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Spatial econometrics of innovation: Recent contributions and perspectives

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  1. Spatial econometrics of innovation: Recent contributions and perspectives Corinne Autant-Bernard GATE-LSE CNRS University of Lyon Workshop Pécs 2011

  2. Introduction: Spatial econometricsis a fastdeveloppingsubfield of econometrics (Besag, 1972, Paelinck, 1979, Anselin, 1988, LeSage 1997, LeSage and Pace, 2010) . It has been applied in variouseconomicfields (agricultural economics, convergence analysis, etc) and especially in regional innovation and growthanalysis. They are several motivations to consider that spatial dependence is inherent to the regional innovation process: -an R&D externalities-based motivation, -omitted variables or spatial heterogeneity may lead to spatial dependence What are the contributions provided by spatial econometric tools to the analysis of regional innovation and growth and what research perspectives arise from the recent developments in spatial dynamics panel models ? Workshop Pécs 2011

  3. Outline: 1.Using spatial econometrictools to quantify spatial knowledgespillovers 2. Using spatial econometrictools to explore the underlyingmechanisms 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion Workshop Pécs 2011

  4. 1. Using spatial econometrictools to quantify spatial knowledgespillovers • 1. 1. Spatial knowledge production functions • Spatial econometrictools in KPF • Fromspatiallylaggedindependent variables and SEM models…: Anselin, Varga, Acs (1997), Maggioni, Nosvelli, Uberti (2007), Gallie and Legros (2007) • … to SAR and SDM models : • Mairesse and Mulkay (2007), Autant-Bernard and LeSage (2010) • Improvements in the ability to measure the spatial dimension of knowledgespillovers • Direct and indirect effets • Spatial profile of impacts • Correct calculation of impacts • Empiricalevaluation of the differencesbetween spatial and non spatial estimations based on Autant-Bernard and LeSage (2010) • Panel of 94 French NUTS3 regionsobserved over the period 1987-2000. Workshop Pécs 2011

  5. 1. Using spatial econometrictools to quantify spatial knowledgespillovers 1. 1. Spatial knowledge production functions The Bayesian estimates suggest higherprivate R&D spillovers and lower public R&D spillovers. The Bayesian estimates also suggest less resistance of knowledge flows to distance. Workshop Pécs 2011

  6. 1. Using spatial econometrictools to quantify spatial knowledgespillovers 1. 2. Spatial interaction models • Gravitymodelsusing patent citations as proxy for knowledgespillovers • Fischer, LeSage, Scherngell (2007), Fischer and Griffith (2008), Bergman and Usai (2009) • Improvements in the ability to measure the spatial dimension of knowledgespillovers • Correction for spatial autocorrelationresultingfromheterogeneity of origin and destination regions (LeSace and Pace, 2008) • Empiricalevaluation of the differencesbetween spatial and non spatial estimations • Fischer, LeSage, Scherngell (2007) Workshop Pécs 2011

  7. 1. Using spatial econometrictools to quantify spatial knowledgespillovers 1. 2. Spatial interaction models • Fischer, Scherngell et Jansenberger (2006) vs Fischer, LeSage, Scherngell (2007) • The Bayesian effects model produces smaller coefficient estimates • for both distance and borders: less resistance of knowledge flows to distance and borders when one controls for unobserved heterogeneity using the model containing spatial effects. 2) Regarding the positive impact of technological similarity between the regions, the Bayesian estimate is about : 1.57 times that from the conventional Poisson model, 10 times larger than the distance effect. Workshop Pécs 2011

  8. 1. Using spatial econometrictools to quantify spatial knowledgespillovers 1. 3. Perspectives • Exploit the information contained in the matrix of the direct and indirect impacts • Include the time dimension Workshop Pécs 2011

  9. 2. Using spatial econometrictools to explore the underlyingmechanisms 2. 1. Whydoes distance matter ? - Interpersonalrelationship and labour mobility Zucker, Darby et Armstrong (1994) et Balconi, Breschi et Lissoni (2004), Breschi and Lissoni (2009) - Interpersonalrelationship and innovation networks -patent citation approaches: Singh (2005), Sorenson et. al. (2006), Gomes-Casseres (2006), Agrawal et. al. (2008) -collaboration models: Frenkenet al. (2007), Autant- Bernard et al. (2007), Maggioni et Uberti (2007), Frachisse (2011), … Workshop Pécs 2011

  10. 2. Using spatial econometrictools to explore the underlyingmechanisms 2. 2. Two distinct empiricalstrategies: SKPF and gravitymodels - Is geography a by product of social proximity (relationalweightmatrix)? Maggioni and Uberti (2007, 2009) Test for spatial dependence once controlled for social proximity or comparison of weight matrices (LeSage and Parent, 2004) - Does spatial distance impact on collaboration and network formation (gravitymodels)? Scherngell and Barber (2009) Spatial dependencearising for spatial heterogeneityisaccounted for. Workshop Pécs 2011

  11. 2. Using spatial econometrictools to explore the underlyingmechanisms 2. 3. Empiricalresults - Role of social ties as channels for diffusion of knowledge - Interplay between social and geographical proximity. - Spatial distance impact on collaboration choices and on network formation/structure , but geographywouldmatterlessthan social proximity . 2. 4. Perspectives linking spatial econometrics and network analysis - Accounting for spatial dependencewithin collaboration gravitymodels - Reversing the causality: To whatextent collaborations and networks structure impact on geography (Does the network positionning of one region and itsneighbors impact the regional innovation or the spatial diffusion of knowledge)? (Miguelez and Moreno, 2010) - Refining the relationalweightmatrix, usingtextual informations (Maggioni et al., 2009) - Inclusion of the time dimension Workshop Pécs 2011

  12. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion • 3. 1. New developments in spatio-temporalmodels • Panel data models where the observational units are regions • Space-time dependence in the disturbance structure: • Baltagi et al. (2007), Su and Yang (2007) • Space-time dependence in the dependent variable: • Yu et al. (2008), Yu and Lee (2010) • Parent and LeSage (2010 and 2011) • Considering both individual and time effects is useful for empirical applications where time effects might be important, especially growth theory and regional economics Workshop Pécs 2011

  13. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion 3. 2. Spatio-temporal dimension of knowledge flows • Key issues: • - A tough debate framed by the work of Romer (1990) and Jones (1995) has focused on time dependence in the flow of new ideas. • - How long does it take for knowledge to flow through space? • - To what extent does the nature of the agglomeration forces change through time? • - The introduction of the temporal dimension islikely to modify the resultsobtainedfrom spatial estimation neglecting time. A strongsimultaneous spatial dependencecanresultfrom a strong time dependence and a weak spatial dependence (LeSage and Pace 2010). Workshop Pécs 2011

  14. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion • 3. 2. Spatio-temporal dimension of knowledge flows • Preliminary empirical approaches: • -Glaeser et al. 1992, Henderson et al. 1995, etc: inter-temporal dimension of externalities apprehended by taking into account the impact upon growth of the initial industrial structure: • -difficult to differentiate static from dynamic effects. • -no specific evaluation of knowledge spillovers. • Jaffe, Trajtenberg and Henderson (1992 and 1993) Johnson, Sipirong and Brown (2006): geographical coincidence between citing and cited patents decreases over time • KPF: Bottazzi and Peri (2007), focusing on time dependence only (and international spillovers), Parent (2009) Workshop Pécs 2011

  15. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion 3. 2. Spatio-temporal dimension of knowledge flows Parent (2009): 49 US states over the period 1994-2005 Bayesian MCMC approach to estimate the model parameters which accommodate spatial diffusion of innovative activities in a dynamicframework. Low levels of spatial dependence between neighboring regions can over long time periods lead to a significant amount of inter-connectivity between regions in the long-run knowledge production process. Workshop Pécs 2011

  16. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion 3. 3. Spatio-temporal dimension of knowledge networks • Two main set of questions: • Issues related to the interactions between space and networks: • To what extent does the spatial determinants of collaboration changes through time? • To what extent does the spatial structure of network change through time? • Issues related to the dynamics of networks (social distance instead of spatial distance): • How networks evolve over time: Introducing temporal as well as relational dependence into empirical network analysis is required to investigate most of the theoretical hypotheses (preferential attachment, closure, etc). • How long does it take for knowledge to flow through networks? Workshop Pécs 2011

  17. 3. Using spatial econometrictools to investigate the dynamics of knowledge diffusion 3. 3. Spatio-temporal dimension of knowledge networks -Preliminary empirical results: Hoeckman, Frenken and Tijssen (2010): Gravity model. The results reveal that the effect of territorial borders on co-publishing decreases over time, whereas the effect of distance either remains almost the same or increase in importance. Lata and Scherngell (2010): Gravity model (accounting for spatial dependence but not for panel dimension). The effect of both distance and border on R&D collaboration gradually declines over time. Hanaki, Nakajima and Ogura (2010): Dynamics of co-invention network. Cyclic closure and preferential attachment effect is observed, as well as a positive impact of co-location. Workshop Pécs 2011

  18. Conclusion: • Spatial econometrictools have improved the ability to : • Quantifyknowledgespillovers • Measure of their spatial extent • Explore the underlyingmechanisms and especially the interactions betweengeographical and social distance. • The recentdevelopment of spatio-dynamicmodels opens new researchlines to investigate the temporal dimension of both spatial knowledgeflows and innovation networks. • Bayesianeconometrictoolsmayplay a key part in these new developments as theyovercomeseveraldifficultiesfaced by frequentistapproaches (heterogeneity, null observations, etc.). Workshop Pécs 2011

  19. Spatial econometrics of innovation: Recent contributions and perspectives Corinne Autant-Bernard GATE-LSE CNRS University of Lyon Workshop Pécs 2011