Table 3.

Results of a spatial regression model with a spatially-autocorrelated error term and spatially-lagged concentrated disadvantage variables and energy burden in 2020

Independent variables	Unstandardized coefficient (b)	S.E.	z-Statistic	p value
Public assistance	−0.009	0.006	−1.468	0.142
No insurance	0.055	0.012	4.465	0.000
Minority status	0.013	0.004	2.961	0.003
Female household heads	−0.019	0.012	−1.620	0.105
Disable	0.114	0.014	8.222	0.000
Below poverty	0.149	0.012	12.662	0.000
Age 65 or older	0.241	0.013	18.660	0.000
Age 17 or younger	0.014	0.016	0.874	0.382
COVID-19 cases	−0.022	0.020	−1.079	0.280
COVID-19 death	0.621	0.556	1.118	0.264
w-public assistance	−0.046	0.011	−4.361	0.000
w-minority group	0.016	0.005	3.192	0.001
w-age 17 or younger	0.041	0.020	2.043	0.041
w-poverty	0.012	0.019	0.635	0.526
w-female household head	−0.110	0.011	−9.977	0.000
w-P2020 (energy burden)	0.544	0.023	23.947	0.000
Lambda	−0.099
F-statistic	(67, 3074) = , 72.848∗∗∗
N	3142
Adjust R2	0.593

∗p < 0.05, ∗∗p < 0.01, ∗∗∗P < 0.001; coefficients of categorical variables, including 50 states and Washington D.C, were considered as controlled variables, and therefore, were not listed here. Each of the `w-` variables relate the values at a given county to those in surrounding counties. In contrast to the coefficients of the other variables, which reflect global averages, variables such as w-P2020 reflect the average energy burden of every county's neighboring counties. A 1% increase in the energy burden of surrounding counties correlates to a 0.544% increase in energy burden for the average county, and this interpretation can be held for all other `w-` variables.