Table 2.

Variables and modeling results

Variables		Classical regression model (CRM)	Spatial lag model (SLM)	Spatial error model (SEM)
Constant	Coefficient	0.626072	− 18.3936	− 13.5801
	Probability	0.9782	0.3751	0.5933
Population	Coefficient	− 0.0775117	− 0.0392796	− 0.0648459
	Probability	0.4506	0.6582	0.4236
Population density	Coefficient	0.016737	0.0159396	0.0171707
	Probability	0.0000	0.0000	0.0000
People age ≥ 60	Coefficient	− 0.0356168	− 0.039213	− 0.0461551
	Probability	0.0949	0.0303	0.0549
No. of urban people	Coefficient	0.0029676	0.00264053	5.99E−05
	Probability	0.1312	0.1159	0.9745
No. of working age people	Coefficient	0.000895814	0.00406054	0.00481118
	Probability	0.8330	0.2821	0.2972
No. of industrial workers	Coefficient	− 0.000259399	− 0.00575691	− 0.00254137
	Probability	0.9538	0.1824	0.5808
No. of poor people	Coefficient	− 0.00103537	− 0.000795051	− 0.00125877
	Probability	0.3094	0.3677	0.1914
Access to television	Coefficient	− 0.00215033	− 0.00246411	− 0.00112741
	Probability	0.0930	0.0260	0.2994
Literacy rate	Coefficient	0.252333	0.270138	0.209675
	Probability	0.1788	0.0918	0.2793
Lag coeff. (Rho\ρ)	Coefficient	–	0.284915	–
	Probability	–	0.0060	–
Lambda (λ)	Coefficient	–	–	0.519562
	Probability	–	–	0.0001