Table 1.

OLS estimates

	All Second Wave		1st Sept. - 3rd Nov.
	(1)	(2)	(3)	(4)
	Regional FE	Baseline	FE	Baseline
Temperature		-13.12***		-4.945**
		(0.003)		(0.012)
Income per Capita		3.468***		2.608***
		(0.000)		(0.000)
Agriculture Share Population		-0.555		-0.275
		(0.107)		(0.185)
Services Share Population		0.423**		0.262**
		(0.021)		(0.014)
Share families 5+ components		14.53***		6.671***
		(0.000)		(0.004)
Cases First Wave		-0.466**		-0.222***
		(0.011)		(0.003)
Public Transport Trips Concentration		16.15***		10.41***
		(0.000)		(0.000)
Observations	104	104	104	104
$R^2$	.58	.768	.51	.727
$R^2\left(adj\right)$	.491	.693	.406	.639
Region FE	Yes	Yes	Yes	Yes
$H\_0$	-	=(1)	-	= (3)
F-Test	-	9.1 ***	-	9 ***
Critical value (1% sign.)	-	2.9		2.9

Note: Significance levels: * = 0$·$10; ** = 0$·$05; *** = 0$·$01. All specifications use Conley Spatial Standard Errors with a cutoff of 150km. P-values of coefficients in parenthesis. All regressions are controlled for region fixed effects. Therefore, the $\beta$ coefficient on each variable can be interpreted as contributing to increasing (decreasing) Covid-19 cases per capita beyond (below) the regional mean. Specification (1) shows how mean regional differences explain 58% of the variance (49% adjusted for DOF). In specifications 2, we introduce other province-level characteristics and test whether all coefficients are jointly significant to explain more within-region variance in the dependent variable than simple fixed effects ($H_0$, the test statistic and critical value at 0$·$01 significance level reported at the end of the table). Specifications 3 and 4 perform the same exercise but for the pre-regional policy period only (1/09/2020 - 3/11/2020).