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

Abstract

Background: Assessing the impact of government responses to Covid-19 is crucial to contain the pandemic and improve preparedness for future crises. We investigate here the impact of non-pharmaceutical interventions (NPIs) and infection threats on the daily evolution of cross-border movements of people during the Covid-19 pandemic. We use a unique database on Facebook users' mobility, and rely on regression and machine learning models to identify the role of infection threats and containment policies. Permutation techniques allow us to compare the impact and predictive power of these two categories of variables.

Results: In contrast with studies on within-border mobility, our models point to a stronger importance of containment policies in explaining changes in cross-border traffic as compared with international travel bans and fears of being infected. The latter are proxied by the numbers of Covid-19 cases and deaths at destination. Although the ranking among coercive policies varies across modelling techniques, containment measures in the destination country (such as cancelling of events, restrictions on internal movements and public gatherings), and school closures in the origin country (influencing parental leaves) have the strongest impacts on cross-border movements.

Conclusion: While descriptive in nature, our findings have policy-relevant implications. Cross-border movements of people predominantly consist of labor commuting flows and business travels. These economic and essential flows are marginally influenced by the fear of infection and international travel bans. They are mostly governed by the stringency of internal containment policies and the ability to travel.

Keywords: Containment policies; Covid-19; Cross-border mobility; Non-Parmaceutical interventions.

Fig. 1

Aggregate Traffic Deviations from Pre-Covid Levels. Source: Facebook data on daily border crossings. Notes: Y-axis represents the average of τ_ijt, the percentage change (times 100) in the 7-day moving average traffic compared to t=0 over all destinations. The weights are the traffic levels observed in pre-Covid-19 period (i.e., t=0)

Fig. 2

Traffic Deviations from Pre-Covid Levels for Selected Corridors. Source: Facebook data on daily border crossings. Notes: Y-axis represents τ_ijt, i.e. percentage change (times 100) in corridor 7-day moving average traffic compared to t=0 in corridor ij

Fig. 3

Evolution of the average value of Covid cases and government policy measures. Source: Oxford Covid-19 Government Response Tracker (OxCGRT). Note: The values of each indicator is scaled between 0 and 1, and the average is computed using the 30 countries included in our sample

Fig. 4

Correlation Matrix of the different variables. Source: Own computations. Notes: Unilateral traffic growth for each country i is the relative deviation in aggregate traffic involving country i, $\sum _{j=1}^n{T}_{\mathit{\text{ijt}}}$, as compared to the pre-Covid-19 period (t=0)

Fig. 5

Daily traffic estimates (X-axis) and FB data (Y-axis) by corridor (1 March 2020). Sources: Numbers of daily commuters are extracted from Eurostat data by Nuts2 region in 2019; Numbers of air passengers are extracted from Eurostat monthly statistics on air passenger transport on March 1, 2020; Data on international migrants are extrapolated from [34] for the year 2015, assuming a conservative 50% growth in the flows between 2015 and 2020. FB data are the Facebook data on daily border crossings on March 1, 2020. Note: All variables are expressed in logs

Fig. 6

Distribution of Weights. Sources: Numbers of daily commuters are extracted from Eurostat data by Nuts2 region in 2019; Numbers of air passengers are extracted from Eurostat monthly statistics on air passenger transport on March 1, 2020; Data on international migrants are extrapolated from [34] for the year 2015, assuming a conservative 50% growth in the flows between 2015 and 2020. Notes: Countries on X-Axis are ordered as country_icountry_j, thus linked to ω_i→j. The opposite weight ω_j→i=1−ω_i→j refers to the weight of country_jcountry_i