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The Geography of R&D; Tobit Analysis and Bayesian Approach to Mapping R&D Activities for The Netherlands

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  • Hans Ouwersloot

    (Universiteit Maastricht)

  • Piet Rietveld

    (Vrije Universiteit Amsterdam)

Abstract

Regions vary strongly according to the participation of firms in R&Dactivity.By linking data on R&D activity at thefirm level with GIS based data on economic, and other locationfeatures of zoneswe are able to investigate the impactof local factors on R&D involvement for various types of firms.The relative importance of local factors as determinants of R&Dinvolvement ofthe firms is estimated by means of atobit model. Rather strong differences are found between zones in thesame urbanregion. For example modernmanufacturing firms located in the centre of the large cities haverelativelylow levels of R&D, the opposite holds forrings of zones at a certain distance from the cities. Bayesianmethods are usedfor map presentations of the survey data.

Suggested Citation

  • Hans Ouwersloot & Piet Rietveld, 2000. "The Geography of R&D; Tobit Analysis and Bayesian Approach to Mapping R&D Activities for The Netherlands," Tinbergen Institute Discussion Papers 00-043/3, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20000043
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    5. Josep-Maria Arauzo-Carod & Elisabet Viladecans-Marsal, 2009. "Industrial Location at the Intra-Metropolitan Level: The Role of Agglomeration Economies," Regional Studies, Taylor & Francis Journals, vol. 43(4), pages 545-558.
    6. Elisabet Viladecans Marsal & Josep Maria Arauzo Carod, "undated". "Industrial Location at the Intra-metropolitan Level: A Negative Binomial Approach," Studies on the Spanish Economy 224, FEDEA.
    7. Kleoniki Kalapouti & Nikos Varsakelis, 2015. "Intra and inter: regional knowledge spillovers in European Union," The Journal of Technology Transfer, Springer, vol. 40(5), pages 760-781, October.
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    9. Michael Fritsch & Viktor Slavtchev, 2010. "How does industry specialization affect the efficiency of regional innovation systems?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(1), pages 87-108, August.
    10. Kleoniki Kalapouti & Konstantinos Petridis & Chrisovalantis Malesios & Prasanta Kumar Dey, 2020. "Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions," Annals of Operations Research, Springer, vol. 294(1), pages 297-320, November.
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