LOCATION PATTERNS OF ISRAELI PHARMACEUTICAL AND ELRCTRONIC FIRMS

1. LOCATION PATTERNS OF ISRAELI PHARMACEUTICAL AND ELRCTRONIC FIRMS קטי גילמן ומירה ברון הפקולטה להנדסת תעשייה וניהול, הטכניון

2. Introduction • Understanding and explaining spatial organization of firms is central in industrial location economics • Pharmaceutical and electronic industries are among the most significant and prominent branches of Israeli economy, understanding their location pattern is essential in firms’ location in Israel.

3. Agglomeration • Agglomeration Economies • May be defined as cost reductions that occur when many economic activities are carried on in one place (Blair, 1995). • May be derived from the geographical concentration of firms engaged in similar activities or within the industry, leading to further local clustering of related firms and accumulation of knowledge (Odile, 2002). • Agglomeration economies are cost-reducing factors that diminish uncertainty and increase production efficiencies (Shefer and Frenkel, 1999). • McCann and Shefer (2004) mention information spillovers, non-traded local inputs and skilled local labor pool.

4. Hypotheses • The major question examined was does autoregressive spatial models provide a better explanation then models that disregard the spatial effects. • The models are examined on the pharmaceutical and electronic industries in Israel.

5. Models Examined • Traditional Tobit Regression • Models with spatial effects using LeSage Matlab Spatial econometric toolbox (1999) • SAR-Spatial Autoregressive Model • SDM-Spatial Autoregressive Durbin Model • SEM- Spatial Autoregressive Error Model

6. Hypotheses • Location patterns of pharmaceutical industry and electronics industry in Israel are similar. • Both industries are characterized by agglomerative location patterns. • The following would affect firms’ location decision: • Location of a university • Location of a hospital, generating demand for drugs and enabling collaboration in research • Size of population is a proxy for the pool of workers • Presence of firms in other industries in the same region

7. Overview of Israeli Pharmaceutical industry • There are about 30 manufacturing companies in Israel that are devoted to the production of pharmaceutical products. They produce mainly human drugs, veterinary products and active pharmaceutical ingredients. The five leading companies are Teva, Agis, Dexxon, Taro and Rakah. • The major amount of the firms are R&D small-middle size companies

8. Dependent variables • Number of pharmaceutical companies in each natural region • Mostly small middle sizedcompanies with a varied line of activity such as R&D, manufacturing, marketing etc that are located within Israel. • Joins a number of pharmaceutical related sectors such as biotechnological and biological companies that mainly involved in drug research and development activities and chemical companies that manufacture biochemical, organic and other chemicals

9. Phamaceutical Industry • The database includes about 170 companies • The database sources are Dun and Bradstreet guide and Matimop internet site (http://www2.matimop.org.il). • Besides pharmaceutical firms, firms of related sectors were included such as biotechnological and biological companies that are mainly involved in drug research and development activities and chemical companies that manufacture biochemical, organic and other chemicals

10. Overview of Israeli Electronic industry • Israel's fabless sector is third only to the USA and Taiwan. • The basis of the Israeli semiconductor industry are the very strong microelectronic academic departments in Israel since the early 60s. They contributed to a skilled manpower that later often migrated to the Silicon Valley, gained experience there and returned to Israel. • Three noticeable representatives of electronic industry - Motorola, Intel and Tower

11. Electronic Industry • Includes Semiconductors design, Semiconductor Manufacturing, Passive Components, Industrial Equipment, General Electronics Equipment and Electronics Assembly (EMS) • The database includes about 250 companies • The source of data is Israel Science and Technology website (www.science.co.il) • Contains firms engaged with activities such as R&D, manufacturing and marketing

12. Independent Variables • Population (Pop)–the number of persons distributed by natural regions (in thouthands). • Plants (Plants) –thenumber of establishments with more than 5 employees distributed by natural regions. • Control variables • University (UNIV) – Distribution of Israeli universities. The variable receives value ‘1” in case university is present in the region, and “0” otherwise. • Hospital (HOSP)– since this work focuses on pharma industry, hospitals represent one of the customers for the pharma firms and also research partner. The variable receives “1” in case of presence of hospital in a given region.

13. Traditional Regression • PharmaceuticalTobit Regression

14. * Variable significant at p<0.05

15. . • Weight Matrix • The proximity term in this work bases on natural regions distribution. • ISR CBS (Central Bureau of Statistics) provides a map of Israel divided by regions, sub-regions and natural regions • Rook method was used to examine spatial contiguity

16. Spatial Autoregressive Durbin Model • SDM Tobit Pharma Regression * Variable significant at p<0.05 * *Variable significant at p<0.1 • SDM Tobit Electronic Regression * Variable significant at p<0.05 * *Variable significant at p<0.1 • Tobit method was used due to distribution of observations since this method allows dealing with cases where sample observations are censored or truncated. Given that the number of companies cannot be less than zero, the dependent variable is bounded on the lower end by zero and justifies the use of Tobit specifications for the models used.

17. Results • Spatial lag parameter ρ • Pharmaceutical industry • Positive and significant coefficients • Presence of spatial agglomeration economies at work for the dependent variable (the number of companies in a certain region depends on the number in neighboring regions). • Electronic industry • Positive but insignificant at the 10 percent level • Suggesting that the number of electronic companies in one region was not influenced by number of companies of nearby regions. .

18. Results • Population • Pharmaceutical industry • Negative and insignificant • W-population is positive and significant at 10% level • Positive influence of population presence in nearby regions on firm’s decision to locate in a certain area • Electronic industry • Negative and insignificant

19. Results • University • Insignificant, however positive in both industries • Contradicts a common hypothesis in spatial literature that proximity to universities or research institutions is beneficial for plants (Jaffe, 1989, Feldman, 1994, Jaffe, Trajtenberg and Henderson, 1993) • Contradicts thesis hypothesis • Consistent with previous research that was conducted in Israel (Frenkel, 2001, Felstnstein, 1996)

20. Results • Plants • Represents the presence of other firms in the area • Positive and significant at 5% level in both industries • Consistent with study hypothesis that presence of other firms in the same region will have a positive influence on the firm. • In the pharmaceutical industry WPlants is significant but negative, it measures the impact of remote plants.

21. Results • Hospital • Represents proximity to the client and also proximity to the source of innovation (similar to influence of universities). • Positive and significant in pharmaceutical industry • Aligned with study hypothesis of positive correlation between hospitals and pharmaceuticals firms

22. Main Conclusions • The presence of spatial agglomeration economies between the different natural regions • Confirmed in the pharmaceutical industry • Not confirmed in electronic industry

23. Conclusions – cont. • Autoregressive models are more appropriate to examine agglomeration effects. • Though the traditional Tobit model results in the pharma industry in R-squared of 0.69, SDM results in R-squared of 0.7326. The spatial regression results in better goodness of fit, i.e. higher R-squared and in more significant results regarding the spatial effect.

24. Study implications • Pharmaceutical firms • Would find it beneficial to collocate with firms that engage in the same type of activity • Creating specialized industrial parks or centers would be valuable for this industry. • Electronics firms • Would not see in location with other electronic firms a valuable advantage • Existence of better infrastructure would attract electronic firms to the region.

25. Q&A

26. Literature review – cont. • Location of Innovativeness • The “Diamond of Competitive Advantage” Theory -cont. • The success of an individual firm may be partially traced to the size, depth, and nature of the cluster of related and supporting both public and private enterprises. Clusters provide constituent firms a competitive advantage not afforded by dispersed firms. Location advantages accrued from being in a cluster and provide less costly access to specialized inputs like components, machinery, business services, and skilled personnel in comparison to dispersed participants (Porter, 2000).

27. Spatial Econometrics Spatial Autoregressive Regressions • The standard linier regression has the following form: y = Xβ + ε ε ~N(0,2) • The most general statement of spatial auto regression is : y = ρW1y +Xβ + u u = λW2u+ε ε ~ N(0,σ2In)

28. Spatial Econometrics – Spatial Autoregressive Regressions – cont. • Model with restrictions: • SAR Model - Spatial Autoregressive Model: y = ρW1y + Xβ + ε ε ~ N(0, σ2In) • SEM model - Spatial Error Model y = Xβ + u u = λWu+ε ε ~ N(0,σ2In). • SDM model - Spatial Durbin Model y = ρW1y + Xβ1 +W1Xβ2 + ε ε ~ N(0, σ2In)

29. Spatial Econometrics – cont.Weight matrix • Contiguity matrix • NxN symmetric matrix where wij = 1 when i and j are neighbors and 0 when they are not • Makes for a fairly sparse matrix • W matrix is usually standardized so all columns sum to 1 • wsij = wij / Σj wij • Makes operations with the W matrix as an average of neighboring values

30. Spatial Econometrics Weight matrix - cont. • Types of contiguity matrix • Rook contiguity - define Wij = 1 for regions that share a common side with the region of interest. • Bishop contiguity - define Wij = 1 for entities that share a common vertex with the region of interest. • Queen contiguity - for entities that share a common side or vertex with the region of interest define Wij = 1.

31. Context for Firm Strategy and Rivalry • Related and Supporting Industries • Factor (Input) Conditions • Demand Conditions The “Diamond of Competitive Advantage” Theory

32. Pharmaceutical Firms Data Distribution Axis X -Range of number of companies Axis Y -Number of natural regions

33. Electronic Firms' Data Distribution Axis X -Range of number of companies Axis Y -Number of natural regions

34. Population Distribution Axis X -Range of population number (in thousands) Axis Y -Number of natural regions

35. Plants Distribution Axis X -Range of plants number Axis Y -Number of natural regions

36. Maps of Israel natural region

37. Weight Matrix

38. Employed Persons by District