The Competitive Advantage of Industrial Districts: by Carlo Pietrobelli (auth.), Prof. Michele Bagella, Dr.
By Carlo Pietrobelli (auth.), Prof. Michele Bagella, Dr. Leonardo Becchetti (eds.)
Several fascinating effects at the economics of commercial districts are accrued during this booklet. the 1st half investigates over inner determinants of commercial district competitiveness taking a look at inner productiveness, at styles of innovation and at these elements which create a good business surroundings. the second one a part of the booklet investigates over overseas competitiveness of commercial districts targeting the functionality of export and of alternative different types of internationalisation.
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Additional info for The Competitive Advantage of Industrial Districts: Theoretical and Empirical Analysis
1920, Principles of Economics, 8th edition. (Procupine Press, Philadelphia, 1982). 44 M. Bagella and 1. , 1993, Distretti Industriali e Mercato Unico Europeo, Istituto G. Tagliacarne, Milano, F. Angeli. Momigliano F. , 1983, Tecnologia e organizzazione Industriale Internazionale, Bologna, II Mulino. , Viesti G. M, 1992, Distretti Industriali: Crisi CESPRl, Egea,1992. 0 Evoluzione? Porter M, 1991, II vantaggio Competitivo delle Nazioni, Milano, Mondadori. Pyke F. , 1992, Industrial District and local Economic Regeneration, Geneva, International Institute for labour Studies.
To the imitator in the (e,ne) solution of the game, r may be alternatively interpreted as the differential between R&D expenditure and imitation costs in order to avoid a too restrictive assumption on costless imitation. 3 We are implicitly assuming to live in an economy in which technological innovation in telecommunications has not eliminated positive effects of geographical agglomeration on capacity of absorbing technological spillovers. In other terms following Gaspar-Glaeser (1996), we assume that geographical proximity matters because electronic proximity is a complement and not a substitute of it.
3 Results from Econometric Analysis Longitudinal regressions try to provide a breakdown by size, macroarea and macrosector for the net effect of geographical agglomeration on private fIrm R&D expenditure and R&D participation. A synthetic description of the results is presented in Tab. 7-8 while full results are presented in the Appendix. Tab. 7 present coefficients and T -statistics on the net impact of geographical agglomeration (GA) on private fIrm R&D intensity from the following Tobit specifIcation: 22 3 i=l i=l Rsotat=CXo + LaiDse(+ UkDareak +filSize+fi2Group+fi3GA+& (8) where Rstotat is the three year (1989-91) average of the share of R&D expenditure on total assets, Dset are 22 sector dummies, Darea are three macroarea dummies, GA is a dummy which takes value of 1 if the fIrm is located in an industrial district (is geographically agglomerated) and zero otherwise, Size is three year (1989-91) average of fIrm's employees, Group is a dummy which takes value of one for fIrms affiliated to groups (subsidiaries or parent companies) and zero otherwise.