How did consumers react to the COVID‐19 pandemic over time?
| Published date | 01 October 2022 |
| Author | George Kapetanios,Nora Neuteboom,Feiko Ritsema,Alexia Ventouri |
| Date | 01 October 2022 |
| DOI | http://doi.org/10.1111/obes.12507 |
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 84, 5 (2022) 0305-9049
doi: 10.1111/obes.12507
How did consumers react to the COVID-19 pandemic
over time?
GEORGE KAPETANIOS,† NORA NEUTEBOOM,†,‡ FEIKO RITSEMA‡ and
ALEXIA VENTOURI†
†Kings Business School, Kings College London, Bush House, 30 Aldwych WC2B 4BG, London,
WC2B 4BG, United Kingdom (e-mail: george.kapetanios@kcl.ac.uk)
‡ABN AMRO Bank, Gustav Mahlerlaan 10 Amsterdam, 1082 PP, Netherlands (e-mail:
nora.neuteboom@nl.abnamro.com)
Abstract
Non-pharmaceutical interventions (NPIs) have been the key policy instrument utilized to
contain the impact of the COVID-19 pandemic. This paper disentangles the effects of NPIs
from that of the virus and looks at the specific channels through which the virus impacts
consumption. Using geo-located transaction data, we find that consumers’ behaviour
towards the virus has explanatory power for the drop in consumption in the early stages
of the pandemic. This effect disappears in the later stages of the pandemic, suggesting
that consumers have adapted their behaviour. As the COVID-19 pandemic progressed,
consumers tended to make ‘safer’ consumption decisions, by avoiding crowded places.
I. Introduction
The novel coronavirus outbreak, along with lockdown measures, that is, non-
pharmaceutical interventions (NPIs) intended to contain the spread of COVID-19, resulted
in significant and unprecedented changes in society and the economy. NPIs have been for
most countries the key policy instrument utilized to contain the impact of the COVID-19
pandemic. The NPIs involved closing (sub)sectors or placing supply restrictions on others,
reducing the opportunity for consumers to spend. To understand the potential negative
economic impact of COVID-19, it is important to understand the economic transmission
channels through which the shocks will adversely affect the economy.
Studies on the effect of COVID-19 on consumption have looked specifically at the
effects of NPIs on domestic consumption Chetty et al. (2020); Bounie et al. (2020);
Neuteboom et al. (2020); Coibion et al. (2020) and Chen, Qian and Wen (n.d.). They
find that during the first stages of the COVID-19 pandemic, consumption has been hit
directly by both the NPIs and the behavioural response function of consumers towards the
virus, that is, voluntary stay at-home-policies by economic agents. According to some,
in the absence of NPIs, self-imposed stay at-home-policies would have been at least
JEL Classification numbers: C33, C61, D12, D14, E21.
961
©2022 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
962 Bulletin
as damaging to the economy (see Lin and Meissner, 2020 and Igan et al.,2020). This
evidence suggests that NPIs are only part of the story, and the behavioural reaction of
consumers towards the virus is perhaps even more important to understand the effects of
the COVID-19 pandemic on the economy.
In this study, we look more closely at the effect of NPIs and the behavioural response
of consumers on consumption, by using bank transaction data. We use a novel approach to
geo-locate physical pin transactions and compare consumption patterns in different Dutch
municipalities with the local COVID-19 outbreak. The Dutch government’s strategy
of imposing nationwide NPIs measures presents us with a unique empirical estimation
strategy. Both the incidence of the illness and its timing varied substantially across different
municipalities, while the NPIs induced homogeneous expenditure dynamics across all
municipalities in the Netherlands. We look specifically at the spatial heterogeneity of
COVID-19, as the virus has had different effects in different municipalities across the
country. While similar empirical studies have been done in other countries, for instance,
in China and France (Chen et al.,n.d. and Bounie et al.,2020), the Netherlands was the
only country were NPI policy that was consistent among regions in terms of strictness and
imposition date. Therefore, we do not have to manually control for differences between
regions within the country.
This paper makes two main contributions. First, we show, in line with previous studies,
that changes in behaviour by consumers towards to virus explain a large part of the drop
in consumption during the early stages of the pandemic. An explanation put forward to
describe this effect is that, when the pandemic eases/surges, people are less willing/more
willing to comply with the imposed NPIs and therefore consumption increases/decreases
(Coibion et al.,2020). We do not find any evidence for this explanation, as our results
show that COVID-19 does not correlate with spending in sectors that were subject to
NPIs. Secondly, while a variety of studies have examined the effects of the first lockdown
on economic behaviour and evaluating the economic damage, there are no detailed studies
that look at the effects over time. In this study, we incorporate the period from the start
of the pandemic until the vaccine roll-out. We find no effect of COVID-19 on aggregate
consumption in the second COVID-19 wave, which suggests that consumers have changed
their behaviour as the pandemic progressed. We do however find an effect of COVID-19
on the substitution of offline spending towards online spending, from which we conclude
that consumers continued their consumption patterns but perhaps in a more ‘safer’ way.
This finding taps into the literature of how consumer update their beliefs and behaviour
on COVID-19 (Akesson et al.,2020). As the COVID-19 pandemic progressed, more
information became available on the infectiousness and on which (social) activities may
increase the chances of attracting the virus. Our results suggest that perhaps consumers
have adapted their consumer behaviours, avoiding physical consumption in certain sectors
that have a high chance of transmissibility of the virus, but continue to shop in sectors
that are relatively safe.
This paper is structured as follows: In section II, we review the most relevant literature
on the economic impact of the pandemic and the associated fall in consumption. In
section III, we describe the geolocation model and our methodology for estimating the
differences in consumption between municipalities. Section IV discusses the results of
the regressions. In section V, we show that the results are robust to changing the time
©2022 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
How did consumers react to the COVID-19 pandemic over time? 963
frequency and cross-sectional dependence. We also explore the time lags of the dependent
variable. Finally, in section VI, we discuss our findings in light of existing research and
outline avenues for future work.
II. Literature review
Our work builds on and contributes to a rapidly evolving literature on measuring the
economic impacts of COVID-191.
In general, the literature on the economic impact of COVID-19 can be broadly
divided among four different main transmission channels through which a pandemic
can cause economic disruptions (see: Carlsson-Szlezak and Philipp, 2020a,b; Guerrieri
et al.,2020): (i) direct impact through economic agents falling ill; (ii) direct impact due
to an increase in uncertainty; (iii) direct impact through restrictions by the government
to contain the spread of the virus (NPIs); and (iv) the indirect impact through worsening
economic conditions. Below we will discuss the four different channels in more
detail.
The direct impact of infections is caused by workers who become infected by the virus
and do not show up at work, causing supply-side disruptions for producers (Guerrieri
et al.,2020). On top of that, consumers who have contracted the virus are not eligible
to leave their houses and consume, causing demand-side disruptions. This channel is, for
example, taken into account by the canonical epidemiology macro model by Eichenbaum,
Rebelo and Trabandt (2020). In their model, the prevalence of infection depends on
the degree of interaction between agents when consuming and working, as well as the
random chance of contracting the virus. Therefore, the susceptible population can lower
the chances of infection by reducing their spending activities and their labour supply
(outside of their houses).
The second channel through which the virus cause economic disruption is uncertainty
(Dardanoni, 1991; Mengel, Tsakas and Vostroknutov, 2016). A pandemic is a negative
unusual and unexpected event that disrupts the status quo; therefore, economic agents
may become more uncertain about their future earnings and hence more risk-adverse.
There is a large stand of literature on uncertainty due to sudden unexpected negative
events. In summary, the literature finds that uncertainty is damaging for short-run
growth, reducing output, investment, hiring, consumption and trade (Bloom, 2014).
Uncertainty impacts the economy in several ways: (i) real-options effects, which act to
make firms more cautious about hiring and investing, and consumers more cautious
about buying durable goods (Bernanke, 1983), (ii) risk-premium effects raise the
cost of finance (Arellano, Bai and Kehoe, 2019) and (iii) from consumers’ desire
for precautionary saving, which itself reduces consumption expenditure (Bansal and
Yaron (2000).
According to Baker et al. (2020), COVID-19 has led to massive spikes in uncertainty,
and there are no close historical parallels. They show that volatility is affected by specific
economic indicators and is sensitive to COVID-19 news. Both negative and positive
1For a good literature review see Brodeur et al. (2020), and for an overview on the economic effect of COVID-19,
see our previous work on this topic (Neuteboom et al.,2020).
©2022 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd.
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