moran scatterplot map, 2002-2004
DESCRIPTION
Moran scatterplot map, 2002-2004. Moran scatterplot map Europe, 2002-2004. Moran LISA map, 2002-2004. Moran LISA map Europe, 2002-2004. Convergence in innnovative efforts? National level. Convergence in innnovative efforts? Regional level. Summary of main novelties…. - PowerPoint PPT PresentationTRANSCRIPT
21/04/23 Geography of innovation in OECD regions
Pag.1
Moran scatterplot map, 2002-2004
21/04/23 Geography of innovation in OECD regions
Pag.2
Moran scatterplot map Europe, 2002-2004
21/04/23 Geography of innovation in OECD regions
Pag.3
Moran LISA map, 2002-2004
21/04/23 Geography of innovation in OECD regions
Pag.4
Moran LISA map Europe, 2002-2004
Convergence in innnovative efforts?National level
21/04/23 Geography of innovation in OECD regions
Pag.5
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France Germany
Greece
Hungary
Iceland
IrelandItaly
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
-50
05
01
00
15
0P
CT
pe
r ca
pita
va
r% 9
8-0
0/0
2-0
4
0 100 200 300PCT per capita 98-00
Convergence in innnovative efforts?Regional level
21/04/23 Geography of innovation in OECD regions
Pag.6
-150.00
-100.00
-50.00
0.00
50.00
100.00
150.00
200.00
250.00
300.00
0.00 100.00 200.00 300.00 400.00 500.00 600.00
PCT per capita 98-00
PC
T p
er c
apit
a va
r% 9
8-00
/02-
04
21/04/23 Geography of innovation in OECD regions
Pag.7
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.
21/04/23 Geography of innovation in OECD regions
Pag.8
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
n
c tjjcc
stjstjstjstj
qtjstjqtjtj
NAT
DGDPDCAPDRDU
DENSHKRDI
1 ,
,7,6,5,4
,3,2,1,
•Note:• Variables in log• Time lags are considered
21/04/23 Geography of innovation in OECD regions
Pag.9
Estimation strategy
1. OLS to assess significance of coefficients and the presence of spatial dependence
2. Discriminate between spatial lag model or spatial error model and re-estimate with ML
n
c tjtjjcc
stjstjstjstj
qtjstjqtjtj
WINAT
DGDPDCAPDRDU
DENSHKRDI
1 ,,4
,7,6,5,4
,3,2,1,
Econometric results
OLS ML OLS ML OLS ML
Log (RD) 0.486 0.446 0.498 0.461 0.548 0.479
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
log (HK) 1.094 0.991 1.072 0.886 1.061 1.086
(0.000) (0.000) (0.000) (0.000) (0.262) (0.008)
log (DENS) 0.070 0.073 0.054 0.059 0.069 0.076
(0.092) (0.045) (0.438) (0.320) (0.182) (0.093)
W log (I) 0.182 0.229 0.153
(0.000) (0.000) (0.016)
Rural dummy -0.201 -0.202 -0.142 -0.130 -0.236 -0.279
(0.050) (0.026) (0.280) (0.248) (0.197) (0.080)
Urban dummy 0.099 0.049 0.268 0.230 -0.271 -0.342
(0.452) (0.679) (0.104) (0.103) (0.243) (0.092)
Capital dummy -0.543 -0.419 -0.515 -0.338 -0.815 -0.821
(0.003) (0.010) (0.019) (0.073) (0.440) (0.018)
GDP dummy 0.810 0.652 0.935 0.713 0.466 0.375
(0.000) (0.000) (0.000) (0.000) (0.078) (0.103)
NAT dummies yes yes yes yes yes yes
Obs 271 271 201 201 61 61
R2-adj 0.889 0.906 0.901 0.920 0.679 0.747
Moran’s I 4.074 3.619 1.656
(0.000) (0.000) (0.098)
LM-ERR 0.002 0.090 0.401 0.065 0.013 0.143
(0.968) (0.764) (0.526) (0.799) (0.909) (0.706)
LM-LAG 20.551 22.653 3.990
(0.000) (0.000) (0.046)
Europe North AmericaVariables
OECD
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)
KPF estimation with interactive dummies
OLS ML OLS ML OLS ML
Log (RD) 0.571 0.586 0.600 0.619 0.768 0.605
(0.000) (0.000) (0.000) (0.000) (0.292) (0.332)
log (HK) 1.087 0.953 0.969 0.780 1.020 1.408
(0.000) (0.000) (0.000) (0.000) (0.474) (0.253)
log (DENS) 0.100 0.106 0.126 0.114 0.073 0.081
(0.015) (0.004) (0.080) (0.062) (0.171) (0.074)
W log (I) 0.176 0.223 0.160
(0.000) (0.000) (0.013)
DGDP*log(RD) -0.104 -0.191 -0.155 -0.262 -0.230 -0.141
(0.399) (0.085) (0.291) (0.041) (0.753) (0.823)
DGDP*log(HK) -0.488 0.359 -0.433 -0.201 0.000 -0.400
(0.002) (0.011) (0.027) (0.246) (0.999) (0.747)
Controls
Rural dummy -0.203 -0.210 -0.950 -0.102 -0.232 -0.277
(0.042) (0.017) (0.462) (0.357) (0.213) (0.081)
Urban dummy 0.078 0.021 0.187 0.152 -0.264 -0.339
(0.548) (0.854) (0.253) (0.278) (0.263) (0.093)
Capital dummy -0.478 -0.377 -0.445 -0.300 -0.784 -0.763
(0.007) (0.018) (0.038) (0.105) (0.062) (0.031)
GDP dummy 1.953 1.507 1.895 1.192 0.473 1.455
(0.000) (0.000) (0.000) (0.002) (0.902) (0.663)
NAT dummies yes yes yes yes yes yes
Obs 271 271 201 201 61 61
R2-adj 0.893 0.911 0.905 0.923 0.668 0.750
LIK -199.977 -187.269 -151.127 -138.559 -37.986 -35.142
(0.000) (0.001) (0.107)
(0.692) (0.900) (0.460) (0.793) (0.990) (0.856)
(0.000) (0.000) (0.034)
Europe North AmericaVariables
OECD
KPF estimation with spatial lag of RD
OECDNorth
AmericaOLS OLS
Log (RD) 0.603 0.633 0.627 0.507
(0.000) (0.000) (0.000) (0.000)
log (HK) 1.064 0.940 0.964 1.011
(0.000) (0.000) (0.000) (0.033)
log (DENS) 0.089 0.118 0.126 0.057
(0.031) (0.926) (0.072) (0.277)
W log (RD) 0.253 0.312 0.289 0.214
(0.006) (0.010) (0.160) (0.200)
W2 log (RD) 0.280
(0.051)
DGDP*log(RD) -0.155 -0.180 -0.162
(0.209) (0.217) (0.261)
DGDP*log(HK) -0.483 -0.424 -0.393
(0.002) (0.028) (0.041)
Controls
Rural dummy -0.201 -0.092 -0.641 -0.245
(0.041) (0.471) (0.613) (0.178)
Urban dummy 0.062 0.163 0.151 -0.283
(0.627) (0.311) (0.343) (0.220)
Capital dummy -0.434 -0.396 -0.415 -0.858
(0.014) (0.062) (0.048) (0.034)
GDP dummy 1.923 1.818 1.690 0.513
(0.000) (0.000) (0.000) (0.054)
NAT dummies yes yes yes yes
Obs 271 201 201 61
R2-adj 0.897 0.908 0.909 0.683
LIK -195.657 -147.144 -144.877 -37.154
(0.001) (0.001) (0.001) (0.285)
(0.556) (0.452) (0.495) (0.726)
(0.000) (0.000) (0.000) (0.099)
VariablesEurope
OLS
OECDNorth
AmericaOLS OLS
Log (RD) 0.603 0.633 0.627 0.507
(0.000) (0.000) (0.000) (0.000)
(0.000) (0.000) (0.000) (0.033)
(0.031) (0.926) (0.072) (0.277)
(0.006) (0.010) (0.160) (0.200)
(0.051)
(0.209) (0.217) (0.261)
(0.002) (0.028) (0.041)
(0.041) (0.471) (0.613) (0.178)
(0.627) (0.311) (0.343) (0.220)
(0.014) (0.062) (0.048) (0.034)
(0.000) (0.000) (0.000) (0.054)
(0.001) (0.001) (0.001) (0.285)
(0.556) (0.452) (0.495) (0.726)
(0.000) (0.000) (0.000) (0.099)
VariablesEurope
OLS
(0.000) (0.000) (0.000) (0.000)
(0.000) (0.000) (0.000) (0.033)
(0.031) (0.926) (0.072) (0.277)
(0.006) (0.010) (0.160) (0.200)
(0.051)
(0.209) (0.217) (0.261)
(0.002) (0.028) (0.041)
(0.041) (0.471) (0.613) (0.178)
(0.627) (0.311) (0.343) (0.220)
(0.014) (0.062) (0.048) (0.034)
(0.000) (0.000) (0.000) (0.054)
Obs 271 201 201 61
R2-adj 0.897 0.908 0.909 0.683
LIK -195.657 -147.144 -144.877 -37.154
AIC 461.314 356.289 353.755 94.307
SC 587.388 458.691 459.460 115.416
Moran’s I 3.306 3.379 3.290 1.069
(0.001) (0.001) (0.001) (0.285)
LM-ERR 0.347 0.566 0.466 0.123
(0.556) (0.452) (0.495) (0.726)
LM-LAG 14.480 15.139 12.472 2.724
(0.000) (0.000) (0.000) (0.099)
KPF estimation with distance matrix
North AmericaOLS ML OLS
Log (RD) 0.600 0.677 0.548
(0.000) (0.000) (0.000)
log (HK) 0.969 0.624 1.061
(0.000) (0.001) (0.262)
log (DENS) 0.126 0.075 0.069
(0.080) (0.229) (0.182)
W log (I) 0.012
(0.000)
DGDP*log(RD) -0.155 -0.209
(0.291) (0.103)
DGDP*log(HK) -0.433 -0.169
(0.027) (0.340)
Controls
Rural dummy -0.950 -0.088 -0.236
(0.462) (0.433) (0.197)
Urban dummy 0.187 0.189 -0.271
(0.253) (0.183) (0.243)
Capital dummy -0.445 -0.232 -0.815
(0.038) (0.223) (0.044)
GDP dummy 1.895 1.032 0.466
(0.000) (0.012) (0.078)
NAT dummies yes yes yes
Obs 201 201 61
R2-adj 0.905 0.922 0.679
LIK -151.127 -139.517 -38.144
(0.000) (0.004)
(0.377) (0.793) (0.244)
(0.000) (0.836)
EuropeVariables
North AmericaOLS ML OLS
(0.000) (0.000) (0.000)
(0.000) (0.001) (0.262)
(0.080) (0.229) (0.182)
(0.000)
(0.291) (0.103)
(0.027) (0.340)
(0.462) (0.433) (0.197)
(0.253) (0.183) (0.243)
(0.038) (0.223) (0.044)
(0.000) (0.012) (0.078)
(0.000) (0.004)
(0.377) (0.793) (0.244)
(0.000) (0.836)
EuropeVariables
(0.000) (0.000) (0.000)
(0.000) (0.001) (0.262)
(0.080) (0.229) (0.182)
(0.000)
(0.291) (0.103)
(0.027) (0.340)
(0.462) (0.433) (0.197)
(0.253) (0.183) (0.243)
(0.038) (0.223) (0.044)
(0.000) (0.012) (0.078)
Obs 201 201 61
R2-adj 0.905 0.922 0.679
LIK -151.127 -139.517 -38.144
AIC 362.255 341.034 94.288
SC 461.354 443.436 113.286
Moran’s I 7.125 2.852
(0.000) (0.004)
LM-ERR 0.780 0.069 1.355
(0.377) (0.793) (0.244)
LM-LAG 21.236 0.043
(0.000) (0.836)
KPF estimation with Japan and Korea
OLS ML
Log (RD) 0.556 0.574
(0.000) (0.000)
log (HK) 1.114 0.954
(0.000) (0.000)
log (DENS) 0.093 0.098
(0.030) (0.009)
W log (I) 0.185
(0.000)
DGDP*log(RD) -0.113 -0.203
(0.378) (0.074)
DGDP*log(HK) -0.411 -0.293
(0.011) (0.039)
Controls
Rural dummy -0.203 -0.228
(0.045) (0.010)
Urban dummy 0.084 0.016
(0.511) (0.885)
Capital dummy -0.358 -0.250
(0.042) (0.106)
GDP dummy 1.757 1.333
(0.000) (0.000)
NAT dummies yes yes
Obs 287 287
R2-adj 0.878 0.902
LIK -222.798 -206.251
(0.000)
(0.629) (0.824)
(0.000)
VariablesOECD
OLS ML
(0.000) (0.000)
(0.000) (0.000)
(0.030) (0.009)
(0.000)
(0.378) (0.074)
(0.011) (0.039)
(0.045) (0.010)
(0.511) (0.885)
(0.042) (0.106)
(0.000) (0.000)
(0.000)
(0.629) (0.824)
(0.000)
VariablesOECD
(0.000) (0.000)
(0.000) (0.000)
(0.030) (0.009)
(0.000)
(0.378) (0.074)
(0.011) (0.039)
(0.045) (0.010)
(0.511) (0.885)
(0.042) (0.106)
(0.000) (0.000)
Obs 287 287
R2-adj 0.878 0.902
LIK -222.798 -206.251
AIC 517.596 486.502
SC 649.338 621.903
Moran’s I 4.007
(0.000)
LM-ERR 0.234 0.049
(0.629) (0.824)
LM-LAG 28.261
(0.000)
KPF estimation with PCT per worker
OLS ML OLS ML OLS ML
Log (RD) 0.531 0.542 0.564 0.580 0.840 0.686
(0.000) (0.000) (0.000) (0.000) (0.238) (0.263)
log (HK) 1.068 0.930 0.963 0.764 0.592 0.949
(0.000) (0.000) (0.000) (0.000) (0.670) (0.432)
log (DENS) 0.110 0.166 0.146 0.137 0.074 0.082
(0.008) (0.002) (0.042) (0.027) (0.154) (0.067)
W log (I) 0.146 0.188 0.133
(0.000) (0.000) (0.019)
DGDP*log(RD) -0.059 -0.134 -0.120 -0.219 -0.296 -0.208
(0.630) (0.227) (0.415) (0.090) (0.678) (0.736)
DGDP*log(HK) -0.488 -0.371 -0.402 -0.193 0.233 -0.119
(0.002) (0.009) (0.040) (0.269) (0.866) (0.922)
(0.056) (0.250) (0.530) (0.408) (0.170) (0.064)
(0.750) (0.936) (0.446) (0.509) (0.300) (0.128)
(0.007) (0.013) (0.031) (0.076) (0.054) (0.024)
(0.000) (0.000) (0.000) (0.003) (0.953) (0.843)
(0.000) (0.001) (0.132)
(0.912) (0.753) (0.596) (0.856) (0.956) (0.833)
(0.000) (0.000) (0.041)
Europe North AmericaVariables
OECD
(0.000) (0.000) (0.000) (0.000) (0.238) (0.263)
(0.000) (0.000) (0.000) (0.000) (0.670) (0.432)
(0.008) (0.002) (0.042) (0.027) (0.154) (0.067)
(0.000) (0.000) (0.019)
(0.630) (0.227) (0.415) (0.090) (0.678) (0.736)
(0.002) (0.009) (0.040) (0.269) (0.866) (0.922)
Controls
Rural dummy -0.189 -0.199 -0.081 -0.093 -0.250 -0.288
(0.056) (0.250) (0.530) (0.408) (0.170) (0.064)
Urban dummy 0.041 -0.009 0.124 0.093 -0.239 -0.301
(0.750) (0.936) (0.446) (0.509) (0.300) (0.128)
Capital dummy -0.484 -0.394 -0.464 -0.332 -0.791 -0.781
(0.007) (0.013) (0.031) (0.076) (0.054) (0.024)
GDP dummy 1.908 1.511 1.789 1.167 -0.220 0.646
(0.000) (0.000) (0.000) (0.003) (0.953) (0.843)
NAT dummies yes yes yes yes yes yes
Obs 270 270 201 201 61 61
R2-adj 0.897 0.905 0.909 0.918 0.661 0.741
LIK -198.248 -187.226 -151.044 -140.113 -36.415 -33.841
(0.000) (0.001) (0.132)
(0.912) (0.753) (0.596) (0.856) (0.956) (0.833)
(0.000) (0.000) (0.041)
(0.000) (0.000) (0.000) (0.000) (0.238) (0.263)
(0.000) (0.000) (0.000) (0.000) (0.670) (0.432)
(0.008) (0.002) (0.042) (0.027) (0.154) (0.067)
(0.000) (0.000) (0.019)
(0.630) (0.227) (0.415) (0.090) (0.678) (0.736)
(0.002) (0.009) (0.040) (0.269) (0.866) (0.922)
(0.056) (0.250) (0.530) (0.408) (0.170) (0.064)
(0.750) (0.936) (0.446) (0.509) (0.300) (0.128)
(0.007) (0.013) (0.031) (0.076) (0.054) (0.024)
(0.000) (0.000) (0.000) (0.003) (0.953) (0.843)
Moran’s I 3.583 3.300 1.505
(0.000) (0.001) (0.132)
LM-ERR 0.012 0.099 0.281 0.033 0.003 0.044
(0.912) (0.753) (0.596) (0.856) (0.956) (0.833)
LM-LAG 20.691 18.788 4.197
(0.000) (0.000) (0.041)
21/04/23 Geography of innovation in OECD regions
Pag.17
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
21/04/23 Geography of innovation in OECD regions
Pag.18
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?
21/04/23 Geography of innovation in OECD regions
Pag.19
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?
Knowledge flows
• Knowledge flows occur when an idea generated by one particular institution is learned by another institution.
• The learning process creates the availability of the new idea that becomes part of what is called ‘accessible knowledge’
• Knowledge may flow through at least four different channels: traded goods, labor mobility, transaction-based flows and knowledge spillovers
• Channels may be internal or external with respect to firms
IAREG 22 Intangible assets & regional economic growth
IAREG 23 Intangible assets & regional economic growth
• To provide a review of the main contributions in the literature
• To contribute to the analysis of knowledge flows (proxied by citations) across European regions and to investigate on their main determinants
• To examine whether geographical distance and spatial contiguity influence knowledge links
• To investigate on the evolution of such flows along time
• To investigate on specific sector features of such flows
• To investigate on cross-border flows
Research line
Knowledge flows
• Knowledge flows occur when an idea generated by one particular institution is learned by another institution.
• The learning process creates the availability of the new idea that becomes part of what is called ‘accessible knowledge’
• Knowledge may flow through at least four different channels: traded goods, labor mobility, transaction-based flows and knowledge spillovers (depend on organisational, social, institutional and geographical proximity)
• Channels may be intra- or inter-firms
IAREG 24 Intangible assets & regional economic growth
IAREG 25 Intangible assets & regional economic growth
Distribution of citations for country of origin and destination, 1980-2000
national international
number of
regions abs. values % of total abs. values % of total
Austria 9 2.552 1,1 7.243 3,2 Belgium 11 4.311 1,8 8.102 3,6 Czech Rep. 8 25 0,0 203 0,1 Denmark 1 2.428 1,0 4.574 2,0 Finland 5 3.005 1,3 5.895 2,6 France 22 34.406 14,4 35.430 15,6 Germany 39 126.589 53,1 68.139 30,0 Greece 13 28 0,0 262 0,1 Hungary 7 185 0,1 716 0,3 Ireland 2 192 0,1 856 0,4 Italy 21 12.210 5,1 19.996 8,8 Luxembourg 1 170 0,1 358 0,2 Netherlands 12 9.823 4,1 15.100 6,6 Norway 7 616 0,3 1.854 0,8 Poland 16 23 0,0 139 0,1 Portugal 5 1 0,0 52 0,0 Slovak Rep. 4 2 0,0 68 0,0 Spain 17 773 0,3 3.595 1,6 Sweden 8 6.294 2,6 10.766 4,7 Switzerland 7 11.288 4,7 17.032 7,5 Turkey 26 1 0,0 65 0,0 UK 37 23.280 9,8 26.832 11,8 TOTAL 278 238.203 100 227.276 100
IAREG 26
national international intraregional contiguous reg. not contiguous reg. contiguous reg. not contiguous reg.
Country abs. val. % of tot abs. val. % tot abs. val. % tot abs. val. % tot abs. val. % tot Austria 1987 20,3% 351 3,6% 213 2,2% 276 2,8% 6966 71,1% Belgium 2964 23,9% 877 7,1% 470 3,8% 190 1,5% 7913 63,7% Czech Rep. 20 8,6% 2 0,7% 4 1,7% 1 0,3% 203 88,7% Denmark 2428 34,7% - - - - 54 0,8% 4520 64,5% Finland 2309 25,9% 479 5,4% 217 2,4% 2 0,0% 5893 66,2% France 21818 31,2% 3209 4,6% 9379 13,4% 600 0,9% 34830 49,9% Germany 53678 27,6% 22122 11,4% 50789 26,1% 1213 0,6% 66927 34,4% Greece 27 9,2% 0 0,0% 1 0,4% 0 0,0% 262 90,4% Hungary 160 17,7% 19 2,1% 6 0,7% 0 0,0% 716 79,4% Ireland 179 17,0% 14 1,3% 0 0,0% 1 0,1% 855 81,5% Italy 8249 25,6% 2073 6,4% 1888 5,9% 416 1,3% 19580 60,8% Luxembourg 170 32,2% - - - - 30 5,8% 328 62,0% Netherlands 7489 30,0% 1492 6,0% 842 3,4% 265 1,1% 14835 59,5% Norway 464 18,8% 73 3,0% 79 3,2% 16 0,6% 1838 74,4% Poland 22 13,6% 0 0,3% 0 0,3% 0 0,0% 139 85,8% Portugal 1 1,8% 0 0,0% 0 0,0% 0 0,0% 52 98,2% Slovak Rep. 2 3,4% 0 0,0% 0 0,0% 1 1,4% 67 95,2% Spain 643 14,7% 33 0,8% 98 2,2% 18 0,4% 3577 81,9% Sweden 4373 25,6% 853 5,0% 1068 6,3% 16 0,1% 10750 63,0% Switzerland 6220 22,0% 2434 8,6% 2634 9,3% 803 2,8% 16229 57,3% Turkey 1 0,8% 0 0,3% 0 0,0% 0 0,0% 65 99,0% UK 10850 21,7% 3922 7,8% 8509 17,0% 1 0,0% 26831 53,5% TOTAL 124053 26,7% 37954 8,2% 76197 16,4% 3903 0,8% 223374 48,0%
Distribution of citations for country of origin and destination, 1980-2000
IAREG 27 Intangible assets & regional economic growth
Descriptive statistics (citazioni per capita )
1980 - 19851980 - 19851985 - 19901985 - 19901990 - 19951990 - 19951995 - 20001995 - 2000
IAREG 28 Intangible assets & regional economic growth
Distribution of citations for destination,% on total, 1980-2000
Fig 1 – Distribution of patent citations for destination in percentage on total, 1980-2000
IAREG 29 Intangible assets & regional economic growth
Econometric analysis
• An improvement of previous analysis with an original extended database
• The analysis is performed with an original econometric methodology applied to spatial data in a gravity model developed by Le Sage and Page (2008).
IAREG 30 Intangible assets & regional economic growth
Estimation and variables
• Our dependent variable is the number of citations originated in region i and received by region j. This flow is measured in two periods: 1990-1995 and 1995-2000.
• We consider 219 territorial units (Turkey excluded)• We replicate our analysis for some sectors: two high
tech sectors such as Chemicals and Machinery and a set of sectors which we name Traditionals (which include Food and Beverage, Textiles, Apparels, Leather, Woods and Paper).
IAREG 31 Intangible assets & regional economic growth
Variables
• As for the explanatory variables– GDP per capita– Quota of R&D expenditure– Distance in kilometers.
• As a robustness exercise we test our results– by substituting the R&D variable with the stock of
patents.– to see if there are institutional, structural and
cultural determinants affecting knowledge flows across regions national dummies are inserted
– Results are also tested with respect to the presence of zero’s
IAREG 32 Intangible assets & regional economic growth
Period 1990-1994, total citations, regressors GDPpc, R&D
log flows beta hat t-statistics t-prob
constant 0.0424 13.3954 0.0000
ia 2.0097 40.1695 0.0000
D_GDPpc1 0.0009 2.1444 0.0320
D_RDexp1 0.0911 22.7039 0.0000
O_GDPpc1 0.0013 2.9367 0.0033
O_RDexp1 0.0794 19.9047 0.0000
I_GDPpc1 0.1278 19.9598 0.0000
I_RDexp1 0.2069 3.5702 0.0004
distance -0.0229 -5.1739 0.0000
rho1 0.5256 106.6943 0.0000
rho2 0.5252 107.3323 0.0000
rho3 -0.279 -32.1295 0.0000
log-likelihood function value -31247
IAREG 33 Intangible assets & regional economic growth
Period 1995-2000, total citations, regressors GDPpc, R&D
log flows beta hat t-statistics t-prob
constant 0.076 18.4125 0.0000
ia 2.2203 36.7908 0.0000
D_GDPpc2 0.0026 5.4453 0.0000
D_RDexp2 0.13 26.569 0.0000
O_GDPpc2 0.0022 4.6705 0.0000
O_RDexp2 0.1268 25.9939 0.0000
I_GDPpc2 0.0968 14.1261 0.0000
I_RDexp2 0.3088 4.4234 0.0000
distance -0.0434 -7.9897 0.0000
rho1 0.5529 118.0959 0.0000
rho2 0.5733 125.9773 0.0000
rho3 -0.3346 -42.8144 0.0000
log-likelihood function value -39627
IAREG 35 Intangible assets & regional economic growth
Period 1995-2000, sector Chemicals
log flows beta hat t-statistics t-prob
Constant -0.1021 -16.9834 0.0000
Ia 1.9988 21.0783 0.0000
D_GDPpc2 -0.0055 -7.5918 0.0000
D_RDexp2 0.0889 11.7249 0.0000
O_GDPpc2 -0.0045 -6.1694 0.0000
O_RDexp2 0.0824 10.8767 0.0000
I_GDPpc2 0.1285 11.782 0.0000
I_RDexp2 0.5903 5.276 0.0000
Distance -0.0282 -3.5706 0.0004
rho1 0.3563 58.5878 0.0000
rho2 0.3854 65.6154 0.0000
rho3 -0.0617 -5.361 0.0000
log-likelihood function value -60188
IAREG 37 Intangible assets & regional economic growth
Regressions: Period 1995-2000, sector Machinery
log flows beta hat t-statistics t-prob
constant -0.2353 -24.1137 0.0000
ia 1.7877 14.078 0.0000
D_GDPpc2 -0.0106 -10.7777 0.0000
D_RDexp2 0.0561 5.5399 0.0000
O_GDPpc2 -0.0091 -9.3045 0.0000
O_RDexp2 0.0618 6.1074 0.0000
I_GDPpc2 0.1585 10.8575 0.0000
I_RDexp2 0.6228 4.1534 0.0000
distance -0.0146 -1.4111 0.1582
rho1 0.2825 43.4005 0.0000
rho2 0.3165 50.198 0.0000
rho3 0.0319 2.5548 0.0106
log-likelihood function value -73685
IAREG 39 Intangible assets & regional economic growth
Period 1995-2000, sector Traditional
log flows beta hat t-statistics t-prob
Constant -0.3392 -28.7154 0.000
ia 1.8551 15.085 0.000
D_GDPpc -0.0154 -15.6371 0.000
D_RDexp -0.0044 -0.4527 0.651
O_GDPpc -0.0129 -13.3066 0.000
O_RDexp -0.0104 -1.0639 0.287
I_GDPpc 0.151 10.7148 0.000
I_RDexp 0.7617 5.2557 0.000
distance 0.0446 4.4696 0.000
rho1 0.2965 46.1239 0.000
rho2 0.3392 55.1283 0.000
rho3 0.0072 0.6137 0.539
log-likelihood function value -72167
IAREG 41 Intangible assets & regional economic growth
Period 1995-2000, total citations, regressors: GDPpc, PAT
log flows beta hat t-statistics t-prob
constant 0.1016 23.7058 0.0000
ia 2.2572 37.7623 0.0000
D_GDPpc 0.002 4.4423 0.0000
D_PAT 0.0001 37.9751 0.0000
O_GDPpc 0.0020 4.505 0.0000
O_PAT 0.0001 35.901 0.0000
I_GDPpc 0.1151 17.6248 0.0000
I_PAT 0.0000 0.0751 0.9401
distance -0.0430 -7.9135 0.0000
rho1 0.4994 90.1703 0.0000
rho2 0.5099 92.007 0.0000
rho3 -0.2797 -33.1295 0.0000
log-likelihood function value -38343
IAREG 43 Intangible assets & regional economic growth
Regressions: Period 1995-2000, total citations, regressors GDPpc, R&D, dummy NAT
log flows beta hat t-statistics t-prob
constant 0.0941 21.9189 0.0000
ia 2.1175 32.8255 0.0000
D_GDPpc 0.0093 10.7386 0.0000
D_RD 0.1441 26.2104 0.0000
O_GDPpc 0.0067 7.7307 0.0000
O_RD 0.1426 25.9789 0.0000
I_GDPpc 0.002 0.1583 0.8743
I_RD 0.2743 3.4971 0.0005
distance -0.0631 -9.7949 0.0000
rho1 0.5366 111.8577 0.0000
rho2 0.5568 119.1844 0.0000
rho3 -0.3443 -42.4851 0.0000
National dummies yes
log-likelihood function value -39783
IAREG 45 Intangible assets & regional economic growth
Regressions: Period 1995-2000, total citations, regressors GDPpc, PAT, dummy NAT
log flows beta hat t-statistics t-prob
constant 0.1143 25.8284 0.0000
ia 1.9653 30.6003 0.0000
D_GDPpc 0.0001 0.0877 0.9301
D_PAT 0.0001 34.2396 0.0000
O_GDPpc -0.0017 -1.8394 0.0659
O_PAT 0.0001 32.1882 0.0000
I_GDPpc 0.0215 1.6215 0.1049
I_PAT 0 -0.5961 0.5511
distance -0.0881 -13.5575 0.0000
rho1 0.4894 85.5786 0.0000
rho2 0.4992 86.9675 0.0000
rho3 -0.2865 -32.1263 0.0000
National dummies yes
log-likelihood function value -38727
Main results/1
• Citations as well as patents are concentrated across space but that a process of slow but gradually progressive diffusion is ongoing.
• Clusters of innovative regions appear both at the national and the international level.
• There is a lot of heterogeneity among regional flows and that such differences can be related both to diverse geographical, institutional and industrial settings
IAREG 46 Intangible assets & regional economic growth
IAREG 47 Intangible assets & regional economic growth
• The econometric analysis proves that knowledge flows depend on the weight of origin and destinations regions measured by GDP per capita and R&D investments.
• Moreover, knowledge flows depend on geographic distance and on the weights of neighbouring regions both of the origin and the destination regions.
• Results are maintained when some robustness exercise is performed.
• Finally, sector analysis shows that some results are not robust with respect to the specific feature of the economic structure.
Main results/2
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:
21/04/23 Geography of innovation in OECD regions
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