ISSN (Print) 0473-453X

Discussion Paper No. 1144

ISSN (Online) 2435-0982

FEAR OF COVID-19 CONTAGION:
THE IDIOSYNCRATIC EFFECTS OF
AN AGGREGATE PANDEMIC SHOCK
Junichi Kikuchi
Ryoya Nagao
Yoshiyuki Nakazono

October 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Fear of COVID-19 Contagion: The Idiosyncratic
Effects of an Aggregate Pandemic Shock*Junichi Kikuchi†

Ryoya Nagao‡

Yoshiyuki Nakazono§

October 2021

Abstract
We examine how the fear of COVID-19 contagion inﬂuences consumer expenditure patterns. We show that the consumption expenditure responses to the spread of the COVID-19
pandemic are signiﬁcantly heterogeneous across generations. We ﬁnd that the elderly spend
less than the younger generation by at least 5% as COVID-19 spread. In fact, those aged
above 60 signiﬁcantly decreased their spending even on food and drink products by 13%.
We also ﬁnd that the elderly forgo shopping in favor of the younger generation. These heterogeneous responses are likely to be due to the fear of the COVID-19 infection.
JEL Classiﬁcation:

D12; E21

Keywords:

COVID-19; consumption gap across generations; expenditure;fear; spending behavior* Nakazono acknowledges the ﬁnancial support from JSPS KAKENHI, grant numbers 19K13649 and 21H04397.

We would like to thank Hirokazu Ishise, Munechika Katayama, So Kubota, Rui Ota, Michio Suzuki, Qing-Yuan
Sui, Kozo Ueda, Koji Yashiki, seminar participants at Waseda University, and participants at the Japanese Economic
Association 2021 Spring Meeting for their comments and suggestions. The views expressed in this paper are thoseof the authors and do not reﬂect those of the Cabinet Ofﬁce or the Government of Japan.
†
Yokohama City University, m205162g@yokohama-cu.ac.jp‡
Yokohama City University; m215118b@yokohama-cu.ac.jp§
Cabinet Ofﬁce, Government of Japan; Yokohama City University; nakazono@yokohama-cu.ac.jp

1 Introduction
How does the COVID-19 pandemic inﬂuence consumption expenditure? We show that the
response to the spread of COVID-19 has been signiﬁcantly heterogeneous across generationsin Japan. We ﬁnd that the consumption expenditure of the elderly is negatively associatedwith the number of new COVID-19 cases: the elderly spend less than the younger generation
by at least by 5% as COVID-19 spread throughout the country. In fact, those aged above
60 signiﬁcantly decreased their spending even on food and drink products by 13%. We
also ﬁnd that the elderly forgo shopping to the younger generation. These heterogeneous
responses are likely to be due to the fear of COVID-19 infection. The ﬁndings suggest thatheterogeneous perceptions about fear of health-related consequences transform an aggregate
shock into an idiosyncratic one.
A COVID-19 shock is interpreted as an aggregate shock. Figure 1 suggests a COVID19 shock is a purely exogenous one, with a severe impact on aggregate demand. In fact,
gross domestic demand in Japan fell sharply by more than 57 trillion yen from 2019Q3
to 2020Q2. The ﬁgure has been that the magnitude of this effect on the gross domestic
product (GDP) is larger than that of the global ﬁnancial crisis in 2009. The world economy
experiences a similar pattern. Speciﬁcally, the IMF (2021) reports that world output in 2020
declined sharply by −3.5% year-on-year. The decline is more severe by −0.1% than that of
the global ﬁnancial crisis in 2009. The evidence supports the view that a COVID-19 is anaggregate shock to the global economy.
Looking at consumption expenditure, there are four mechanisms to explain the responsesof consumption expenditure to the COVID-19 pandemic (Immordino et al., 2021): (1) thesuspension of many production and commercial activities on the supply side; (2) a negative
income shock, which is not covered by compensated by government transfers; (3) increased
precautionary savings due to increased uncertainty about future earnings, current and future
employment status, credit conditions, and uncertainty about the length of the pandemic; and(4) an infection-concern motive, that is, the response to the risk of contracting the virus.
Mechanisms (1)–(3) can be interpreted as aggregate shocks, while (4) can be classiﬁed as
an idiosyncratic shock. The COVID-19 pandemic obviously works as an aggregate shock.
As for (1) the suspension of many production and commercial activities on the supply side,
Cavallo (2020), for example, shows that consumers in the United States have been spending
relatively more on food and relatively less on transportation and other categories during the
COVID-19 pandemic. The production suspension on the supply side can be thus interpreted2

as an aggregate supply shock. As for (2) a negative income shock, the literature suggests thatnegative income shocks that are not insured have considerable impacts on spending (Baker
et al., 2020; Coibion et al., 2020; Kubota et al., 2021).1 As the standard model predicts, theexpenditure responses to an aggregate (negative) demand shock differ between consumers
with and without liquidity-constraints. As for (3) increased precautionary savings, Cox et al.
(2020) and Immordino et al. (2021) show a drastic increase in savings during the pandemic.
Speciﬁcally, Cox et al. (2020) discuss that the pandemic shock increased the aggregate uncertainty about future earnings, job status, and the length of the pandemic. This suggeststhat an aggregate uncertainty shock induces consumers to increase precautionary savings.
While the spending responses are heterogeneous among consumers, the original source ofthe responses stems from aggregate shocks: (1) an aggregate supply shock, (2) an aggregate
demand shock, and (3) an aggregate uncertainty shock.
As for (4) an infection-concern motive, the growing literature suggests that the fear of
the COVID-19 pandemic plays an important role in explaining the effects of lockdown orders or voluntary lockdowns on personal mobility and interactions.2 Alexander and Karger(2021), Alfaro et al. (2020), Barrios et al. (2021), Cicala et al. (2020), Goolsbee and Syverson (2021), Watanabe and Yabu (2021a), and Watanabe and Yabu (2021b) use location
data from smartphone devices to quantify the impact of the fear of contagion on consumer
choices. While the literature suggests the overall relationship between the fear of infection and consumer choices, the fear of the infection has potentially heterogeneous effects
on expenditure among generations in terms of in-person shopping, eating out, and traveling.
However, there is scant evidence that the fear of contagion has heterogenous impacts on
actual expenditures.3
1

For example. Baker et al. (2020) and Coibion et al. (2020) examine whether the consumption responses to the2020 economic stimulus payments are heterogeneous among consumers in the United States. Kubota et al. (2021)
identify the heterogeneity of spending among Japanese consumers.
2
Binder (2020) shows that fear inﬂuences the macroeconomic expectations of consumers by focusing on theeffects of fear as an aggregate shock on macroeconomic expectations.
3
Goolsbee and Syverson (2021) show that the fear of infection during the COVID-19 pandemic is important
to explain heterogeneous choice behaviors among consumers using mobile location data. However, they do not
examine the effect of the fear of infection on actual expenditure. Watanabe and Yabu (2021a) and Watanabe andYabu (2021b) show that the elderly refrained from going out more than the young in response to the information
about the pandemic, while they do not examine the heterogenous effects of (voluntary) lockdowns on consumption
expenditures. To the best of our knowledge, the only exception is Immordino et al. (2021), who examine theoverall relationship between the fear of contagion and consumption behavior. They show that the probabilities
of consumption decreases and savings increases are positively associated to the fear of contagion, particularly for
shopping, eating out, and traveling.

3This study ﬁlls this research gap. In contrast to the literature, we focus on the idiosyncratic reactions of actual expenditure to the fear of the COVID-19 pandemic. The infectionconcern motive corresponds to the risk of contracting the virus. It is well known that theprobability to become severely ill by COVID-19 infection increases with age. Therefore,
fear of contagion must be heterogeneous across generations and work as an idiosyncratic
shock. Based on a granular data source, this study provides a detailed analysis and determines how the fear of the COVID-19 pandemic as an idiosyncratic shock affects consumer
expenditure. The evidence suggests that the fear of infection actually becomes an idiosyncratic shock among the older generation . That is, an idiosyncratic shock that stems from theinfection-concern motive generates heterogeneous responses in consumption behavior. In
fact, the elderly signiﬁcantly decrease expenditure and forgo shopping to the younger generation due to fear of COVID-19 contagion. This suggests that the heterogeneous perceptions
about the fear of infection transform an aggregate shock into an idiosyncratic one for any
novel infectious disease.
The remaining of the paper is organized as follows: Section 2 presents the survey data weuse. Section 3 shows the development of expenditure before and after COVID-19. Section
4 explain the tests we use to verify whether the fear of COVID-19 infection inﬂuences consumption expenditure and Section 5 shows the results. Section 6 examines whether the fearof COVID-19 infection inﬂuences the frequency of consumer visits at retail stores. Section
7 concludes the paper.

2 Data2.1 Data about the consumption expenditure
We use panel data (SCI-personal) on consumption expenditure collected by a marketingcompany, Intage. Speciﬁcally, we use day-to-day shopping information collected on an ongoing basis from consumers aged 15–79 all over Japan. The Family Income and Expenditure
Survey (FIES) conducted by the Statistics Bureau of Japan records the consumption expenditure of approximately 6,000 households based on household heads, while the panel data
we use records individual expenditure for more than 50,000 consumers. The data capture the
proﬁle of these consumers in detail, including aspects such as income, education, and ﬁnancial assets. Speciﬁcally, we can determine who bought what, when, where, how many, and4

at what price. These data cover items that households purchase frequently, such as food (except for fresh food, prepared food, and lunch boxes), beverages, daily miscellaneous goods,
cosmetics, pharmaceutical products, and cigarettes.45 We use the data to test whether theelderly decreased their expenditure due to the fear of COVID-19 compared to the younger
generation.
There are two caveats in the data on consumption expenditure.6 First, from Table 2,
women outnumber the men. As in Kaplan and Schulhofer-Wohl (2017) and D’Acunto etal. (2021), our data also show that the expenditure by women is larger than that of men.
Second, the coverage of the data relative to Japanese households’ consumption is not large .
Diamond et al. (2020) use SCI-personal and report that the items included in the data cover
approximately 30% of the weight of the Japanese Consumer Price Index.7

2.2 Survey on the effects of COVID-19 pandemic on daily life
Intage Inc. surveys the effects of the COVID-19 pandemic on daily life from October 23 toNovember 4, 2020. A total of 35,389 respondents out of 83,501 completed the online-survey.
The response rate is 42.4%. The median time to take the survey was 4.3 minutes.
The survey contributes to identifying how the fear of COVID-19 inﬂuences consumption
expenditure across generations. First, the survey can be matched with the survey on household expenditure. In fact, more than half of those who took the survey are also respondents tothe survey on household expenditure in Subsection 2.1. We successfully match the two survey data for 29,864 respondents. The matched data allow us to examine the heterogeneous
responses of consumption expenditure during the COVID-19 pandemic.
Second, the survey allows us to identify who idiosyncratic shocks due to the COVID-19
pandemic.8 It requires respondents to provide information about changes in their daily lives.
Questions (1)–(17) about work or school identify changes in employment status and income
level, how one works (at the ofﬁce or at home), ofﬁce (school) closure, and ofﬁce (school)
reopening. The answers to the questions are interpreted as shocks that each respondent faces
4

Because our scanner data cover daily necessities, they do not cover housing, utilities, durables, clothing, andservices.
5
Table 2 shows the descriptive statistics of the data from January to July 2020.
6
O’Connell et al. (2021) also use household scanner data similar to the data we use.
7
D’Acunto et al. (2021) use similar scanner data from U.S. households, and report that the scanner data coveraround 25% of the US households’ consumption.
8
The survey questionnaire is presented in Appendix A.

5due to the COVID-19 pandemic. Because the COVID-19 pandemic is completely exogenousand has heterogeneous effects, the identiﬁed changes in employment status, income level,
and ofﬁce closure are idiosyncratic shocks. To focus on the heterogeneous effects of consumption expenditure to the fear of COVID-19, the identiﬁed shocks are vital for controlling
idiosyncratic shocks such as unemployment and income shocks.9
Third, the survey identiﬁes how respondents live during the COVID-19 pandemic. Respondents are required to provide information about how they respond to COVID-19. For
example, the survey asks them to provide information about changes in opportunities to eat
out, cook at home, and take trips. The information reﬂects the endogenous responses of respondents to idiosyncratic shocks and the policy interventions in response to the COVID-19
pandemic. Furthermore, the survey asks respondents to provide information about how theirfamily members who live with them change their lives in response to COVID-19. Therefore,
we can use information on not only the respondents but also their family members as a set ofcomprehensive covariates to control for the endogenous reactions to shocks and the policies
that may inﬂuence the consumption level.10

3 Household expenditure before and after the COVID19 pandemic
Figure 2 shows the chronological development of expenditure before and during the COVID19 pandemic. There are two spikes in expenditure from 2019 to 2020. First, there is a peak
and a trough before and after the consumption tax hike in October 2019.11 The expenditures
increase by approximately 15% in September 2019 compared to the previous month and thenfell sharply by more than 5%. This reﬂects the typical responses of household expenditure
to the consumption tax hike, as was also case for the tax hike in April 2014.12 . Second, theexpenditure increased by more than 5% in February 2020. Starting in late February, signs of
9

Tango and Nakazono (2021) examine how unemployment and (positive/negative) income shocks affect consumption expenditure during the COVID-19 pandemic using the same survey.
10
The descriptive statistics are presented in Table B.1 in Appendix B.
11
In Japan, the consumption tax rates increased from 8% to 10% on October 1, 2019. Tax reduction rates apply
to food and other daily necessities. Therefore, the tax on services and products other than food and other daily
necessities increased.
12
Cashin and Unayama (2016) also ﬁnd a peak and a trough before and after the consumption tax hike in April
2014.

6

an epidemic were detected in some areas, especially in Hokkaido. As shown in Table 1, thegovernment of Japan announced the basic policy measures for COVID-19 on February 25,
2020. On the next day, the governor of Hokkaido Prefecture requested public elementaryand junior high school to close in response to a rapid increase in the number of COVID-19
cases in the prefecture. The responses to the policy interventions seem to induce households
to forgo going out and eating out and to subsequently increase expenditure for cooking athome, as reﬂected in Figure 2.
However, Figure 3 suggests that the development of the expenditure by generation changed
drastically after April 2020. First, the expenditure diverges in April 2020, when the nationwide state of emergency was declared. The expenditure levels increased by approximately
7% in February and March 2020 compared to January 2020 for all generations. However,they diverge in April 2020: while those aged below 40 signiﬁcantly increased their expenditure, those aged 70 or above sharply decreased their expenditure. In fact, the expenditureof the consumers in their 20s surged at 115, while that of those in their 70s reduced to 102
in April. The difference reaches 18% at most. Figure 3 illustrates that the changes in expenditure are predicted by age. The fact that the younger spend more and the elderly lessmay suggest that the fear of the COVID-19 pandemic entails this expenditure gap between
generations. Second, the increase and decrease in the expenditure gaps repeat three times
with the increase in the number new COVID-19 cases. Figures 3 suggests that the change inthe expenditure gaps and the new COVID-19 cases comove. In fact, while the gap decreases
after lifting the nationwide state of emergency in late May, an increase in the number of
new COVID-19 cases in late July seems to widen the expenditure gap. This is the case after
September 2020. While the gap temporally diminished in September, there is a divergence
between generations along with an increase in the number of new COVID-19 cases in late
October. The evidence that the divergence of expenditures between generations is associatedwith the new COVID-19 cases may support the view that fear of contagion are heterogeneous
across generations and work as an idiosyncratic shock.
Note that the government of Japan never prohibited people from going out, even during
the state of emergency.13 While the government of Japan declared the state of emergency inApril 2020, it only asked for people to exercise self-restraint from non-essential outings. In
response to calls to close their business during the state of emergency, large scale retailers
such as department stores and shopping malls stopped their operations. However, several
13

Throughout 2020, the government of Japan declared a state of emergency only in April.

7

restaurants and retailers such as grocery stores, supermarkets, and drug stores remained
open during the state of emergency. Furthermore, the government of Japan never imposed
a lockdown; transportation services such as train, bus, and subway were provided as usual.
This means that the elderly, as well as the younger generation, were free to go shopping
anytime and anywhere they wanted to even during the state of emergency. In spite of that,
if the elderly forgo shopping to the young, this is most likely due to the fear of COVID-19
infection. The following section veriﬁes whether this fear inﬂuences the spending of theelderly.

4 Estimation strategy
This section formally tests whether the elderly decreased their expenditure due to the fearof COVID-19 compared to the younger generation. To this end, we estimate the following
equation based on a dynamic difference-in-differences (DDID) approach:ln∑Consi,t=α × DElderly +βj × DM onthj
Consi,t−12j∑+γj × DM onthj × DElderly + Xδx + Yδy + εi,t ,(1)jwhere Consi,t is the (nominal) consumption expenditure of individual i in month t. DElderly
is a dummy variable that takes 1 when individual i is 60 or above, and 0 otherwise. DM onthj
and X are a calendar (month) dummy variable and control variables such as ﬁxed effects,
gender, income, educational attainments, family size, and new cases of COVID-19 at theprefecture level in Japan. Our interest is in the coefﬁcient γ on the cross-term between
DM onth and DElderly . A positive γ suggests that the elderly spend more than the young,and vice versa a negative γ.
Here, Y is a set of COVID-19-oriented shocks constructed from the survey in Appendix
A. We use 16 (eight by two) dummy variables and one categorical variable from the respondent and the family members who live with the respondent, respectively. The dummy variables for the respondent and the family members are DtU nemployment , DtGettingJob , DtSchoolClose ,DtSchoolReopne , DtIncreaseEatout , DtDecreaseEatout , DtIncomeU p , and DtIncomeDown . The categorical variables measure how often the respondent and the family members work from8

home.14 These variables allow us to control for the potential effects on the consumption
expenditure of school closure, change in employment status, positive and negative income
shocks, and the frequency of eating out. Therefore, estimating Equation (1) identiﬁes theelderly’s fear effect on consumption expenditure.

5 Results
First, we show the results for all products. Figure 4 shows the developments of the γ coefﬁcient using January 2020 as 0.15 The ﬁgure shows that the monthly expenditure of theelderly in 2020 is negatively correlated to the number of new COVID-19 cases. The expenditure of the elderly does not differ from that of consumers below 60 up to March 2020.
However, they spent less by approximately 5% compared to those aged below 60 during
April and May, when the government declared the state of emergency. Once the government
lifted the state of emergency, in June, the expenditure of the elderly recovered. However, thecycle repeats after June. That is, the expenditure of the elderly fell again by 5% in Augustand bounced back in October.16
We also estimate Equation (1) using a subsample from food and drink products andproducts other than food and drink. Figure 5 supports the view that the elderly spend lessthan those aged less than 60. The top and bottom panels present the γ coefﬁcients using
the subsample of food and drink products and the others, respectively. The top panel shows
signiﬁcant drops in the expenditure of the elderly of more than 10% in April, May, andAugust, compared to January. The impact is at most 13%; those aged above 60 signiﬁcantly
decreased their spending even on food and drink products by 13% in May, compared to
January. This is also the case in the bottom panel using a subsample of products otherthan food and drink. While the decrease in the expenditure of the elderly is insigniﬁcant,the bottom panel shows that their expenditure decreases in April and August compared to14

We deﬁne the dummy variables in Appendix A. The categorical variables regarding working from home take
1, 2, or 3 when a respondent answers “YES” to Questions (13), (14), or (15) in Survey (I) for her/himself and in
Survey (II) for the family members, respectively.
15
Table B.2 summarizes the estimation results by estimating Equation (1). Figures (4)–(6) and Figures (B.1)–(B.3) in Appendix B depict the development of main coefﬁcient γ on the cross-terms between the month dummies
and DElderly .
16
The above results are robust when we set March, June, and October as base months in Figure B.1. The ﬁgureshows that the expenditure of the elderly signiﬁcantly fell by more than 5% in April, May, and August.

9

January.17
We check the robustness of the results using multiple age dummies in the following
equation:ln∑∑Consi,tβj × DM onthj +=γk × DCohortk
Consi,t−12jk∑∑+γk,j × DM onthj × DCohortk + Xδx + Yδy + εi,t ,kjwhere DCohortk is a dummy variable that takes 1 when individual i belongs to cohort k, and0 otherwise.18 DM onthj and X are a calendar (month) dummy variable and control variables
such as ﬁxed effects, gender, income, educational attainments, and family size, respectively.
Y is a set of COVID-19-oriented shocks constructed based on the survey in Appendix A.
Our interest is in the γ coefﬁcient on the cross-term between DM onth and DCohort6 andDCohort7 for those in their 60s and 70s. A positive γ suggests that those in their 60s and 70s
spend more than the younger generation, while a negative γ means that they spend less.
Figure 6 supports the view that the elderly spend less than the young. The top and bottom
panels present the coefﬁcients γ on the interaction terms between the month dummies andDCohort6 and DCohort7 for those in their 60s and 70s for all products. Both panels show asigniﬁcant drop in the expenditure of the elderly by approximately 5% in April, May, andAugust, compared to January. Furthermore, Figure 6 suggests that in their 70s spend lessthan those in their 60s. The results support the view that more elderly consumers decreased
their consumption expenditure relatively more probably due to the fear of the COVID-19
pandemic.19
We check the robustness of the results using another survey about COVID-19 which was
conducted in late March to early April 2020. Intage Inc. surveys the effects of the COVID17

The above results are robust when we set March, June, and October as base months in Figure B.2. The ﬁgureshows that the expenditure of the elderly signiﬁcantly fell by more than 10% in May and August when the subsample
of food and drink products is considered.
18 Cohortk
Dis a dummy variable at 10-year intervals starting for those in their 20s. Speciﬁcally, a set of
CohortkD(k = 2, 3, 5, 6, 7) corresponds to those in their 20s, 30s, 50s, 60s, and 70s, respectively. Therefore,
DCohort4 for those in their 40s is set as a base dummy.
19
The above results are robust when we set March, June, and October as base months in Figure B.3. The ﬁgureshows that the expenditure of the elderly signiﬁcantly fell as COVID-19 spread. The results are robust when the
consumption measures are adjusted using the same equivalence scale as in Banks et al. (1998), although we do notreport the regression results due to space considerations.

10

19 pandemic on daily life after infection spread from 31 March to 7 April 2021.20 The survey contributes to directly identifying how respondents fear in response to the onset of the
spread of COVID-19. The survey asks respondents to answer how you are concerned about
the spread oc COVID-19.21 We successfully further match the former survey conducted in
October with this survey conducted in late March for 25,192 respondents. The matched data
allow us to examine the relationship between fear of COVID-19 and consumption expenditure in the onset of the COVID-19 pandemic.22 We estimate the following equation using adummy variable DF ear :ln∑Consi,t=α × DF ear +βj × DM onthj
Consi,t−12j∑+γj × DM onthj × DF ear + Xδx + Yδy + εi,t .

(2)jA positive γ suggests that those who fear COVID-19 spend more, while a negative γ meansthat they do less. Figure 7 shows the developments of the γ coefﬁcient using February 2020
as 0. The ﬁgure shows that the monthly expenditure of those who fear COVID-19 is negatively correlated to the number of new COVID-19 cases. The expenditure of those who
fear COVID-19 does not differ from those who do not up to March 2020. However, theyspent less by approximately 5% compared to those who do not fear COVID-19 during April,May, and June when the government declared the state of emergency.23 The results support the view that consumers who fear COVID-19 decreased their consumption expenditure
relatively more.

20

A total of 33,563 respondents out of 75,431 completed the online-survey. The response rate is 44.5%.
The survey asks what to extent respondents are concerned about the COVID-19. Respondents choose the mostappropriate one from (1) feel very concerned; (2) feel concerned; (3) feel a little concerned; (4) can’t say either; (5)not feel too concerned; (6) not feel concerned; (7) not feel concerned at all. Based on the answer to the survey, weconstruct a dummy variable DF ear which takes 1 when a respondent chooses (1), (2), or (3), and 0 otherwise. The
sample mean of DDear is approximately 92.2%.
22
In what follows, we assume that the fear of COVID-19 which is identiﬁed using the survey in March 2020 istime-invariant at least for the subsequent three months from April to June 2020.
23
The above results are robust when we set March as a base month, although we do not report the regression
results due to space considerations.
21

11

6 Frequency of visiting retail stores
The fact that the elderly spend less as the number of new COVID-19 cases increases suggests
they may forgo going out due to an increase due to fear of infection. If so, the elderly
generally go shopping with a lower frequency during the COVID-19 pandemic. To formallytest whether the frequency of shopping at retail stores decreased as COVID-19 spread, we
estimate the frequency of visits at retail stores for each consumer.
The transaction-level scanner data on consumption expenditures allow us to count how
frequently consumers visit stores per month. As explained in Section 2.1, SCI-personal
records when and where respondents purchase on a daily basis. This means we can estimate
how often respondents visit retail stores per month.24 We count the number of stores where
respondent i goes shopping during day d in month t, V isiti,d,t . We sum up V isiti,d,t foreach month from January to December 2020.25 Therefore, V isiti,t is the estimated numberof visits at retail stores by individual i in a month.
To examine whether the elderly visit retail stores less compared to the younger generation, we estimate the following equation:ln∑
V isiti,t=α × DElderly +βj × DM onthj
V isiti,t−12j∑M onthj+× DElderly + Xδx + Yδy + εi,t ,γj × D(3)

jwhere V isiti,t is the frequency of retail stores visits by individual i in month t. Similar toEquation (1), X are a calendar (month) dummy variable and control variables such as ﬁxed
effects, gender, income, educational attainments and family size and Y is a set of COVID19-oriented shocks. Our interest is in the γ coefﬁcient on the cross-term between DM onthand DElderly . A positive γ suggests that the elderly spend more than the young, and viceversa for a negative γ.
Figure 8 presents the development of the γ coefﬁcient using January 2020 as 0. The ﬁgure shows that the monthly visits of the elderly are negatively correlated to the new COVID19 cases in 2020. However, the frequency of visits at retail stores by the elderly is not
24

The survey we use includes data from online shopping. Our estimation results are robust when expenditures
and the frequency of visits to retail stores from online shopping are excluded, although these are not reported tosave space. Using the same data we use, Tsukawaki et al. (2021) estimate that approximately 13% of expenditure isfrom online shopping.
25
Table 2 in Appendix B shows the descriptive statistics for the frequency of visits at retail stores.

12different from those of consumers aged below 60 up to March 202. The number of visitsthen reduced by 4% compared to those aged below 60 in April and May, when the government declared the state of emergency. Once the government lifted the state of emergency,
the frequency of visits by the elderly recovered by June. However, the cycle repeated after
June. That is, the expenditure of the elderly fell again by 4% in August, bouncing back in
October.26
Figure 8 supports the view that the elderly visit stores less than the younger generations
during the COVID-19 pandemic. The evidence of infrequent visits to retail stores by theelderly suggests that the heterogeneous expenditure responses are likely to be due to fear of
the COVID-19 infection: the elderly may forgo shopping to the young. This ﬁnding implies
that the heterogeneous perceptions of the fear of health-related consequences transforms anaggregate shock into an idiosyncratic one for any novel infectious disease.

7 Conclusions
This study examines how the spread of COVID-19 inﬂuences the consumption expenditure of Japanese households. Using large-scale monthly panel data collected from morethan 50,000 Japanese households, we examine how consumption expenditure changes frombefore and to after the onset of COVID-19. We show that the response to the spread of
COVID-19 is signiﬁcantly heterogeneous across generations. We also ﬁnd that, during thestate of nationwide emergency, the consumption expenditure of the elderly signiﬁcantly decreased compared to before and to that of the younger generations. In fact, those aged above
60 signiﬁcantly decreased spending even on food and drink products by 13%. Further, theelderly forgo shopping to the young, likely due to the fear of COVID-19 contagion. The
evidence suggests the heterogeneous perceptions of the fear of health-related consequencestransform an aggregate pandemic shock into an idiosyncratic one for any a novel infectious
disease.

26

The above results are robust when we set March, June, and October as base months in Figure B.4. The ﬁgureshows that the frequency of visits by the elderly signiﬁcantly fell approximately by 5% in May and August.

13

References
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Alfaro, Laura, Ester Faia, Nora Lamersdorf, and Farzad Saidi. (2020). “Social Interactionsin Pandemics: Fear, Altruism, and Reciprocity.” NBER Working Papers, 27134.
Barrios, John M., Efraim Benmelech, Yael V. Hochberg, Paola Sapienza, and Luigi Zingales.
(2021) “Civic Capital and Social Distancing during the Covid-19 Pandemic,” Journal of
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Baker, Scott R., Robert A. Farrokhnia, Steffen Meyer, Michaela Pagel, and Constantine Yannelis. (2020). “Income, Liquidity, and the Consumption Response to the 2020 Economic
Stimulus Payments.” NBER Working Paper Series No.27097, May 2020.
Banks, James, Richard Blundell, and Sarah Tanner. (1998). “Is there a Retirement-savings
Puzzle?” American Economic Review 88(4), 769–788.
Binder, Carola.(2020). “Coronavirus Fears and Macroeconomic Expectations.” Review of
Economics and Statistics 102(4), 721–730.
Cashin, David, and Takashi Unayama. (2016). “Measuring Intertemporal Substitution in
Consumption: Evidence from a VAT Increase in Japan.” Review of Economics and Statistics 98, 285–297.
Cavallo, Alberto. (2020). “Inﬂation with Covid Consumption Baskets.” NBER Working Paper Series No.27352, June 2020.
Cicala, Steve, Stephen P. Holland, Erin T. Mansur, Nicholas Z. Muller, and Andrew J. Yates.
(2020). “Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing.” NBER Working Paper Series No.27135, May 2020.
Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber. (2020). “How Did U.S. Consumers Use Their Stimulus Payments?” NBER Working Paper Series No.27693, August
2020.

14

Cox, Natalie, Peter Ganong, Pascal Noel, Joseph Vavra, Arlene Wong, Diana Farrell, andFiona Greig. (2020). “Initial Impacts of the Pandemic on Consumer Behavior: Evidence
from Linked Income, Spending, and Savings Data.” Brookings Papers on Economic Activity Summer 2020, 35–69.
D’Acunto, Francesco, Ulrike Malmendier, Juan Ospina, and Michael Weber. (2021). “Exposure to Grocery Prices and Inﬂation Expectations.” Journal of Political Economy 129(5),1615–1639.
Diamond, Jess, Kota Watanabe, and Tsutomu Watanabe. (2020). “The Formation of Consumer Inﬂation Expectations: New Evidence from Japan’s Deﬂation Experience.” International Economic Review 61(1), 241–281.
Goolsbee, Austan, and Chad Syverson. (2021). “Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020.” Journal of Public Economics
193(104311), 1–8.
International Monetary Fund. (2021). “World Economic Outlool Update.” World Economic
Outlook Reports January 2021.
Immordino, Giovanni, Tullio Jappelli, Tommaso Oliviero and Alberto Zazzaro “Fear of
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CSEF Working Paper Series No.601.
Kaplan, Greg, and Sam Schulhofer-Wohl. (2017). “Inﬂation at the Household Level.” Journal of Monetary Economics 91, 19–38.
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Idiosyncratic Shocks during COVID-19.” unpublised manuscript.

15

Tsukawaki, Masato, Yuta Miura, and Yoshiyuki Nakazono. (2021). “Online Consumption
Expenditure: Evidence from Japan.” unpublised manuscript.
Watanabe, Tsutomu, and Tomoyoshi Yabu. (2021). “Japan’s Voluntary Lockdown,” PLOS
ONE forthcoming.
Watanabe, Tsutomu, and Tomoyoshi Yabu. (2021). “Japan’s Voluntary Lockdown: Further
Evidence Based on Age-Speciﬁc Mobile Location Data,” Japanese Economic Review
forthcoming.

16

17

Table 1: Chronological developments surrounding COVID-19 and the state of emergency in 2020
Date
News and policy announcements in response to COVID-19
January 16, 2020
Announcement of the ﬁrst infected individual in Japan.
January 28, 2020
Announcement of the ﬁrst infected Japanese.
January 30, 2020
The government of Japan established the Novel Coronavirus Response Headquarters (NCRH).
February 5, 2020
Quarantine on the Diamond Princess.
February 25, 2020 NCRH announced the government’s basic policy for COVID-19.
February 26, 2020 Public elementary and junior high school closure requests in Hokkaido.
February 27, 2020 Prime Minister Abe requests schools nationwide to close temporarily.
March 9, 2020
Expert meeting presents “three conditions” for a high risk of infection.
March 12, 2020
WHO certiﬁed the outbreak as a “pandemic.”
March 25, 2020
Governor of Tokyo Koike requests staying at home on weekends.
April 3, 2020
The number of infected people worldwide exceeds 1 million.
April 7, 2020
The government announced a state of emergency in seven prefectures.
April 16, 2020
The government expands the state of emergency nationwide.
April 18, 2020
The number of infected people in Japan exceeds 10,000.
April 20, 2020
Economic policy: JPY 100,000 to be paid to all citizens.
May 4, 2020
The government decides to extend the state of emergency until May 31, 2020.
May 21, 2020
The government changes the area for the emergency state.
May 22, 2020
An emergency meeting of the Bank of Japan Policy Board (eliminate the prospect of JGB holdings).
May 25, 2020
The government lifts the state of emergency.

18

17,741
20,212
9,002
16,59021,16022,50023,25021,793

24,59920,752
20,92824,330High school graduate or below
Four-year college graduate or above

Household annual income below JPY 4 million
Household annual income of at least JPY 7 million

Age 20–29
12,626
Age 30–39
19,826
Age 40–49
24,535
Age 50–59
26,194
Age 60–69
26,744
Age 70–79
24,883
Note: The data are from January 2019 to December 2020.

21,103
16,888

24,104
13,30027,13916,761

FemaleMale

18,92322,521

All

15.59319.81122.59624.03922.92820.32020.85321.93421.66221.39721.38121.27621.33713.00018.00021.00022.00021.00018.00019.00020.00020.00019.00020.00019.00020.00074,280141,699
182,028
154,416
108,683
56,935

207,102
25,0695

190,811
310,357

421,202306,651

727,853

Table 2: Descriptive statistics of household expenditure and frequency of visits at retail stores. Source: Intage Inc .
Purchase amountfrequency of visits
Observations
Mean Median
MeanMedian(Trillion yen)
560
540
520
500
480
460
440

94

96

98

00

02

04

06

08

10

12

14

16

18

20

Figure 1: Development of GDP from 1994 to 2020 in Japan. Source: Cabinet Ofﬁce, the Government of Japan .

19

120
115
110
105
100
95
90

I

II

III

IV

2019

I

II

III

IV

2020

Figure 2: Development of expenditure from January 2019 to December 2020. The series are seasonally adjusted and standardized using the average expenditure from January 2019 to December
2019 as 100%.

20

8000

120

6000

115

4000

110
105

2000

100
95

0

90
2019Q1 2019Q2 2019Q3 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4
Age 20-29
Age 40-49
Age 60-69
New cases of Covid-19 (right axis)

Age 30-39
Age 50-59
Age 70-79

Figure 3: Development of expenditure by ages (left axis) and new cases of COVID-19 (rightaxis) from January 2019 to December 2020. The series of expenditure are seasonally adjusted
and standardized using the average expenditure from January 2019 to December 2019 as 100.

21

.1
.05
coefficient
0
-.05
-.1
Jan

Feb

Mar

Apr

May

Jun JulMonth

Coefficient

Aug

Sep

Oct

Nov

Dec

90% CI

Figure 4: The ﬁgure reports the γ coefﬁcients on the interaction terms between the month dummies and DElderly from estimating Equation (1) using all products. The dotted lines represent
the 90% conﬁdence interval bands.

22

.2
.15
.1
coefficient-.05 0
.05
-.1
-.15
-.2
Jan

Feb

Mar

Apr

May

Jun Jul
Month

Aug

Oct

Nov

Dec

Oct

Nov

Dec

90% CI

-.2

-.15

-.1

coefficient-.05 0
.05

.1

.15

.2

Coefficient

Sep

Jan

Feb

Mar

Apr

May

Jun JulMonth

Coefficient

Aug

Sep
90% CI

Figure 5: The top and bottom panels report the γ coefﬁcients on the interaction terms between
the month dummies and DElderly from estimating Equation (1) using the subsample of food anddrink products and products other than food and drinks, respectively. The dotted lines represent
the 90% conﬁdence interval bands.
23

.1
.05
coefficient
0
-.05
-.1
Jan

Feb

Mar

Apr

May

Jun Jul
Month

Aug

Oct

Nov

Dec

Oct

Nov

Dec

90% CI-.1-.05

coefficient
0

.05

.1

Coefficient

Sep

Jan

Feb

Mar

Apr

May

Jun JulMonth

Coefficient

Aug

Sep
90% CI

Figure 6: The top and bottom panels present the development of the γ coefﬁcients on the interaction terms between the month dummies and DCohort6 and DCohort7 for those in their 60s and 70s
using all products, respectively. The dotted lines represent the 90% conﬁdence interval bands.
24

.1
.05
coefficient
0
-.05
-.1

Feb

Mar

Apr
Month

May

Jun

Figure 7: The ﬁgure reports the γ coefﬁcients on the interaction terms between the month dummies and DF ear from estimating Equation (2) using all products. The dotted lines represent the
90% conﬁdence interval bands.

25

.1
.05
coefficient
0
-.05
-.1
Jan

Feb

Mar

Apr

May

Jun JulMonth

Coefficient

Aug

Sep

Oct

Nov

Dec

90% CI

Figure 8: The ﬁgure reports the γ coefﬁcients on the interaction terms between the month dummies and DElderly from estimating Equation (3) using all products. The dotted lines represent
the 90% conﬁdence interval bands.

26

A Questionnaire: Survey on the effects of the COVID19 pandemic on daily life
Intage Inc. surveys the effects of the COVID-19 pandemic on daily life from October 23,
2020 to November 4, 2020. The questions are as follows.

Survey (I): How has the COVID-19 pandemic affected your daily life? Pleaseindicate the situation for yourself for each period: (A) April–May, (B) June, (C)July–August, and (D) September–Present. If both suspension and resumption
occurred during the same period, please select both.
(1) School closed(2) School reopened(3) Culture lesson (including tutoring school) closed(4) Culture lesson (including tutoring school) reopened(5) Had to take a leave of absence from work (including from part-time job)
(6) Resumed work (including part-time job)
(7) Lost job (including part-time job)
(8) Got a new job (including part-time job)(9) Income decreased(10) Income increased(11) Workload increased(12) Workload decreased(13) Worked from home more than 20 days per month(14) Worked from home around half of the month(15) Worked from home around one day per week(16) Changed method of commuting to work (or school)(17) Staggered commute to work (or school)(18) Fewer opportunities to eat out

27

(19) Increased opportunities to eat out(20) Fewer opportunities to cook at home(21) Increased opportunities to cook at home(22) Fewer leisure activities and trips(23) Increased number of leisure activities and trips(24) Fewer trips to nearby places such as window shopping(25) Went out more for window shopping and other local activities(26) Decreased frequency of wearing make-up or dressing up(27) Increased frequency of make-up and dressing up(28) Decreased frequency of skin care(29) Increased frequency of skincare(30) Increased time spent with family(31) Decreased time spent with family(32) Increased time spent watching TV(33) Increased time spent on the Internet and similar activities.
(34) Have not been affected at all(35) Do not want to answer

Survey (II): How has the COVID-19 pandemic affected you daily life? Pleaseindicate the situation for the family members who live with you for each period:(A) April–May, (B) June, (C) July–August, and (D) September–Present. If bothsuspension and resumption occurred during the same period, please select both.
(1) School closed(2) School reopened(3) Culture lesson (including tutoring school) closed(4) Culture lesson (including tutoring school) reopened(5) Had to take a leave of absence from work (including from part-time job)
(6) Resumed work (including part-time job)28

(7) Lost a job (including part-time job)
(8) Got a new job (including part-time job)(9) Income decreased(10) Income increased(11) Workload increased(12) Workload decreased(13) Worked from home for more than 20 days per month(14) Worked from home around half of the month(15) Worked from home about one day per week(16) Changed method of commuting to work (or school)(17) Staggered commute to work (or school)(18) Fewer opportunities to eat out(19) Increased opportunities to eat out(20) Fewer opportunities to cook at home(21) Increased opportunities to cook at home(22) Fewer leisure activities and trips(23) Increased number of leisure activities and trips(24) Fewer trips to nearby places such as window shopping(25) Went out more for window shopping and other local activities(26) Decreased frequency of wearing make-up or dressing up(27) Increased frequency of make-up and dressing up(28) Decreased frequency of skin care(29) Increased frequency of skincare(30) Decreased frequency (opportunity) of skin care for family members who live with you(31) Increased frequency (opportunity) of skin care for family members who live with you(32) Have not been affected at all(33) No family members who live with you

29

(34) Do not want to answer
Based on the answers to Surveys (I) and (II) about school closure in Questions (1)and (2), employment status in Questions (7) and (8), income shocks in Questions (9) and(10), and the frequency of eating out in Questions (18) and (19), we construct 16 (eightby two) dummy variables. The dummy variables for the respondent and the family members correspond to school closure (DtSchoolClose and DtSchoolReopne ), employment status(DtU nemployment and DtGettingJob ), income shocks (DtIncomeU p and DtIncomeDown ), and thefrequency of eating out (DtIncreaseEatout and DtDecreaseEatout ). The dummy variables take
1 when a respondent answers “YES” to each question in period t, and 0 otherwise. For example, assuming that a respondent answers “YES” to Question (1) regarding Period (D) in
Survey (I), DtSchoolClose takes 1 from September to November.

30

B Additional tables and ﬁgures
Table B.1: Basic statistics of the survey on the effects of the COVID-19 pandemic on daily life.
Source: Intage Inc.
Respondent

Apr.–May 2020

June 2020

July–Aug. 2020

Sept.–Nov. 2020

Unemployment
Return to work

1.38%
1.00%

0.82%
0.65%

0.83%
0.92%

0.69%
1.11%

School close
School reopen

2.81%
0.58%

0.46%
1.83%

0.38%
0.50%

0.15%
0.72%

Eating out more
Eating out less

1.06%
59.35%

5.10%
40.29%

5.83%
35.45%

18.82%
20.49%

Increase in income
Decrease in income

1.29%
16.52%

1.86%
11.32%

2.04%
9.79%

2.71%
7.57%

Work from home (≥ 20 days per month)Work from home (≥ 10 days per month)Work from home (once a week)

5.34%
5.21%
4.38%

2.95%
3.16%
2.87%

2.56%
2.52%
2.61%

2.15%
2.23%
2.25%

32,91133,35834,02846,743Observations

Family members who live with the respondent Apr.–May 2020 June 2020 July–Aug. 2020 Sept.–Nov. 2020
Unemployment
Return to work

0.84%
0.34%

0.43%
0.29%

0.45%
0.34%

0.39%
0.49%

School close
School reopen

20.93%
2.72%

3.37%
14.23%

2.33%
3.00%

5.88%
2.90%

Eating out more
Eating out less

0.85%
36.16%

2.70%
25.64%

4.44%
22.15%

12.16%
14.23%

Increase in income
Decrease in income

0.57%
11.08%

0.81%
7.78%

0.88%
6.42%

1.34%
4.70%

Work from home (≥ 20 days per month)Work from home (≥ 10 days per month)Work from home (once a week)

5.73%
5.14%
4.09%

3.15%
3.15%
3.16%

2.69%
2.51%
2.39%

2.13%
1.85%
2.00%

32,91133,35834,02846,743Observations

31

Table B.2: Estimation results from a dynamic difference-in-differences approach
All products Food and drink Other than food and drink
γ1 : January (Base month)
0.000
0.000
0.000
(0.000)(0.000)(0.000)γ2 : February−0.007(0.009)−0.052*(0.026)0.009(0.034)γ3 : March

0.011(0.009)−0.026(0.026)−0.014(0.034)γ4 : April−0.050***(0.010)−0.113***(0.029)−0.054(0.037)γ5 : May−0.055***(0.010)−0.130***(0.030)−0.025(0.037)γ6 : June

0.019*(0.010)0.0120(0.030)0.019(0.037)γ7 : July−0.013(0.010)−0.035(0.030)−0.030(0.038)−0.046***(0.010)−0.104***(0.029)−0.052(0.037)γ9 : September−0.008(0.010)−0.014(0.031)

0.037(0.042)

γ7 : October

0.016(0.010)0.043(0.029)−0.004(0.036)

γ8 : November

0.008(0.010)−0.033(0.030)−0.013(0.036)

γ9 : December−0.014(0.011)−0.018(0.030)−0.037(0.036)

γ8 : August

Observations
338,926
226,656
166,897
Notes: The table reports the γ coefﬁcients on the cross-term between month dummies
and DElderly The standard errors between parentheses are clustered at the individual level.
*, **, and *** indicate signiﬁcance at the 10%, 5%, and 1% levels, respectively.

32

.1
.05
coefficient
0
-.05
-.1

Apr

Month

May

Jun

-.1

-.05

coefficient
0

.05

.1

Mar

Jul

Aug
Month

Sep

Oct

-.1

-.05

coefficient
0

.05

.1

Jun

Oct

Nov
Month
Coefficient

Dec
90% CI

Figure B.1: The ﬁgure reports the γ coefﬁcients on the interaction terms between the month
dummies and DElderly from estimating Equation (1) using all products. The top, middle, andbottom panels present the development of the γ coefﬁcients with March, June, and October as
the base months, respectively. The dotted lines represent the 90% conﬁdence interval bands.
33

.2
-.2

-.15

-.1

coefficient
-.05
0
.05

.1

.15

.2
.15
.1
coefficient
-.05
0
.05
-.1
-.15
-.2

Month

May

Jun

Mar

Apr

Month

May

Jun

.15
.1
coefficient
-.05
0
.05
-.1
-.15
-.2

-.2

-.15

-.1

coefficient
-.05
0
.05

.1

.15

.2

Apr

.2

Mar

Aug
Month

Sep

Oct

Jun

Jul

Aug
Month

Sep

Oct

.15
.1
coefficient-.05 0
.05
-.1
-.15
-.2

-.2

-.15

-.1

coefficient-.05 0
.05

.1

.15

.2

Jul

.2

Jun

Oct

Nov
Month
Coefficient

Dec

Oct

90% CI

Nov
Month
Coefficient

Dec
90% CI

Figure B.2: The left- and right-hand side panels represent the γ coefﬁcients on the interaction
terms between the month dummies and DElderly from estimating Equation (1) using the subsample of food and drink products and products other than food and drink, respectively. The top,
middle, and bottom panels present the development of the γ coefﬁcients with March, June, andOctober as the base months, respectively. The dotted lines represent the 90% conﬁdence intervalbands.

34

.2
-.2

-.15

-.1

coefficient
-.05
0
.05

.1

.15

.2
.15
.1
coefficient
-.05
0
.05
-.1
-.15
-.2

Month

May

Jun

Mar

Apr

Month

May

Jun

.15
.1
coefficient
-.05
0
.05
-.1
-.15
-.2

-.2

-.15

-.1

coefficient
-.05
0
.05

.1

.15

.2

Apr

.2

Mar

Aug
Month

Sep

Oct

Jun

Jul

Aug
Month

Sep

Oct

.15
.1
coefficient-.05 0
.05
-.1
-.15
-.2

-.2

-.15

-.1

coefficient-.05 0
.05

.1

.15

.2

Jul

.2

Jun

Oct

Nov
Month
Coefficient

Dec

Oct

90% CI

Nov
Month
Coefficient

Dec
90% CI

Figure B.3: The left- and right-hand side panels present the development of the γ coefﬁcients on
the interaction terms between the month dummies and DCohort6 and DCohort7 for those in their
60s and 70s using all products, respectively. The top, middle, and bottom panels present the development of the γ coefﬁcients with March, June, and October as the base months, respectively.
The dotted lines represent the 90% conﬁdence interval bands.

35

.1
.05
coefficient
0
-.05
-.1

Apr

Month

May

Jun

-.1

-.05

coefficient
0

.05

.1

Mar

Jul

Aug
Month

Sep

Oct

-.1

-.05

coefficient
0

.05

.1

Jun

Oct

Nov
Month
Coefficient

Dec
90% CI

Figure B.4: The ﬁgure reports the γ coefﬁcients on the interaction terms between the month
dummies and DElderly from estimating Equation (3). The top, middle, and bottom panels present
the development of the γ coefﬁcients with March, June, and October as the base months, respectively. The dotted lines represent the 90% conﬁdence interval bands.
36