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 8

Results of subregional SDM model.

Eastern region				Middle region				Western region
CO	2.76 (2.13)	WGOV	2.16 (1.05)	CO	3.92 (2.64)	WGOV	9.72 (2.31)	CO	0.34 (0.28)	WGOV	2.38 (0.31)
CO^2	−2.20 (−2.41)	WIND	−3.43 (−1.44)	CO^2	−3.21 (−2.58)	WIND	−0.98 (−0.67)	CO^2	−0.28 (−0.32)	WIND	3.26 (1.53)
HUM	−0.01 (−0.77)	WOPE	−0.13 (−1.47)	HUM	−0.04 (−0.61)	WOPE	1.58 (4.0341)	HUM	0.05 (0.75)	WOPE	0.13 (0.45)
HUM^2	7.00E − 04 (2.05)	Wdep.var.	−0.24 (−1.29)	HUM^2	3.30E − 03 (0.61)	Wdep.var.	−1.00 (−3.15)	HUM^2	−5.10E − 03 (−0.83)	Wdep.var.	0.03 (0.16)
HUM^3	−9.00E − 06 (−2.08)	Fixed time	Y	HUM^3	−1.00E − 04 (−0.57)	Fixed time	Y	HUM^3	1.00E − 04 (0.86)	Fixed time	Y
BASE	2.40 (2.22)	Fixed region	Y	BASE	4.49 (1.17)	Fixed region	Y	BASE	0.56 (0.67)	Fixed region	Y
GOV	0.53 (0.44)	Observations	77	GOV	−1.98 (−1.66)	Observations	63	GOV	−7.51 (−2.32)	Observations	70
IND	0.94 (0.81)	Log-likelihood	191.04	IND	−0.64 (−1.43)	Log-likelihood	170.38	IND	−0.41 (−0.69)	Log-likelihood	166.42
OPE	0.02 (0.44)	R^2	0.95	OPE	−0.33 (−2.97)	R^2	0.92	OPE	0.18 (1.36)	R^2	0.93
WCO	−1.65 (−0.66)	Sigma^2	5.00E − 04	WCO	4.33 (0.95)	Sigma^2	4.00E − 04	WCO	−5.22 (−1.51)	Sigma^2	5.00E − 04
WCO^2	2.64 (1.53)	LR_lag	53.49	WCO^2	−4.67 (−1.19)	LR_lag	34.26	WCO^2	4.34 (1.70)	LR_lag	11.8583
WHUM	0.07 (3.50)	Wald_lag	44.32	WHUM	−0.19 (−0.60)	Wald_lag	31.39	WHUM	−0.08 (−0.61)	Wald_lag	9.1877
WHUM^2	−0.01 (−5.20)	LR_error	55.24	WHUM^2	0.02 (0.74)	LR_error	30.03	WHUM^2	0.01 (0.59)	LR_error	12.1627
WHUM^3	7.40E − 05 (5.46)	Wald_error	35.91	WHUM^3	−0.02 (−0.87)	Wald_error	24.89	WHUM^3	−2.00E − 04 (−0.56)	Wald_error	8.9445
WBASE	2.56 (1.22)	Hausman	−55.27	WBASE	11.87 (1.22)	Hausman	−10.54	WBASE	7.29 (2.37)	Hausman	−24.7434

Note., , and are statistically significant at the 10%, 5%, and 1% levels, respectively. The t-statistic is in parentheses.