Skip to main content

Advertisement

SpringerLink
Log in

Implementation of MGWR-SAR models for investigating a local particularity of European regional innovation processes

  • Published:
Central European Journal of Operations Research Aims and scope Submit manuscript

Abstract

This study puts emphasis on spatial effects, spatial autocorrelation and heterogeneity in the context of European regional innovation activities. The main aim of the paper was a simultaneous consideration of both spatial effects following a new class of data generating processes, mixed geographically weighted regression—spatial autoregressive model. The basis of the analysis was 220 European regions. As a proxy for innovation output Patent Cooperation Treaty applications were considered. As three innovative inputs were opted: scientific publications among the top-10% most cited publications worldwide; Research & Development expenditure in the business sector, and small and medium-sized enterprises introducing product or process innovations. The paper was intended to answer the research question such as the question of spatial differentiation of the model parameters, as we assumed heterogeneous responses of regional innovation output to innovation inputs. This is very important in terms of regional policy measures, which should be heterogeneous if spatially varying parameters are verified. In relation to spatial spillover effects, not only the question whether spatial spillover effects affect the regional innovation was addressed but also whether a unique spatial process drives the dependence across the full study area or there are separate spatial processes driving each region. The results suggested a spatial differentiation of all the parameters under the consideration and spatial spillovers have been indicated as a significant factor in increasing innovation. Consequently, this implies more place-based innovation and industrial regional policy strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: author’s elaboration based on the RIS 2019 data (Hollanders et al. 2019) in RStudio

Fig. 2

Source: author’s calculations based on the RIS 2019 data (Hollanders et al. 2019) in RStudio

Fig. 3

Source: own calculations in RStudio and GeoDa

Fig. 4

Source: own calculations in RStudio and GeoDa

Fig. 5

Source: own calculations in RStudio and GeoDa

Fig. 6

Source: own calculations in RStudio and GeoDa

Fig. 7

Source: own calculations in RStudio and GeoDa

Similar content being viewed by others

Notes

  1. Official NUTS codes for Serbia are not yet available and therefore unofficial codes were used (see Hollanders et al. 2019).

  2. For calculation of Moran´I statistics, spatial weighting matrix of queen contiguity scheme was used. This form of matrix is used in all parts of our spatial analysis (for more details see, e.g., Anselin and Rey 2014).

  3. According to the classical OLS assumptions (see, e.g., Anselin and Rey 2014), the elements of the error term u are distributed independently and identically (i.i.d.) with expected values of zero and constant variance \(\sigma_{u}^{2}\).

  4. Presented in relation to our empirical analysis.

  5. Due to possible problems with isolated units as well as with high variability of neighbouring regions resulting from other approaches, the queen contiguity form seemed to be a suitable for determining spatial regional structures. In the case of spatial weights, for instance based on a distance function, inverse or radial (see, e.g., Pavlovčič-Prešeren et al. 2019), there can be a problem with the bimodal distribution, when some regions have very few neighbours, and on the other hand, the other regions have very many neighbouring units. In our case, i.e., the queen case, the range of the number of the neighbours varied from 1 to 12. Only 12 regions had one neighbour, which was only 5.5% of the total number of regions.

References

  • Acs ZJ, Anselin L, Varga A (2002) Patents and innovation counts as measures of regional production of new knowledge. Res Policy 31:1069–1085

    Article  Google Scholar 

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    Book  Google Scholar 

  • Anselin L, Rey SJ (2014) Modern spatial econometrics in practice. GeoDa Press LLC, Chicago

    Google Scholar 

  • Anselin L, Varga A (1997) Local geographic spillovers between university research and high technology innovations. J Urban Econ 42:422–448

    Article  Google Scholar 

  • Anselin L, Varga A, Acs Z (2000a) Geographical spillovers and university research: a spatial econometric perspective. Growth Chang 31:501–515

    Article  Google Scholar 

  • Anselin L, Varga A, Acs Z (2000b) Geographic and sectoral characteristics of academic knowledge externalities. Pap Reg Sci 79:435–443

    Article  Google Scholar 

  • Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640

    Google Scholar 

  • Audretsch DB, Feldman MP (2004) Knowledge spillovers and the geography of innovation. Cities Geogr 4:2713–2739

    Article  Google Scholar 

  • Basile R, Durbán M, Mínguez R, Montero JM, Mur J (2014) Modeling regional economic dynamics: spatial dependence, spatial heterogeneity and nonlinearities. J Econ Dyn Control 48:229–245

    Article  Google Scholar 

  • Brunsdon C, Fotheringham A, Charlton M (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298

    Article  Google Scholar 

  • Brunsdon C, Fotheringham S, Charlton M (1998) Spatial nonstationarity and autoregressive models. Environ Plan A 30(6):957–973

    Article  Google Scholar 

  • Brunsdon C, Fotheringham A, Charlton M (1999) Some notes on parametric significance tests for geographically weighted regression. J Reg Sci 39(3):497–524

    Article  Google Scholar 

  • Charlot S, Crescenzi R, Musoleli A (2015) Econometric modelling of the regional knowledge production function in Europe. J Econ Geogr 15:1227–1259

    Article  Google Scholar 

  • Cho SH, Lambert D, Roberts R, Kim S (2010) Moderating urban sprawl: Is there a balance between shared open space and housing parcel size? J Econ Geogr 10(5):763–783

    Article  Google Scholar 

  • Chocholatá M (2020) Spatial variations in the educational performance in Slovak districts. Statistika Stat Econ J 100(2):193–203

    Google Scholar 

  • Feldman MP (1994) The geography of innovation. Kluwer, Dordrecht

    Book  Google Scholar 

  • Feldman MP, Florida R (1994) The geographic sources of innovation: technological infrastructure and product innovation in the United States. Ann Assoc Am Geogr 84:210–229

    Article  Google Scholar 

  • Formánek T (2020) Neighborhood definitions in econometric models: the search for an accurate spatial structure with application to labor market. In: Reiff M, Gežík P (eds) Quantitative methods in economics—multiple criteria decision making XX (QME 2020). University of Economics, Bratislava, Bratislava, pp 70–77

    Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton M (1999) Some notes on parametric significance tests for geographically weighted regression. J Reg Sci 39:497–524

    Article  Google Scholar 

  • Furková A (2016) The Innovative clusters in the EU: the sensitivity analysis of the spatial weight matrix construction. In: Quantitative methods in economics: multiple criteria decision making XVIII. International scientific conference. Vrátna, Slovakia, PP 106–113

  • Furková A (2019) Spatial spillovers and European Union regional innovation activities. CEJOR 27(3):815–834

    Article  Google Scholar 

  • Geniaux G, Martinetti D (2018) A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Reg Sci Urban Econ 72:74–85. https://doi.org/10.1016/j.regsciurbeco.2017.04.001

    Article  Google Scholar 

  • Geniaux G, Napoléone C (2011) A spatial hedonic approach on land use change anticipations. J Reg Sci 51(5):967–986

    Article  Google Scholar 

  • Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116

    Article  Google Scholar 

  • Groenendijk N (2006) Regional differences in R&D expenditure. Implications for regional governance. https://www.researchgate.net/publication/239851879_Regional_differences_in_RD_expenditure_Implications_for_regional_governance. Accessed 26 Sept 2020

  • Guastella G, van Oort F (2015) Regional heterogeneity and interregional research spillovers in European innovation: modelling and policy implications. Reg Stud 49(11):1–16

    Article  Google Scholar 

  • Gurgul H, Lach Ł (2019) Regional patterns in technological progress of Poland: the role of EU structural funds. Cent Eur J Oper Res 27:1195–1220. https://doi.org/10.1007/s10100-018-0556-5

    Article  Google Scholar 

  • Harris P (2019) A simulation study on specifying a regression model for spatial data: choosing between autocorrelation and heterogeneity effects. Geogr Anal 51(2):151–181. https://doi.org/10.1111/gean.12163

    Article  Google Scholar 

  • Hollanders H, Es-Sadki N, Mekelbach I (2019) Regional Innovation Scoreboard 2019. https://ec.europa.eu/growth/sites/growth/files/ris2019.pdf. Accessed 1 March 2020

  • Jaffe AB (1989) Real effects of academic research. Am Econ Rev 79:957–970

    Google Scholar 

  • Jaffe AB, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Quart J Econ 63:577–598

    Article  Google Scholar 

  • Jong M, Slavova K (2014) Res Policy 43(4):645–654

    Article  Google Scholar 

  • Khan BZ (2012) Of Time and space: a spatial analysis of technological spillovers among patents and unpatented innovations in the nineteenth century. http://wwwnberorg/papers/w20732. Accessed 10 March 2017

  • Kinkel S, Lay G, Wengel J (2005) Innovation: more than research and development. Growth opportunities on different innovation paths. https://core.ac.uk/download/pdf/6625245.pdf. Accessed 10 Jan 2021

  • Kumar I (2008) Innovation clusters: a study of patents and citations ESRI international user conference. https://wwwpcrdpurdueedu/files/media/Innovation-Clusters-A-Study-of-Patents-and-Citationspdf. Accessed 10 March 2017

  • LeSage J, Pace K (2009) Introduction to spatial econometrics. Chapman and Hall/CRC, Boca Raton

    Book  Google Scholar 

  • LeSage JP, Pace RK (2014) The biggest myth in spatial econometrics. Econometrics 2(4):217–249

    Article  Google Scholar 

  • Leung Y, Mei C, Zhang W (2000) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environ Plan A 32:871–890

    Article  Google Scholar 

  • Mei C, He S, Fang K (2004) A note on the mixed geographically weighted regression model. J Reg Sci 44:143–157

    Article  Google Scholar 

  • Moreno R, Paci R, Usai S (2005a) Spatial spillovers and innovation activity in European regions. Environ Plan A 37:1793–1812

    Article  Google Scholar 

  • Moreno R, Paci R, Usai S (2005b) Innovation clusters in the European regions. http://crenosunicait/crenos/sites/default/files/wp/05-12pdf. Accessed 22 April 2017

  • Pakes A, Griliches Z (1980) Patents and R&D at the firm level: a first look. Econ Lett 4:377–381

    Article  Google Scholar 

  • Pavlovčič-Prešeren P, Stopar B, Sterle O (2019) Application of different radial basis function networks in the illegal waste dump-surface modelling. CEJOR 27:783–795. https://doi.org/10.1007/s10100-018-0586-z

    Article  Google Scholar 

  • Żółtaszek A, Olejnik A (2021) Regional effectiveness of innovation: leaders and followers of the EU NUTS 0 and NUTS 2 regions, Innovation. Eur J Soc Sci Res. https://doi.org/10.1080/13511610.2020.1870440

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Grant Agency of Slovak Republic—VEGA 1/0193/20 “Impact of spatial spillover effects on innovation activities and development of EU regions”.

Author information

Authors and Affiliations

  1. Department of Operations Research and Econometrics, University of Economics in Bratislava, Dolnozemská cesta 1, 852 35, Bratislava, Slovakia

    Andrea Furková

Authors
  1. Andrea Furková
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Furková.

Ethics declarations

Conflict of interest

The author declares no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Furková, A. Implementation of MGWR-SAR models for investigating a local particularity of European regional innovation processes. Cent Eur J Oper Res 30, 733–755 (2022). https://doi.org/10.1007/s10100-021-00764-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10100-021-00764-3

Keywords

JEL Classification

Search

Navigation