Face mask use and physical distancing before and after mandatory masking: No evidence on risk compensation in public waiting lines

Abstract

During the COVID-19 pandemic, the introduction of mandatory face mask usage triggered a heated debate. A major point of controversy is whether community use of masks creates a false sense of security that would diminish physical distancing, counteracting any potential direct benefit from masking. We conducted a randomized field experiment in Berlin, Germany, to investigate how masks affect distancing and whether the mask effect interacts with the introduction of an indoor mask mandate. Joining waiting lines in front of stores, we measured distances kept from the experimenter in two treatment conditions - the experimenter wore a mask in one and no face covering in the other - in two time spans - before and after mask use becoming mandatory in stores. We find no evidence that mandatory masking has a negative effect on distance kept toward a masked person. To the contrary, masks significantly increase distancing and the effect does not differ between the two periods. However, we show that after the mandate distances are shorter in locations where more non-essential stores, which were closed before the mandate, had reopened. We argue that the relaxations in general restrictions that coincided with the mask mandate led individuals to reduce other precautions, like keeping a safe distance.

Keywords: COVID-19; Face masks; Field experiment; Health policy; Risk compensation; Social distancing.

Fig. 1

Mask usage by age.

Fig. 2

Summary of distance kept from the experimenter. The white dots represent the averages, the gray bars the interquartile ranges, and the light gray areas the kernel density of the distributions.

Fig. 3

Cumulative distribution functions (CDF) of distances kept by the subject from the experimenter in NoMask (blue) and Mask (red) conditions (in centimeter), separately in the April (Policy$=0$), the May (Policy$=1$), and the full sample. Cumulative distributions are exact and densities are estimated univariate Epanechnikov kernel density estimation (KDE) functions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)