2004 by DIW Berlin (survey „Situation and Perspectives of Firms in East Germany“)

9. Location Factors, Innovativeness and Firm Performance. Cluster - Definition „Clusters are geographic concentrations of interconnected companies and institutions in a particular field.“ Examples forworld-famous clusters are: Hollywood, Wall Street, Ferragamo und Gucci or automotive manufacturers in Southern Germany

10. Location Factors, Innovativeness and Firm Performance. Porter‘s Diamond Model Chance Context for Firm Strategy and Rivalry Demand Conditions Factor (Input) Conditions Related and Supporting Industries Government

11. Location Factors, Innovativeness and Firm Performance. Structural Equation Model • The determinants of the Diamond Model as the exogenous latent variables: • Factor conditions and government impact (6 indicators) • Demand conditions (3 indicators) • Firm strategy and rivalry (4 indicators) • Related and supporting industries (7 indicators) • Two important aspects of the cluster approach as the endogenous latent variables: • Innovativeness (5 indicators) • Firm performance (5 indicators) • All latent constructs are operationalized as formative measurement models

12. Location Factors, Innovativeness and Firm Performance. Structural Equation Model Factor Conditions Related & Supporting Industries + + + + Innovativeness Performance + + + + Demand Conditions Strategy & Rivalry Assigment of manistest variables to latent variables see attachement 1

13. Location Factors, Innovativeness and Firm Performance. PLS Estimation - Notes • Division of firms into two groups – firms from highly innovative and low-innovative sectors of economy (see attachement 2) • We expect that the hypothesized relationships between the variables should be stronger in the model for the highly innovative firms than those for the low-innovative firms • The preliminary analysis: the results for the innovative and non-innovative firms are presented in attachment 3 • Only the significant relationships are pictured • The order of the listed indicators is with regards to their relationship strength with the appriopriate latent variable

14. Location Factors, Innovativeness and Firm Performance. PLS Estimation Results – Innovative Firms(Structural Model) • Five of nine hypothesized paths turn out to be significant at the 5% level, additionally the path between „Rivals/Strategy” and „Innovativeness” is only significant at the 10% level • The influence of „Demand Conditions” on „Innovativeness” is negative • The impact of „Rivals/Strategy” on „Innovativeness” and „Firm Performance” is also negative • The assumed positive impact of „Innovativeness” on „Firm Performance” is not confirmed • R2 of „Innovativeness” is on average (0.353) and R2 of „Firm Performance” is low (0.189)

15. Location Factors, Innovativeness and Firm Performance. PLS Estimation Results – Non-innovative Firms(Structural Model) • Four of nine hypothesized relationships are significant first at the 10% level • The influence of „Demand Conditions” on „Innovativeness” is negative • The impact of „Rivals/Strategy” on „Innovativeness” or „Firm Performance” is not confirmed • R2 of „Innovativeness” is extremely low (0.083) and R2 of „Firm Performance” is on average (0.306)

16. Location Factors, Innovativeness and Firm Performance. Estimation Results – Innovative vs. Non-innovative Firms(Measurement Models) • Higher intensity of cooperation activities and more significant cooperation fields in the case of the innovative firms • For innovative firms the closeness to universities and research establishments are the most important factors, for non-innovative ones the closeness to research establishments and the supra-regional transportation system are most important • All manifest variables of „Innovativeness” are significant for the innovative firms, for the non-innovative ones only the patent count • „Firm Performance” is measured by the market volume in the case of the innovative firms and by the employment growth for their non-innovative counterparts

17. Location Factors, Innovativeness and Firm Performance. Model Comparision – Innovative vs. Non-innovative Firms • As expected the relationships between the variables in the model for the firms from highly innovative sectors of economy are stronger compared to those for the less-innovative firms

18. Location Factors, Innovativeness and Firm Performance. Survey of Polish firms • The questionnaire for Polish firms is based on the survey that was sent in 2004 to East-German firms by the German Institute for Economic Research (DIW Berlin) • The survey of 2000 Polish firms is planned in February/March 2007 • We took the firm adresses from the database on the webpage http://www.teleadreson.pl/considering the following criteria: • the economic sectors (NACE): 22.3 24.4 29.1 29.4 29.7 30 31 32 33 34 35.3 72.2 72.3 72.4 73 74.3 • the ownership form: private, mixed, foreign capital • the legal form: corporations, individual enterprises, agencies of foreign enterprises

19. Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level Data from East Germany and Poland. Thank you for your attention!

20. Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level Data from East Germany and Poland. Backup

21. Location Factors, Innovativeness and Firm Performance. The Determinantsof the Porter’s DiamondModel (I) • Factor Conditions The locationin clustersenables the access to specialized and cost-effective inputs • Demand Conditions Demanding local customers put pressure on the firms to innovate and to bring new products on the market as well as to establish new production processes • Context for Firm Strategy and Rivalry Powerful local competitors put visible pressure on each otherresulting in new innovations, lower costs and quality improvements

22. Location Factors, Innovativeness and Firm Performance. The Determinands of the Porter’s Diamond Model (II) • Related and Supporting Industries efficient accessto cost-effective inputs and machinery; lower transaction costs; in the close cooperation the suppliershelp the firms to perceive new methods or technologies • Government influence all determinands of the DiamondModel, e.g. by the subventions, the education policy, the regulationsof the product norms or the tax policy • Chance providing the discontinuities, cause of the changes in the competition situation (e.g. the sudden changes of the input costs or wars)

23. Location Factors, Innovativeness and Firm Performance. Partial Least Squares Approach • PLS and LISREL as path models with latent variables that are indirectly observed by manifest variables, called indicators • PLS as „soft-modeling“, no assumptions concerning the distributional properties of the variables • PLS is prediction-oriented, has an explanatory nature – no detailed knowledge about the relationships in the structural and measurement models is required • The PLS algorithm proceeds in three stages: • stage 1 (iterative): the case values of the LV are estimated • stage 2: estimation of loadings and weights • stage 3: estimation „location parameters“ • PLS method generates explicit case values for LV • Reflective and formative measurement models can be operationalized

24. (a) Blood alcohol level + - Drunkenness Ability to respond +/- … (b) Consumed beer + + Consumed wine Drunkenness … +/- Fig.: Reflektive (a) vs. formative (b) measurement model Location Factors, Innovativeness and Firm Performance. Reflektive vs. Formative Measurement Models