HIAS-E-116

COVID-19 Uncertainty Index in Japan:
Newspaper-Based Measures andEconomic Activities

Hiroshi Morita (a), Taiki Ono (b)(b)(a)
Hosei University
PhD student at Hitotsubashi University

January, 2022

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

COVID-19 Uncertainty Index in Japan:
Newspaper-Based Measures and Economic Activities∗
Hiroshi Morita†‡
Hosei University
Taiki Ono §PhD student at Hitotsubashi University
This version: January 2022

Abstract
Measuring uncertainty and its economic impact are of major concern during the unprecedented crisis triggered by the coronavirus disease 2019 (COVID-19) pandemic.
This paper constructs a newspaper-based measure that captures the uncertainty induced by COVID-19 and examines its economic impacts using a structural VAR
model applied to Japanese data. We develop two types of uncertainty indices andidentify two types of structural shocks in the VAR model: one measuring an epidemiological uncertainty, the other a policy-related uncertainty. Our ﬁndings are
summarized as follows. First, the constructed series of uncertainty shows a spike after COVID-19 related events, indicating that our indices work well as a measure of
COVID-19 induced uncertainty. Second, stock market variables show statisticallysigniﬁcant responses to a policy-related uncertainty shock rather than an epidemiological uncertainty shock. Third, in contrast, real variables such as mobility andconsumption tend to respond signiﬁcantly to an epidemiological uncertainty shock.
These ﬁndings highlight the importance of considering diﬀerent types of uncertainty in order to properly assess the impact of COVID-19 induced uncertainty on
economic activity.

Keywords: COVID-19, uncertainty, newspaper-based approach, VAR model.
JEL classiﬁcation: C32, D80, E44,∗
This research was funded by Grant-in-Aid for Young Scientists 19K13652 and Grant-in-Aid for
Scientiﬁc Research (A) 20H00073. Errors, if any, are entirely the authors’ responsibility.
†
Address: Hosei University, Faculty of Economics, 4342 Aihara, Machida, Tokyo 194-0298, Japan.
E-mail address: morita-h@hosei.ac.jp‡
Corresponding author.
§
Address: Hitotsubashi University, Graduate School of Economics, 2-1 Naka, Kunitachi, Tokyo 1868601, Japan. E-mail address: ttaiki.ono@gmail.com

1

1

Introduction

After the outbreak of coronavirus disease 2019 (COVID-19), a great deal of uncertainty
surrounds humam activities, causing both economic and non-economic problems worldwide. In the case of Japan, the government declared a nationwide COVID-19 state of
emergency multiple times, requesting that people refrain from non-essential outings andthat restaurants shorten their business hours and not serve alcohol. In addition, events
such as sports matches or concerts, including even the Olympic Games, were canceled,postponed, or held with an attendance cap. Moreover, the state of emergency was repeatedly extended more than planned, and there remained uncertainties until the last minutewhether the Olympic Games would be held. Such micro-level uncertainties, which have asigniﬁcant impact on people and business, also possibly lead to macro-level uncertainty. 1
In fact, real GDP growth in the second quarter of 2020 shrank an annualized 27.8% fromthe previous quarter, while it grew an annualized 21.4% in the subsequent third quarter.2 The former ﬁgure is the largest shrinkage on record and the latter is the highest
recovery since 1968, both these ﬁgures have shown considerable volatility and deviatedsigniﬁcantly from the private forecasts by the think-tank, such as the Japan Center for
Economic Research, just before the release of the data.3
Amid this unprecedented spike of uncertainty, this paper investigates how COVID-19induced uncertainty impacts economic activities measured by ﬁnancial and real variablesin the spirit of Bloom (2009) who emphasize the role of uncertainty on economic activity.
To this end, we develop a newspaper-based index for quantifying the uncertainty induced
by COVID-19 and estimate a VAR model to assess the eﬀect of the uncertainty on economic activities. Our approach is similar to that whereby Baker et al. (2016) construct
the economic policy uncertainty (EPU) index, but our procedure makes it possible to extract uncertainty induced by an epidemiological feature of COVID-19 and by government
1

Miyakawa et al. (2021) estimate the potential number of ﬁrm exits during the pandemic. The analysis
shows that the pandemic increased the ﬁrm exit by 20 percent compared to the previous year under someconditions.
2
These GDP growth rates are estimates released in the ﬁrst preliminary estimates of quarterly estimates of GDP.
3
The ﬁrst estimates of GDP growth in the second and third quarters of 2020 were published on August
17th and November 16th, respectively. The monthly survey of professional forecasters in Japan, releasedby the Japan Center for Economic Research, reported forecasts of real GDP growth rate of -26.59% for
the second quarter on August 13th and of 18.03% for the third quarter on November 11th.

2

reaction to the pandemic of COVID-19. There are some features of the newspaper-based
approach relative to other uncertainty measures.4 First, since people perceive many situations via newspapers, it reﬂects households’ perceptions about uncertainty. It is nota technical uncertainty measure in the ﬁnancial market, and it is possible to analyze
its connections more broadly. Indeed, we examine the eﬀect of uncertainty induced by
COVID-19 on stock market variables, the ﬂow of people, and private consumption byincorporating each variable into a VAR model in turn. Second, the approach can collectdaily and more timely data, and the lag between events and their reference is shorter,
usually a day. This characteristic allows us to satisfy the request of the government andhouseholds for a timely measure of the uncertainty the economy faces under the infection
situation and with response by the government unfolding and changing so rapidly. Third,
it is easier to identify the source of uncertainty since the approach rests on text mining.
We can specify the cause of the uncertainty and how it occurs by selecting terms in newspaper articles. Utilizing this property, we construct two types of uncertainty indices that
capture the epidemiological and policy-related uncertainty of COVID-19, and clarify thesource of uncertainty.
Although many studies have pointed out the increment in the uncertainty during the
pandemic (e.g., Baker et al., 2020b; Chen et al., 2021; Morikawa, 2021), to our knowledge,
there have been only two studies of the economic impacts of uncertainty induced by
COVID-19, by Baker et al. (2020a) and Caggiano et al. (2020).5 Even such studies only
examine the eﬀects on output or stock prices using existing uncertainty indices. This
paper constructs a unique measurement for daily time series of uncertainty induced only
by COVID-19 (the Economic COVID-19 Uncertainty index, ECU) and comprehensively
examines its economic impact. Notably, we construct the following two types of ECU
indices by exploiting a newspaper-based approach: ECU with policy and ECU without
4

There are multiple measures of uncertainty, but what uncertainty means diﬀers among them. For
example, volatility indices, such as the Nikkei Volatility Index for Nikkei 225 and VIX for S&P 500, reﬂect
uncertainty in the ﬁnancial market, and what they identify as uncertainty is the near-term price volatilityof stock prices. Contrary to the approach incorporated in the ﬁnancial markets, forecast disagreement
is an uncertainty measure based on the dispersion of the point forecast of the macroeconomic variables
among experts. Baker et al. (2020b) and Altig et al. (2020) detail the characteristics of each uncertainty
measure.
5
Many studies not limited to uncertainty have examined the impact of COVID-19 pandemic. See
Brodeur et al. (2021) and Goldstein et al. (2021) for surveys.

3

policy. The ECU with policy is constructed based on the number of articles that contains“Economics”, “COVID-19”, and “Uncertainty” terms, while the ECU without policy isconstructed by removing the articles that contain “Policy” terms from the articles of
ECU with policy. Moreover, we identify two types of COVID-19 uncertainty shocks byincorporating these two measures of COVID-19-related uncertainty into the VAR model.
We refer to each uncertainty shock as a COVID-19-speciﬁc uncertainty shock and a
COVID-19-related policy uncertainty shock. The former shock is considered to capture
the epidemiological uncertainty of COVID-19 in the sense of being independent of policyrelated factors, while the latter literally captures the policy-related uncertainty. This
more speciﬁcally identiﬁed source of uncertainty is crucial for real-time data analysis anda more detailed and pertinent policy reaction. To the best of our knowledge, no study
has decomposed the source of uncertainty induced by COVID-19. 6
Our ﬁndings in this paper are summarized as follows. First, the constructed seriesof COVID-19-related uncertainty, both the ECU with and without policy, shows elevateduncertainty in the early stage of the COVID-19 pandemic, which is consistent with theliterature and our intuition. We also conﬁrm that the two indices peak at diﬀerent
time, and the movements of these two uncertainty indices are compatible with speciﬁc
events that may increase uncertainty. Therefore, the two types of uncertainty indicesindeed capture diﬀerent types of COVID-19-related uncertainty. The comparison withthe varying measure of uncertainty reveals that the ECU with policy is highly correlatedwith the EPU, implying that the policy uncertainty and COVID-19-related uncertainty
overlap with each other during the pandemic. This implication is also observed in theUS, as reported by Baker et al. (2020a). In contrast, the ECU without policy seems to
be uncorrelated with the EPU index, suggesting that it successfully embeds uncertainty
caused by the epidemiological features of COVID-19 rather than policy. In addition, both
uncertainty indices are unrelated to the ﬂuctuation of infection status, such as new casesof COVID-19, the number of patients with severe cases, and the number of deaths, which
are also regarded as a proxy of COVID-19-related uncertainty. Second, based on the VAR
analysis, we ﬁnd that ﬁnancial variables are aﬀected signiﬁcantly by a COVID-19-related
6

Baker et al. (2020a) argue that the epidemiological feature of COVID-19 cannot explain the stock
market reaction to COVID-19, but their discussion is simply derived from a comparison of the lethality
with Spanish ﬂu.

4policy uncertainty shock while real variables, such as mobility and consumption, areaﬀected by a COVID-19-speciﬁc uncertainty shock. Our ﬁndings on stock price reactions
support the argument of Baker et al. (2020a) that the epidemiological threat of COVID-19
is not a dominant factor in a signiﬁcant drop of stock prices in the pandemic. The noveltyof this paper is that we directly clarify the source of uncertainty that plays a dominantrole in the variation of stock prices rather than guessing its source from a comparison
with past pandemic events as in Baker et al. (2020a). We also uncover from the analysisof the stock market variables that the volatility index shows a signiﬁcant response only
to a COVID-19-related policy uncertainty shock, thereby indicating that the use of stock
market volatility (e.g., as in Caggiano et al., 2020) possibly fails to evaluate the entire
impact of uncertainty induced by COVID-19. The real variables, in contrust, chieﬂy
respond to a COVID-19-speciﬁc uncertainty shock. In response to a COVID-19-speciﬁc
uncertainty shock, the ﬂow of people to retail and recreation places, transit stations, andworkplaces declines, and the ﬂow of people in residential areas increases. The results
are fairly similar to the response of consumption. We can regard a COVID-19-speciﬁc
uncertainty shock as capturing uncertainty for the epidemiological feature of COVID-19,
so the results suggest that people have changed their behavior due to the perceived threatof infection.
This paper is closely related to two strands of literature: one measuring economic
uncertainty, especially in the COVID-19 era, and the other measuring the impact of the
COVID-19 pandemic. As summarized in Baker et al. (2020b), economic uncertainty isoften quantiﬁed by stock market volatility, newspaper-based measures, and business expectation surveys. Among these uncertainty measures, this paper employs a newspaperbased approach, originally developed by Baker et al. (2016) and well known as the EPU
index. Arbatli et al. (2017) have published the Japan EPU index. Recently, Baker et al.
(2019) construct a newspaper-based Equity Market Volatility tracker that harmonizes
with the movement of the VIX, and Baker et al. (2020a) apply it to the evaluation of therole of the COVID-19 in the US stock market. Also, there exist some studies that measure uncertainty using a ﬁrm-level survey of ﬁrms’ economic outlook (e.g., Chen et al.,2021; Morikawa, 2021). All these studies, despite their methodological diﬀerences, report the same results as in this paper, a massive hike in uncertainty at the early stage5

of the pandemic. There are also several studies that have examined the impact of the
COVID-19 pandemic on economic activities. As comprehensively surveyed in Brodeur
et al. (2021), the growing literature on this subject has analyzed not only uncertainty but
also the consequences of the social distancing policy and other related policy interventions on infection status, mobility pattern, and economic activities. For example, Coibion
et al. (2020) report that the lockdown policy itself has a signiﬁcant negative impact on
consumption, employment, and households’ expectation based on survey data in the US.
Watanabe and Yabu (2021) indicate that under Japan’s state of emergency, which is notlegally binding, information about the risk of serious illness and death from COVID-19
is more likely than the government’s request to lead older people to voluntarily refrainfrom going out. Haddad et al. (2021) reveal the impacts of the COVID-19 outbreak andsubsequent intervention by the Fed on the corporate bond market. If we limit ourselves
to studies that focus on the economic impact of uncertainty induced by COVID-19, Altig
et al. (2020) show that increased COVID-19 uncertainty causes declines in US industrial
production by 12–19%, and both Baker et al. (2020b) for the US output and Caggiano
et al. (2020) for the world output report almost the same results. This paper aims tocontribute to the literature by providing two types of uncertainty indices that can identify
the source of the COVID-19-induced uncertainty and clarifying its eﬀects on economic
activities in Japan.
The rest of this paper is structured as follows. Section 2 explains how to construct both
uncertainty indices and discusses the properties of the constructed indices by comparing
them with other uncertainty indices and actual events. Section 3 reports our empiricalanalysis. We ﬁrst explain our VAR model and identiﬁcation strategy and then show theimpulse responses of stock market variables, mobility data, and consumption to two typesof COVID-19-related uncertainty shocks. Section 4 concludes.

6

2

COVID-19 Uncertainty Index

2.1

Construction of the ECU index

We construct the index of COVID-19-related economic uncertainty in the same manneras the EPU index developed by Baker et al. (2016). The EPU index is a newspaperbased measure of policy-related uncertainty constructed by counting newspaper articles
with terms about “economic”, “uncertainty” and “policy”. Following Baker et al. (2016),
our economic COVID-19 uncertainty (ECU) measure is also built based on newspaper
coverage frequency, counting articles that contain the terms related to “economic”, “uncertainty” and “COVID-19” instead of “policy”. However, the outbreak of the pandemic
entailed policy uncertainty in addition to its epidemiological uncertainty. For instance,
the infectiousness, prevalence, and lethality of COVID-19 reﬂect uncertainty about theproperties of the virus (i.e., COVID-19-speciﬁc uncertainty), while the issues about thedeclaration of a state of emergency and vaccine supply are uncertainty about policy reactions in coping with the virus (i.e., COVID-19-related policy uncertainty). The collectionof the articles containing the terms “economic”, “uncertainty” and “COVID-19” does
not exclude the articles related to “policy”, resulting in a mix of the two types of uncertainty in the constructed index. According to Baker et al. (2020a), more than 90% of US
newspaper articles containing “economic”, “uncertainty” and “policy” terms also contain “COVID-19”-related terms in March and April 2020. To assess the eﬀects of those
two uncertainties on economic activities separately, we also construct a policy-unrelated
COVID-19 uncertainty measure by eliminating articles with “policy” terms from the articles containing “economic”, “uncertainty” and “COVID-19”, which mainly captures the
epidemiological aspect of COVID-19-induced uncertainty. We name COVID-19-related
economic uncertainty with and without policy-related terms, in literal fashion, ECU with
policy and ECU without policy, respectively.
The detailed procedure for constructing the indices consists of two steps: a counting
step and a normalizing step. In the ﬁrst step, we collect articles from four leading Japanesenewspapers (Nikkei, Asahi, Mainichi, and Yomiuri) that satisfy the following conditions.
The condition for selecting an article for ECU with policy is that it contains at least one
term in all brackets for each of the following three terms: Economy term: {Economy,
7Economics}, Uncertainty term: {Unclear, Uncertain, Uncertainty}, and COVID-19 term:{Coronavirus, Novel Coronavirus, Novel Pneumonia}.7 It should be stressed that thearticles selected using this condition may well contain policy-related terms. Accordingly,
we refer to the uncertainty measure produced by this criterion as ECU with policy. In
contrust, the condition for inclusion in ECU without policy is that article also contains
Economy, Uncertainty, and COVID-19 terms, but does not contain any Policy term. 8 In
the second step, for each newspaper, we divide the total number of articles satisfying
the respective condition by the total number of daily articles, and then normalize thisdaily coverage ratio to have a mean of 0 and a variance of 1. Finally, these normalized
coverage ratios for each newspaper are aggregated with equal weight each day, and further
normalized so that the mean of the index equals 100. We follow this procedure for boththe morning and evening editions of newspapers for the period from January 1, 2020,
to August 31, 2021, and construct the daily frequency measure of COVID-19-related
uncertainty.9

2.2

The properties of ECU index

In this section, we investigate some properties of the ECU index and discuss certain
details. Figure 1 shows the constructed time-series of the ECU without policy (Figure1a) and the ECU with policy (Figure 1b). Both indices surge from around February
2020, peak in May, and are stable after the rise, while the ﬂuctuations of each index
are distinct. We can also see from the ﬁgures that the indices show clear spikes aroundseveral featuring events. For example, large drops in stock market generate large spikes
7

Although we understand that the selection of articles under this condition may include articles thatconvey the news of “reducing uncertainty”, this is a standard approach in the literature (e.g., Baker
et al., 2016; Arbatli et al., 2017), and the constructed indices seem to capture well the events associatedwith the increased uncertainty caused by COVID-19, as shown below. Hence, we adopt this manner toconstruct our uncertainty indices.
8
The list of Policy terms is long, so it is summarized in detail in Appendix A.1, along with the Japanesecounterparts for each of the English keywords for Economy, Uncertainty, and COVID-19. The Japaneseterms that correspond to original English keywords are in accordance with those documented in Arbatli
et al. (2017) in constructing the Japan’s EPU index. Also, the daily articles for Nikkei, Asahi, Mainichi,
and Yomiuri are collected from their online database libraries, Nikkei Telecon, Kikuzo II, Maisaku, andYomidas-rekishikan, respectively.
9
During our sample period, January 2 was a newspaper holiday, and all the newspapers used in thispaper did not publish either a morning or an evening edition. Therefore, both of the ECU indices are
missing for January 2 and are thus excluded from the following estimation.

8

in both ECU indices. As for the state of emergency, ECU without policy shows spikes
only for events related to the lifting of the state of emergency, while ECU with policy
tends to react to all the events related to the state of emergency. This suggests thatpeople may fear further spreading the infection with the lift of the state of emergency,
inducing uncertainty regarding infectious COVID-19. Interestingly, only the ﬁrst stateof emergency had an impact on the indices. We also ﬁnd that political transitions, PM
Abe’s resignation, and the inauguration of the Suga cabinet led to an increment in thepolicy related uncertainty. Furthermore, it should be noted that the ECU without policy
tends to rise a few days after events that raise the ECU with policy (e.g., declarations
and extensions of a state of emergency). This is likely because many articles about
these events written from a non-political perspective are published a few days after thosepolitical events. Overall, our indices capture well the featuring events related to COVID19 and thus can be regarded as a valid measure of uncertainty.

9

(a) ECU without policy

108(c) Lifting the SoE (39 prefectures)

106(d) Decision to lift the SoE (8 prefectures)(h) Large drops in stock prices

104(g) Partially suspending travel campaign

102
100
98
Jan

Apr

Jul
2020

Oct

Jan

Apr
Jul
2021

(b) ECU with policy

108(b) Extending the SoE

106(c) Lifting the SoE (39 prefectures)(a) Declaration of the SoE(e) PM Abe's resignation(f) Inauguration of the Suga cabinet(g) Partially suspending travel campaign

104
102
100
98

(h) Large drops in stock prices

Jan

Apr
Jul
2020

Oct

Jan

Apr
2021

Jul

Figure 1: The time series of ECU indicesNotes: This ﬁgure shows the time-series of the ECU index without policy (upper panel) and the
ECU with policy (bottom panel). The dates when newspapers published articles on the eventsin the ﬁgures are as follows: (a) April 8, 2020.; (b) May 5, 2020.; (c) May 15, 2020; (d) May
24, 2020.; (e) August 29, 2020.; (f) September 17, 2020.; (g) November 22, 2020.; (h) March
10, 2020, June 12, 2020, and September 22, 2020. Note that here we have the dates when theevents were published in the newspapers, so the events themselves were possibly considered tohave happened the day before.

Table 1 summarizes the calculated correlation coeﬃcients among the ECU indicesand other indicators: EPU index, the number of newly infected people (new cases),
the number of participants with severe cases (severe cases), and the number of deaths10(deaths).10 For the EPU index, the sample correlation coeﬃcients between the dailychange rates of the ECU index and the EPU index are calculated. For other indicators of
infection status, moving averages of the past week are ﬁrst constructed to remove weekly
seasonality, and the correlation coeﬃcients between the daily change rates of the ECU
index and of the average of these indicators are calculated.
We ﬁrst ﬁnd that the ECU without policy index is more highly orthogonal to EPU
index than the ECU without policy index, with their correlations equal to 0.071 and
0.654 respectively. In this regard, the ECU without policy index allows the extractionof the uncertainty induced by COVID-19 itself from COVID-19 economic uncertainty
as a whole. Moreover, the results tell us that there is little correlation between the
representative indicators of infection status and the ECU indices with and without policy,
implying that our ECU index captures other information than what the infection statusmeasures.
Table 1: Correlation coeﬃcients of the ECU indices and other indicators
EPU

New cases

Severe cases

DeathsECU without policy

0.071

0.030

−0.0000.030

ECU with policy

0.654

0.031

0.006

−0.005

Notes: This table shows the correlation coeﬃcients between each ECU index and other indicators. Herewe chose the EPU index, the number of newly infected people, the number of patients with severe cases,
and the number of deaths as the indicators, which are often used as proxies for COVID-19-induced
uncertainty. The correlation coeﬃcients with EPU (ﬁrst column) are computed using the daily change
rate of both variables. For the remaining three indicators, because of their weekly seasonality, we ﬁrst
calculate their average value over the past week and then compute the correlation coeﬃcients using thedaily change rate of the weekly average values. The daily series of the EPU index is constructed following
the procedure by Arbatli et al. (2017), and the indicators for infection situation are collected from thewebsite of Ministry of Health, Labour and Welfare.

10

We construct the daily series of the EPU index entirely according to Arbatli et al. (2017) because they provide Japan’s EPU index only on a monthly basis. The data for the COVID-19
infection status are collected from the database of the Ministry of Health, Labour and Welfare(https://www.mhlw.go.jp/stf/covid-19/open-data.html).

11

3

Eﬀects of ECU on economic activities

3.1

Structural VAR analysis

We estimate a structural vector autoregressive (SVAR) model to examine the eﬀects
of COVID-19-related uncertainties on economic activities. By incorporating two kindsof ECU indices into the VAR system, we can separately identify (i) COVID-19-speciﬁc
uncertainty shock and (ii) COVID-19-related policy uncertainty shock and assess the roleof each uncertainty on economic ﬂuctuation amid the pandemic. Identifying two types ofstructural shocks allows us to clearly distinguish between the economic impact of these
diﬀerent types of uncertainties induced by COVID-19.
We employ a three-variable VAR model including ECU without policy, ECU with
policy, and the variable of interest in this order and identify the structural shocks by recursive restriction. Letting Yt = [ECU w/o policyt , ECU w/ policyt , variable of interestt ]′′and et = [e1t , e2t , e3t ] , our VAR model can be written:
Yt = c + B(L)Yt−1 + Aet ,(1)

where c is a constant vector term, B(L) is a polynomial in the lag operator, and A
is a lower triangular matrix that speciﬁes the contemporaneous relationship among thevariables. Under this speciﬁcation, the ﬁrst structural shock (e1t ) has a contemporaneous
impact on both the ECU without policy and the ECU with policy, while the second
structural shock (e2t ) has no simultaneous eﬀect on ECU without policy; thus, we regard
the ﬁrst shock as COVID-19-speciﬁc uncertainty shock and the second as COVID-19related policy shock. This interpretation of structural shocks is based on the notion thatthe epidemiological uncertainty of COVID-19 can trigger policy uncertainty, but not viceversa. For example, the spread of the delta variant, which is considered highly infectious,
would increase uncertainty about the epidemiological feature of the virus as well as policy
uncertainty over such issues as the strength of movement restrictions, but it is unlikelythat the strength of movement restrictions is not expected to increase uncertainty on
the nature of the virus. This parsimonious identiﬁcation strategy can be justiﬁed by thecomplete removal of policy-related articles in the process of constructing the index of12

ECU without policy.
In what follows, we examine the impact of COVID-19 uncertainty on ﬁnancial market
and real activities by incorporating in turn stock market variables, mobility data, andconsumption as the third variable in the VAR system. A detailed description of the data
is provided in Appendix A.2. As an additional speciﬁcation issue, we incorporate thenatual logarithm of two ECU indices multiplied by 100 into the VAR model. The formof the third variable in the VAR model will be discussed separately below. Also, the lag
length is set based on the Schwartz Information Criterion (SIC) by each estimation. The
SIC chooses shorter lags than the Akaike Information Criterion (AIC), but our results arerobust, with little diﬀerent qualitative or quantitative diﬀerence, when using the AIC.

3.2

Eﬀects on stock prices and volatility

We begin by examining the eﬀect of COVID-19-induced uncertainty shocks the on Nikkei
225 Average (Nikkei Average) and Nikkei Volatility Index (Nikkei VI), both of which are
representative measures of stock prices and uncertainty in the Japanese stock market.
We estimate the VAR model by including respective variable in the third endogenous
variable and compute the impulse response function to two types of uncertainty shocks.
Each ﬁnancial variable is incorporated into the model in term of the natural logarithmof the level, multiplied by 100. The sample period is from January 1, 2020 to August 31,
2021, with weekends, holidays, and newspaper holidays removed.
Figure 2 shows the impulse responses of the Nikkei average and Nikkei VI to a COVID19-speciﬁc uncertainty shock (upper panel) and a COVID-19-related policy uncertainty
shock (bottom panel). The solid line denotes the point estimator of the response, andthe dashed lines denote 95% conﬁdence intervals calculated analytically. We ﬁnd from
Figures 2 that both Nikkei average and Nikkei VI have statistically signiﬁcant reactions
with the expected signs to a COVID-19-related policy uncertainty shock, but not to a
COVID-19-speciﬁc uncertainty shock. In response to a COVID-19-related policy shock,the Nikkei VI shows an immediate and persistent positive reaction, and the Nikkei VI
shows a permanent negative reaction after an insigniﬁcant response on impact. Namely,an unfavorable increase of uncertainty, particularly related to policy, leads to spreading13

out the distribution of expected returns of stocks and shifts the mean to the left, thereby
causing declining stock prices and rising stock market volatility. In contrast, the responseof both variables to a COVID-19-related policy uncertainty shock is accompanied by wide
conﬁdence intervals.
(i) Responses to a COVID-19-speciﬁc uncertainty shock(a) Nikkei Average(b) Nikkei VI

.3

2.5

.2

2.0
1.5

.1

1.0

.0

0.5

-.1

0.0

-.2
-.3

-0.5
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7

8

9

-1.0

10

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10

(ii) Responses to a COVID-19-related policy uncertainty shock(c) Nikkei Average
.2
.1
.0
-.1
-.2
-.3
-.4
-.5
-.6
-.7

(d) Nikkei VI
4.0
3.5
3.0
2.5
2.0
1.5
1.0

1

2

3

4

5

6

7

8

9

0.5

10

1

2

3

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7

8

Figure 2: Impulse responses of ﬁnancial variables to COVID-19-induced uncertainty
shocks
Notes: The ﬁgures show the impulse responses of Nikkei Average and Nikkei VI to COVID19-speciﬁc uncertainty shock (upper panel) and COVID-19-related policy uncertainty shock(bottom panel). The sample period is from January 1, 2020, to August 31, 2021, adjusted forweekends, holidays, and newspaper holidays, and the scale on the horizontal axis is one businessday. The solid line is the point estimate of the responses, and the dashed lines are the 95%
conﬁdence intervals, computed analytically.

These results suggest that the policy-related uncertainty may play a chief role in stock
market ﬂuctuations during the pandemic, both in terms of prices and volatility, whichis in accord with Baker et al. (2020a)’s argument that the US stock market ﬂuctuation
during the pandemic cannot be explained by the epidemiological properties of the virus.
Our analysis empirically supports Baker et al. (2020a)’s view, derived from comparison
with past pandemics, by identifying the source of the uncertainty induced by COVID-19.
14

A further important implication of this analysis is that uncertainty measures in ﬁnancial
markets (e.g., Nikkei VI) may only capture the policy-related part of all the uncertainty
induced by COVID-19. As discussed in detail below, real economic activities tend tobe aﬀected by COVID-19-speciﬁc uncertainty shock, and thus, the results here indicatethat the use of easily accessible ﬁnancial market uncertainty measures such as Nikkei VI
may fail to (or only partially) gauge the true impact of COVID-19-induced uncertainty.
Hence, our attempt to construct a diﬀerent type of uncertainty index related to COVID19 contributes to assessing the true impact of its uncertainty.

3.3

Eﬀects on the ﬂow of people

We now investigate how the COVID-19-induced uncertainty aﬀects the mobility of people.
An increase in the ﬂow of people is possibly considered to lead to the spread of theinfection, and therefore, policymakers have consistently monitored changes in the ﬂow
of people during the COVID-19 pandemic. For mobility data that can capture the ﬂow
of people, we use the COVID-19 Community Mobility Reports provided by Google LLC.
Since the data are collected daily, they can be included in the VAR model without
time aggregation. We should comment on the preparation of the mobility data in theestimation before arguing the results of VAR analysis. In the following estimation, we use
the residuals obtained by regressing the original mobility data on the lockdown index, the“stringency index” published by University of Oxford, because the behavior restriction
by government possibly aﬀects the mobility of people in addition to the uncertainty that
we consider.11 The sample period in this estimation is from February 15, 2020, to August
31, 2021, due to data availability. Unlike the stock market variables, mobility data arerecorded every day over the sample period, so only newspaper holidays are removed in
the estimation.
Figures 3 and 4 show the responses of the mobility variables to two types of COVID-19
uncertainty shocks. We consider the responses of the mobility in six categories of places:(a) retail and recreation, (b) groceries and pharmacies, (c) parks, (d) transit stations, (e)11The stringency index is collected from the “COVID-19 Government Response Tracker” website, published by Oxford University (https://www.bsg.ox.ac.uk/research/research-projects/covid-19government-response-tracker)

15

workplaces, and (f) residential.
(a) Retail & recreation(b) Grocery & pharmacy

1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0

2

4

6

8

.6
.4
.2
.0
-.2
-.4
-.6
-.8

10

2

(c) Parks

8

10

0.4
0.0
-0.4
-0.8
-1.2
-1.6

2
1
0
-1
2

4

6

8

-2.0

10

2

(e) Workplaces
0.4
0.0
-0.4
-0.8
-1.2
-1.6
-2.0
-2.4

6

(d) Transit stations

3

-2

4

4

6

8

10

8

10

(f) Residential
.8
.6
.4
.2

2

4

6

8

.0

10

2

4

6

Figure 3: Responses of mobility indices to COVID-19-speciﬁc uncertainty shock
Notes: The ﬁgures show the impulse responses of mobility indices to a COVID-19-speciﬁc
uncertainty shock. The sample period is from February 15, 2020, to August 31, 2021, adjusted
for newspaper holidays, and the scale on the horizontal axis is one business day. The solid lineis the point estimate of the response, and the dashed lines are the 95% conﬁdence intervals,
computed analytically.

16

(a) Retail & recreation(b) Grocery & pharmacy

0.6

.4
.2
.0
-.2
-.4
-.6

0.2
-0.2
-0.6
-1.0

2

4

6

8

-.8

10

2

(c) Parks
0.4

1.2

0.0

0.4

-0.4

-0.4

-0.8
2

4

6

8

-1.2

10

2

(e) Workplaces

8

10

4

6

8

10

8

10

(f) Residential

1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5

6

(d) Transit stations

2.0

-1.2

4

.5
.3
.1
-.1
2

4

6

8

-.3

10

2

4

6

Figure 4: Responses of Mobility indices to COVID-19-related policy uncertainty shock
Notes: The ﬁgures show the impulse responses of mobility indices to a COVID-19-related policy
uncertainty shock. The sample period is from February 15, 2020, to August 31, 2021, adjusted
for newspaper holidays, and the scale on the horizontal axis is one business day. The solid lineis the point estimate of the response, and the dashed lines are the 95% conﬁdence intervals,
computed analytically.

Figures 3 show the responses to a COVID-19-speciﬁc uncertainty shock, revealing thatin contrast to stock market variables, the mobility variables tend to respond signiﬁcantly
to this shock. In response to a COVID-19-speciﬁc shock, after some initial statisticallyinsigniﬁcant responses, the mobility for retail and recreation areas (e.g., restaurant, cafe,
museum, and so on) persistently declines (Figure 3a). The results are similar to the17responses for transit stations and workplaces (Figures 3d and 3e), though the mobilityof workplaces immediately declines after the shock. Not surprisingly, the mobility inresidential areas signiﬁcantly rises after increased COVID-19-speciﬁc uncertainty (Figure3f). Thus, the results can be plausibly interpreted as indicating that epidemiological concerns over COVID-19 lead people to avoid visiting crowded areas with a high probabilityof infection. However, workplaces and residential areas show a hump-shape response,suggesting that even in the pandemic, workers are likely to return to their workplaces inabout ten days. We also ﬁnd that the mobility for groceries and pharmacies declines after
the ﬁfth day, while the mobility to parks increases slightly on impact. People might have
reduced their shopping frequency and quickly changed their destinations in holidays fromcrowded recreation places to parks just after perceiving an increased COVID-19-speciﬁc
uncertainty.
Figure 4 shows the responses to a COVID-19-related policy uncertainty shock. Unlike
Figure 3, mobility to groceries and pharmacies, parks, and workplaces does not show
a signiﬁcant reaction to the shock over the horizon considered (Figures 4b, 4c and 4e).
The residential response exhibits a slight statistically signiﬁcant rise only after a week
(Figure 4f). We also observe that retail and recreation areas and transit stations haveslower responses to a COVID-19-related policy uncertainty shock than the responses toa COVID-19-speciﬁc uncertainty shock, having statistically insigniﬁcant responses forapproximately three to ﬁve days after the shock (Figures 4a, 4d). These responses,
however, are totally diﬀerent from those to a COVID-19-speciﬁc shock in terms of themagnitude, speed, and shape of the responses.
In sum, we conclude from Figures 3 and 4 that changes in the ﬂow of people aremainly driven by increased COVID-19-speciﬁc uncertainty. In other words, people hadconcerns about the uncertainty of infection or seriousness of the disease rather thanuncertainty in government action when they are going out. This result is compatible with
Watanabe and Yabu (2021), who report that the increase in the number of critically illpatients suppressed mobility (especially of the elderly). However, it should be noted that
our uncertainty measure is the compound of risk and ambiguity and therefore capturesuncertainty induced by COVID-19 in a broader sense than that captured by Watanabe
and Yabu (2021), who focuses on the impact of risk on the ﬂow of people. Here, however,18

recall that we initially control for the eﬀect of lockdown policy, which is exogenously
imposed on the behaviors of people. Thus, our analysis highlights how people voluntarily
react to changes in their perceived uncertainty stemming from COVID-19, while at thesame time the eﬀect of lockdown policy, which is also one of the major concerns in theliterature, is outside our scope.

3.4

Eﬀects on consumption

Finally, we examine the eﬀect of COVID-19 uncertainty on consumption. The data for
consumption are obtained from the Family Income and Expenditure Survey, released by
the Statistical Bureau of Japan, which records daily consumption expenditures based on
the information of the household account books of about 9,000 households. Althoughnominal consumption is used in the analysis due to data availability, this is not a serious
problem because our sample period is at most one and a half years, and prices hardly
ﬂuctuate over the sample period, showing a mean and standard deviation of monthly
change of CPI (all item) less than -0.05% and 0.35%. As in the analysis of mobility, we
initially control the eﬀect of lockdown policy by regressing consumption on the stringency
index and using the residuals in the estimation. The sample period for this estimation
is from January 1, 2020, to August 31, 2021, and only newspaper holidays are adjusted
because consumption is recorded every day, as in the mobility data. In the following,among the various categories of consumption provided by the data source, we show theresults for selected consumptions that yield statistically signiﬁcant reactions.
Figure 5 shows the responses to a COVID-19-speciﬁc uncertainty shock, while Figure
6 shows those to a COVID-19-related policy uncertainty shock. Overall, almost thesame argument for the mobility data holds for the analysis using consumption data:
Consumption also seems to be aﬀected more by COVID-19-speciﬁc uncertainty than a
COVID-19-related policy uncertainty. Total expenditure drops signiﬁcantly in response
to a COVID-19-speciﬁc uncertainty shock (Figure 5a), but not to a policy-related one(Figure 6a). Thus, we conclude that epidemiological uncertainty included in COVID-19
is a main factor driving the ﬂuctuations in real activity under the pandemic.
Furthermore, we note that there is a close link between ﬂow of the people and con-19

sumption. For both types of uncertainty shocks, the responses of consumption for transportation in Figures 5c and 6c plot paths similar to those for transit stations in Figures
3d and 4d, and the responses of culture and recreation in Figures 5d and 6d also seemto be counterparts for the responses of retail and recreation in Figures 3a and 4a. It isreasonable to consider that less frequently visiting to these places results in a decline of
expenditure for those categories of consumption.
The expenditure for medical care also exhibits an interesting response to a COVID19-speciﬁc uncertainty shock. Medical service, i.e., medical payments at hospitals, is adominant part of expenditure for medical care, and thus the negative response in medicalcare expenditure on impact suggests that people might have been temporarily hesitant
to go to the hospital just after receiving information about increased epidemiologicaluncertainty about COVID-19. Although the VAR analysis cannot yield anything further,
we can guess from this ﬁnding that people might try to avoid infection by not going to
the hospital.

20

(a) Total expenditure(b) Medical care
6
4
2
0
-2
-4
-6
-8

0.4
-0.4
-1.2
-2.0
-2.8

2

4

6

8

10

(c) Transportation
2

0

0

-4

-2

-8

-4
2

4

6

4

6

8

10

(d) Culture and recreation

4

-12

2

8

-6

10

2

4

6

8

10

Figure 5: Responses of consumption to COVID-19-speciﬁc uncertainty shock
Notes: The ﬁgures show the impulse responses of selected consumption categories to a COVID19-speciﬁc uncertainty shock. The sample period is from January 1, 2020, to August 31, 2021,
adjusted for newspaper holidays, and the scale on the horizontal axis is one business day. The
solid line is the point estimate of the response, and the dashed lines are the 95% conﬁdence
intervals, computed analytically.

21

(a) Total expenditure(b) Medical care

2.5

4
2
0
-2
-4
-6

1.5
0.5
-0.5
-1.5

2

4

6

8

-8

10

(c) Transportation

4

6

8

10

(d) Culture and recreation

10

3
2
1
0
-1
-2

6
2
-2
-6

2

2

4

6

8

-3

10

2

4

6

8

10

Figure 6: Responses of consumption to COVID-19-related policy uncertainty shock
Notes: The ﬁgures show the impulse responses of selected consumption categories to a COVID19-related policy uncertainty shock. The sample period is from January 1, 2020, to August 31,
2021, adjusted for newspaper holidays, and the scale on the horizontal axis is one business day.
The solid line is the point estimate of the response, and the dashed lines are the 95% conﬁdence
intervals, computed analytically.

4

Conclusion

We have constructed two types of newspaper-based uncertainty indices induced by COVID19 and analyzed their connections with ﬁnancial and non-ﬁnancial variables. Our results
indicate the following. First, the created uncertainty series traces the feature events associated with COVID-19-induced uncertainty well. Second, stock market variables show
statistically signiﬁcant responses to a policy-related COVID-19 uncertainty shock rather
than an epidemiological COVID-19 uncertainty shock. Third, in contrast, real variables
such as mobility and consumption tend to respond signiﬁcantly to epidemiological uncertainty shocks in COVID-19. Hence, our ﬁndings highlight the importance of considering
diﬀerent types of uncertainty in order to properly assess the impact of COVID-19-induced

22uncertainty on economic activity. Furthermore, we would like to emphasize that our constructed series of uncertainty can be applied to a wide range of analyses beyond the scopeof our empirical analysis in this paper.
An important objective for future work mainly lies in methodology. Even though
Baker et al. (2016) indicate the validity of the EPU constructed by computers, showingthat the correlation between EPU calculated by computer and humans is relatively high,there is a possibility that our index wrongly captures uncertainty. This is because ourapproach mainly rests on whether each article contains certain speciﬁc terms, and thisprocedure accidentally includes articles referring to not being uncertain when constructing
the index. One possible way to cope with the problem is by applying natural language
processing such as a topic model when building the index, which would enable us toevaluate each article more precisely, and the more accurately extracted index would be
more valid as an index.

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Journal of Public Economics, 191:104274.
Arbatli, E. C., Davis, S. J., Ito, A., and Miake, N. (2017). Policy uncertainty in Japan.
Working Paper 23411, National Bureau of Economic Research.
Baker, S. R., Bloom, N., and Davis, S. J. (2016). Measuring economic policy uncertainty.
The Quarterly Journal of Economics, 131(4):1593–1636.
Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M., and Viratyosin, T. (2020a).
The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing
Studies, 10(4):742–758.
Baker, S. R., Bloom, N., Davis, S. J., and Kost, K. J. (2019). Policy news and stock
market volatility. Working Paper 25720, National Bureau of Economic Research.

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Baker, S. R., Bloom, N., Davis, S. J., and Terry, S. J. (2020b). Covid-induced economic
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24

A

Appendix

A.1

Term set for newspaper-based uncertainty index
Table A.1: Terms for EPU

Term GenreEnglish Term

Japanese Term

Economy(E)“economy” or “economic”

経済 or 景気Uncertainty(U)“unclear”

不透明“uncertain” or “uncertainty”

不確実 or 不確定 or 不安“coronavirus”

コロナウイルス“Novel Coronavirus”

新型コロナウイルス“Novel Pneumonia”

新型肺炎“Covid-19”

Covid-19“tax(es)”

税“taxation”

税制

Corona(C)Policy(P)“government spending” or

歳出“government expenditure”“government revenue(s)”歳入 or 財源

“government budget”

予算 or 財政

“policy debt”

公的債務
国債 or 国の借金 or“government debt”

国の債務 or 政府債務 or 政府の債務“government deﬁcit(s)”

財政赤字

“BOJ”

日銀

“Bank of Japan”

日本銀行“central bank”

中央銀行“The Fed”

連銀“Federal Reserve”

連邦準備“regulation(s)” or “regulatory” or

規制 or 自由化

“regulate” or “deregulation” or “deregulate”“structural reform”

構造改革“legislation”

法案“upper house”

参議院 or 参院“lower house”

衆議院 or 衆院“Diet”

国会“Prime minister”

首相 or 総理

“Prime minister’s oﬃce”

官邸

Notes: The Japanese terms corresponding to the original English keywords are in accordance with those
documented in Arbatli et al. (2017).

25

A.2

Data appendix

Newspaper articles
The daily articles for Nikkei, Asahi, Mainichi, and Yomiuri are collected from their online database libraries, Nikkei Telecom, Kikuzo II, Maisaku, and Yomidasu-rekishikan,
respectively. We used the data from January 1, 2020, to August 31, 2021. Note that
January 2 is a newspaper holiday, on which day all the newspapers used in this paper
publish neither morning nor evening editions.
Infection status
The data for the infection status of COVID-19 in Table 1 are collected from the databaseof the Ministry of Health, Labour and Welfare (https://www.mhlw.go.jp/stf/covid-19/opendata.html). We use the daily nationwide series of the number of newly infected people,
the number of participants with severe cases, and the number of deaths.
Stock market variables
Financial data are collected from Nikkei NEEDS-Financial Quest online database. The
database provides the daily series of Nikkei 225 average and Nikkei Volatility Index. We
used data for the business days from January 1, 2020, to August 31, 2021.
Flow of people
The mobility data are obtained from the COVID-19 Community Mobility Reports, released by Google LCC (https://www.google.com/covid19/mobility/), which provides us
with the ﬂow of people at six categories of locations: (a) Retail and recreation, (b) Grocery and pharmacy, (c) Parks, (d) Transit station, (e) Workplaces, and (f) Residential,in the form of change rates compared to the baseline values for each day of the week.
The baselines are the median values of the ﬂow of people on each day from January 3
to February 6, 2020. The data are released daily from February 15, 2020, and thus theestimated sample period using this mobility data is from February 15, 2020, to August
31, 2021, excluding the newspaper holiday on January 2.

26

Consumption expenditure
The data on consumption expenditure are drawn from the Family Income and Expenditure
Survey of the Statistical Bureau of Japan (https://www.stat.go.jp/data/kakei/index.html).12
This survey provides a daily series of (nominal) consumption ﬁgures recorded based onhousehold account books. Although the category of consumption is highly subdivided
by item, we show here the results derived from four major categories of consumption for“two-or-more-person households” to obtain statistically signiﬁcant estimates. The results
for the other major categories of consumption are available from the authors on request.

12

The data themselves are downloaded from the Nikkei NEEDS-Financial Quest online database.

27