Table 1.

Models testing the relationship between a hot week and moderate-to-severe food insecurity

	Dependent variable:
	Moderate-to-severe food insecuritya
	(1)	(2)	(3)
Intercept	0.2914	0.1215	0.1261
	(0.2521, 0.3306)	(0.0827, 0.1603)	(0.0860, 0.1661)
	P < 0.0001	P < 0.0001	P < 0.0001
Hot week	0.0054	0.0075	0.0058
	(0.0023, 0.0085)	(0.0040, 0.0110)	(0.0030, 0.0086)
	P = 0.0007	P < 0.00013	P = 0.0001
Area: urban		−0.0345	−0.0331
		(−0.0374, −0.0317)	(−0.0360, −0.0302)
		P < 0.0001	P < 0.0001
Age		0.0011	0.0012
		(0.0010, 0.0011)	(0.0011, 0.0012)
		P < 0.0001	P < 0.0001
Partner: yes		−0.0237	−0.0236
		(−0.0261, −0.0213)	(−0.0260, −0.0212)
		P < 0.0001	P < 0.0001
Gender: male		−0.0167	−0.0166
		(−0.0189, −0.0144)	(−0.0188, −0.0143)
		P < 0.0001	P < 0.0001
Children: yes		0.0424	0.0377
		(0.0398, 0.0451)	(0.0351, 0.0404)
		P < 0.0001	P < 0.0001
Hot days in the last 365 days		0.0061	0.0061
		(0.0050, 0.0071)	(0.0050, 0.0073)
		P < 0.0001	P < 0.0001
Number of countries	150	149	149
Standard deviation of countries	0.244	0.2031	0.2069
Observations	543,852	427,832	427,832
Log likelihood	−233,823.8000	−179,687.0000	−242,523.4000
Akaike information criterion	467,657.5000	359,405.9000	485,078.8000
Bayesian information criterion	467,713.6000	359,581.4000	485,254.3000

^aModerate-to-severe food insecurity with (1) no controls, (2) controls and (3) controls and CBPS-adjustment for covariate imbalance. Two-sided t-tests and 95% CIs and P values are reported under each coefficient. Year and precipitation variables are not shown in the table.