Table 5.

Averaged feature ranking across models and specifications without directional priors

	Panel A: Corridor & Day dummies					Panel B: Corridor dummies
Features	Linear	KNN	G-Boost	MLP	Avg.	Linear	KNN	G-Boost	MLP	Avg.
C1 School closures	100%	78%	98%	91%	93%	100%	100%	86%	86%	93%
C3 Cancel public events	14%	100%	79%	84%	69%	21%	100%	60%	76%	64%
C6 Stay home requirement	56%	18%	78%	71%	56%	25%	23%	64%	38%	38%
C7 Restr. Internal movement	6%	97%	20%	100%	56%	1%	84%	27%	100%	53%
C4 Restrict gatherings	28%	60%	80%	49%	54%	9%	76%	65%	64%	53%
H2 Testing policy	11%	4%	64%	64%	36%	26%	2%	49%	30%	36%
New Covid deaths	3%	10%	100%	0%	28%	0%	77%	100%	0%	44%
C8 Inter. travel controls	0%	8%	47%	44%	25%	3%	17%	44%	32%	24%
H3 Contact tracing	18%	20%	20%	35%	23%	12%	1%	19%	32%	16%
New Covid cases	11%	0%	46%	35%	23%	11%	76%	63%	87%	59%
C5 Close public transport	26%	7%	0%	43%	19%	15%	0%	0%	31%	11%
C2 Workplace closing	2%	34%	12%	21%	17%	2%	46%	23%	33%	26%

Notes: The different features are ranked following the permutation importance method. For each approach, we provide results obtained with the model including day/corridor dummies (cols. 1-5) and the version including corridors dummies only (cols. 6-10). Directional priors are not included. The importance values of each feature is computed over 10 permutations using the negative mean absolute error (MAE). The origin- and destination-specific features importance are aggregated by taking the mean between the 2. Finally, the resulted values are scaled between 0% and 100% separately for each model. The last column in each panel presents the mean value of importance averaged over the four models. The features are ranked according to the average importance of the models including the corridor and day dummies