Why Is Collaborative Agglomeration of Innovation so Important for Improving Regional Innovation Capabilities? A Perspective Based on Collaborative Agglomeration of Industry-University-Research Institution
Table 7
Results of spatial models.
Knowledge innovation stage
Outcome transformation stage
OLS
SAR
SEM
SDM
SDEM
OLS
SAR
SEM
SDM
SDEM
CO
2.71 (4.27)
2.32 (4.28)
2.22 (3.77)
2.01 (3.15)
2.09 (3.25)
−1.70 (−2.83)
−1.70 (−2.89)
−1.71 (−2.91)
−1.66 (−2.23)
−1.63 (−2.19)
CO2
−1.89 (−4.08)
−1.64 (−4.17)
−1.57 (−3.66)
−1.51 (−3.05)
−1.56 (−3.13)
1.20 (2.76)
1.20 (2.81)
1.22 (2.85)
1.08 (1.87)
1.05 (1.82)
HUM
0.01 (1.31)
2.30E − 03 (0.44)
−1.00E − 03 (−0.17)
−0.01 (−2.28)
−0.01 (−2.06)
3.10E − 03 (0.53)
3.10E − 03 (0.54)
2.90E − 03 (0.51)
0.03 (0.35)
2.90E − 03 (0.38)
HUM2
−5.00E − 04 (−1.6601)
−2.00E − 04 (−0.71)
−3.50E − 05 (−0.12)
7.00E − 04 (2.25)
6.00E − 04 (2.03)
−2.00E − 04 (−0.71)
−2.00E − 04 (−0.73)
−2.00E − 04 (−0.68)
−2.00E − 04 (−0.60)
−2.00E − 04 (−0.65)
HUM3
7.00E − 06 (1.81)
3.00E − 06 (0.89)
1.00E − 06 (0.35)
−7.00E − 06 (−1.85)
−7.00E − 06 (−1.66)
3.00E − 06 (0.81)
3.00E − 06 (0.83)
3.00E − 06 (0.75)
5.00E − 06 (1.00)
5.00E − 06 (1.05)
BASE
−0.56 (−0.89)
−0.42 (−0.80)
−0.60 (−1.03)
0.10 (0.15)
0.18 (0.28)
1.05 (1.78)
1.05 (1.82)
1.10 (1.93)
0.89 (1.23)
0.85 (1.17)
GOV
1.46 (2.15)
1.22 (2.12)
1.05 (1.69)
0.94 (1.48)
0.99 (1.55)
2.92 (4.56)
2.92 (4.60)
2.96 (4.72)
2.63 (3.54)
2.63 (3.53)
IND
0.07 (0.22)
0.04 (0.16)
0.12 (0.39)
−0.01 (−0.01)
−0.04 (−0.11)
0.60 (1.94)
0.60 (1.97)
0.63 (2.08)
0.34 (0.96)
0.33 (0.94)
OPE
0.05 (1.75)
0.04 (1.69)
0.05 (1.69)
0.03 (0.80)
0.03 (0.80)
−0.07 (−2.39)
−0.07 (−2.44)
−0.07 (−2.48)
−0.08 (−1.99)
−0.08 (−1.97)
WCO
0.39 (0.26)
1.36 (0.83)
−0.09 (−0.05)
0.17 (0.11)
WCO2
0.03 (0.03)
−0.65 (−0.51)
0.44 (0.33)
0.25 (0.20)
WHUM
0.08 (5.19)
0.09 (5.37)
5.00E − 04 (0.03)
1.00E − 04 (0.01)
WHUM2
−4.00E − 03 (−5.33)
−4.60E − 03 (−5.53)
2.00E − 04 (0.26)
2.00E − 04 (0.32)
WHUM3
5.20E − 05 (5.17)
5.90E − 05 (5.41)
−6.00E − 06 (−0.58)
−7.00E − 06 (−0.65)
WBASE
1.92 (1.40)
2.19 (1.43)
0.70 (0.44)
0.41 (0.27)
WGOV
1.23 (0.98)
1.95 (1.37)
0.96 (0.64)
0.28 (0.21)
WIND
−1.06 (−1.22)
−1.29 (−1.32)
1.08 (1.07)
0.90 (0.97)
WOPE
−0.06 (−1.04)
−0.06 (−0.89)
−0.03 (−0.40)
−0.01 (−0.17)
Wdep.var.
0.39 (4.85)
0.27 (2.99)
0.0030 (0.03)
−0.21 (−1.60)
spat.aut.
0.38 (4.57)
0.23 (2.39)
−0.09 (−0.75)
−0.19 (−1.43)
Fixed time
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Fixed region
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Observations
210
210
210
210
210
210
210
210
210
210
Log-likelihood
437.66
435.60
456.81
456.04
441.72
441.98
447.39
447.56
R2
0.13
0.91
0.88
0.92
0.91
0.21
0.93
0.93
0.93
0.93
Sigma2
1.00E − 03
7.00E − 04
8.00E − 04
7.00E − 04
7.00E − 04
9.00E − 04
9.00E − 04
9.00E − 04
9.00E − 04
9.00E − 04
LM_lag/LR_lag
68.65
22.28
41.85
38.30
36.75
21.02
5.68
5.33
11.35
11.68
R_LM_lag/Wald_lag
20.36
5.77
0.37
30.31
41.33
3.79
0.11
0.10
9.85
10.13
LM_error/LR_error
55.45
19.78
71.83
42.42
40.88
17.72
17.58
16.93
10.83
11.15
R_LM_error/Wald_error
7.17
3.26
30.35
31.21
45.56
0.49
12.01
11.70
8.92
9.06
Hausman
−113.77
−47.60
−338.09
−38.15
−34.20
−69.48
−42.11
−92.46
Note.,, and are statistically significant at the 10%, 5%, and 1% levels, respectively. The t-statistic is in parentheses.
