Table 2.

The link between death rate, stringency measures, vaccination coverage and tourism recovery from the pandemic (whole sample).

Panel A.	Dependent variable: Hotel		Dependent variable: Overall
	(1)	(2)	(3)	(4)
Death	−0.0198***	−0.0193***	−0.0281***	−0.0284***
	(0.00297)	(0.00348)	(0.00276)	(0.00330)
Stringency	−0.0494***	−0.0542***	−0.0366***	−0.0400***
	(0.00373)	(0.00443)	(0.00347)	(0.00420)
GDPpc		15.57***		15.09***
		(2.836)		(2.888)
Observations	108,315	81,780	108,315	81,780
R-squared (within)	0.005		0.005
Chi squared		489.5		492.2

Panel B.	Dependent variable: Hotel		Dependent variable: Overall
	(1)	(2)	(3)	(4)
Vaccinated	−0.292***	−0.318***	−0.270***	−0.299***
	(0.00244)	(0.00287)	(0.00227)	(0.00273)
Stringency	−0.0324***	−0.0340***	−0.0286***	−0.0297***
	(0.00272)	(0.00317)	(0.00254)	(0.00301)
GDPpc		17.02***		16.45***
		(2.861)		(2.911)
Observations	108,315	81,780	108,315	81,780
R-squared (within)	0.121		0.12
Chi-squared		12,819.3		12,461.7

Notes: (1) Standard errors are in parentheses, country-specific effects are controlled for in all FE estimations; significance levels: * p < 0.05, ** p < 0.01, *** p < 0.001. (2). All variables are log transformed. Basically speaking, we report coefficients of estimates and their standard errors to the third decimal digit. However, for variables which have much smaller absolute values of coefficients and standard errors than other variables, it is reported to the first three significant figures. The accordance of this mixing way of presentation can be found in Cole (2014) introduction to the rule of rounding number. (3) R-squared values are not large in this table. This is acceptable. A detailed discussion about this issue is provided by Frost (2020), p.131, and can be seen in Frost's (2020) blog at https://statisticsbyjim.com/regression/interpret-r-squared-regression/.