Spatial differentiation and driving factors of the high-quality development of undertakings for the aged of China

Table 7 Estimated coefficients of the spatial SEM, SAR, SDM, SAC models

Type of variables	Variables	OLS	SEM	SAR	SDM	SAC1	SAC2
Space lag term	Rho			0.148** (0.072)	0.235 *** (0.058)	0.746 *** (0.086)	0.739 *** (0.153)
Space lag term	Lambda		0.300 *** (0.061)			-0.702 *** (0.173)	-0.487 (0.567)
Economy factors	The GDP per capita	0.034 ** (0.016)	0.038 ** (0.012)	0.033 ** (0.014)	0.049 ** (0.021)	0.036 *** (0.013)	0.034 ** (0.014)
Economy factors	The proportion of the tertiary industry	0.023 *** (0.004)	0.023 *** (0.005)	0.022 *** (0.005)	0.020 *** (0.006)	0.018 *** (0.005)	0.022 *** (0.006)
Population aging factors	The dependency ratio of the older adults	-0.003 (0.012)	0.001 (0.013)	0.002 (0.014)	-0.004 (0.012)	0.006 (0.010)	-0.002 (0.013)
Population aging factors	The proportion of older adults	-0.074 *** (0.014)	-0.091 *** (0.012)	-0.078 *** (0.013)	-0.098 *** (0.011)	-0.081 *** (0.009)	-0.079 *** (0.012)
The digital technology factors	Ln(the fixed asset investment in digital industries)	0.040 * (0.023)	0.037 ** (0.018)	0.035 * (0.019)	0.027 (0.017)	0.028 * (0.016)	0.036 * (0.018)
The digital technology factors	Ln(the number of employees in digital industries)	-0.089 (0.047)	-0.039 (0.093)	0.086 (0.105)	-0.046 (0.103)	0.071 (0.075)	-0.046 (0.095)
R-square		0.309	0.375	0.292	0.201	0.462	0.3465
AIC		-246.339	-254.533	-247.704	-267.524	-267.159	-259.985
BIC		-222.680	-227.494	-220.664	-220.2055	-236.740	-229.566
Log-likelihood		130.169	135.267	131.852	147.762	142.579	138.993

Note: *, ** and *** indicate significance at the level of 0.1, 0.05, 0.01, respectively. Rho, Spatial Autoregressive Parameters; Lambda, Residual lag parameter; Ln, natural logarithm; the SAC1 model is based on binary adjacency matrix, the SAC2 model is based on the geographical distance matrix. The value in parentheses represents the standard deviation

ISSN: 1475-9276