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Synthetic indicators/1

Synthetic indicators/1. Synthetic indicators/2. PCT per million in 30 best performing regions, 1998-2000. PCT per million in 30 best performing regions, 2002-2004. OECD Regions: PCT per million population variability, 1998-2000.

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Synthetic indicators/1

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  1. Synthetic indicators/1 Geography of innovation in OECD regions

  2. Synthetic indicators/2 Geography of innovation in OECD regions

  3. PCT per million in 30 best performing regions, 1998-2000 Geography of innovation in OECD regions

  4. PCT per million in 30 best performing regions, 2002-2004 Geography of innovation in OECD regions

  5. OECD Regions: PCT per million population variability, 1998-2000 Geography of innovation in OECD regions

  6. OECD Regions: PCT per million population variability, 2002-2004 Geography of innovation in OECD regions

  7. Spatial distribution of innovation/1 • The degree of disparities in the regional distribution of innovative activities has increased across OECD countries for three out of four indexes. CV decreases mainly because the average value has changed • This phenomenon has not been homogeneous across macroareas (in particular it decreases in the United States) • We would like to perform the same analysis across sectors to assess potential differences Geography of innovation in OECD regions

  8. Spatial distribution of innovative activity/1 Geography of innovation in OECD regions

  9. Spatial dependence of innovative activity/2 • Presence of strong and positive spatial autocorrelation among contiguous areas. Spatial dependence extends until the 3th order of contiguity • The extent of such a dependence is stable along time • Spatial dependence is also detected when distances are used instead of contiguities • This process has favoured the formation of clusters of innovative regions…(we need sector data in order to see if such a process is differentiated across sectors and how much) • Let us see these clusters Geography of innovation in OECD regions

  10. Moran scatterplot map, 2002-2004 Geography of innovation in OECD regions

  11. Moran scatterplot map Europe, 2002-2004 Geography of innovation in OECD regions

  12. Moran LISA map, 2002-2004 Geography of innovation in OECD regions

  13. Moran LISA map Europe, 2002-2004 Geography of innovation in OECD regions

  14. Convergence in innnovative efforts?National level Geography of innovation in OECD regions

  15. Convergence in innnovative efforts?Regional level Geography of innovation in OECD regions

  16. Summary of main novelties… • We focus on OECD regions. • We have a set of homogeneous indicators for all the countries. • We are going to estimate KPF at both the regional level (and later potentially at the industry level) • We are going to use specific econometric techniques to analyse the nature and the spatial scope of knowledge creation and diffusion. Geography of innovation in OECD regions

  17. The determinants of innovative activity at the local level: knowledge production function I = local patents (per capita) in region j • RD= quota of R&D on GDP (j) • HK= tertiary education (j) • DENS= population density (j) • NAT = national dummies; • DU, DR, DCAP= dummies for urban, rural, capital regions • DGDP= dummy for above and below average GDP per capita • Note: • Variables in log • Time lags are considered Geography of innovation in OECD regions

  18. Estimation strategy • OLS to assess significance of coefficients and the presence of spatial dependence • Discriminate between spatial lag model or spatial error model and re-estimate with ML Geography of innovation in OECD regions

  19. Econometric results

  20. Some robustness checks • Interactive dummies: • DGDP*HK and DGDP*RD • Spatial Lag of RD • KPF with distance matrix (only for EU and North America) • KPF including Japan and Korea (estimation of some variables) • KPF with PCT per worker (instead of per capita)

  21. KPF estimation with interactive dummies

  22. KPF estimation with spatial lag of RD

  23. KPF estimation with distance matrix

  24. KPF estimation with Japan and Korea

  25. KPF estimation with PCT per worker

  26. Final remarks • Clusters of regional innovative systems have formed across OECD countries • Main determinants of knowledge creation are at work both at the local and at the external level • Human capital has larger effects than R&D • Such determinants are within national innovation systems Geography of innovation in OECD regions

  27. Final remarks and questions • Clusters of regional innovative systems have formed across OECD countries • Main determinants of knowledge creation are at work both at the local and at the external level • Are they different with respect to industrial specialisation? • Are they within national innovation systems? • Are they getting stronger or bigger? Geography of innovation in OECD regions

  28. The research agenda forwhat we have done so far • There are still some missing values in the database (Korea and Switzerland, for example) • No detail about RD • Public vs private (possible for some countries) • Not all spatial externalities are appropriately measured • Citations can be used to measure spillovers both within and across regions • No measure of other local public knowledge • University and research centers? Geography of innovation in OECD regions

  29. The research agenda: main options • To deepen and to improve the analysis of the general KPF in order to assess the presence of differences across macroregions • To replicate the descriptive analysis at a more disaggregated territorial level (that is TL3)…the replication of the econometric analysis is problematic since most data for explanatory variables are lacking • To focus on industrial disaggregation and to replicate the analysis for all sectors or for a set of them (some high tech). This can be done both for the descriptive and the econometric analysis. The database has to be built at the regional level Geography of innovation in OECD regions

  30. The determinants of innovative activity at the local industry level I = local industry patents (per capita) in sector i and region j • IST = technological specialisation index based on location quotient (ij) • DIV= diversity index based on herfhindhal (ij) • GDP= GDP per capita (j) • DENS= population density (j) • EDU= tertiary education • RD = quota of R&D on GDP (j) • NAT = national dummies; • Other controls for macroareas, urban and rural regions, citations • Note: • Variables in log • Time lags are considered Geography of innovation in OECD regions

  31. For your interests • Oecd patent database includes also data on citations regionalised for TL2 regions • If you are interested in this topic and getting hold on the data you can contact me: stefanousai@unica.it Geography of innovation in OECD regions

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