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Simultaneous consideration of spatial heterogeneity and spatial autocorrelation in European innovation: a spatial econometric approach based on the MGWR-SAR estimation

Andrea Furková ()
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Andrea Furková: University of Economics in Bratislava

Review of Regional Research: Jahrbuch für Regionalwissenschaft, 2021, vol. 41, issue 2, No 3, 157-184

Abstract: Abstract The paper focuses on a simultaneous consideration of both spatial heterogeneity and spatial autocorrelation in the context of European regional innovation activities. A new class of data generating processes, the Mixed Geographically Weighted Regression—Spatial Autoregressive model, is the main instrument of the analysis. We deal with 220 European regions, and the components of the Regional Innovation Scoreboard 2019 are the basis of the analysis. Patent Cooperation Treaty applications are used as a measure of innovation output, and Scientific publications among the top-10% most cited publications worldwide, Research & Development expenditure in the business sector, Small and Medium-Sized Enterprises introducing product or process innovations and Human resources in science and technology are included as innovation inputs. The main research hypothesis is that there are spatial innovation spillovers among the European regions. Changes in innovation inputs in a specific region affect the patent applications not only in this region but these changes might also significantly impact neighbouring regions. At the same time, we assume that the effects of changes in innovation inputs differ across regions, mainly between regions belonging to the top innovators and lagging regions. The results indicate that a spatial differentiation of the model parameters and spatial spillovers matter for innovation output. We detect significant differences between advanced and lagging regions. Therefore, heterogeneous responses to regional policy measures should be considered, and it is necessary to apply a more individual approach to the regional development of innovation activities.

Keywords: Mixed Geographically Weighted Regression - Spatial Autoregressive Model; Spatial Heterogeneity; Spatial Autocorrelation; European innovation (search for similar items in EconPapers)
JEL-codes: O31 R12 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10037-021-00160-z

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