Consumer mobility and expenditure during the COVID-19 containments: Evidence from French transaction data

Abstract

This paper investigates the effects of the pandemic containment periods in France on individuals' movements, expenditure and adaptation to the shock, using billions of French bank card transactions measured before and during the COVID-19 pandemic. We measure not only the effect on consumer expenditure, but also on quantities directly related to the containment restrictions, such as consumer mobility, number of retailers visited, and inter-regional purchases. The results show large effects on these measures of consumers' movements, as well as on both online and offline measures of expenditure, particularly in the first containment period. We also find evidence that consumers adjusted rapidly during the first containment period, mitigating the effects of mobility restrictions via an increasing shift toward online purchasing, and that the nature of the adaptation differed for different types of purchase.

Keywords: COVID-19; Consumer mobility; Consumption expenditure; Containment policy; Inter-regional trade; Transaction data.

Fig. 1

Timeline of sample and core containment dates, 2020.

Fig. 2

Daily fluctuations in aggregate transactions, January–December 2019–20.

Fig. 3

Daily aggregate POS transactions, 2019–2020.

Fig. 4

Daily aggregate online transactions, 2019–2020.

Fig. 5

Intra-Week Consumption Fluctuations, January–December 2019–20 (Value).

Fig. 6

Distribution of distances travelled by cards between point-of-sale transactions.

Fig. 7

Distribution of number of cities visited per card.

Fig. 8

Distribution of the value of consumer expenditures made within or outside the cardholders home city.

Fig. 9

Estimated daily expenditure response dynamics. Notes: This figure plots the coefficients $exp\left({\hat{\beta }}_j\right)-1$ estimated from Eq. (3), with $j$ = January 27 2020, January 28 2020, …, May 10 2020, along with their corresponding 95 percent confidence intervals. The $x$-axis denotes the day and the $y$-axis shows the estimated daily percentage response.

Fig. 10

Estimated weekly expenditure response dynamics. Notes: This figure plots the coefficients $exp\left({\hat{\beta }}_j\right)-1$ estimated from the weekly form of Eq. (3), with $j$ indicating weekly values for the weeks beginning January 27 2020 through December 21 2020, along with their corresponding 95 percent confidence intervals. The $x$-axis denotes the date at weekly intervals and the $y$-axis shows the weekly percentage spending response.

Fig. 11

Estimated weekly expenditure dynamics, by sector. Notes: This figure plots the coefficients $exp\left({\hat{\beta }}_j\right)-1$ estimated from Eq. (3), using a sectoral disaggregation of the total value of transactions, with $j$ indicating weekly values for the weeks beginning January 27 2020 through December 21 2020, along with their corresponding 95 percent confidence intervals. The $x$-axis denotes the date at weekly intervals and the $y$-axis shows the weekly percentage spending response.

Fig. 12

Evolution of inter-regional effects.

Fig. 13

Number of retailers visited outside home city.

Fig. 14

Number of retailers visited inside home city.