PRI Discussion Paper Series (No.24A-01)

Dynamic Relationship between Information Dissemination by
Local Governors and Mobility during the COVID-19 Pandemic
Visiting Scholar, Policy Research Institute, Ministry of FinanceYasuhiro Hara

July 2024

The views expressed in this paper are those of theauthors and not those of the Ministry of Finance or
the Policy Research Institute.

Research Department Policy Research Institute, MOF
3-1-1 Kasumigaseki, Chiyoda-ku, Tokyo 100-8940, JapanTEL 03-3581-4111

Dynamic Relationship between Information Dissemination by Local
Governors and Mobility during the COVID-19 Pandemic 1
Yasuhiro HARA

Abstract
The COVID-19 pandemic has prompted countries to implement a variety of containment measures, including
non-pharmaceutical interventions such as stay-at-home orders. Japan has avoided legally enforcing strictmeasures such as complete or partial lockdowns, instead relying on voluntary restraint from going out during thestate of emergency.
We evaluate the impact of information dissemination on people’s mobility. First, we apply the latest findings
in natural language processing research to precisely measure the information dissemination effect for eachprefecture in Japan. Second, we analyse the dynamic relationship between information dissemination andmobility in each prefecture in Japan using econometric methods. Third, we divide the sample into an early and alater period when the Delta variant emerged in order to analyse the time-varying dynamics of the information
effect.
Our investigation yields two major findings: First, the stay-at-home information dissemination significantly
suppressed people’s mobility. Second, we found a remarkable change in the magnitude of the information effectover time. The information effect weakens after the dominance of the Delta variant compared with the early stageof the pandemic.
Keywords: COVID-19, impulse response analysis, mobility control policy, sentiment analysis, BERTJEL Classification: C23, C55, C61, H12, I18

1

I would like to express my appreciation to Tatsuyoshi Okimoto (Professor of Economics and Finance at Keio University) for
providing valuable guidance and advice. I also received helpful comments from Takeshi Yagihashi (Professor of Economics at
Hosei University), Hideaki Matsuoka (Associate Professor of Economics at Hitotsubashi University), Wataru Takahashi (Junior
Associate Professor of Economics at Takasaki City University of Economics) and participants at the 2024 Japanese Economic
Association Spring Meeting.
 Visiting Scholar, Policy Research Institute, Ministry of Finance, Japan.

1

I. Introduction
The COVID-19 pandemic has prompted countries to implement a variety of containment measures, of which
non-pharmaceutical interventions (NPIs), such as stay-at-home orders, have proven to be an effective tool. While
China, the United States, and European countries have legally enforced strict measures such as complete orpartial lockdowns, Japan has avoided such a strategy, instead relying on voluntary restraint from going out during
the state of emergency. This study analyses the effectiveness of policies sans legal enforcement, as in the case ofJapan. Of especial importance to us are Watanabe and Yabu’s (2020, 2021) 1 ) studies, which examine themechanisms through which such policies induce behavioural changes in people. Watanabe and Yabu (2020, 2021)
use mobility data as an indicator of the extent of people's stay-at-home behaviour and decompose the changes inthese data into two channels: an intervention effect and an information effect. The former refers to changes inbehaviour arising from government orders or requests, while the latter refers to voluntary changes in people’s
behaviour that arise when people update their risk perceptions based on information about the pandemic, suchas government announcements and the number of new infections. Watanabe and Yabu (2020, p.1) find that the
information effect is more significant than the intervention effect; that is, ‘what is necessary to contain the spreadof COVID-19 is not strong, legally binding measures but the provision of appropriate information thatencourages people to change their behaviour’.
Our primary contribution to the exhaustive and growing corpus of literature on the effectiveness of COVID19 policymaking is that we re-examine the information effects analysed by Watanabe and Yabu (2020, 2021),
but this time, we account for the dynamic relationship between information dissemination by local governors
and mobility in 18 prefectures that had relatively high numbers of infections in Japan during the pandemic.
First, we develop a more refined measure of government information dissemination. While Watanabe andYabu (2020, 2021) measure information dissemination by using dummy variables for the issuance and lifting ofspecific policies (e.g. declaration of a state of emergency), we apply sentiment analysis, a natural language
processing (NLP) technique. While the presence or absence of specific policies is one element that represents
the “information” disseminated by a government, the government disseminates information in various forms ona daily basis. Watanabe and Yabu (2020, 2021) may have missed a significant portion of this information.
Sentiment analysis, our preferred technique, transforms text into quantitative data2). It allows us to measure
information not only in terms of specific policies but also on a daily basis, including daily alerts on the threat ofa pandemic. We use bidirectional encoder representations from transformers (BERT3)), a deep learning-based
sentiment analysis approach, to conduct sentiment analysis on text data from press conferences in order toprecisely measure government information dissemination. BERT-based sentiment analysis is increasingly being
used in economics literature for nowcasting economic trends, as exemplified by Seki et al.’s (2022) S-APIR
Index. However, very few scholars use this approach to evaluate policy effects. By exploring the dynamic
relationship between information dissemination and mobility in each prefecture in Japan using econometricmethods, we evaluate the impact of government information dissemination on people’s mobility.
Second, we also employ the local projection (LP) method in line with Inoue and Okimoto (2023), who also
analyse the dynamic relationship between changes in mobility and the spread of COVID-19. The LP method
helps us estimate the duration of the information effect after the information is disseminated, that is, as an impulse
response function. Other factors and their impacts that concern our investigation include the information effectof factors other than government announcements (e.g. information on the infection status, including daily number
1)
2)
3)

A similar study based on Watanabe and Yabu (2020) was conducted by the Cabinet Office, Government of Japan (2021).
Grimmer and Stewart (2013)
BERT is explained in detail in Section II.

2

of infected people and vaccination rate), intervention effect of the state of emergency declaration, and others (e.g.
climatic factors).
Third, we analyse the time-varying dynamics of the information effect. We divide the sample period into theearly stage of the pandemic and the later stage after the Delta variant outbreak in July 20214), primarily todetermine whether the magnitude of the information effect differs between these two periods. We already knowthat the magnitude of an intervention effect changes over time5), as evidenced by Watanabe and Yabu (2021),
albeit in the context of the early stage of the pandemic. To the best of our knowledge, very few studies examine
the time-varying effects of information during the later stages of the pandemic when vaccination was widespreadand rapidly mutating strains, such as the Delta and Omicron variants, emerged. In summary, our analysis sheds
light on the time-varying dynamics of the information effect over a longer period than most literature tackles.
Our investigation yields two major findings: First, the stay-at-home information dissemination significantly
suppressed people’s mobility. Second, we found a remarkable change in the magnitude of the information effectover time. The information effect weakens after the dominance of the Delta variant compared with the early stageof the pandemic.
The remaining article is organized as follows: In section II, we construct a model that quantifies the extent towhich local governors aim to suppress mobility in their information dissemination. In section III, we analyse thedynamic relationship between information dissemination and mobility. In section IV, we examine the timevarying dynamics of the relationship between information dissemination and mobility. Section V concludes.

II. Measuring information dissemination
II.1. Model
We apply sentiment analysis, an NLP technique, to constructs a model that quantifies the extent to which local
governors aim to suppress mobility in their information dissemination.
Al-Qablan et al. (2023) classify sentiment analysis techniques into three categories: lexicon-based, machinelearning (ML) based, and deep learning (DL) based. Lexicon-based approaches utilize predefined sentimentdictionaries or rules to assign sentiment scores. This method has advantages such as simplicity and speed, but it
is usually designed for specific domains. In this article, we require a COVID-19-specific Japanese dictionary,
which has not yet been established in the literature. Using a dictionary unrelated to COVID-19 may have trouble
with terms such as 'Three Cs' (known as 'sanmitsu' in Japanese), making it difficult to accurately measure
sentiment.
The majority of ML-based approaches provide high accuracy by learning from labelled data. DL-based
approaches have the potential to automatically learn complex patterns, capture contextual information, anddemonstrate impressive performance. In this study, we utilize BERT, a DL-based model developed by Devlin etal. (2018). BERT has been applied in economics in various ways. For instance, Alaparthi and Mishra (2021)
analysed movie review data from IMDb, while Seki et al. (2022) constructed the S-APIR Index. Alaparthi and
Mishra (2021) compared BERT with lexicon-based and ML-based approaches and found that BERT
outperformed the others. Based on these characteristics, we selected BERT for its strong performance insentiment analysis, as demonstrated in the study by Al-Qablan et al. (2023), and its suitability for index creation,
as proven in the study by Seki et al. (2022). We utilized the Japanese BERT tokenizer released by the NLP Group

4)
5)

National Institute of Infectious Diseases (2022) defines July 3 - August 15, 2021 as the Delta strain epidemic period.
Yeyati, and Sartorio (2020), Goldstein et al. (2021)

3

at Tohoku University6) for our implementation. To assess the model's performance, we divided the data into three
sets: 70% for training, 15% for validation, and 15% for testing. Stratified sampling is employed to maintain thesame distribution of class labels in the divided data as in the original data.

II.2. Data
This study utilizes two sets of data: textual data on information dissemination regarding COVID-19 andindices of the stringency of the COVID-19 response measures. The textual data consists of daily announcements
made by the Prime Minister and relevant ministers in Japan. The data on the information disseminated by thePrime Minister are obtained from the Prime Minister's Office website.
The data on information disseminated by ministers are collected from two sources on the website of theJapanese government. The first source is the Ministry of Health, Labour and Welfare (MHLW) website, whichcontains information disseminated by the former Minister before March 17, 2020. The second source is thewebsite of the Cabinet Agency for Infectious Disease Crisis Management, which contains information
disseminated by the minister in charge of the COVID-19 response. To assess the government's proactive
information dissemination, we collect data based solely on the opening statements of press conferences,
excluding question-and-answer sessions. For the purpose of this study, which aims to create an index of
information dissemination at the prefectural level, the text classification model uses data on the dissemination of
information by the central government, while data from local governments are only used when calculating theindex based on the constructed model.
Regarding the second type of data, we utilize the COVID: Stringency Index7) to label the textual data. A higher
index value indicates a stricter government policy against COVID-19, while a lower value indicates a looser
policy. The index is transformed into daily differenced data and labelled as follows: 'Strict' should be used if thedifference is greater than 0, 'neutral' if the difference is 0, and 'lenient' if the difference is less than 0. It should
be noted that policy changes may not occur on the same day as information dissemination. Thus, we align thedissemination of information with the policy change that triggered the index update. Mathieu et al. (2020) provide
notes that form the basis for changes in the index. On this basis, the text data should be labelled as 'strict' when
they provide information on policies that contributed to strict index changes, 'lenient' when they provide
information on policies that contributed to lenient index changes, and 'neutral' in all other cases. To analyse theimpact of information dissemination on human mobility, this study focuses on Japanese citizens, who are theprimary recipients of this information. Therefore, we exclude any index changes based on border control
measures and related text data from our analysis. Our model construction utilizes only the information provided
by the central government. We exclude information related to local government measures as the index also takes
them into account. Additionally, it is possible that the index may have missed some policy changes. If a policychange involves the issuance or expansion of a state of emergency (SOE) or quasi-state of emergency (QSOE),
the labels will be adjusted to 'strict'. If a policy change involves lifting the SOE or QSOE, or reducing the target
regions, the labels will be partially corrected to 'lenient'.

II.3. Prediction accuracy of the text classification model
6)

Tohoku NLP Group. “cl-tohoku/bert-japanese: BERT models for Japanese text”. Retrieved from: ‘https://github.com/cltohoku/bert-Japanese’. (Accessed: November 17, 2023)
7)
Mathieu et al. (2020)4

Table 1 presents the classification performance metrics for the developed model. The evaluation metrics
comprise precision, recall, and F1 score. We focus on the F1 score, which provides a balance between precisionand recall, and is an amalgamation of these two measures. Specifically, we focus on the macro F1 score, whichis the average of the F1 scores calculated for each class. The data used in this study is unbalanced, with theneutral class having the most data and the remaining two classes having less data. To avoid overestimating theaccuracy of the class with the most samples, a simple average is more suitable than a weighted average.
Table 1: Prediction accuracy of the text classification model
macro avg.
weighted avg.

Precision
0.98
0.95

Recall0.87
0.95

F1 score
0.92
0.95

As shown in Table 1, the macro F1 score of the model is measured at 0.92, indicating a high accuracy. This
result is consistent with the results reported by Alaparthi and Mishra (2021), who constructed a text classification
model for IMDb review data.

II.4. Calculation of information dissemination index based on the constructed model
The following describes the process of creating the local governor’s information dissemination index (‘local
governor’s index’) using the constructed model. The 18 prefectures with the highest cumulative number of newcases at the end of the analysis period, October 13, 2022, were targeted, excluding two prefectures. As this study
focuses on changes in mobility during the COVID-19 pandemic, the sample is limited by the number of cases,
and it is assumed that regions with a high number of cases have more active NPIs. Regarding the two excluded
prefectures, regular press conferences are published in text format on their websites, while press conferences
related to COVID-19 are only published in video format. For the purpose of this study, which aims to analyse
the dynamic changes in information dissemination and human mobility during the COVID-19 pandemic, it isdeemed appropriate to exclude the data from these two prefectures. The textual data are obtained from thewebsites of the 18 prefectures8) mentioned above.
To measure local governor’s index, we define the index as:
𝑙𝑔_𝑖𝑛𝑓𝑜

𝑃 𝑠𝑡𝑟𝑖𝑐𝑡 ∙ 1

𝑃 𝑛𝑒𝑢𝑡𝑟𝑎𝑙 ∙ 0

𝑃 𝑙𝑒𝑛𝑖𝑒𝑛𝑡 ∙1(1)

where 𝑃 𝑠𝑡𝑟𝑖𝑐𝑡 denotes the probability that the given text encourages staying at home, 𝑃 𝑙𝑒𝑛𝑖𝑒𝑛𝑡 denotes
the probability that the text encourages going out, and 𝑃 𝑛𝑒𝑢𝑡𝑟𝑎𝑙 denotes the probability that the text is neither
strict nor lenient.
Note that the maximum token length is set to 512 when the model is created. To construct a model with highaccuracy, it is sufficient to read the beginning and end of the document, as the main message is often concentrated
in these sections9). For documents longer than 512 tokens, the local governor's index for each prefecture in thisstudy is calculated using the average value estimated by the first 512 tokens and the value estimated by the last
512 tokens.

8)

These are Aichi, Chiba, Fukuoka, Gifu, Hiroshima, Hokkaido, Ibaraki, Kanagawa, Kumamoto, Kyoto, Mie, Miyagi,
Okayama, Okinawa, Osaka, Saitama, Shizuoka, and Tokyo.
9)
Sun et al. (2019)5

III. Dynamic relationship between information dissemination and mobility
III.1. Model
We apply the LP method10), following Inoue and Okimoto (2023), to investigate the responses of the level ofchange in the mobility ℎ periods ahead to a change in causal variables at time 𝑡 . The R package 'lpirfs',
developed by Adämmer (2019), is used for implementation. The following assumptions are made for each of thevariables.
We assume that the level of mobility factor from 𝑡 to 𝑡ℎ

1 collectively consists of multiple factors

identified in previous studies. Specifically, information effect, including the local governor’s index, intervention
effects, represented by the SOE declaration, and other factors such as weather and autocorrelation are considered.
For the measure of mobility 𝑚 , , we use the Google COVID-19 Community Mobility Reports11) index as thedependent variable.
To measure the information effect at time 𝑡, we use the local governor's index constructed in section II. The
data is aggregated into weekly totals in each prefecture.
The COVID-19 infection status is also included as a variable related to the information effect. The perceptionof infection risk among individuals may change based on the information they receive about the spread orcontainment of the infection, and their behaviour may adapt accordingly. This study focuses on the daily numberof new cases and deaths and the vaccination rate. As the number of cases and deaths increases, people may be
more likely to stay at home as they perceive a higher risk of infection. We denote the total number of daily cases
or deaths per 1 million in the population at time 𝑡 in prefecture 𝑖 by 𝑥 . Following Watanabe and Yabu (2020,2021), we transform 𝑥
𝑙𝑛 𝑥𝑥

using the inverse hyperbolic sine transformation. Specifically, we define 𝑋

1 . On the other hand, a higher vaccination rate is associated with a lower perceived risk ofinfection, which may lead to an increase in mobility.
It is important to note that the variables of the daily numbers of new cases and deaths are considered with atime lag, as it is assumed that people need time to recognize the number of cases and deaths and adjust their
behaviour accordingly. Additionally, there is a delay in behavioural changes following vaccination, and recovery
at home may be necessary due to side effects. Moreover, the relationship between the infection status andmobility variables is interdependent, and the potential issue of endogeneity is acknowledged. The objective of
this study is to examine the dynamic relationship between various variables and mobility, rather than to draw
strict causal inferences. However, the use of time lags can help to mitigate the problem of endogeneity to someextent.
To assess the impact of the intervention, we also construct a variable that counts the number of days that thedeclaration of a SOE was implemented in week 𝑡. Additionally, we include a variable for the number of days of
a QSOE, which was introduced from April 2021 as an equivalent measure. Our analysis differs from that of
Watanabe and Yabu (2020, 2021) in that we use weekly data instead of daily data.
As for the weather, in addition to the precipitation data used by Watanabe and Yabu (2020, 2021), we also
incorporate maximum temperature data as a weather factor that affects the change in people's mobility used byInoue and Okimoto (2023). However, while Inoue and Okimoto (2023) used temperature to capture thepossibility that extreme temperature may lower immunity and make ventilation difficult due to room temperature
control problems, we examine the relationship between the number of hot summer days and ice days per week10)

Jordà (2005)
Google LLC. “Google COVID-19 Community Mobility Reports”. Retrieved from:‘https://www.google.com/covid19/mobility/’. (Accessed: October 17, 2023)
11)

6and mobility considering the possibility that extreme temperature may discourage people from going out,
regardless of COVID-19 prevalence.
Furthermore, as the Google COVID-19 Community Mobility Reports indices used in this study are time series
data, we consider the autocorrelation and use the mobility data of the previous period. The regression model that
integrates the above is written as:
𝑚,

𝑚,

∑

𝛽 𝑙𝑔_𝑖𝑛𝑓𝑜

𝛾

𝑋,

𝛿

𝑍

𝜀

𝜀

𝜖

(2)

where 𝛽, 𝛾 and 𝛿 are the regression coefficients. Variable 𝑚 , denotes the mobility observed in the categoryof place 𝑎 of prefecture 𝑖 during week 𝑡.
Variable 𝑙𝑔_𝑖𝑛𝑓𝑜

denotes the level of information dissemination by local governors, which is the main

explanatory variable in this study. The control variables are represented by 𝑋 ,variables, 𝑋 ,12)is the lagged

control variable and comprises three variables: the number of cases, the numberof deaths, and vaccination rate; 𝑍than 𝑋 ,𝜀and 𝑍 . Among the control

is the control variable that does not consider lags, which are variables other.

is a time-fixed effect representing factors such as the nationwide changes in mobility due to the changes

in the perception of the risk of infection or serious illness due to the appearance of mutant strains, theimplementation or changes in policies, and the number of national holidays at different times of the year that are
not explicitly considered in Eq. (2). 𝜀

is a prefecture fixed effect representing the specificity andheterogeneity of each prefecture, including demographics, lifestyle (e.g. commuting modes, presence ofdowntown areas), external human mobility, average household size, and others. Lastly, 𝜖Note that the left side of Eq. (2) is the amount of change in mobility from 𝑡 to 𝑡

is an error term.

ℎ. In LP, the values of theregression coefficients vary across the forecast horizon ℎ, necessitating the addition of ℎ as a superscript todistinguish them.

III.2. Data
This study uses weekly panel data for 18 prefectures in Japan from April 17, 2020 to October 13, 2022. The
data used in this analysis are described below.
The Google COVID-19 Community Mobility Reports indices are utilized to measure prefecture-level mobility
on a daily basis. This index classifies the mobility of the categories of places into six categories: retail andrecreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. The index was createdusing the early stage of the COVID-19 pandemic (from January 3 to February 6, 2020) as the base period. The
indices are calculated based on the percentage increase or decrease from the median of the corresponding day of
the week during this period. In this study, we represent the weekly mobility level by averaging the daily values
per week.
The analysis focuses on data up until October 15, 2022, the latest Thursday in the data release period forweekly aggregation. Figure 1 illustrates mobility trends by prefecture and category. Following Watanabe andYabu (2020), we use level data instead of differenced data. Three types of unit root tests13) were conducted, eachof which rejected the null hypothesis of non-stationarity.

12)

We treat the lag order of the variables as 𝑃 1. To assess robustness, we also estimate the models with the case where themaximum lag order 𝑃 2,3, and obtain qualitatively similar outcomes.
13)
Levin et al. (2002), Im et al. (2003), and Maddala and Wu (1999)

7

Figure 1: Trends in mobility by prefecture and category

This analysis focuses on the local governor’s index, which is constructed in section II. The model construction
method and the data used are detailed in the same section. We use the weekly aggregated data from this index,
as explained in section III. If there were no press conferences during the week, we treat the data as an NA14).
Figure 2 shows the trends of the local governor’s index for each prefecture.

14)

To assess robustness, we also estimate the models with the index of NAs filled with 0, and obtain qualitatively similar
outcomes.

8

Figure 2: Trends in local governor’s index by prefecture

Data is collected from the ‘Data Shows – COVID-19 Information’ section of the MHLW website. Regarding
the daily number of new cases, the ‘Trend of New Positive Cases (Daily)’ data is used, while for the number ofdeaths, the ‘Trend of Deaths’ data is referenced. Both datasets are available at the prefectural level. According to
the website, the number of cases is aggregated based on HER-SYS data, except for the period between September
21, 2022, and September 25, 2022, when individual cases reported by each local government are used. The
number of deaths is the aggregated value reported by each local government and excludes charter flights. To
calculate rates per 1 million population, we divide the number of cases and deaths by the total population of eachprefecture, and we also multiply the result by 100 million to enhance clarity.
Data on the total population by prefecture is obtained from the ‘Population by Age Group’ section of the ‘Basic
Resident Register (2022) (City/Ward/Town/Village)’ published on the website of the Ministry of Internal Affairs
and Communications.
Following Inoue and Okimoto (2023), we use weekly aggregated values for the number of cases and deaths,
as these tend to have a strong weekly effect. We follow Watanabe and Yabu (2020, 2021) by taking the inverse
hyperbolic sine transformed values. The website reports the number of cases from January 16, 2020. However,for our analysis, we have limited the data from April 17, 2020, when the infection had spread nationwide and at
least one weekly positive case was recorded in each of the 18 prefectures analysed. Our analysis accounts forapproximately 75% of the total number of infected individuals and deaths nationwide as of October 13, 2022.
Figure 3 illustrates the trend of the number of new cases and deaths per million population in 18 prefectures.

9Figure 3: Trends in the number of new cases and deaths(per million population, inverse hyperbolic sine transformed)

The vaccination rate is calculated based on the data obtained from two sources. The weekly number ofvaccinations is collected from the ‘Open Data – Prefectural Vaccination Number Details’ published by the
Vaccination Record System (VRS) of the Digital Agency, Japan. This data is used to determine the number ofpeople who completed the second vaccination dose in each prefecture. To calculate the vaccination rate for eachprefecture, ‘the Basic Resident Register (2022) (City/Ward/Town/Village)’ is used, following the same method
as for the number of cases and deaths.
Regarding the SOE and QSOE, data are obtained from the website of the Cabinet Agency for Infectious
Disease Crisis Management. The number of days on which SOE or QSOE declarations were implemented inweek 𝑡 is taken into consideration.
It is important to note that the local governor’s index we constructed had almost no linear relationship withother variables related to the information effect and the variables related to the intervention effect. This outcome
can be attributed to the delay between information dissemination and policy intervention, as well as the difference
in information volume between raw data, such as the number of new cases, and processed data, such as the actual
textual information disseminated by the government.
The maximum daily temperature and total daily precipitation data for each prefecture are obtained from theJapan Meteorological Agency website. The observation data of the prefectural capital are used as therepresentative weather condition of the prefecture. The number of days with extremely high and low temperatures
per week is calculated from the daily maximum temperature. The weekly precipitation is calculated byaggregating the daily total precipitation for each week. Figure 4 shows the trends of the number of days withextremely high and low temperatures and precipitation.

10

Figure 4: Trends in climatic factors by prefecture

Table 2 displays the descriptive statistics for the aforementioned data.
Table 2: Descriptive statistics
Variablem1(retail_and_recreation)m2(grocery_and_pharmacy)m3(parks)m4(transit_stations)m5(workplaces)m6(residential)
lg_infolog(cases)log(deaths)
vaccination
SOE
QSOE
hot summer / ice days
precipitation

Obs.
2340
2340
2340
2340
2340
2340
1228
2340
2340
2340
2340
2340
2340
2340

Mean
-11
1.2
-9.4
-24
-14
6.7
0.019
5.1
1
0.35
0.98
0.8
1.5
36

Std.Dev.
9.2
5.1
16
11
9.2
3.4
0.18
2.5
0.92
0.35
2.4
2.1
2.4
51

Min.
-59
-17
-54
-66
-59
0.86
-0.99
0
0
0
0
0
0
0

Max.
26
23
72
34
1.3
28
1.5
10
3.6
0.81
7
7
7
480

III.3. Estimation results
III.3.1. Estimation results for one-week ahead model
Table 3 presents the estimation results of Eq. (2) for the one-week-ahead model (ℎ1). The R2 values for allmodels, except for transit stations and workplaces mobility, are above 0.6, indicating that this specification has
high explanatory power15).
The information effects of the local governor’s index show a significant positive coefficient for residential
mobility and significant negative coefficients for the mobility of other categories. This suggests that the mobilityof residential category is increased while the mobility of other categories is decreased, indicating that the stayat-home information dissemination significantly suppressed people’s mobility of all categories of places. Note
15)

The VIF values were calculated and were all below 1.5, so it is considered that there is little concern about multicollinearity.

11

that the local governor’s index we constructed had almost no linear relationship with other variables related tothe information effect and the variables related to the intervention effect (see Appendix D). This can beinterpreted as confirming, to a certain extent, the unique effect of information dissemination. The confirmationof the information effect even after extracting the unique effects of information dissemination further strengthensprior findings.
Regarding the number of new cases, with the exception of the grocery and pharmacy mobility, a significant
effect of discouraging people from going out was confirmed. Upon examination of the coefficients of the local
governor’s index and the number of cases in the residential mobility, it was found that the local governor’s index
was 0.345 and the number of cases was 0.328. This indicates that a suppression effect on people’s going out
behaviour can be achieved at the same level when the number of cases is 1.0.
.

1.052, as when the local

governor’s index is 1.0. Although the units differ, a local governor’s index of 1.0 indicates information
dissemination equivalent to declaring a SOE, while an index of -1.0 indicates information disseminationequivalent to lifting such a declaration. Regarding the number of cases, a value of 1.052 means that 1.335 infected
people per million population were observed in one week, which translates to 18,419 infected people in Tokyo.
Conversely, no significant effect was confirmed for the number of deaths.
The vaccination rate had a significant impact on people’s behaviour in different categories of places. In termsof the mobility of retail and recreation and residential categories, vaccination was found to reduce people’s
mobility, whereas vaccination increased the mobility of workplaces category. This finding regarding theworkplaces and the residential mobility is consistent with the assumption that vaccination reduces the perceived
risk of infection. However, there may be some kind of bias in retail and recreation mobility, where residents who
are more willing to be vaccinated may be more likely to refrain from going out to prevent the spread of infection,and this altruistic behaviour may influence their vaccination behaviour.
While the intervention effects of the declaration of a SOE and QSOE may vary from category to category,they generally have a suppressive effect on people’s mobility. Specifically, the declaration of a SOE has asignificant negative effect on the mobility of retail and recreation, transit stations, and workplaces categories,
while it has a positive effect on residential mobility. Similarly, a QSOE has a significant negative effect on
mobility in transit stations category at the 10% level, while it has a positive effect on residential mobility.
Although it may be difficult to compare the values of the coefficients directly with the local governor's index,
the index is equal to 1.0 when the dissemination of information is equivalent to the declaration of a SOE.
Analogously, when a SOE is declared, the value of the SOE variable is also 1.0, indicating that the units of bothare similar. Based on Table 3, the coefficient for the local governor's index is consistently larger in magnitude
than that for the declaration of a SOE in all categories. This suggests that the local governor's index is moresignificant in terms of the regression coefficient.
The weather factors of hot summer days and ice days had no significant effect, while an increase inprecipitation had a suppressing effect on the mobility of all categories of places. For the lagged mobility, thecoefficients were consistently positive for all mobility indices.

12

Table 3: Estimation results for one-week ahead model(1)
retail_and_recreation-1.077***(0.236)-1.217***(0.221)
0.003(0.109)-12.271**(6.081)-0.200***(0.054)-0.046(0.036)0.015(0.116)-0.012***(0.003)0.558***(0.051)1212
0.620

lg_infolog(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-0.675**-2.080***-0.897**-0.451***(0.327)(0.708)(0.434)(0.161)-0.026-0.484**-1.341***-0.445***(0.083)(0.22)(0.169)(0.155)-0.0180.168-0.038-0.065(0.087)(0.358)(0.236)(0.104)3.086-7.579-14.792
7.252**(2.84)(13.316)(11.859)(3.332)0.022
0.034-0.081***-0.130***(0.016)(0.102)(0.029)(0.03)-0.0070.006-0.084*-0.008(0.022)(0.065)(0.048)(0.023)0.033-0.503-0.105-0.054(0.054)(0.485)(0.125)(0.036)-0.012***-0.062***-0.017***-0.005**(0.002)(0.012)(0.004)(0.002)0.720***
0.682***
0.566***
0.497***(0.049)(0.029)(0.04)(0.077)
1212
1212
1212
1212
0.604
0.640
0.503
0.390

(6)
residential
0.345***(0.081)
0.328***(0.076)0.002(0.049)-2.012***(0.404)0.081***(0.018)0.023**(0.01)
0.003(0.027)0.005***(0.001)0.579***(0.069)1212
0.641

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

III.3.2. Impulse response analysis
This subsection conducts an impulse response analysis based on the LP method. Figure 5 shows the impulse
response functions (IRFs) of the level of change in the mobility of each of the six categories of places when thelocal governor’s index increases by 1.0. The impulse response and its confidence interval are calculated from thecoefficient 𝛽𝑎1, 2, … , 6; ℎ

1, 2, … , 5 and its standard error, estimated by panel OLS based on Eq. (2),and show how much mobility changes when the local governor’s index increases by one unit.
Figure 5 displays that an increase in the local governor’s index resulted in a statistically significant decreasein the mobility of categories other than residential and workplaces categories for at least three weeks after theincrease. In the workplace category, mobility decreased significantly in the first, fourth, and fifth weeks after theindex increase. By contrast, mobility in residential category increased significantly from the first week to at least
the fifth week after the increase in the local governor's index.
In summary, the analysis of the impulse response indicates that an increase in the local governor's index
significantly reduces mobility for at least one week ahead. Although the strength of the effect varies across
categories of places, it persists afterwards.

13

Figure 5: IRFs of the rate of change in the level mobility to an increase in the local governor’s index

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

IV. The time-varying effects of information dissemination
Finally, we analyse the changes in the information effect over time by dividing the sample into the early phaseof the pandemic and the period after the delta variant outbreak in July 2021. As explained in section I, previous
studies have also suggested that the information effect changes over time. We estimate the one-week-ahead modelfor each period and perform an impulse response analysis using the LP method. The study period comprised 62
periods during the early pandemic and 68 periods after the delta variant outbreak. Tables 4 and 5 show theestimation results for each period and Figure 6 shows the results of the impulse response analysis.
The results of the one-week-ahead model's estimation are presented in Tables 4 and 5. We find a remarkablechange in the magnitude of the information effect over time. However, the information effect weakens after thedominance of the Delta variant compared with the early stage of the pandemic.
Furthermore, Figure 6’s impulse response analysis indicates that mobility was more suppressed during theearly stages of the pandemic than after the Delta variant outbreak. Regarding residential mobility, the study founda significant decrease in mobility one to four weeks after the local governor's index increased during the early
pandemic period. However, in the sample after the Delta variant outbreak, this significant decrease in mobility
was no longer observed at one week or four weeks. Regarding mobility other than the residential category, theIRFs for the early pandemic period are generally lower and the period during which the IRFs are below 0 is
longer. Previous studies have shown that the information effect varies over time, which is confirmed by thisstatement16).

16)

𝑃

To assess robustness, we also estimated the model the NAs filled with 0, and with the case where the maximum lag order
2,3. The outcomes obtained with these models were qualitatively similar.

14

Table 4: Estimation results for one-week ahead model in the early pandemic period

lg_infolog(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.540***(0.444)-1.163***(0.211)
0.136(0.178)-26.956(27.966)-0.194***(0.044)0.017(0.042)-0.040(0.129)-0.01***(0.003)0.585***(0.043)622
0.670

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-1.099***-3.391***-1.621**-1.189***(0.301)(0.803)(0.761)(0.445)-0.346***-1.123***-1.150***-0.419***(0.128)(0.28)(0.124)(0.125)-0.0770.675*-0.045-0.080(0.113)(0.349)(0.244)(0.234)-14.394-80.919-72.434**-48.576(22.731)(118.225)(34.62)(29.548)0.036-0.028-0.093-0.167***(0.038)(0.137)(0.072)(0.042)0.125***
0.084
0.076
0.035
(0.028)(0.129)(0.072)(0.037)-0.122-1.041-0.068-0.065(0.15)(0.862)(0.174)(0.055)-0.012***-0.061***-0.012***-0.006***(0.002)(0.017)(0.003)(0.002)
0.465***
0.601***
0.623***
0.538***(0.078)(0.033)(0.049)(0.06)
622
622
622
622
0.429
0.547
0.599
0.478

(6)
residential
0.576***(0.201)0.301***(0.065)-0.067(0.08)
23.721**(9.307)0.089***(0.019)0.019(0.015)0.011(0.04)
0.005***(0.001)0.618***(0.055)622
0.655

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Table 5: Estimation results for one-week ahead model in the period after the Delta variant outbreak

lg_infolog(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-0.302(0.274)-1.089***(0.144)-0.266*(0.151)-2.693(10.056)-0.390**(0.163)-0.160***(0.062)-0.022(0.153)-0.015***(0.004)0.401***(0.072)582
0.467

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces
0.100-0.3550.553
0.471(0.381)(0.923)(1.187)(0.334)0.055-0.144-1.543***-0.270(0.139)(0.383)(0.453)(0.196)-0.102-0.522-0.053-0.148**(0.163)(0.54)(0.232)(0.067)-0.383-4.166
4.887
11.219***(7.97)(17.215)(12.139)(3.987)0.044-0.089-0.222-0.069(0.075)(0.192)(0.217)(0.05)-0.035-0.084-0.203-0.081(0.049)(0.116)(0.124)(0.067)0.108***-0.112-0.096-0.121**(0.032)(0.217)(0.155)(0.06)-0.014***-0.062***-0.024***-0.007*(0.002)(0.011)(0.007)(0.004)0.704***
0.712***
0.455***
0.239***(0.058)(0.049)(0.048)(0.067)
582
582
582
582
0.575
0.690
0.381
0.139

(6)
residential
0.031(0.145)0.311***(0.074)0.105***(0.023)-3.405*(1.936)
0.099**(0.046)0.043(0.03)
0.022(0.023)0.006***(0.001)0.433***(0.091)582
0.528

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

15

Figure 6: IRFs in the early pandemic period and the period after the Delta variant outbreak

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

Watanabe and Yabu (2021) put forward several hypotheses to explain the absence of the information effectafter the delta variant outbreak, despite its presence in the early stages of the pandemic. These hypotheses
included a possible decline in altruism17) over time and the emergence of a gap between people's perceptions of
the severity of the infection and the actual situation.
Our investigation focuses on the impact of information dissemination on the information effect. Thus, we
examine additional factors beyond those examined by Watanabe and Yabu (2021), who analysed the information
effects of announcements of new cases and deaths. In the early stages of the pandemic, people struggled to makeinformed decisions based solely on announcements such as the number of cases. As a result, the disseminationof government information was critical. However, as people became better able to make decisions based on
announcements of infection status, the importance of government information dissemination declined relative to
its initial role.
Tables 4 and 5 show that the information effect of information dissemination was not observed after the Delta
variant outbreak. However, information effect made by daily announcements of the number of cases continued
to affect the majority of categories. Additionally, the impact of daily announcements of the number of deaths onmobility decreased significantly in more categories after the Delta variant outbreak compared to the early phase.
This could be evidence of a decline in the relative importance of information dissemination.

V.

Conclusion
In this article, we applied the latest findings in NLP research to precisely measure the information

dissemination effect for each prefecture in Japan in the context of the COVID-19 pandemic. We then analysed
17)

Alfaro et al. (2024) examine altruism as a potential motivator for changes in human behaviour. Altruism refers to theconcern that one's own infection may endanger those around them.

16the dynamic relationship between the index of information dissemination constructed based on the above andmobility, and followed through with an econometric analysis of the importance of the information effectpresented in Watanabe and Yabu’s (2020, 2021) study.
First, let us consider the proposed index of information dissemination for each prefecture. Unlike Watanabe
and Yabu (2020, 2021), we did not simply use the presence or absence of certain policies as a dummy variable
to create a new index, but applied the latest research in NLP to textual data. This method has gained popularity,especially for nowcasting economic trends. We built this model based on findings in extant literature on NLP.
The accuracy of the model built for index construction was evaluated using the macro F1 score, which measured
at 0.92, the same as Alaparthi and Mishra (2021), who constructed a text classification model for IMDb review
data.
Second, econometric methods helped us study the dynamic relationship between the newly developed index
representing information dissemination and mobility. We subsequently confirmed that the stay-at-home
information dissemination done by local governors during the COVID-19 pandemic effectively suppressedpeople’s mobility.
Our results are not unexpected. Watanabe and Yabu (2020, 2021) also impress upon the importance of theinformation effect through information dissemination. However, our approach is novel because it allowed us toprecisely and quantitatively assess the impact of information dissemination on mobility based on existing data.
We also confirmed that the information effect of announcing the number of new cases and the intervention
effect of declaring a SOE suppressed mobility, but the significance of this effect varied by the mobility of thecategories of places. Moreover, an increase in precipitation had a suppressing effect on the mobility of all
categories of places. These findings reaffirm Watanabe and Yabu’s (2020, 2021) study through the use of our
precise index representing information dissemination.
Significant results were obtained for the intervention of the declaration of a SOE only for some categories ofplaces. However, the local governor’s index significantly suppressed the mobility of all categories of places,
further strengthening Watanabe and Yabu’s (2020, 2021) claim that appropriate information encouraged
behavioural change rather than legally binding countermeasures against COVID-19.
The local governor’s index we constructed had almost no linear relationship with other variables related to theinformation effect and the variables related to the intervention effect. This can be interpreted as confirming, to acertain extent, the unique effect of information dissemination. The confirmation of the information effect evenafter extracting the unique effects of information dissemination further strengthens prior findings.
Third, we split the sample into an early and a later period when the Delta variant emerged in order to analysechanges in the information effect over time. A comparison of the one-week-ahead models revealed a significant
effect of suppressing mobility even after splitting the sample in the early pandemic period. No significant effectwas confirmed in any area after the Delta variant outbreak. After including periods that have not been analysedin many previous studies, the finding holds that the information effect changes over time. Again, we strengthen
the findings in extant literature.
Finally, we simultaneously analysed the changes over time of both the information effect caused by theannouncement of the infection status and that caused by the dissemination of information. The weakening of theinformation effect caused by information dissemination over time is more significant than that caused by theannouncement of the infection status. That is, both exploiting an information effect and knowing when and whatkind of information to disseminate are equally important to maximize the overall effect. This finding has serious
policy implications.
Our study is unique in applying findings of NLP research to create an index of information dissemination, andthen using this index as a proxy for the information effect. We followed through with an econometric analysis to17verify the information effect.
Nevertheless, it is essential to note the limitations of our methodology. Regarding the first analysis, we used asmall number of samples for model construction. Although BERT can help construct highly accurate models
even with a small number of samples, and we were indeed able to construct highly accurate prediction models,
using more samples would be beneficial for constructing a more refined model.
The computer memory was also a limiting factor in model construction. We were unable to read text longer
than 512 tokens. Following Sun et al. (2019), we compressed the input data for text data longer than a certain
unit. If there were no such limitations, we could construct a model that is not dependent on the length of the text.
Regarding the second and third analyses, although we considered two fixed effects—time and prefecture—thetime fixed effect may still differ by prefecture, which we did not examine.
Finally, we used impulse response analysis to analyse the impact of the local governor's index on mobility up
to five weeks ahead. It may also be that information dissemination occurs in anticipation of the following week's
mobility, so considering lead effects as well as lags would be interesting. Not all prefectures analysed are now
governed by the same governor as when the study was undertaken. In such prefectures, we suggest including theeffect of changes in information disseminators as an explanatory variable.
We hope that future research in this field will develop further once our limitations are considered whendeveloping new methodologies for analysis.

18

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20Appendix

21

Appendixes
A.

Parameter settings

Table A.1 shows the values of the parameters used to construct the text classification model in Section II.
Table A.1: Parameter settings
Parameters
Transformer
Batch size
Token length
EpochsLearning rate

B.

Settings
Hugging Face
32
512
15
0.00005

Examples of estimated values of the text classification model
The model is applied to the actual statements of local governors announcing the issuance and lifting of a SOE.

Examples of actual statements are as follows.
Example 1: Statement by the Governor of Tokyo, April 23, 2021 (provisional translation)
The government has just decided to declare a state of emergency under the Act on Special Measures against
Novel Influenza for Tokyo, Osaka, Kyoto and Hyogo prefectures. The Tokyo Metropolitan Government has
decided to implement emergency measures under the Act on Special Measures against Novel Influenza from
Sunday, April 25 to Tuesday, May 11. In order to ensure that the people’s mobility in Tokyo is controlled, wewould like to request all citizens of Tokyo to stay at home during the period of this declaration.
Example 2: Statement by the Governor of Okinawa, May 15, 2020 (provisional translation)
On May 14, the government decided to change its basic policy on measures against COVID-19 and to
remove 39 prefectures, including Okinawa Prefecture, from the area where emergency measures should be
implemented based on the Act on Special Measures against Novel Influenza. This means that the emergency
measures in Okinawa Prefecture will also be lifted, and daily life will gradually return to normal.
Example 1 is classified as ‘strict’ as it announces the implementation of a SOE in Tokyo, which requires
individuals to stay indoors. In contrast, Example 2 is labelled as ‘lenient’ as it declares the lifting of the SOE in
Okinawa.
Table B.1: Prediction results of the text classification model
Example 1
Example 2

𝑃 𝑠𝑡𝑟𝑖𝑐𝑡0.99550.0022

𝑃 𝑛𝑒𝑢𝑡𝑟𝑎𝑙
0.00020.0015

𝑃 𝑙𝑒𝑛𝑖𝑒𝑛𝑡
0.00430.9963

Table B.1 shows that the announcement of the application of the SOE in Example 1 has the highest probabilityof being classified as 'strict', while the announcement of the lifting of the SOE in Example 2 has the highest
probability of being classified as 'lenient', which is in line with expectations. It was confirmed that the model
construction was intuitively reasonable.

22

C.

Panel unit root tests

As outlined in section III, we use level data instead of differenced data, following the approach of Watanabe
and Yabu (2020). Table C.1 presents the results of the panel unit root tests. We conducted three types of unit root
tests18), each of which rejected the null hypothesis of non-stationarity.
Table C.1: The panel unit root test results
m1
retail andrecreation
m2
grocery andpharmacy
m3
parks
m4
transit stations
m5
workplacesm6
residential

Levin-Lin-Chu-17.42

Intercept & trend
Im-Pesaran-Shin-16.389

Maddala-Wu
349.41

Levin-Lin-Chu-14.787

Intercept
Im-Pesaran-Shin-16.298

Maddala-Wu
376.5(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***-14.969-14.525351.17-9.0733-10.366

230.79(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***-25.004-25.723667.79-18.523-21.388583.47(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***-8.6732-8.5914157.39-7.0808-8.0056

147.91(< 2.2e-16)***(< 2.2e-16)***(7.573e-16)***(7.167e-13)***(5.942e-16)***(2.717e-14)***-41.19-35.196989.18-35.334-33.389

1056.4(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***-21.356-18.449

406.04-18.71-19.051466.9(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***(< 2.2e-16)***

Note: Asterisks denote statistical significance: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

18)

Levin et al. (2002), Im et al. (2003), and Maddala and Wu (1999)

23

D.

Correlation analysis

The local governor’s index we constructed had almost no linear relationship with other variables related to theinformation effect and the variables related to the intervention effect. The correlation coefficients between thevariables have an absolute value of less than 0.2 in all cases. As explained in section II, the local governor's index
is a numerical representation of the dissemination of information on various policies, including intervention
measures. It is expected to reflect the intervention effect to an extent, as well as the information effect to somedegree, given that intervention measures are based on infection status information. However, the correlation
analysis results indicate that there is only a minimal linear relationship between the local governor's index andother variables associated with information and intervention effects. Scatter plots of the local governor's index
and the four variables are shown in Fig. D.1.
Fig. D.1: The scatter plots of the local governor’s index and the other variables

Note: Straight lines represent regression lines.

E.

Robustness checks
This section evaluates the robustness of the estimation results of the one-week ahead model from two

perspectives. First, the handling of NAs in the local governor's index is assessed. The second perspective is therobustness of the order of lags.
Robustness on handling of NAs
In section III, the local governor's index is treated as NAs if there was no information dissemination in that
period to distinguish between no information dissemination and information dissemination equivalent to a valueof 0. However, there is a possibility of introducing bias in the estimation results if the data for weeks with missing
governor indices are not included in the analysis. Therefore, in this section, we reanalyse the data by filling in
the NAs of the local governor's index with 0 and confirm the robustness of the analysis in section III.
24

Table E.1: Estimation results for one-week ahead model with NAs imputed with 0

lg_info (filled)
log(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.153***(0.26)-6.278(4.626)-0.862***(0.129)0.032(0.095)-0.188***(0.05)-0.012(0.038)-0.01(0.103)-0.011***(0.002)0.615***(0.036)2322
0.563

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-0.674**-2.288***-0.687-0.335***(0.327)(0.693)(0.441)(0.12)
3.797-2.529-4.7174.267(2.413)(10.159)(12.148)(3.458)0.055-0.094-0.854***-0.307***(0.056)(0.215)(0.161)(0.103)-0.0020.174-0.103-0.093(0.059)(0.208)(0.196)(0.061)0.033***
0.045-0.086***-0.132***(0.013)(0.06)(0.031)(0.021)0.015
0.109*-0.017-0.016(0.02)(0.065)(0.047)(0.015)0.009-0.388-0.176-0.049(0.057)(0.383)(0.128)(0.034)-0.01***-0.051***-0.016***-0.004*(0.002)(0.009)(0.003)(0.002)0.752***
0.718***
0.577***
0.501***(0.047)(0.042)(0.041)(0.076)
2322
2322
2322
2322
0.601
0.608
0.410
0.372

(6)
residential
0.302***(0.083)-1.386**(0.588)0.261***(0.048)0.034(0.023)0.081***(0.015)0.019***(0.007)0.007(0.027)0.004***(0.001)0.569***(0.057)2322
0.605

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Figure E.1: IRFs of the rate of change in the level mobility to an increase in the local governor’s index

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

The results of the one-week-ahead model indicated that the local governor's index, with NAs filled in,significantly suppressed the mobility of categories other than the transit station category. The results for the othervariables are similar to those in section III. The difference between the estimation results in section III and Table
E.1 is that the number of deaths was found to significantly suppress the mobility of four categories of places
when NAs were filled: retail and recreation, transit transportation, workplaces, and residential. The impulse
response analysis in Figure E.1 also confirmed the effect of suppressing mobility, even when using the local
governor's index with imputed NAs, similar to the results in section III.

25

Robustness on lag order
In Section III, the lag order of the two variables related to infection status (number of new cases and numberof deaths) considered as part of the information effect was treated as 𝑃

1. On the other hand, there is little

theoretical basis for how many lag periods should be included for these variables related to infection status, and
it is necessary to make an empirical judgement. Therefore, we reanalyse the case in which the maximum lag
order is 𝑃2, 3. We assume that the period for which the number of new cases and deaths can be recognized

and used as a reference for behavioural change is at most three weeks, and we check the robustness of the analysis
in Section III. Note that the lag order of the vaccination rate is fixed at 𝑃

1 because the variable is cumulativeand likely to be autocorrelated.
Table E.2: Estimation results for one-week ahead model (𝑃lg_infolog(cases)_lag1
log(cases)_lag2
log(deaths)_lag1
log(deaths)_lag2
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.047***(0.228)-1.400***(0.272)0.227(0.265)0.063(0.14)-0.155(0.124)-12.137**(5.827)-0.207***(0.056)-0.052(0.036)0.016(0.118)-0.012***(0.003)0.554***(0.054)
1198
0.617

2)

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-0.633**-1.969***-0.872**-0.490***(0.298)(0.641)(0.441)(0.188)-0.206-0.618-1.415***-0.094(0.153)(0.454)(0.238)(0.173)
0.247
0.175
0.070
-0.459***(0.153)(0.508)(0.274)(0.172)0.078
0.770*
0.072
0.003(0.097)(0.449)(0.275)(0.096)-0.222-1.049*-0.181-0.021(0.146)(0.582)(0.139)(0.115)3.852-6.771-14.7316.783**(2.868)(12.285)(11.653)(3.169)0.018
0.051-0.077***-0.123***(0.016)(0.103)(0.029)(0.032)-0.0120.007-0.084-0.002(0.023)(0.068)(0.051)(0.023)0.035-0.515-0.105-0.059*(0.053)(0.475)(0.124)(0.035)-0.012***-0.062***-0.017***-0.005**(0.002)(0.012)(0.004)(0.002)0.720***
0.677***
0.565***
0.478***(0.05)(0.029)(0.042)(0.083)
1198
1198
1198
1198
0.605
0.643
0.502
0.374

(6)
residential
0.346***(0.085)0.260***(0.099)0.098(0.06)-0.037(0.029)0.054(0.046)-1.986***(0.434)0.081***(0.019)0.023**(0.011)
0.005(0.028)0.005***(0.001)
0.558***(0.072)1198
0.631

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Table E.3: Estimation results for one-week ahead model (𝑃lg_infolog(cases)_lag1
log(cases)_lag2
log(cases)_lag3
log(deaths)_lag1
log(deaths)_lag2
log(deaths)_lag3
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.011***(0.211)-1.337***(0.257)-0.129(0.227)0.395**(0.198)-0.011(0.142)-0.266**(0.111)0.145(0.139)-11.973**(5.821)-0.216***(0.058)-0.053(0.036)0.018(0.122)-0.012***(0.003)0.547***(0.058)
1190
0.619

3)

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-0.616**-1.850***-0.816*-0.467**(0.295)(0.606)(0.439)(0.182)-0.164-0.632-1.444***-0.172(0.165)(0.441)(0.239)(0.139)0.088-0.475-0.308-0.264(0.239)(0.615)(0.377)(0.169)0.166
0.844*
0.499*-0.163(0.13)(0.492)(0.266)(0.21)
0.064
0.559
0.012
-0.015(0.104)(0.412)(0.275)(0.099)-0.229**-1.472**-0.287-0.09(0.107)(0.65)(0.177)(0.121)-0.0050.739*
0.132
0.174**(0.134)(0.428)(0.168)(0.085)3.847-5.797-14.7536.089**(2.832)(11.784)(11.381)(2.986)0.017
0.047-0.074**-0.121***(0.016)(0.104)(0.029)(0.03)-0.0120.013-0.0810.000(0.023)(0.067)(0.052)(0.024)0.036-0.504-0.097-0.058*(0.053)(0.473)(0.123)(0.033)-0.013***-0.062***-0.017***-0.006*(0.002)(0.012)(0.004)(0.003)0.719***
0.678***
0.564***
0.477***(0.05)(0.031)(0.042)(0.092)
1190
1190
1190
1190
0.607
0.643
0.505
0.380

(6)
residential
0.332***(0.078)0.291***(0.081)0.106(0.076)-0.039(0.073)-0.026(0.025)0.081**(0.039)-0.051(0.032)-1.801***(0.453)0.080***(0.02)
0.022*(0.011)
0.004(0.027)0.005***(0.001)0.557***(0.074)1190
0.638

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

26

Figure E.2: IRFs of the rate of change in the level mobility to an increase in the local governor’s index (𝑃2)

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

Figure E.3: IRFs of the rate of change in the level mobility to an increase in the local governor’s index (𝑃3)

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

The results of the one-week-ahead model, as presented in Tables E.2 and E.3, are consistent with those in
section III, even when the lag order is set to 𝑃

2, 3. In both cases, the local governor's index had a significant

effect on mobility suppression. The results for the other variables are similar to those in section III.
The coefficient of the number of new cases with a lag order of P=3, as shown in Table E.3, was contrary to
the results in section III in three areas: retail and recreation, parks, and transit stations. The analysis indicated27that an increase in the number of new cases with a lag order of 𝑃

1 significantly suppressed mobility.

However, an increase in the number of new cases three weeks ago may have created the impression that the
number of new cases was relatively low compared to three weeks ago, even if the number of new cases in thesubsequent two weeks was high. Therefore, it is possible that the increase in the number of new cases three weeks
prior appears to increase mobility.
The impulse response analysis shown in Figures E.2 and E.3 confirms the impact of mobility suppression,even when the maximum lag order is 𝑃F.

2,3, which is consistent with the findings in section III.

Robustness on the analysis of the time-varying effects of information dissemination
As in the robustness checks conducted for the benchmark model in section III, we examine the robustness of

the analysis of time-varying effects of information dissemination in section IV from two perspectives.
Robustness checks on handling of NAs
Examining the estimation results of the one-week-ahead models, it was found that in the model for the early
pandemic period, the local governor's index, with NAs filled in, had a significant effect on suppressing
mobility in almost all categories of places. However, in the period after the Delta-variant outbreak, no
significant effects were observed. The main results for the other variables are similar to the estimation results in
section IV. In addition, the impulse response analysis presented in Figure F.1 adheres to a conventional
structure and suggests that mobility was suppressed more in the early stages of the pandemic. In summary, theprimary conclusions drawn from these analyses were nearly identical to those outlined in section IV.
Table F.1: Estimation results for one-week ahead model with NAs imputed with 0 in the early pandemic period

lg_info (filled)
log(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.571***(0.433)-0.949***(0.131)
0.153(0.152)-0.636(14.975)-0.194***(0.037)0.047(0.033)0.024(0.11)-0.009***(0.002)0.629***(0.035)1116
0.616

Google COVID-19 Community Mobility Reports
(4)
(5)
(2)
(3)
grocery_and_pharmacyparkstransit_stations
workplaces-1.08***-3.52***-0.921-0.788***(0.346)(0.794)(0.702)(0.288)-0.245***-0.992***-0.987***-0.323***(0.091)(0.131)(0.164)(0.082)-0.0510.455*-0.172-0.039(0.1)(0.236)(0.235)(0.14)-18.59-6.499-72.988***-27.731(15.667)(45.242)(18.817)(17.896)0.037-0.053-0.133***-0.157***(0.024)(0.115)(0.037)(0.021)0.111***
0.153
0.028
0
(0.029)(0.098)(0.062)(0.03)-0.055-0.604-0.039-0.032(0.125)(0.593)(0.129)(0.046)-0.01***-0.054***-0.013***-0.004**(0.002)(0.013)(0.002)(0.002)0.486***
0.631***
0.596***
0.525***(0.069)(0.045)(0.044)(0.056)
1116
1116
1116
1116
0.387
0.535
0.495
0.431

(6)
residential
0.435***(0.166)
0.264***(0.044)-0.019(0.045)7.816*(4.236)0.088***(0.014)0.021*(0.011)-0.001(0.04)
0.004***(0.001)0.584***(0.046)1116
0.609

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

28

Table F.2: Estimation results for one-week ahead model with NAs imputed with 0 in the period after the Delta
variant outbreak

lg_info (filled)
log(cases)_lag1
log(deaths)_lag1
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-0.554(0.341)-0.615***(0.114)-0.097(0.139)3.757(7.335)-0.304**(0.136)-0.132**(0.058)-0.107(0.15)-0.013***(0.003)0.444***(0.049)1188
0.355

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces
0.018
-0.83
0.021
0.236
(0.401)(0.946)(1.011)(0.356)0.126*
0.496-0.644***-0.238***(0.068)(0.339)(0.19)(0.079)-0.032-0.241-0.009-0.152***(0.108)(0.387)(0.232)(0.057)2.552-1.89613.4059.602***(5.247)(14.86)(8.83)(2.921)0.07
0.083-0.132-0.08*(0.055)(0.172)(0.167)(0.042)-0.011
0.033-0.102-0.058(0.034)(0.123)(0.093)(0.038)0.008-0.446*-0.268-0.091(0.054)(0.268)(0.187)(0.058)-0.01***-0.051***-0.019***-0.005(0.002)(0.008)(0.005)(0.003)0.73***
0.727***
0.477***
0.234***(0.054)(0.063)(0.047)(0.062)
1188
1188
1188
1188
0.569
0.621
0.282
0.129

(6)
residential
0.138(0.146)0.231***(0.05)
0.091***(0.021)-2.911***(1.018)0.086**(0.034)0.028(0.019)0.026(0.025)0.005***(0.001)0.458***(0.08)
1188
0.490

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Figure F.1: IRFs of the rate of change in the level mobility to an increase in the local governor’s index with
NAs imputed with 0

Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

29

Robustness on lag order
Examining the estimation results of the one-week-ahead models with maximum lag order 𝑃

2,3, asignificant effect of the local governor's index in suppressing mobility was confirmed in the model for the early
pandemic period in all categories of places. However, no significant effect was observed in the period after the
Delta-variant outbreak. The main results for the other variables are similar to the estimation results in section
IV. Furthermore, the impulse response analysis depicted in Figures E.2 and E.3 indicated that mobility wassignificantly suppressed during the early pandemic period. In summary, the primary findings of these analyses
are nearly identical to those presented in section IV.
Table F.3: Estimation results for one-week ahead model P

lg_infolog(cases)_lag1
log(cases)_lag2
log(deaths)_lag1
log(deaths)_lag2
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.499***(0.443)-1.328***(0.314)0.202(0.279)0.124(0.204)-0.055(0.172)-29.845(27.564)-0.2***(0.044)0.008(0.037)-0.035(0.134)-0.01***(0.003)0.581***(0.047)608
0.667

2 in the early pandemic period

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces-1.041***-3.201***-1.619**-1.224**(0.289)(0.724)(0.786)(0.49)-0.396**-0.887-1.353***-0.22(0.194)(0.79)(0.199)(0.14)
0.075-0.3790.237-0.274*(0.182)(0.901)(0.356)(0.156)0.112
1.359**
0.261-0.036(0.147)(0.637)(0.253)(0.152)-0.365**-1.138-0.57***
0.019(0.147)(0.822)(0.184)(0.221)-15.917-80.376-74.112**-45.961(22.357)(118.228)(36.748)(29.543)
0.042
0.023
-0.081-0.167***(0.038)(0.14)(0.071)(0.045)0.126***
0.101
0.076
0.042
(0.026)(0.134)(0.071)(0.042)-0.12-1.09-0.055-0.071(0.146)(0.86)(0.177)(0.054)-0.011***-0.061***-0.011***-0.006***(0.002)(0.016)(0.003)(0.002)0.457***
0.591***
0.622***
0.527***(0.08)(0.035)(0.052)(0.069)
608
608
608
608
0.431
0.553
0.602
0.456

(6)
residential
0.57***(0.201)0.271***(0.102)0.05(0.062)-0.086(0.059)0.024(0.081)23.426**(9.925)0.09***(0.021)0.019(0.016)0.013(0.043)0.005***(0.001)0.603***(0.058)608
0.642

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Table F.4: Estimation results for one-week ahead model P

lg_infolog(cases)_lag1
log(cases)_lag2
log(deaths)_lag1
log(deaths)_lag2
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-0.253(0.274)-1.22***(0.29)
0.175(0.3)-0.045(0.147)-0.353**(0.175)-0.618(9.769)-0.399**(0.165)-0.173***(0.06)-0.029(0.157)-0.015***(0.004)0.4***(0.072)574
0.467

2 in the period after the Delta variant outbreak

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces
0.167-0.0790.644
0.443(0.375)(1.005)(1.234)(0.363)-0.156-0.477-1.493***0.451(0.212)(0.73)(0.576)(0.325)0.258
0.438-0.072-0.937***(0.281)(0.548)(0.41)(0.311)-0.0530.209-0.096-0.1(0.136)(0.376)(0.36)(0.065)-0.068-1.119**
0.136
0.038
(0.202)(0.523)(0.29)(0.096)
1.056
4.197
6.243
10.305**(8.004)(15.433)(12.425)(4.151)
0.037-0.061-0.209-0.022(0.071)(0.162)(0.219)(0.049)-0.051-0.134-0.206-0.058(0.053)(0.119)(0.13)(0.061)0.108***-0.126-0.119-0.128*(0.033)(0.213)(0.173)(0.071)-0.014***-0.064***-0.024***-0.008*(0.002)(0.012)(0.007)(0.004)0.703***
0.711***
0.452***
0.216***(0.058)(0.05)(0.048)(0.076)
574
574
574
574
0.571
0.693
0.382
0.164

(6)
residential
0.048(0.149)0.133(0.135)0.229(0.141)
0.047(0.032)0.083**(0.041)-3.174(2.206)
0.092**(0.045)0.038(0.028)0.027(0.025)0.006***(0.001)0.411***(0.098)574
0.533

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

30

Fig. F.2: IRFs of the rate of change in the level mobility to an increase in the local governor’s index (𝑃Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

Table F.5: Estimation results for one-week ahead model P

lg_infolog(cases)_lag1
log(cases)_lag2
log(cases)_lag3
log(deaths)_lag1
log(deaths)_lag2
log(deaths)_lag3
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-1.505***(0.425)-1.265***(0.313)-0.1(0.281)0.365(0.288)-0.037(0.222)-0.273*(0.149)0.345(0.242)-31.717(29.569)-0.211***(0.046)0.001(0.034)-0.029(0.142)-0.011***(0.003)0.567***(0.05)
600
0.674

3 in the early pandemic period

Google COVID-19 Community Mobility Reports
(4)
(5)
(2)
(3)
grocery_and_pharmacyparkstransit_stations
workplaces-1.04***-3.07***-1.587**-1.224**(0.291)(0.628)(0.74)(0.504)-0.377*-1.032-1.409***-0.308***(0.199)(0.864)(0.247)(0.113)0.041-0.9620.141
0.023(0.256)(1.13)(0.483)(0.197)
0.033
0.943
0.279
-0.235(0.165)(0.852)(0.349)(0.265)0.115
1.126**
0.054-0.092(0.161)(0.568)(0.235)(0.176)-0.339***-1.625*-0.987***-0.124(0.115)(0.91)(0.281)(0.213)-0.0450.802
0.817**
0.405***(0.14)(0.674)(0.349)(0.156)-18.242-79.527-69.861*-47.251(23.203)(119.516)(36.337)(33.241)
0.041
0.023
-0.08
-0.166***(0.039)(0.139)(0.07)(0.04)
0.125***
0.09
0.069
0.038
(0.026)(0.129)(0.072)(0.042)-0.116-1.06-0.053-0.077(0.144)(0.855)(0.178)(0.05)-0.012***-0.06***-0.012***-0.008***(0.002)(0.017)(0.003)(0.003)0.454***
0.593***
0.628***
0.537***(0.079)(0.036)(0.056)(0.078)
600
600
600
600
0.432
0.554
0.612
0.479

(6)
residential
0.556***(0.192)0.32***(0.086)0.027(0.094)-0.034(0.076)-0.057(0.061)
0.096(0.068)-0.151***(0.038)23.877**(10.987)
0.089***(0.02)
0.021(0.015)0.011(0.042)0.006***(0.001)0.606***(0.056)600
0.658

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

31

2)

Table F.6: Estimation results for one-week ahead model P

lg_infolog(cases)_lag1
log(cases)_lag2
log(cases)_lag3
log(deaths)_lag1
log(deaths)_lag2
log(deaths)_lag3
vaccination_lag1
SOE
QSOE
hot_ice_days
precipitation
mobility_lag1
Observations
R2(1)
retail_and_recreation-0.186(0.281)-1.217***(0.28)-0.338(0.339)0.561**(0.257)0.01(0.14)-0.302*(0.164)0.002(0.177)2.849(8.561)-0.324**(0.143)-0.173***(0.059)-0.094(0.133)-0.014***(0.004)0.405***(0.073)560
0.461

3 in the period after the Delta variant outbreak

Google COVID-19 Community Mobility Reports
(2)
(3)
(4)
(5)
grocery_and_pharmacyparkstransit_stations
workplaces
0.153
0.132
1.092
0.448
(0.356)(1.173)(1.547)(0.396)-0.154-0.491-1.38***
0.386(0.207)(0.736)(0.506)(0.303)-0.0750.174-0.928*-0.662**(0.32)(0.89)(0.558)(0.278)0.349*
0.279
0.997**-0.303*(0.199)(0.681)(0.429)(0.177)
0.031
0.357
0.143
-0.087(0.138)(0.377)(0.414)(0.066)0.015-1.256**
0.535
0.018
(0.168)(0.635)(0.364)(0.104)-0.1140.298-0.687**0.074(0.171)(0.54)(0.329)(0.09)
5.628
10.355
7.941
11.693**(7.268)(13.674)(11.01)(4.687)0.08
0.077-0.091
0.003(0.054)(0.141)(0.164)(0.051)-0.06-0.116-0.16-0.058(0.053)(0.118)(0.127)(0.063)0.044-0.266-0.211-0.147**(0.031)(0.229)(0.154)(0.067)-0.013***-0.062***-0.023***-0.007**(0.002)(0.012)(0.006)(0.004)0.693***
0.706***
0.457***
0.206***(0.055)(0.049)(0.05)(0.076)
560
560
560
560
0.573
0.693
0.386
0.164

(6)
residential-0.046(0.17)
0.159(0.131)
0.26**(0.131)-0.045(0.081)
0.008(0.046)0.043(0.042)0.047(0.04)-4.145*(2.187)0.059**(0.03)
0.029(0.027)0.048**(0.019)0.006***(0.001)0.427***(0.087)560
0.532

Note: Heteroscedasticity-consistent robust standard errors in parentheses. Asterisks denote statistical significance:∗p<0.1;∗∗p<0.05;∗∗∗p<0.01.

Figure F.3: IRFs of the rate of change in the level mobility to an increase in the local governor’s index (𝑃Note: The solid lines correspond to the point estimate and the shaded areas correspond to the 90% confidence interval.

32

3)

G.

Source of text data for each prefecture used to calculate the local governor's index19)

Aichi Government．“知事記者会見トップページ [Governor's Press Conference]”. Retrieved from:‘https://www.pref.aichi.jp/site/chiji-kaiken/’.
Chiba Government．“知事定例記者会見 [Governor's Regular Press Conference]”. Retrieved from:‘https://www.pref.chiba.lg.jp/cate/kt/goannnai/chijishitsu/teirei/index.html’.
Fukuoka Government．“知事記者会見録 [Governor's Press Conference Transcript]”. Retrieved from:‘https://www.pref.fukuoka.lg.jp/site/chiji-kisha/kaiken-new.html’.
Gifu Government．“知事記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.pref.gifu.lg.jp/site/kaiken/’.
Hiroshima Government．“知事記者会見一覧 [List of Governor's Press Conferences]”. Retrieved from:‘https://www.pref.hiroshima.lg.jp/site/kishakaiken/index-2.html’.
Hokkaido Government．“知事記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.pref.hokkaido.lg.jp/category/d006/c026/s084/’.
Ibaraki Government．“知事記者会見録 [Governor's Press Conference Transcript]”. Retrieved from:‘https://www.pref.ibaraki.jp/shiru/annai/chiji/kishakaiken/_index.html’.
Kanagawa Government．“記者会見 [Press Conference]”. Retrieved from:‘https://www.pref.kanagawa.jp/chiji/press-conference/’.
Kumamoto Government．“記者会見 [Press Conference]”. Retrieved from:‘https://www.pref.kumamoto.jp/site/chiji/list57.html’.
Kyoto Government．“知事記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.pref.kyoto.jp/koho/kaiken/’.
Mie Government．“知事定例記者会見 [Governor's Regular Press Conference]”. Retrieved from:‘https://www.pref.mie.lg.jp/common/09/ci400000468.htm’.
Miyagi Government．“知事定例記者会見 [Governor's Regular Press Conference]”. Retrieved from:‘https://www.pref.miyagi.jp/site/chiji-kaiken/’.
Okayama Government．“知事記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.pref.okayama.jp/site/chijikaiken/’.
Okinawa Government．“新型コロナウイルス感染症にかかる知事コメント [Governor's comments on
COVID-19]”. Retrieved from:‘https://www.pref.okinawa.jp/site/chijiko/kohokoryu/koho/2020_new_korona_virs.html’.
Osaka Government．“知事の記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.pref.osaka.lg.jp/koho/kaiken2/’.
Saitama Government．“定例記者会見 [Regular Press Conference]”. Retrieved from:‘https://www.pref.saitama.lg.jp/kense/koho/kaiken/index.html’.
Shizuoka Government．“記者会見 [Press Conference]”. Retrieved from:‘https://www.pref.shizuoka.jp/kensei/introduction/governor/kaiken/index.html’.
Tokyo Government．“知事の記者会見 [Governor's Press Conference]”. Retrieved from:‘https://www.metro.tokyo.lg.jp/tosei/governor/governor/kishakaiken/index.html’.

19)

All sources are in Japanese (Accessed: January 31, 2024).

33