Chia Laguna Resort: un caso studio per il turismo sostenibile
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Transcript Chia Laguna Resort: un caso studio per il turismo sostenibile
Moran scatterplot map, 2002-2004
21/07/2015
Pag.1
Geography of innovation in OECD regions
Moran scatterplot map Europe, 2002-2004
21/07/2015
Pag.2
Geography of innovation in OECD regions
Moran LISA map, 2002-2004
21/07/2015
Pag.3
Geography of innovation in OECD regions
Moran LISA map Europe, 2002-2004
21/07/2015
Pag.4
Geography of innovation in OECD regions
150
Convergence in innnovative efforts?
National level
Korea
100
Japan
Turkey
50
Mexico
Italy
Poland
Spain
0
Slovak Republic
Hungary
Czech Republic
Austria
Iceland
Ireland
New Zealand
Canada
Australia
France
Switzerland
Denmark
Germ
any
United States
Belgium
United Kingdom
Netherlands
Finland
Norway
Greece
Sweden
Portugal
-50
Luxem bourg
0
100
200
PCT per capita 98-00
21/07/2015
Pag.5
Geography of innovation in OECD regions
300
Convergence in innnovative efforts?
Regional level
300.00
250.00
PCT per capita var% 98-00/02-04
200.00
150.00
100.00
50.00
0.00
0.00
100.00
200.00
300.00
-50.00
-100.00
-150.00
PCT per capita 98-00
21/07/2015
Pag.6
Geography of innovation in OECD regions
400.00
500.00
600.00
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.
21/07/2015
Pag.7
Geography of innovation in OECD regions
The determinants of innovative activity at the
local level: knowledge production function
I RD HK DENS
j ,t
j ,t q
1
j ,t s
2
3
j ,t q
DU DR DCAP DGDP
j ,t s
4
j ,t s
5
NAT
6
j ,t s
7
j ,t s
n
c 1
c
jc
j ,t
I = local patents (per capita) in region j
• RD= quota of R&D on GDP (j)
• HK= tertiary education (j)
• DENS= population density (j)
•Note:
• Variables in log
• Time lags are considered
• NAT = national dummies;
• DU, DR, DCAP= dummies for urban, rural, capital regions
• DGDP= dummy for above and below average GDP per capita
21/07/2015
Pag.8
Geography of innovation in OECD regions
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
I RD HK DENS
j ,t
j ,t q
1
j ,t s
2
3
j ,t q
DU DR DCAP DGDP
j ,t s
4
j ,t s
5
j ,t s
6
NAT WI
n
c 1
21/07/2015
Pag.9
c
jc
4
j ,t
Geography of innovation in OECD regions
j ,t
7
j ,t s
Econometric results
Variables
Log (RD)
log (HK)
log (DENS)
OECD
OLS
0.486
(0.000)
1.094
(0.000)
0.070
(0.092)
Urban dummy
Capital dummy
GDP dummy
NAT dummies
Obs
R2-adj
Moran’s I
LM-ERR
LM-LAG
Europe
OLS
0.498
(0.000)
1.072
(0.000)
0.054
(0.438)
-0.201
(0.050)
0.099
(0.452)
-0.543
(0.003)
0.810
(0.000)
yes
0.446
(0.000)
0.991
(0.000)
0.073
(0.045)
0.182
(0.000)
-0.202
(0.026)
0.049
(0.679)
-0.419
(0.010)
0.652
(0.000)
yes
271
0.889
271
0.906
W log (I)
Rural dummy
ML
4.074
(0.000)
0.002
(0.968)
20.551
0.090
(0.764)
ML
North America
OLS
ML
0.548
(0.000)
1.061
(0.262)
0.069
(0.182)
-0.142
(0.280)
0.268
(0.104)
-0.515
(0.019)
0.935
(0.000)
yes
0.461
(0.000)
0.886
(0.000)
0.059
(0.320)
0.229
(0.000)
-0.130
(0.248)
0.230
(0.103)
-0.338
(0.073)
0.713
(0.000)
yes
-0.236
(0.197)
-0.271
(0.243)
-0.815
(0.440)
0.466
(0.078)
yes
0.479
(0.000)
1.086
(0.008)
0.076
(0.093)
0.153
(0.016)
-0.279
(0.080)
-0.342
(0.092)
-0.821
(0.018)
0.375
(0.103)
yes
201
0.901
201
0.920
61
0.679
61
0.747
3.619
(0.000)
0.401
(0.526)
22.653
0.065
(0.799)
1.656
(0.098)
0.013
(0.909)
3.990
0.143
(0.706)
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
OECD
Variables
Log (RD)
log (HK)
log (DENS)
OLS
Europe
ML
OLS
0.571
0.586
0.600
0.619
0.768
0.605
(0.000)
(0.000)
(0.000)
(0.292)
(0.332)
1.087
0.953
0.969
0.780
1.020
1.408
(0.000)
(0.000)
(0.000)
(0.000)
(0.474)
(0.253)
0.100
0.106
0.126
0.114
0.073
0.081
(0.015)
(0.004)
(0.080)
(0.062)
(0.171)
(0.074)
0.176
0.223
(0.000)
DGDP*log(HK)
North America
OLS
ML
(0.000)
W log (I)
DGDP*log(RD)
ML
0.160
(0.000)
(0.013)
-0.104
-0.191
-0.155
-0.262
-0.230
-0.141
(0.399)
(0.085)
(0.291)
(0.041)
(0.753)
(0.823)
-0.488
0.359
-0.433
-0.201
0.000
-0.400
(0.002)
(0.011)
(0.027)
(0.246)
(0.999)
(0.747)
-0.203
-0.210
-0.950
-0.102
-0.232
-0.277
(0.042)
(0.017)
(0.462)
(0.357)
(0.213)
(0.081)
Controls
Rural dummy
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)
-0.478
-0.377
-0.445
-0.300
-0.784
-0.763
(0.007)
(0.018)
(0.038)
(0.105)
(0.062)
(0.031)
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
2
0.893
0.911
0.905
0.923
0.668
0.750
Capital dummy
GDP dummy
R -adj
LIK
Urban dummy
0.062
0.163
0.151
-0.283
(0.627)
(0.311)
(0.343)
(0.220)
KPF estimation with spatial lag of RD
Capital dummy
GDP dummy
North
America
OLS
Variables
OECD
OLS
Log (RD)
0.603
0.633
0.627
0.507
(0.000)
(0.000)
(0.000)
(0.000)
1.064
0.940
0.964
1.011
(0.000)
(0.000)
(0.000)
(0.033)
0.089
0.118
0.126
0.057
(0.031)
(0.926)
(0.072)
(0.277)
log (HK)
log (DENS)
W log (RD)
Europe
OLS
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)
DGDP*log(HK)
-0.155
-0.180
-0.162
(0.209)
(0.217)
(0.261)
-0.483
-0.424
-0.393
(0.002)
(0.028)
(0.041)
Urban dummy
Capital dummy
GDP dummy
NAT dummies
Obs
R2-adj
Obs
2 (RD)
Log
R
-adj
LIK
log
AIC(HK)
-0.396
-0.415
-0.858
(0.062)
(0.048)
(0.034)
1.923
1.818
1.690
0.513
(0.000)
(0.000)
(0.000)
(0.054)
North
yes
America
OLS
yes
OECD
OLS
yes Europe yes
OLS
271
0.603
0.897
(0.000)
-195.657
201
0.633
0.908
(0.000)
-147.144
201
0.627
0.909
(0.000)
-144.877
61
0.507
0.683
(0.000)
-37.154
1.064
461.314
(0.000)
587.388
0.940
356.289
(0.000)
458.691
0.964
353.755
(0.000)
459.460
0.089
0.118
0.126
1.011
94.307
(0.033)
115.416
0.057
Moran’s I
W log (RD)
(0.031)
3.306
0.253
(0.001)
(0.926)
3.379
0.312
(0.001)
(0.072)
3.290
0.289
(0.001)
(0.277)
1.069
0.214
(0.285)
LM-ERR
W2 log (RD)
(0.006)
0.347
(0.010)
0.566
(0.200)
0.123
(0.556)
(0.452)
(0.160)
0.466
0.280
(0.495)
LM-LAG
DGDP*log(RD)
14.480
-0.155
(0.000)
15.139
-0.180
(0.000)
(0.051)
12.472
-0.162
(0.000)
2.724
(0.209)
(0.217)
(0.261)
-0.483
-0.424
-0.393
(0.002)
(0.028)
(0.041)
SC
log (DENS)
DGDP*log(HK)
Controls
Rural dummy
NAT dummies
Variables
-0.434
(0.014)
(0.726)
(0.099)
Controls
-0.201
-0.092
-0.641
-0.245
(0.041)
(0.471)
(0.613)
(0.178)
0.062
0.163
0.151
-0.283
(0.627)
(0.311)
(0.343)
(0.220)
-0.434
-0.396
-0.415
-0.858
(0.014)
(0.062)
(0.048)
(0.034)
Rural dummy
Urban dummy
Capital dummy
1.923
1.818
1.690
0.513
(0.000)
(0.000)
(0.000)
(0.054)
yes
yes
yes
yes
NAT dummies
Obs
271
201
201
61
0.897
0.908
0.909
0.683
GDP dummy
R2-adj
-0.201
-0.092
-0.641
-0.245
(0.041)
(0.471)
(0.613)
(0.178)
0.062
0.163
0.151
-0.283
(0.627)
(0.311)
(0.343)
(0.220)
-0.434
-0.396
-0.415
-0.858
(0.014)
(0.062)
(0.048)
(0.034)
1.923
1.818
1.690
0.513
(0.000)
(0.000)
(0.000)
(0.054)
yes
yes
yes
yes
271
201
201
61
0.897
0.908
0.909
0.683
Urban dummy
(0.462)
(0.433)
(0.197)
0.187
0.189
-0.271
KPF estimation with distance matrix
Capital dummy
GDP dummy
Europe
Variables
Log (RD)
log (HK)
log (DENS)
OLS
ML
North America
OLS
0.600
0.677
0.548
(0.000)
(0.000)
(0.000)
0.969
0.624
1.061
(0.000)
(0.001)
(0.262)
0.126
0.075
0.069
(0.080)
(0.229)
(0.182)
W log (I)
0.012
-0.155
(0.291)
DGDP*log(HK)
-0.209
-0.169
(0.027)
(0.340)
LIK
log (HK)
AIC
SC
log (DENS)
Moran’s I
W log (I)
LM-LAG
DGDP*log(HK)
(0.103)
-0.433
Obs
Log
(RD)
R2-adj
LM-ERR
DGDP*log(RD)
(0.000)
DGDP*log(RD)
NAT dummies
Variables
(0.253)
(0.183)
(0.243)
-0.445
-0.232
-0.815
(0.038)
(0.223)
(0.044)
1.895
1.032
0.466
(0.000)
(0.012)
yes Europe yes
OLS
ML
(0.078)
Northyes
America
OLS
201
0.600
0.905
(0.000)
-151.127
0.969
362.255
(0.000)
461.354
0.126
201
0.677
0.922
(0.000)
-139.517
0.624
341.034
(0.001)
443.436
0.075
61
0.548
0.679
(0.000)
-38.144
1.061
94.288
(0.262)
113.286
0.069
(0.080)
7.125
(0.229)
(0.182)
2.852
(0.000)
0.780
-0.155
(0.377)
(0.291)
21.236
-0.433
(0.000)
(0.027)
0.012
(0.000)
0.069
-0.209
(0.793)
(0.103)
-0.169
(0.004)
1.355
(0.244)
0.043
(0.836)
(0.340)
Controls
Controls
Rural dummy
Urban dummy
Capital dummy
GDP dummy
Rural dummy
-0.950
-0.088
-0.236
(0.462)
(0.433)
(0.197)
0.187
0.189
-0.271
(0.253)
(0.183)
(0.243)
-0.445
-0.232
-0.815
(0.038)
(0.223)
(0.044)
1.895
1.032
0.466
(0.000)
(0.012)
(0.078)
NAT dummies
yes
yes
yes
Obs
201
201
61
0.905
0.922
0.679
-151.127
-139.517
-38.144
Urban dummy
Capital dummy
GDP dummy
NAT dummies
Obs
R2-adj
LIK
-0.950
-0.088
-0.236
(0.462)
(0.433)
(0.197)
0.187
0.189
-0.271
(0.253)
(0.183)
(0.243)
-0.445
-0.232
-0.815
(0.038)
(0.223)
(0.044)
1.895
1.032
0.466
(0.000)
(0.012)
(0.078)
yes
yes
yes
201
201
61
0.905
0.922
0.679
LIK
-151.127
-139.517
-38.144
AIC
362.255
341.034
94.288
R2-adj
Rural dummy
-0.203
-0.228
(0.045)
(0.010)
KPF estimation with Japan and Korea
Urban dummy
Capital dummy
OECD
Variables
OLS
ML
Log (RD)
0.556
0.574
(0.000)
(0.000)
1.114
0.954
(0.000)
(0.000)
0.093
0.098
(0.030)
(0.009)
log (HK)
log (DENS)
W log (I)
0.185
(0.000)
DGDP*log(RD)
DGDP*log(HK)
-0.113
-0.203
(0.378)
(0.074)
-0.411
-0.293
(0.011)
(0.039)
GDP dummy
NAT dummies
Variables
Obs
Log
(RD)
R2-adj
LIK
log (HK)
AIC
SC
log (DENS)
Moran’s I
W log (I)
LM-ERR
DGDP*log(RD)
LM-LAG
DGDP*log(HK)
0.084
0.016
(0.511)
(0.885)
-0.358
-0.250
(0.042)
(0.106)
1.757
1.333
(0.000)
(0.000)
yes
OLS
OECD
yes
ML
287
0.556
0.878
(0.000)
-222.798
1.114
517.596
(0.000)
649.338
0.093
287
0.574
0.902
(0.000)
-206.251
0.954
486.502
(0.000)
621.903
0.098
(0.030)
4.007
(0.009)
(0.000)
0.234
-0.113
(0.629)
(0.378)
28.261
-0.411
(0.000)
(0.011)
0.185
(0.000)
0.049
-0.203
(0.824)
(0.074)
-0.293
(0.039)
Controls
Rural dummy
-0.203
-0.228
(0.045)
(0.010)
0.084
0.016
(0.511)
(0.885)
-0.358
-0.250
(0.042)
(0.106)
1.757
1.333
(0.000)
(0.000)
NAT dummies
yes
yes
Obs
287
287
0.878
0.902
Urban dummy
Capital dummy
GDP dummy
Controls
Rural dummy
Urban dummy
Capital dummy
GDP dummy
NAT dummies
R2-adj
LIK
-222.798
-206.251
Obs
R2-adj
-0.203
-0.228
(0.045)
(0.010)
0.084
0.016
(0.511)
(0.885)
-0.358
-0.250
(0.042)
(0.106)
1.757
1.333
(0.000)
(0.000)
yes
yes
287
287
0.878
0.902
OECD
Variables
OLS
Europe
ML
OLS
ML
North America
OLS
ML
KPF estimation with PCT per worker
Log (RD)
0.531
0.542
0.564
0.580
0.840
0.686
(0.000) OECD(0.000)
OLS
ML
1.068
0.930
(0.000)Europe
(0.000)
OLS
ML
0.963
0.764
North America
(0.238)
(0.263)
OLS
ML
0.592
0.949
Rural dummy
(0.000)
(0.000)
0.531OECD 0.542
0.110
0.166
OLS
ML
(0.000)
(0.000)
(0.008)
(0.002)
1.068
0.930
0.146
0.531
0.542
(0.000)
(0.000)
(0.000)
(0.000)
0.110
0.166
-0.059
-0.134
1.068
0.930
(0.008)
(0.002)
(0.630)
(0.227)
(0.000)
(0.000)
0.146
-0.488
-0.371
0.110
0.166
(0.000)
(0.002)
(0.009)
(0.008)
(0.002)
-0.059
-0.134
0.146
(0.630)
(0.227)
(0.000)
-0.488
-0.371
-0.059
-0.134
(0.002)
(0.009)
-0.189
-0.199
(0.630)
(0.227)
(0.000)
(0.000)
0.564Europe 0.580
0.146
0.137
OLS
ML
(0.000)
(0.000)
(0.042)
(0.027)
0.963
0.764
0.188
0.564
0.580
(0.000)
(0.000)
(0.000)
(0.000)
0.146
0.137
-0.120
-0.219
0.963
0.764
(0.042)
(0.027)
(0.415)
(0.090)
(0.000)
(0.000)
0.188
-0.402
-0.193
0.146
0.137
(0.000)
(0.040)
(0.269)
(0.042)
(0.027)
-0.120
-0.219
0.188
(0.415)
(0.090)
(0.000)
-0.402
-0.193
-0.120
-0.219
(0.040)
(0.269)
-0.081
-0.093
(0.415)
(0.090)
(0.670)
(0.432)
North America
0.840
0.686
0.074
0.082
OLS
ML
(0.238)
(0.263)
(0.154)
(0.067)
0.592
0.949
0.133
0.840
0.686
(0.670)
(0.432)
(0.019)
(0.238)
(0.263)
0.074
0.082
-0.296
-0.208
0.592
0.949
(0.154)
(0.067)
(0.678)
(0.736)
(0.670)
(0.432)
0.133
0.233
-0.119
0.074
0.082
(0.019)
(0.866)
(0.922)
(0.154)
(0.067)
-0.296
-0.208
0.133
(0.678)
(0.736)
(0.019)
0.233
-0.119
-0.296
-0.208
(0.866)
(0.922)
-0.250
-0.288
(0.678)
(0.736)
DGDP*log(HK)
(0.056)
-0.488
(0.250)
-0.371
(0.530)
-0.402
(0.408)
-0.193
(0.170)
0.233
(0.064)
-0.119
Urban dummy
Controls
0.041
(0.002)
-0.009
(0.009)
0.124
(0.040)
0.093
(0.269)
-0.239
(0.866)
-0.301
(0.922)
R2-adj
NAT dummies
LIK
GDP dummy
(0.750)
-0.189
-0.484
(0.056)
(0.007)
0.041
1.908
-0.189
(0.750)
(0.000)
(0.056)
-0.484
yes
0.041
(0.007)
(0.750)
1.908
270
-0.484
(0.000)
0.897
(0.007)
yes
-198.248
1.908
(0.936)
-0.199
-0.394
(0.250)
(0.013)
-0.009
1.511
-0.199
(0.936)
(0.000)
(0.250)
-0.394
yes
-0.009
(0.013)
(0.936)
1.511
270
-0.394
(0.000)
0.905
(0.013)
yes
-187.226
1.511
(0.446)
-0.081
-0.464
(0.530)
(0.031)
0.124
1.789
-0.081
(0.446)
(0.000)
(0.530)
-0.464
yes
0.124
(0.031)
(0.446)
1.789
201
-0.464
(0.000)
0.909
(0.031)
yes
-151.044
1.789
(0.509)
-0.093
-0.332
(0.408)
(0.076)
0.093
1.167
-0.093
(0.509)
(0.003)
(0.408)
-0.332
yes
0.093
(0.076)
(0.509)
1.167
201
-0.332
(0.003)
0.918
(0.076)
yes
-140.113
1.167
(0.300)
-0.250
-0.791
(0.170)
(0.054)
-0.239
-0.220
-0.250
(0.300)
(0.953)
(0.170)
-0.791
yes
-0.239
(0.054)
(0.300)
-0.220
61
-0.791
(0.953)
0.661
(0.054)
yes
-36.415
-0.220
(0.128)
-0.288
-0.781
(0.064)
(0.024)
-0.301
0.646
-0.288
(0.128)
(0.843)
(0.064)
-0.781
yes
-0.301
(0.024)
(0.128)
0.646
61
-0.781
(0.843)
0.741
(0.024)
yes
-33.841
0.646
AIC
Obs
SC
NAT
dummies
R2-adj
462.496
(0.000)
270
581.244
yes
0.897
442.451
(0.000)
270
564.798
yes
0.905
362.087
(0.000)
201
461.186
yes
0.909
342.226
(0.003)
201
444.628
yes
0.918
94.829
(0.953)
61
118.049
yes
0.661
91.682
(0.843)
61
117.013
yes
0.741
LIK
Moran’s I
Obs
AIC
R2-adj
SC
LM-ERR
LIK
-198.248
3.583
270
462.496
(0.000)
0.897
581.244
0.012
-198.248
-187.226
270
442.451
0.905
564.798
0.099
-187.226
-151.044
3.300
201
362.087
(0.001)
0.909
461.186
0.281
-151.044
-140.113
201
342.226
0.918
444.628
0.033
-140.113
-36.415
1.505
61
94.829
(0.132)
0.661
118.049
0.003
-36.415
-33.841
61
91.682
0.741
117.013
0.044
-33.841
AIC
Moran’s I
LM-LAG
SC
(0.912)
462.496
3.583
20.691
581.244
(0.000)
(0.000)
(0.753)
442.451
(0.596)
362.087
3.300
18.788
461.186
(0.001)
(0.000)
(0.856)
342.226
(0.956)
94.829
1.505
4.197
118.049
(0.132)
(0.041)
117.013
Variables
log (HK)
Log (RD)
log (DENS)
Variables
log (HK)
W log
(I)
Log
(RD)
log (DENS)
DGDP*log(RD)
log
(HK)
W log (I)
DGDP*log(HK)
log
(DENS)
DGDP*log(RD)
W log (I)
DGDP*log(HK)
Controls
DGDP*log(RD)
Rural dummy
Capital dummy
Controls
Urban dummy
GDP
Rural dummy
dummy
Capital dummy
NAT dummies
Urban
dummy
GDP dummy
Obs
Capital
dummy
564.798
444.628
(0.833)
91.682
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
21/07/2015
Pag.17
Geography of innovation in OECD regions
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?
21/07/2015
Pag.18
Geography of innovation in OECD regions
The research agenda for
what 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?
21/07/2015
Pag.19
Geography of innovation in OECD regions
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, transactionbased flows and knowledge spillovers
• Channels may be internal or external with respect to
firms
IAREG 22
Intangible assets & regional economic growth
Research line
• 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
IAREG 23
Intangible assets & regional economic growth
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
Distribution of citations for country of origin and
destination, 1980-2000
Austria
Belgium
Czech Rep.
Denmark
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Luxembourg
Netherlands
Norway
Poland
Portugal
Slovak Rep.
Spain
Sweden
Switzerland
Turkey
UK
TOTAL
IAREG 25
national
number of
abs. values
% of total
regions
9
2.552
1,1
11
4.311
1,8
8
25
0,0
1
2.428
1,0
5
3.005
1,3
22
34.406
14,4
39
126.589
53,1
13
28
0,0
7
185
0,1
2
192
0,1
21
12.210
5,1
1
170
0,1
12
9.823
4,1
7
616
0,3
16
23
0,0
5
1
0,0
4
2
0,0
17
773
0,3
8
6.294
2,6
7
11.288
4,7
26
1
0,0
37
23.280
9,8
278
238.203
100
Intangible assets & regional economic growth
international
abs. values
7.243
8.102
203
4.574
5.895
35.430
68.139
262
716
856
19.996
358
15.100
1.854
139
52
68
3.595
10.766
17.032
65
26.832
227.276
% of total
3,2
3,6
0,1
2,0
2,6
15,6
30,0
0,1
0,3
0,4
8,8
0,2
6,6
0,8
0,1
0,0
0,0
1,6
4,7
7,5
0,0
11,8
100
Distribution of citations for country of origin and
destination, 1980-2000
Country
Austria
Belgium
Czech Rep.
Denmark
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Luxembourg
Netherlands
Norway
Poland
Portugal
Slovak Rep.
Spain
Sweden
Switzerland
Turkey
UK
TOTAL
IAREG 26
intraregional
abs. val.
% of tot
1987
20,3%
2964
23,9%
20
8,6%
2428
34,7%
2309
25,9%
21818
31,2%
53678
27,6%
27
9,2%
160
17,7%
179
17,0%
8249
25,6%
170
32,2%
7489
30,0%
464
18,8%
22
13,6%
1
1,8%
2
3,4%
643
14,7%
4373
25,6%
6220
22,0%
1
0,8%
10850
21,7%
124053
26,7%
national
contiguous reg.
not contiguous reg.
abs. val. % tot
abs. val.
% tot
351
3,6%
213
2,2%
877
7,1%
470
3,8%
2
0,7%
4
1,7%
479
5,4%
217
2,4%
3209
4,6%
9379 13,4%
22122 11,4%
50789 26,1%
0
0,0%
1
0,4%
19
2,1%
6
0,7%
14
1,3%
0
0,0%
2073
6,4%
1888
5,9%
1492
6,0%
842
3,4%
73
3,0%
79
3,2%
0
0,3%
0
0,3%
0
0,0%
0
0,0%
0
0,0%
0
0,0%
33
0,8%
98
2,2%
853
5,0%
1068
6,3%
2434
8,6%
2634
9,3%
0
0,3%
0
0,0%
3922
7,8%
8509 17,0%
37954
8,2%
76197 16,4%
international
contiguous reg.
not contiguous reg.
abs. val.
% tot abs. val.
% tot
276 2,8%
6966 71,1%
190 1,5%
7913 63,7%
1 0,3%
203 88,7%
54 0,8%
4520 64,5%
2 0,0%
5893 66,2%
600 0,9%
34830 49,9%
1213 0,6%
66927 34,4%
0 0,0%
262 90,4%
0 0,0%
716 79,4%
1 0,1%
855 81,5%
416 1,3%
19580 60,8%
30 5,8%
328 62,0%
265 1,1%
14835 59,5%
16 0,6%
1838 74,4%
0 0,0%
139 85,8%
0 0,0%
52 98,2%
1 1,4%
67 95,2%
18 0,4%
3577 81,9%
16 0,1%
10750 63,0%
803 2,8%
16229 57,3%
0 0,0%
65 99,0%
1 0,0%
26831 53,5%
3903 0,8%
223374 48,0%
Descriptive statistics (citazioni per capita )
1985
1980 - 1990
1990
1995
1985
1995
2000
IAREG 27
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 28
Intangible assets & regional economic growth
Econometric analysis
•
•
IAREG 29
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).
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 30
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 31
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
-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
distance
log-likelihood function value
IAREG 32
Intangible assets & regional economic growth
-31247
Period 1995-2000, total citations, regressors
GDPpc, R&D
log flows
beta hat
t-statistics
t-prob
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
-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
constant
distance
log-likelihood function value
IAREG 33
Intangible assets & regional economic growth
-39627
Period 1995-2000, sector Chemicals
log flows
beta hat
t-statistics
t-prob
Constant
-0.1021
-16.9834
0.0000
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
-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
Ia
Distance
log-likelihood function value
IAREG 35
Intangible assets & regional economic growth
-60188
Regressions: Period 1995-2000, sector Machinery
log flows
beta hat
t-statistics
t-prob
constant
-0.2353
-24.1137
0.0000
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
-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
ia
distance
log-likelihood function value
IAREG 37
Intangible assets & regional economic growth
-73685
Period 1995-2000, sector Traditional
log flows
beta hat
t-statistics
t-prob
Constant
-0.3392
-28.7154
0.000
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
ia
log-likelihood function value
IAREG 39
Intangible assets & regional economic growth
-72167
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
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
-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
D_GDPpc
distance
log-likelihood function value
IAREG 41
Intangible assets & regional economic growth
-38343
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
0.002
0.1583
0.8743
0.2743
3.4971
0.0005
-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
I_GDPpc
I_RD
distance
National dummies
yes
log-likelihood function value
IAREG 43
Intangible assets & regional economic growth
-39783
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
-0.0017
-1.8394
0.0659
O_PAT
0.0001
32.1882
0.0000
I_GDPpc
0.0215
1.6215
0.1049
0
-0.5961
0.5511
-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
O_GDPpc
I_PAT
distance
National dummies
yes
log-likelihood function value
IAREG 45
Intangible assets & regional economic growth
-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
Main results/2
• 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.
IAREG 47
Intangible assets & regional economic growth
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:
[email protected]
21/07/2015
Pag.48
Geography of innovation in OECD regions