Abstract
In this paper two models are developed in an attempt to elucidate the factors that influence the regional distribution of R&D labor across the regions of Greece. The first one is based on an adaptation of the [Guerrero and Seró (1997) Regional Studies 31:381–390] model to the Greek context treating the regional distribution of R&D labor as a function of the extent of agglomeration and the prevailing economic conditions. The second model extends the first one by taking into account two additional factors, viz. the production structure and infrastructure. The econometric results indicate the superior performance of the extended model in the context of Greece as well as attribute the location of R&D labor mainly on the diversification of industrial activity and the number of establishments in innovation-intensive sectors. It is therefore suggested that the stimulation of the regional production structure and infrastructure is essential for ‘knowledge-lagging’ regions.
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Notes
For a review of such approaches see Chisholm (1991).
For a more detailed survey see Fine (2000).
Data on R&D expenditures do not cover all administrative divisions of Greece or the entire time period.
According to the latest Standard Industrial Classification (SIC) system of the NSAG, established in 1991, the R&D sector corresponds to the two-digit SIC code 73.
The analysis covers the period 1970–2000 as the NSAG conducts its industrial census approximately every ten years and thus data after 2000 are not available from a reliable source.
Prefecture R9 is the ‘leading region’ of Greece with 40% of the population, 50% of industrial activity and the highest GDP, contributes around 38% to the total national output.
The NSAG provides data on GVA for the 1970 to 2000 period, deflated at 1970 current prices. Ideally, the data should have been deflated using regional price deflators. Unfortunately, as regional price indexes are not available from any official source, national deflators were used.
It is worthy informing here that Capello (2002) also reported similar findings for the case of Italy; and in particular for the localization and urbanization economies’ high spatial concentration, viz. of the high-tech sector in the metropolitan area of Milan.
As a rule of thumb, the best fitting model is the one that yields the minimum values for the AIC or the SBC criterion, calculated as \(AIC = T\ln {\left( {RSS} \right)} + 2n\) and \(SBC = T\ln {\left( {RSS} \right)} + n\ln {\left( T \right)}\), where RSS is the residual sum of squares, T is the number of observations and n stands for the number of parameters estimated. The SBC test has superior properties and is asymptotically consistent, whereas the AIC is biased towards selecting an overparameterized model (Enders 1995).
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Acknowledgements
The authors wish to thank the two anonymous referees for their helpful comments. The findings, interpretations and conclusions are those entirely of the authors and do not necessarily represent the official position, policies or views of the Ministry of Rural Development and Foods and/or the Greek Government.