Cross-border mobility responses to COVID-19 in Europe: new evidence from facebook data

Table 2 Feature ranking by origin and destination

	Panel A					Panel B
	Corridor & Day dummies					Corr. dum.
Features	Linear	KNN	G-Boost	MLP	Avg.	Avg.
Indiv. features
Origin - C1 School closures	100	69	100	59	82	85
Destin - C1 School closures	94	60	36	85	68	68
Destin - C3 Cancel public events	19	64	77	68	57	57
Origin - C3 Cancel public events	10	100	33	65	52	46
Origin - C7 Restr. Internal movement	0	93	16	100	52	51
Origin - H2 Testing policy	14	7	87	92	50	43
Origin - C4 Restrictions gatherings	50	51	40	54	49	44
Origin - C6 Stay home requirements	92	16	12	59	45	21
Destin - C6 Stay home requirements	17	15	96	54	45	45
Destin - C4 Restrictions gatherings	6	48	70	25	37	42
Destin - C7 Restr. Internal movement	13	66	12	58	37	29
Origin - H3 Contact tracing	5	12	27	44	22	22
Origin - C8 International travel bans	1	1	45	40	22	18
Origin - New Covid deaths	0	7	73	5	21	46
Origin - New Covid cases	11	0	21	52	21	75
Destin - New Covid deaths	6	10	64	0	20	49
Destin - C5 Close public transport	34	6	1	39	20	9
Destin - H3 Contact tracing	31	21	2	14	17	7
Destin - C2 Workplace closing	5	33	7	23	17	21
Destin - C8 International travel bans	0	12	20	32	16	24
Destin - New Covid cases	12	0	43	6	15	38
Origin - C5 Close public transport	18	7	0	31	14	12
Origin - C2 Workplace closing	1	23	10	14	12	23
Destin - H2 Testing policy	9	0	2	10	4	6
Synthetic features
Destin - Component 1	100	100	100	100	100	100
Origin - Component 1	13	75	20	49	39	48
Origin - Component 2	1	55	17	19	23	26
Destin - Component 2	9	50	0	0	15	17
Origin - New Covid cases	0	0	18	30	12	75
Destin - New Covid deaths	5	14	6	11	9	49
Origin - New Covid deaths	1	13	3	9	6	46
Destin - New Covid cases	0	2	1	6	2	38

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 (col. 6). Directional priors are always used to identify the effects of origin- and destination-specific features. The importance values of each feature is computed over 10 permutations using the negative mean absolute error (MAE). The resulted values are scaled between 0% and 100% separately for each model. The col. ‘Avg.’ averages the results obtained with the four learning techniques. The features are ranked according to the average importance of the models including the day/corridor dummies (Panel A). In Panel B, we only report the ‘Avg.’ score without reporting the model-specific results

ISSN: 1744-8603