Discussion Paper No. 125
How long do voluntary lockdowns keep people at home?
The role of social capital during the COVID-19 pandemic
Yuta Kuroda, Takaki Sato and Yasumasa Matsuda

May, 2022

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANHow long do voluntary lockdowns keep people at home?
The role of social capital during the COVID-19 pandemic ∗Yuta Kuroda1, Takaki Sato2, Yasumasa Matsuda3

1

Graduate School of Economics, Osaka Metropolitan University
2

3

Faculty of Economics, Masashi University

Graduate School of Economics and Management, Tohoku University

Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 21H03400. We appreciate thecooperation of NTT DOCOMO, Inc. in providing us with valuable data. The views expressed
are those of the authors and do not necessarily reflect those of any organizations with which
the authors are affiliated.

∗ Corresponding author: Yuta Kuroda (kuroyu0725@gmail.com).

1

Abstract
We create a city-by-day-level mobility index for the duration of the COVID-19 pandemic fromdata on over 80 million mobile devices to analyze how social distancing compliance varies
with social capital levels. We find that in the second year of the pandemic, both voluntary
preventative activities and policy compliance were substantially reduced in areas with lowlevels of social capital but not in areas with high levels of social capital. Additionally, in Japan,
mobility was clearly reduced among those supporting a majority party, and there is little
heterogeneity by political preference as related to ideology or position. This suggests thatvaluing conformity with others is an important driver of behavior that is beneficial to thecommunity.

JEL classifications: H12, I18, Z18, A13, D91
Keywords: COVID-19, Stay-at-home orders, Social capital, Civic capital, Social distancing,
Mobility, Compliance21.

Introduction

To fight the COVID-19 pandemic that began in early 2020, world governments have
implemented various public health policies. Since social distancing is a primary policy used tocontrol infectious disease outbreaks (Anderson et al. 2020; Hsiang et al. 2020), many
governments adopted orders restricting mobility, such as lockdowns, stay at home (SAH)
orders, and shelter-in-place orders (SIPOs). In the early stages of the pandemic, mobility
restrictions were viewed as a containment strategy to be used until a vaccine was developed
and distributed, but even after vaccination was underway, many serious and fatal cases of
COVID-19 were still being reported worldwide. As a result, the fight against COVID-19 hasturned into a protracted affair. Many studies have found that from the very beginning, mobility
reductions and policy compliance varied by region (e.g., Allcott et al., 2020; Bargain andAminjonov, 2020; Barrios and Hochberg, 2021), but gradually, changes were also observed
over time. Understanding who will or will not comply with policy during which stages of apandemic is a critical concern, not only during the current COVID-19 pandemic but also for
future public health matters.
When faced with a public health problem and the risk of infection, individuals take selfdefense actions to protect themselves, and they often comply with the government's prevention
policies. However, in the case of infectious diseases, because of the critical externality of3spreading the disease to others, the societal benefits resulting from individual preventive
actions are greater than those that accrue to the individual. Since individual prevention can beconsidered a public good that benefits the community as a whole, prevention simply due to fear
for one's own health cannot provide a socially desirable level of prevention (Jones et al., 2021).
Because there are many asymptomatic COVID-19 carriers and the severity of symptoms varies
widely across demographic groups, reducing mobility and enforcing social distancing haveconsiderable positive externalities (Bethune and Korinek, 2020). Furthermore, the difference
between the expected subjective benefits to an individual and the benefits to society canincrease over time, as increased knowledge of the virus allows people to more accurately
understand whether they are at high or low risk of infection (Chang et al., 2020).
If community members have a stronger interest in public welfare, they are more likely tovoluntarily practice social distancing and even comply with government orders in astraightforward manner. Moreover, members of communities with strong reciprocity and social
solidarity norms are more likely to reduce their mobility because of the higher psychologicalcosts of infecting others (Alfaro et al., 2020). Members of such communities also monitor eachother and stigmatize behavior that violates norms, which can increase the cost of engaging inrisky behavior (Borgonovi and Andrieu, 2020; Herrmann et al., 2008; Putnam, 1993). We
follow related studies (e.g., Bartscher et al., 2021; Durante et al., 2021) and define social capital4

as the beliefs and norms by which communities value collective actions and pursue sociallyvaluable activities. The commitment of individuals to social systems is also known as civic
capital (e.g., Barrios et al., 2021; Gusio et al., 2011; Lichter et al., 2021), and both concepts
refer to the shared values, norms, mutual bonds, trust, etc., within a community and encourage
individuals within that community to help other members and to work together to solve
community-wide problems. Social capital can also be considered a resource necessary for theprovision of public goods, as previous studies have found that it is positively associated withthe efficient provision of public goods (Gächter et al., 2004). In the context of the COVID-19
pandemic, social capital can also overcome the free-rider problem by increasing the willingness
to contribute to social welfare, thereby effectively overcoming the lack of self-defense andpolicy compliance related to the spread of infectious diseases.
This study analyzes how the reduction in people's mobility during the COVID-19 pandemic
varies with the social capital of their region of residence using data from early 2020 to the endof September 2021 in Japan. We generate various indices of daily and city-level mobility fromthe location data of approximately 80 million mobile devices. We use principal component
analysis to combine proxies commonly used in related studies (e.g., Barrios et al., 2021;
Bartscher et al., 2021; Borgonovi and Andrieu, 2020), such as voter turnout and number ofcommunity centers per capita, in order to create a new proxy variable for social capital. We
5estimate the differences in people's mobility and policy compliance across social capital levels
by strictly controlling for daily and city-level fixed effects, as well as by further controlling forthe interactions between each phase of the pandemic and local geographic, demographic, andsocioeconomic characteristics. The analysis reveals several important findings. First, in theearly stages of the pandemic, areas with high levels of social capital areas (i.e., high social
capital areas) reduced their mobility faster and to a greater extent, but the reductions were notvery large (approximately a 1.5-percentage point difference in the rate of decline relative to the
prepandemic baseline). Over time, however, the differences by social capital level increased,
as individuals in high social capital cities continued to reduce their mobility and comply withgovernment orders, while those in low social capital areas rarely complied with government
orders. After mid-2021, individuals in areas with high levels of social capital wereapproximately 5 percentage points less mobile than individuals in areas with low levels ofsocial capital, even in the absence of government orders, and an additional 10 percentage points
less mobile if policies were introduced. These results are robust to 1) adding a prefecture–dateinteraction term, 2) controlling for potential confounders such as population density, taxable
income, age, and education, and 3) using alternative measures of social capital and mobility.
We also examine the effects of political beliefs and ideologies, which have beenemphasized in the context of policy compliance under COVID-19. Unlike previous studies on
6

the United States (Allcott et al., 2020; Barrios and Hochberg, 2021), no significant differencesor trends by political belief or ideology were found in Japan. Instead, the results suggest thatwhether the party an individual supports is in the majority may be an important determinant ofcompliance. Since people who conform to their surroundings are more concerned about being
observed and their reputation, it is plausible that they participate in social activities, practicesocial distancing during pandemics, and comply with policies. In contrast, in areas where many
people are not afraid to act differently from those around them (or who dare to act differently
in retaliation against peer pressure), such forces may be weakened.
This study adds to a growing body of literature on social distancing and policy complianceduring pandemics. 1 Previous studies in the United States and European countries investigating
the early stages of the COVID-19 pandemic have shown that areas with higher social capitallevels reduced mobility voluntarily and/or in compliance with policy (Bargain and Aminjonov,
2020; Barrios et al., 2021; Borgonovi and Andrieu, 2020; Brodeur et al., 2021; Ding et al.,2020; Durante et al., 2021), which in turn reduced the spread of infection and decreased excess
mortality (Bartscher et al. 2021). Previous studies have suggested that in addition to social
capital, mobility changes and policy compliance vary depending on socioeconomic
characteristics such as income and occupation (Brough et al. 2021; Dave et al. 2021), politicaltrust and beliefs (Allcott et al., 2020; Bargain and Aminjonov, 2020; Barrios and Hochberg,
72021), level of individualism (Bazzi et al., 2021; Bian et al., 2022), religion (Laliotis and Minos,2022), culture as defined by language (Deopa and Fortunato, 2021), and ethnic diversity(Egorov et al., 2021). Because these cultural differences are interrelated (Bazzi et al., 2021), it
is difficult to strictly separate the effects of each, and potential confounding factors may resultin the impact of social capital being over- or underestimated. However, unlike the U.S. andEuropean countries, which have been covered by previous studies, Japan, which we analyze,
is highly homogeneous in terms of race, ethnicity, religion, language, culture, and othercharacteristics that are difficult to quantify (Central Intelligence Agency, 2022). In addition,
Alexander and Karger (2021) note that the heterogeneity observed when using state-levelvariation is not found when using county-level variation, while our study finds heterogeneity
by social capital levels at the Japanese city-level (there are approximately 1900 Japanese cities,
a classification similar to counties in the United States). We contribute to the literature byproviding robust evidence of heterogeneity by social capital level through the use of an index
of social capital within a small geographic area in a culturally homogeneous country.
Additionally, most existing studies on social capital and social distancing have focused only
on the early stages of the pandemic. While many studies have suggested that mobility andcompliance vary across regions, Alexander and Karger (2021) find that in the early stages ofthe pandemic, there is little heterogeneity in the response to stay-at-home orders by regional8

characteristics, perhaps because of the lack of information on COVID-19 at the beginning of
the pandemic. If people do not have information about what characteristics increase
vulnerability to severe illness or death, they will proactively social distance to reduce their own
risk of infection, regardless of their willingness to benefit others. Subsequently, as knowledgeof COVID-19 increased, individuals could their own risk for severe illness, and the incentives
for low-risk individuals to change their behavior were primarily altruistic. In this scenario,
civic-minded individuals continuously practiced social distancing even if their own risk was
low, while others may not have social distanced or complied with the policy at all. Therefore,
differences in responses by social capital level may have increased with the passage of timesince the beginning of the pandemic. Because it is impossible for governments to heavily
restrict people's behavior for long periods, understanding the changes in community responses
over time is crucial to developing effective strategies for dealing with prolonged infectious
diseases.
By creating mobility indices from estimated population data that include information onarea of residence, this study is able to analyze not only travel outside of a city but also,
conversely, the travel into the city from somewhere else. Our results suggest that while
government orders may prevent people living in high social capital areas from moving away,
they may have a limited effect on reducing the number of people coming into those areas. Thus,9imposing stricter restrictions on low-compliance areas may not increase social distancing, as
people change their behavior to avoid such restrictions.
Our findings have important implications for the current multiyear struggle against
COVID-19 and for new public health crises that may arise in the future. First, policies that
explicitly inform people about the externalities of collective action to improve policy
compliance are less effective in areas with low social capital levels and become even more
attenuated over time. In such cases, financial policies such as penalties or subsidies may benecessary for individuals to internalize the externalities. Second, understanding that policy
compliance varies by social capital levels and that this variation increases over time, willcontribute to the development of public health models that more effectively predict the spreadof disease. Accurate forecasting is critical for deploying resources appropriately, flattening the
curve, and preserving hospital viability.
The paper proceeds as follows. Section 2 details the data related to policy, mobility, social
capital and other control variables used in the analysis. Section 3 presents the empirical strategy.
Section 4 presents the main results and a series of robustness checks. Section 5 discusses theunderlying mechanisms and policy implications, and Section 6 concludes.

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2.

Data and institutions
We use four main types of data: 1) data related to COVID-19, such as government

declarations and the number of cases, 2) indicators measuring mobility, 3) variables
representing regional characteristics, and 4) other variables that may affect mobility. The
analysis period is the 638 days from January 1, 2020, to the end of September 2021, and thedata cover almost all 1889 cities in Japan. 2 Summary statistics for the major variables used inthis analysis are presented in Appendix A1.

2.1. COVID-19 and policy in Japan
Japan was one of the first countries outside of China to confirm a case of COVID-19
infection, which occurred on January 16, 2020. After that, the number of infected people
increased, exceeding 100 on February 22, at which time the government began full-scalecountermeasures. In contrast to China, the U.S., and European countries, which adopted strict
policies that restricted civil liberties by ordering citizens to stay home with penalties forviolations, Japan attempted to control outbreaks by declaring a state of emergency, a measurewith no legal binding force. Watanabe and Yabu (2021) called this situation in Japan a“voluntary lockdown”, but despite the softness of the policy, Japan survived its first wave withvery few cases by significantly reducing mobility. 3 Barrios et al. (2021) state that “Japan was
11able to contain COVID-19 with voluntary social distancing and without either large-scaletesting or rigid lockdowns.” This may be an example of social capital working effectively.
However, after the first wave, the government encouraged people to travel within the country
in an effort to reactivate the stagnant economy, which led to another wave of infection. With
the second and third waves, mobility gradually increased, and the number of cases and deaths
increased as in other countries. Moving into 2022, the outbreak has continued, and the end of
the COVID-19 epidemic in Japan is not yet in sight.
We obtained information on the daily number of cases (and deaths) in each city from theofficial websites of the public health departments located in those cities. Figure 1-(1) plots thenumber of daily confirmed infections per person, divided into cities with above-median andbelow-median social capital. The figure also shows when different states of emergency were
declared in Tokyo, Japan's capital and the most heavily infected city. There were four large
waves of infection during the analysis period, with a state of emergency declared in all 47
prefectures in the first wave and in prefectures with particularly high infections in the secondand subsequent waves. The prefectures where states of emergency were declared and the timingof implementation are shown in Appendix A2. Figure 1-(1) shows that both cities with highand low levels of social capital followed similar trends but that cities with high social capitallevels had fewer confirmed cases of infection overall. Of course, these differences are not12necessarily caused by social capital levels, as they are the result of a combination of various
geographic, demographic, and socioeconomic characteristics. We can also see that the peak ofeach wave increased over time.
We divide the analysis period into 13 phases based on the introduction of various policies,
such as state of emergency declarations. Policy dates related to the start and end of each phasewere obtained from government and local municipality websites. Table 1 shows the start date,domestic conditions, and expected impact on mobility for each phase. The start dates for phases
1 and 2 are the same for all prefectures, but for subsequent policies, there is a lag ranging froma few days to a few weeks in the start and end of the policy. Additionally, the second, third, andfourth state of emergency declarations covered 11, 10, and 21 prefectures, respectively.
Additionally, the policy to promote domestic travel and consumption in Phase 5 (known as the“GoTo Campaign” in Japan) excluded only Tokyo until September 30, 2020. Taking advantageof the differences in the timing and presence of policies across prefectures, we analyze thechange in mobility during each phase, that is, the change in mobility due to the introduction of
the policy, while controlling for time fixed effects.
Since not all prefectures declared a state of emergency after the second wave, we use two
methods for generating our data. The first is to skip the relevant phase in cities without adeclaration, divide the period in half at an approximately median date, and allocate those dates
13

to the phases before and after. For example, a prefecture without a phase 8 emergency
declaration (from January 8, 2021, to March 21, 2021, in Tokyo) would be in phase 7 from theend of November 2020 to mid-February 2021 and in phase 9 from mid-February 2021 to theend of April 2021. The second method is similar to the triple-differences method, which createsa cross term between a dummy variable indicating the phase and a dummy variable indicating
whether a declaration had been made. In this case, the variation in cities with emergency
declarations during Phase 8 is identified with the interaction term “phase 8 x has a declaration”,
and the variation in cities without declarations is identified with “phase 8”. Since both methods
produce almost identical results, for ease of interpretation, we use the first method as our main
strategy and the second method as a robustness check. While there is concern that areas withdeclarations might include only cities with high or low social capital levels, we have confirmedthat such bias is small and does not affect the results. 4
A state of emergency declaration is an endogenous response to the number of COVID-19
cases, the level of social capital, and other local characteristics. The number of cases and thedeclaration of a state of emergency are related, but higher social capital levels may make iteasier to declare a state of emergency, and the characteristics associated with social capital (e.g.,
average age and occupation) may be correlated with the number of cases. However, in Japan,
relatively strong interventions, such as state of emergency declarations, are implemented by
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prefectures, and except for very weak interventions, cities do not set policies on their own
initiative. Because we exploit the variation in social capital and mobility among cities within aprefecture, we avoid the problem of policy endogeneity.
However, one set of policies for which the timing does not overlap with that of the state of
emergency declarations is the “preemergency measures” issued at the city level. This is an evenless enforceable policy that made “requests”, but areas with particularly high case rates were
designated “priority areas,” and those with low rates were designated “normal areas” where
people were encouraged to cooperate with voluntary prevention measures. To control for theeffects of these measures, we add the explanatory variables “preemergency measures: normal”
and “preemergency measures: priority.”

2.2. Mobility measures
We use population data estimated hourly, Mobile Spatial Statistics, obtained from NTT
DOCOMO, Inc., the largest wireless carrier in Japan with over 80 million subscribers, tomeasure changes in mobility during the pandemic. These data are generated based on thelocation of the subscriber's device, which is continuously and automatically aggregated fromthe cellular operational network. Japan is divided into 500 m x 500 m meshes, and hourly meshlevel information about where a person who lives in a given city is currently located is reported.
15

A detailed and technical description of Mobile Spatial Statistics can be found in the Technical
Journal published by NTT DOCOMO, Inc. (NTT DOCOMO, Inc., 2013). We use data from2019 to define each mesh and to measure “normal” mobility before the pandemic. Each main
analysis use data from January 1, 2020, to September 30, 2021.
We generated several mobility variables from the raw data. The first is the number of people
outside their residential city, which is the most straightforward measure of people's mobility
behavior. The second is the average distance traveled by the residents of a city, calculated as
the distance between the center of gravity of their current mesh and the center of gravity of
their city of residence. The third is the number of people visiting commercial areas; a similar
definition is used by Mizuno et al. (2021), who also use Mobile Spatial Statistics. This iscreated by defining a “commercial area” as a mesh with an average nighttime population of atleast 1 and a day/night population ratio of at least 1.5 in 2019 and taking the difference between
the number of people in the defined mesh and the resident population. While the second andthird indicators exhibit wide regional variation, the first, travel out of the city, is highly
homogeneous across the country. In addition to these three measures, we also measure thenumber of people going out of the prefecture, the number of people around train stations, andthe number of people in hotspots where people from various cities gather, which we use forrobustness checks. Each variable is created at the city level for each hour, and its average value
16

between 09:00 and 17:00 is used to calculate daily values. Detailed definitions and replication
instructions are reported in Appendix B.
We also generated each mobility index for 2019 and used the average values for each
weekday and holiday in 2019 as a measure of mobility during normal times. Taking averages
allows for a more uniform measure of each city's mobility, alleviating extreme swings in 2019
due to major events, natural disasters, or other factors. We do not analyze the absolute level ofmobility but rather use the percentage change after the pandemic relative to normal mobility as
our explained variable. Because our identification relies on within-city variations in mobility
over time rather than on comparisons of mobility between cities, the effect of the absolutemagnitude of the original mobility measure is not a concern.
Graphs (2), (3), and (4) in Figure 3 show the changes in the mobility index (7-day moving
averages) from January 2020 to September 2021. If mobility is unchanged compared to the
prepandemic period, the index takes on a value of 0; if it is halved, the index takes on a valueof -0.5. The figure shows that mobility was declining even before the state of emergency was
declared. We note, however, that this trend includes both voluntary behavior and reactions topolicies such as school closures. Although the patterns in each of the mobility indicators
suggests an association between social capital and mobility, this may be due to any number offactors that could be correlated with both social capital and mobility. 5 The empirical strategy
17described in the next section aims to exclude the effects of confounding factors on therelationship between social capital and pandemic responses.
Mobile Spatial Statistics provide information on the estimated population, not travel, so wecannot identify who has moved or by what routes. However, the nature of the data can be used
to create indicators of incoming as well as outgoing activity in a given area. We use theresidential location information in the data to generate the following variables for each mobility
indicator: 1) the mobility of people who reside in a given area and 2) the mobility of people
who are currently in a given area. The first variable shows, for example, how many people
living in Minato-ku are now traveling to other cities, and the second shows how many people
currently staying in Minato-ku are from other cities. This allows us to distinguish between
cities with high social capital levels whose residents either do not leave the city or reject outside
visitors.

2.3. Social capital measures
We assume that residents in communities with high social capital levels exhibit strongsocial responsibility and therefore are more likely to defend themselves and comply with
policies. To measure social capital, the literature uses voting rates (Barrios et al., 2021;
Bartscher, 2021; Bauernschuster et al., 2014; Guiso et al., 2004: Putnam, 1993), blood and18

organ donations (Buonanno et al., 2009; Guiso et al., 2004; Guiso et al., 2016; Satyanath et al.,2017), association densities (Giuliano and Wacziarg, 2021; Satyanath et al., 2017), newspaper
readership (Durante et al., 2021; Gusio et al., 2004), historical literacy rates (Tabellini, 2010),
and the number of civil offices and religious organizations (Rupasingha et al., 2006). Following
Rupasingha et al. (2006) and the related literature, we use turnout in national elections,participation in the volunteer fire brigade, the number of community centers per capita, and thenumber of meeting facilities per capita. We use the first principal component of these four
factors as the composite social capital measure in our analysis. We also use each indicator
separately in our estimation and confirm the robustness of the results to changes in the social
capital proxy variables. Each variable is described below.
Voting is the most representative example of an activity that is privately costly and has nodirect reward but is socially useful. We obtain national election data from the Ministry ofInternal Affairs and Communications. Voter turnout has the analytical advantage of being
highly representative, accurately observed, and specific to geographic areas. We use theaverage turnout in the last three (prepandemic) national elections (held in 2012, 2014, and2017) as a measure of social capital. For each city and election, turnout is the number of votes
divided by the number of eligible voters.
The number of fire brigades in each city is based on data published by the Fire and Disaster
19

Management Agency of the Ministry of Internal Affairs and Communications. In Japan,
volunteer fire brigades (also known as the fire corps) are the emergency firefightingorganizations for each city, and its members hold other jobs while training part-time to perform
firefighting and disaster management activities during emergencies. Headquarters are locatedin each city, and anyone over the age of 18 may join. In 2021, there were approximately
810,000 volunteer fire brigades in Japan. Since participation is voluntary rather than mandatory,
there is wide variation in participation rates from city to city. Because participation in thevolunteer fire brigade contributes to the community but receives a small compensation (thenational average is approximately $300 per year), participation rates can measure thewillingness to contribute to society and levels of community connectedness.
We also use the number of community centers and meeting facilities in each city based on
the Social Education Survey of 2008. The number of such facilities does not increase or
decrease over short periods, and it is also well known that cultural characteristics such as social
capital are strongly persistent over time (Alesina et al., 2013; Bisin and Verdier, 2000; Tabellini,2008). The number of community centers per capita is a good measure of the strength ofcommunity connections and the willingness to provide public goods. Meeting facilities include
health care clubs, martial arts halls, and multipurpose meeting spaces, which can be used tomeasure the level of community activity.
20

Figure 2 shows the variation in the composite social capital variable at the city level, colorcoded by quintile. In Japan, urban areas tend to have relatively low social capital levels. Since
urban areas attract people from rural areas in search of education and employment, it can beassumed that few people are born and raised in the city and, therefore, have a low sense ofcommunity. However, it is important to note that some urban areas do have high levels of social
capital and that there is variation in social capital levels within a single prefecture. We use thecity-level variation in social capital within prefectures in our empirical approach.

2.4. Other control variables
We control for local characteristics related to mobility, social capital, and the response to
the virus. We use the share of the population 65 or over, population density, the share of whitecollar workers, the unemployment rate, and the share of those with a college education or above,
all obtained from the national census. 6 Additionally, the number of hospital beds in each city,
based on the 2015 Survey of Medical Institutions, and the average taxable income for each city
as published by the Ministry of Internal Affairs and Communications are included. Thesevariables are used as control variables in the main estimation and for creating subsamples whenchecking for robustness.
To account for the risk of exposure to COVID-19, the number of new confirmed cases and21

deaths in a given city on a given day as reported by that city’s health department is used. We
control for the previous day's values because we are interested in the impact of the reported
number of cases and deaths on people's behavior. If a health center manages more than one city,
the number of cases and deaths are prorated according to the population ratios. Although theimpact of mobility on the number of cases is important, the number of cases is used only as acontrol variable in our analysis because of measurement error arising from the upper limits on
inspections, false positives and negatives, and patients who do not receive medical attention.
Data from the Automated Meteorological Data Acquisition System, known as AMeDAS,
operated by the Japan Meteorological Agency, are used to control for the weather, an important
determinant of mobility. We use the precipitation level, average temperature, wind speed, andsnowfall information reported by the weather station closest to each city office to represent thedaily weather conditions for each city. If data from the nearest station were missing, the weather
conditions reported by the second or third nearest station were used instead.
We also measure the share of votes cast for the major parties in each city (the number ofvotes cast for each party divided by the total number of votes cast) on the basis of the
proportional representation vote for national elections. Because proportional representation
voting is for parties rather than candidates, it provides information about preferences for eachparty as a whole rather than for a particular politician. We define the six parties that are on the22

ballot in all prefectures as the major parties in Japan and use them in our analysis. Each party's
average vote share, percentage of seats in the House of Representatives, ideology, and position(right or left) are reported in Appendix A8. 7 These variables are not included in the main
analysis but are used as independent variables in place of social capital to analyze the impactof local party preference on mobility and policy compliance.
When analyzing COVID-19 and mobility, it is important to consider the vaccination
coverage data published by each prefecture. However, to avoid statistical discrimination,
vaccination rates are published only at the prefectural level. Additionally, all prefectures beganvaccination on almost the same day (up to two days later), and vaccination rates increased atsimilar speeds across prefectures, with little variation. Therefore, vaccination rates are not used
for the main estimation but simply to confirm the descriptive statistics and provide suggestive
evidence.

3.

Empirical strategy
We aim to identify the role of social capital in postpandemic changes to mobility and policy

compliance over time. The defined phases are used as temporal variables to measure theaverage effects in those phases. We begin by dividing the sample into high- and low social
capital cities as a baseline to see how their respective mobility changed during each phase. We
23then use the interaction between the phase dummy and the high social capital dummy toidentify heterogeneity by social capital level.
Our baseline specification is expressed by the following equation:
𝑗𝑗=13

𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛼𝛼 + � 𝛽𝛽𝑗𝑗 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 + 𝜃𝜃𝑖𝑖 + 𝛾𝛾𝑡𝑡 + 𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,
𝑗𝑗=1

(1)

where the explained variable 𝑌𝑌𝑖𝑖𝑖𝑖 is the measure of mobility, such as traveling outside the cityor distance traveled and measured as the percentage change relative to prepandemic levels, on
day 𝑡𝑡 in city 𝑖𝑖. Our explanatory variable of interest is 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 , a dummy variable that takes

on a value of 1 when day 𝑡𝑡 in city 𝑖𝑖 corresponds to phase 𝑗𝑗 and that identifies the averagechange in mobility in each phase. Phase 1, which corresponds to the prepandemic period, is thebaseline. 𝜃𝜃𝑖𝑖 represents the city fixed effects, which control for all time-invariant observableand unobservable city characteristics and their impact on mobility. 𝛾𝛾𝑡𝑡 comprises date-related

variables such as holiday dummies and day-of-week fixed effects. 𝛿𝛿𝑖𝑖𝑖𝑖 includes the following

variables for day 𝑡𝑡 in city 𝑖𝑖: the natural logarithm of the number of cases and deaths (on theprevious day), the weather, and the dummy variable representing the city’s status under thepreemergency measures issued. In all specifications, we report cluster-robust standard errors
clustered at the city level. We examine changes in mobility in high- and low social capital areas
separately by estimating equation (1) using two subsamples obtained by dividing the full24

sample into cities with above-median and below-median social capital.
Our identification assumption is that other factors correlated with social capital have nosystematic effect on changes in mobility. However, regional characteristics, including social
capital, risk for infectious diseases, changes in mobility, and the implementation of policy,
interact in various ways. We apply the following specification to address the possibility thatvarious unobservable variables may confound our estimates.
𝑗𝑗=13

𝑗𝑗=13

𝑗𝑗=1

𝑗𝑗=1

𝑌𝑌𝑝𝑝𝑝𝑝𝑝𝑝 = 𝛼𝛼 + � 𝛽𝛽1𝑗𝑗 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 + � 𝛽𝛽2𝑗𝑗 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 × 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖
𝑗𝑗=13+ � 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 × 𝑋𝑋𝑖𝑖 + 𝜃𝜃𝑖𝑖 + 𝛾𝛾𝑡𝑡 + 𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑝𝑝𝑝𝑝 + 𝜀𝜀𝑝𝑝𝑝𝑝𝑝𝑝 ,𝑗𝑗=1

where 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 is a dummy variable that equals 1 if the social capital of city 𝑖𝑖 is above themedian for all cities. The estimates of 𝛽𝛽2𝑗𝑗 indicate whether the average percentage change inmobility in each phase depends on the level of social capital. For example, if the estimatedvalue of 𝛽𝛽22 is -0.1, mobility in Phase 2 is reduced in cities with higher social capital by anaverage of 10 percentage points more than in those with lower social capital.

We consider several factors in addition to the variables included in equation (1). By adding
interaction terms between each phase and major regional characteristics 𝑋𝑋𝑖𝑖 , we control for thetime-varying effects of those characteristics. Specifically, we add 1) the share of the population

over age 65 (as the elderly are at higher risk of infection), 2) population density (as infection25(2)

is more likely to spread in urban areas), 3) the share of white-collar workers (as they are more
likely to work at home), and 4) the number of hospital beds per capita (as a better health careinfrastructure lowers the risk of infection). We further include date fixed effects in 𝛾𝛾𝑡𝑡 tocontrol for changes in mobility that are common to all cities on a given day. Additionally, since
many of the policies other than declarations of a state of emergency (e.g., subsidy payments
and other requests) are implemented at the prefectural level, we absorb their effects by adding
an interaction term between prefecture 𝑝𝑝 and date 𝑡𝑡: 𝜇𝜇𝑝𝑝𝑝𝑝 . In the main analysis, social capitalis a binary variable for ease of interpretation, but an analysis using social capital quintiles isalso performed to capture any nonlinear effects. Our main results are robust to the addition andremoval of these control variables, which are presented in Appendix A3.
To identify differences between areas that did and did not declare a state of emergency after
the second wave of the pandemic, we also use the following triple-difference-like framework.
𝑗𝑗=13

𝑗𝑗=13

𝑗𝑗=1
𝑗𝑗=13

𝑗𝑗=1

𝑌𝑌𝑝𝑝𝑝𝑝𝑝𝑝 = 𝛼𝛼 + � 𝛽𝛽1𝑗𝑗 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 + � 𝛽𝛽2𝑗𝑗 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 × 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 × 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖+ � 𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑗𝑗𝑗𝑗𝑗𝑗 × 𝑋𝑋𝑖𝑖 + 𝜃𝜃𝑖𝑖 + 𝛾𝛾𝑡𝑡 + 𝛿𝛿𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑝𝑝𝑝𝑝 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,𝑗𝑗=1

where 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖 is a dummy variable that equals 1 if the city declared a state of emergency

during the second, third, or fourth wave of infection (phases 8, 10, and 12). This allows us toinvestigate how mobility differed between cities with and without declarations during the26(3)second and subsequent waves of the pandemic. Of course, our specifications do not strictly
identify voluntary prevention and policy compliance, since the declaration of a state of
emergency is endogenously determined by the number of cases. The purpose of thisspecification is to verify that the definition of the phases does not cause the under- or
overestimation of the impact of the state of emergency declarations. Since the results fromequation (3) are almost the same as those from equation (2), equation (2) is our primary
specification.

4.

Results

4.1. Main results
We first estimate equation (1) using two subsamples, obtained by dividing the full sample
into two parts: cities above the median social capital level and cities below. The results of thisestimation are shown in Figure 3. The explained variables for mobility are (1) people travelingoutside their city of residence, (2) distance traveled, and (3) people visiting commercial areas,
and the dots in the graph indicate the estimated coefficients for each phase. The 95% confidence
intervals for each estimate are plotted and are constructed on the basis of standard errors
clustered at the city level. Although these results must be interpreted with caution because anumber of regional characteristics are not controlled for, they provide us with important27

insights into changes in mobility during the pandemic. First, regardless of social capital level,
all estimates are negative, indicating that mobility was always lower than normal after 2020.
Mobility was greatly reduced when the first state of emergency was declared (Phase 3) but had
already declined as a result of voluntary preventative activities during Phase 2. During Phases
4 through 6, after the end of the first wave, mobility increased slightly, partly because thegovernment encouraged domestic travel and consumption, but it did not return to normal levels.
Subsequently, after the end of 2020 (Phase 7), each explained variable and the composite social
capital measure exhibit different movements.
During the first state of emergency, (1) travel outside the city of residence were down
approximately 25% to 30% in both high- and low social capital cities. However, the difference
gradually increased in the second year, with the those from high social capital cities travelingout as much as they did under the first declaration, while those from low social capital cities
reduced their mobility by only approximately 10%. Viewed differently, it may be surprisingthat voluntary preventative activity continued for a year and a half in high social capital cities,
despite the absence of any legal restraints. For (2) travel distance, there appears to be little
difference by social capital level, with both groups of cities exhibiting a 30-40% drop at thetime of the outbreak. Regarding (3) visitors to commercial areas, we see that not only do
individuals from high social capital cities always visit fewer commercial areas, but they also
28

respond more strongly to the third and fourth state of emergency declarations. In contrast, theimpact of the second and subsequent emergency declarations in the low social capital cities
was almost negligible. It is important to note, however, that the estimated results capture only
the average change in mobility during each phase, so the decrease in mobility during the stateof emergency declarations reflects both private precautions and public restrictions. The purposeof Figure 3 is to observe the changes in mobility in cities with high and low social capital levels,
not to identify the causal effects of social capital. We therefore provide more robust evidence
for the differences produced by social capital, controlling for a variety of confounding factors.
Figure 4 presents the estimation results for equation (2) on the left and equation (3) on theright. 8 The explained mobility variables are (1)-(2) the number of people who leave their cityof residence, (3)-(4) the distance traveled, and (5)-(6) the number of visitors to commercial
areas, and are measured in terms of the percentage change relative to normal times. Althougheach mobility measure varies in the magnitude of its coefficient due to differences in variance,
the overall trend is consistent. There is no significant difference between high- and low social
capital cities in the first year of the pandemic (up to Phase 7), but from the second year onward,
individuals from high social capital cities are significantly less mobile. Focusing on (1) and (2),the number of people leaving their city of residence, individuals from high social capital cities
reduced their mobility by 5 percentage points more than those from low social capital cities
29

outside the state of emergency declarations. Additionally, the emergency declarations increased
the resident mobility gap between high- and low social capital cities by an additional 10
percentage points.
The main findings of this study are consistent with those of Barrios et al. (2021), who foundthat regions with high civic capital take voluntary precautions and follow government orders.
Barrios et al. (2021), who analyzed the first wave of infection in the U.S., found an additional
1.3% reduction in mobility in areas with high levels of civic capital when states had a stay-athome order. Although our definitions of social capital, our mobility indicators, and the policies
studied also differ and the results should be interpreted with caution, our results for the firstwave are similar (an additional 1.4% reduction in travel outside the city). However, our novel
evidence shows that the differences increased greatly after the second wave of the pandemic.
During a state of emergency declaration, noncompliance is more costly because the probabilityof infection is higher due to the increased number of infected people and because it is easier todetect and sanction violations. Therefore, one would expect that reliance on social capitalwould be lower during a declaration, but our results suggest the opposite. This indicates that
there are significant differences in policy compliance as well as voluntary preventative activity
depending on social capital levels.

30

4.2. Robustness checks
4.2.1. Nonlinear impact of social capital on changes in mobility
We use dummies for the quintiles of social capital to estimate the nonlinear effects of social
capital on changes in mobility, and the results are reported in Appendix A4. Estimates using
travel outside of cities as the explained variable show that individuals from cities withparticularly high levels of social capital (80-100th percentile) reduced their mobility by 15 to
20 percentage points relative to the baseline in the second year of the pandemic. In contrast,
there is little change among those from cities with lower social capital levels, which means thatcities with particularly high levels of social capital were more responsive to the pandemic. The
results using the other mobility indicators are also roughly consistent, although the magnitude
and significance of the coefficients vary. Comparing the magnitude of the coefficients on thequintile dummies in each phase, we see that in almost all phases, the higher the social capitallevel is, the more mobility is reduced. This reinforces the argument that the results are notdriven by the threshold used to define a high level of social capital.

4.2.2. Above- and below-median subsamples by each characteristic
The main results are robust to the inclusion of controls for city fixed effects and ofinteraction terms between regional characteristics and phases, suggesting that geographic,31

demographic, and socioeconomic characteristics are unlikely to be important confounders. As
further robustness checks and to obtain insight into the underlying mechanisms, we partitioned
the sample by the median of several regional characteristics and estimated the specification inequation (2) for each. In the following, we present results using travel outside of the city ofresidence as an explanatory variable, which universally expresses mobility regardless of city
scale or geographic size. Appendix A5 presents the results of the analysis using the above- andbelow-median subsamples for (1) population density, (2) the percentage of the population over65, (3) the percentage of white-collar workers, (4) the percentage of residents with a college
degree or above, (5) average taxable income, and (6) the number of hospital beds per capita.
All results are consistent with the main results, indicating that in the second year of thepandemic, there were significant differences in mobility by social capital. This indicates thatthe main results are not driven by the potential confounding factors presented here.
Looking at each of the results, first, the impact of social capital is greater in areas withlower population densities. In less populated areas, the risk of becoming infected oneself is low,
but the psychological cost of infecting others is high because residents are more likely to knoweach other. Additionally, social capital has a stronger effect on mobility in areas where people
are more vulnerable (a higher percentage over age 65) or that have a worse medical
infrastructure (fewer beds) because the risk and psychological costs of spreading infection are32

greater. The impact of social capital is stronger in areas with a smaller percentage of whitecollar workers, lower education levels, and lower income, possibly because culture plays asmaller role in more developed and urbanized areas and the impact of community linkages andculture is greater in less urbanized areas.

4.2.3. Results using alternative measures of social capital
To ensure that our results do not depend on the construction of our social capital index, wereestimated our model using each social capital variable in place of the principal components.
The results are presented in Appendix A6 and are consistent with the results using the principal
components for (1) voter turnout in national elections, (2) participation in volunteer firebrigades, (3) the number of community centers per capita, and (4) the number of meeting
facilities per capita. Therefore, our main results are robust to changes in alternative indicatorsof social capital.

4.2.4. Results using alternative measures of mobility
To improve the robustness of the main results, different mobility measures are also
considered. Given the limited data sources available, our best option was to create several
mobility measures with different definitions and use them as explained variables. The results
33

are shown in Appendix A7. The mobility measures used are (1) the number of people visitingfrom other cities, (2) the distance traveled by people visiting from other cities, (3) the numberof people visiting commercial areas from other cities, (4) the number of people leaving theirprefecture of residence, (5) the number of people in hot spots, and (6) the number of people
near train stations. The results using these alternative measures of mobility are presented in (4),(5), and (6) and are consistent with the main results, indicating that our results are not sensitive
to the mobility measure used.
Columns (1) through (3) capture the impact of people coming from outside the city,
indicating that it is easier for high social capital cities to reduce (1) the number of people
visiting from outside the city and (3) the average distance those visitors had traveled. This may
be due to the stronger level of mutual monitoring among residents and the increased criticism
of violators in high social capital cities, which may suppress visits from outsiders. Alternatively,
it could be explained by local cooperation, such as encouraging telework or voluntarily closing
stores and facilities. However, the results in (3) indicate that the number of people visiting
commercial areas (areas with particularly large daytime populations) from other cities does notvary with social capital levels. This suggests that high levels of social capital may keep theresidents of those areas from traveling away but may have a limited effect on reducing thenumber of people visiting from other cities. Since the overall number of visitors has decreased,
34

visitors may be concentrated in certain commercial areas, which is problematic from thestandpoint of controlling the spread of infection.
We attempt to explain these results in terms of Japanese policy and actual conditions.
People are attracted to commercial areas because business activities take place there andbecause bars, restaurants, and other establishments are in operation. A state of emergency
declaration includes a “request” that bars and restaurants reduce their hours of operation or
refrain from serving alcohol. However, it is not a legal prohibition, and there are no legal
penalties for noncompliance, but store managers have a financial incentive to cease operations
to receive compensation for their closure. However, if only one store was open while the others
were closed, the operating profits would likely exceed the compensation for closure because it
would attract all potential customers. In such cases, if the indirect effects of negative reviews,
etc., are not considered, management may make the rational decision to keep the business open
and to continue to attract people. It was frequently reported in Japan that pachinko parlors, bars,
and restaurants remained open and drew crowds even during the declared states of emergency(The Japan Times, 2020). There is also anecdotal evidence that when a state of emergency was
declared in an area and many stores were closed for business, people would travel to stores inneighboring cities and prefectures (Asahi Shinbun, 2021). Therefore, a strong policy with
enforceable penalties may be important to keep people from congregating in commercial areas
35

rather than relying on social capital and individual voluntary efforts.

4.2.5. Political preferences
Although social capital is the primary concern of this paper, the impact of politicalpreferences on mobility and policy compliance is also important, as previous studies (Allcott
et al., 2020; Barrios and Hochberg, 2021) have identified. Here, we confirm that our results are
not driven by political preferences. We create dummies that equal 1 for cities where the shareof votes cast for each party is above the 25th percentile and use those dummies in place of oursocial capital measure. The results are reported in Table 5, and a summary of each party isprovided in Appendix A8. Unlike previous studies covering the U.S., our results find no
specific trends by ideology (e.g., conservative or liberal) or position (right or left) in Japan. For
example, (1) the Liberal Democratic Party (LDP, Japan's ruling party, conservative, right-wing)and (2) the Democratic Party of Japan (DP, the largest opposition party, liberal, center-left)often propose contradictory policies, but the impact of support for those parties on mobility
and policy compliance is similar. However, support for (3) Komeito, which is close to the LDP
in its political leanings, exhibits a trend in its effects opposite that of the LDP. In areas withrelatively large numbers of supporters of the (5) Japan Communist Party, which advocatescommunism, mobility was less likely to decline, and policy compliance was lower.
36

The finding that areas with more supporters of the two largest parties, the LDP and the DP,
are more likely to exhibit reduced mobility suggests that supporting the “majority party” maybe more important than party ideology or agenda in Japan. It is plausible that those who prefera party that many people support rather than a party that conforms to their personal principles
and beliefs are more likely to take voluntary precautions and comply with policies to avoid
acting differently from the majority in their community. Cato et al. (2020), who used aquestionnaire to analyze mobility behavior during the pandemic in Japan, reported that
respondents who believed that “it is important to always avoid doing anything people would
say is wrong” were less likely to eat out during the pandemic. Thus, a culture in which people
care more about how others perceive them than their own beliefs may affect their mobility
choices during the pandemic. An alternative explanation might be that resistance to thepreventative measures may be due to a backlash against the existing hierarchy and elites and a
distrust of authority and science. Such defiant attitudes toward the current regime may reduce
perceptions of the risk of COVID-19 and discourage engaging in voluntary prevention andcomplying with policy (Brzezinski et al., 2021).

5.

Discussion and policy implications
Our results have several important policy implications. As the pandemic has continued for37

more than a year, the impact of social capital on mobility and policy compliance has gradually
become stronger, with decreases in mobility being less likely in cities with lower social capitallevels. In contrast, in high social capital cities, both mobility reductions and policy compliance
have continued for a long time, but it is difficult to prevent visits from outside the city. Thus,
unenforceable (or weak) constraints meant to control the pandemic may be effective only inthe short term. Japan managed its first year of the pandemic with a voluntary lockdown, but
this became ineffective during the second year. Therefore, in the long fight against a highly
infectious disease such as COVID-19, it may be less costly to suppress mobility in the shortterm with strict policies than with long periods of weak policies. Additionally, constantmonitoring to see where mobility is declining or where policy compliance is highest is very
important because initially effective policies may gradually become ineffective.
We have given several explanations as to why differences began to appear after the second
year, which we summarize here. The first reason is a change in people’s understanding of thevirus. In the early stages of the pandemic, little was known about the virus, so everyone
voluntarily restricted their mobility because of the uncertainty regarding who would be infected
and who would become severely sick. This is plausible because the number of cases and deaths
were lowest during the first wave when mobility was also reduced the most. However, as therisk of infection was gradually acknowledged as demographically heterogeneous, altruistic38

individuals with a low risk of infection may have continued to voluntarily restrain themselves
for the sake of others, but egocentric low-risk individuals may have begun to ignore such
restrictions. This hypothesis cannot be empirically tested with our data. However, an
explanation relying on altruism is plausible in light of the finding by Cato et al. (2020) thatpeople with more altruistic characteristics were more likely to reduce their social distanceduring the pandemic.
Another possibility is whether the areas covered by the state of emergency declarations are
geographically large. The first wave necessarily reduced mobility because the state of
emergency was declared for all of Japan, so no one could escape to areas not covered by the
declaration. However, after the second wave, individuals had the option of moving tononemergency areas, which may have caused the differences in mobility. However, this
explanation is unlikely to hold given our sample and results. As mentioned above, someestablishments remained open during the first state of emergency, allowing people tocongregate there. This explanation is also not very convincing considering the differences in
the impact of the third state of emergency declaration, which covered a small number of areas,
and that of the fourth state of emergency declaration, which covered the entire area surrounding
certain cities.
It is also difficult to use vaccination for any kind of explanation, since differences by social
39

capital were present even before vaccination began. However, since vaccination has strong
externalities and its social benefits are very large (White, 2021), it would be useful to consider
the effect of vaccines. To avoid statistical discrimination, data on vaccination coverage is only
published at the prefectural level in Japan and cannot be analyzed in detail. We developed aprefecture-level index of social capital and compared the vaccination coverage of prefectureswith above- and below-median social capital levels. Appendix A9 indicates that vaccination isprogressing more quickly in prefectures with high levels of social capital. It is interesting tonote that vaccination progressed more quickly and widely in high social capital areas, eventhough, on average, infections were more common and the vaccine supply was greater in urban
areas with lower social capital. This is very weak evidence of prefecture-level differences, sothese results must be interpreted with caution, but they do suggest a relationship between
vaccination and social capital.

6.

Conclusion
In response to the global COVID-19 pandemic, countries around the world implemented

various policies, both mandatory and voluntary, to enforce social distancing. To explain thelarge national and regional differences in social distancing and policy compliance, it isimportant to consider the role of culture from a sociological perspective (Bian et al.). Given40

that COVID-19 is still prevalent in 2022 and the threat of similar pandemics in the future isgreat, understanding the effects of cultural heterogeneity is of great value to public health.
We used a wealth of data on the Japanese context to investigate how the effect of social
capital on mobility changed during the first year and a half of the pandemic. The results
revealed that the differences in mobility and policy compliance between cities with high andlow social capital levels increased as the pandemic continued. As Durante et al. (2021)
predicted, social capital is more important during prolonged pandemics. They mention thepossibility of faster economic recovery in communities with higher social capital levels, butaccording to our results, this may be spoiled by noncompliance and free riding among thosefrom cities with lower social capital levels. Because the virus easily crosses community
boundaries, successful prevention in one city may be offset and rendered meaningless bycontact with people from another city.
Since collective action issues are important in human society (Ostrom, 2000), not only for
COVID-19, our results have broader implications. Our finding that high social capital areas
make longer-lasting contributions to the community highlights the importance of the social
capital stock within the community. Although there are continuous and enormous costs
associated with beautifying a community and improving public health, areas with high levelsof social capital may be able to provide such services less expensively. While community
41

bonding and peer pressure are sometimes viewed negatively from an individualistic viewpoint,
encouraging community unity and strengthening social capital can be highly effective foraddressing issues that require collective action.
A potential limitation is that we do not examine the external validity of our study. Comparedto the areas covered by previous studies, Japan exhibits two main differences, its unique culture
and its voluntary lockdowns, and the effects of these factors cannot be identified separately.
However, it is interesting to find large heterogeneity by social capital level, even in a society
with relatively little racial, religious, linguistic, and ethnic heterogeneity. Additionally, while
this study used city-level characteristics, it is important to know about the impacts ofindividual-level demographics and their relationship to the community. More insight into
individual-level characteristics such as knowledge of viruses, personality, and trust in thegovernment and science could be obtained by analyzing individual-level data rather than
population estimates. If individual characteristics such as cooperation and perseverance areassociated with local social capital levels, those characteristics may provide better explanations
for our results. This is an interesting issue for the future.

Declaration of Competing Interests
The authors declare that they have no known competing financial interests or personal42

relationships that could have appeared to influence the work reported in this paper.

1

Brodeur et al. (2021) provides a comprehensive review of the economic research related to

COVID-19.
2

Seven cities in Fukushima were not included in the analysis because they were severely

damaged by the nuclear power plant accident caused by the Great East Japan Earthquake andare still considered evacuation zones.
3

A detailed analysis of the mobility changes during the early stages of the pandemic in Japanis provided by Watanabe and Yabu (2021).
4

There are 1889 cities in the analysis, of which 616 (121), 649 (266), and 1114 (389) werecovered by the second, third, and fourth state of emergency declarations, respectively (with thenumber of cities with high levels of social capital in parentheses).
5

In Figure 2-(B), mobility among residents of high-social capital cities declines sharply at theend of February, which introduces a concern about some kind of error in the data preparation,
but a closer examination of the data shows that this fluctuation actually occurred. This may be
the result of a time lag during which infections spread first in areas with low levels of social
capital and then spread to areas with high levels of social capital. In other words, the spread of
infection may have reduced the high mobility of individuals from low social capital areas sothat it was equal to the mobility of individuals from high-social capital areas. Then, thesubsequent spread of infection in high-social capital areas caused a rapid decline in mobility.
6

The Japanese census is conducted every five years, with the latest data being from 2020, but

to avoid the effects of the pandemic, we used the 2015 census. Because surveys on education43

are conducted only every 10 years, we use data from the 2010 census for the share ofindividuals with a college degree or above.
7

Descriptions of each party's ideology and standing are determined based on information fromeach party's website, election-related materials, and Taniguchi and Winkler (2020).
8

The estimation results presented in Figure 4 are listed in Appendix A4, and estimation results

excluding some fixed effects are also reported.

44

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50

PhasesStart dateConditions

Expected impact
on mobility

1

January 1, 2020

Before the number of confirmed cases of
COVID-19 exceeded 100

Baseline

2

February 22, 2020

Early stages of outbreak(Schools were closed)

Negative3

Early April, 2020

First state of emergency(47 prefectures)

Strongly negative

4

Late May, 2020

No restrictions

5

July 21, 2020

Domestic tourism promoted(without Tokyo)

Positive

6

October 1, 2020

Domestic tourism and consumption
promoted (with Tokyo)

Positive

7

Late November, 2020

No restrictions or promotions

8

Early January, 2021

Second state of emergency(11 prefectures)

9

Late March, 2021

No restrictions

10

Late April, 2021

Third state of emergency(10 prefectures)

Strongly negative

11

Late June, 2021

Weak enforcement policies in some areas

Negative

12

Early August, 2021

Fourth state of emergency(21 prefectures)

Strongly negative

13

Mid-September, 2021 Weak enforcement policies in some areas

Strongly negative

Negative

Table 1. Phases of the COVID-19 pandemic and the corresponding policies in Japan
Note: This table shows the 13 phases of the analysis period (January 1, 2020, toSeptember 30, 2021), which are defined according to each city's conditions. The start datefor each phase is defined based on information published by the government and by eachmunicipality.
51

Figure 1. Changes in the number of confirmed cases per capita and various
mobility measures plotted by social capital (7-day moving averages)
Note: This figure describes the changes over time in the number of COVID-19 cases andthe mobility of people in Japan. Solid lines indicate cities with high levels of social capital,
dotted lines indicate cities with low levels of social capital, and shaded areas indicate
periods when states of emergency were declared in Tokyo.
52

Figure 2. Geographic distribution of social capital in Japan
Note: This figure illustrates the geographic distribution of the quintiles of our main social
capital measure. For visual ease, Okinawa Prefecture, a remote chain of islands at thesouthern tip of Japan, is shown in the area enclosed by the line in the upper left corner ofthe figure.

53

Figure 3. Changes in mobility among residents of cities with high and low social
capital during the pandemic
Note: Using city-by-day level data, we plot the coefficients for each phase dummy from
regressions that include weather variables, pre-emergency measure dummies (indicating
normal vs. priority status), holiday dummy, day-of-week fixed effects, city fixed effects,and (the natural logarithm of) the previous day's COVID-19 cases and deaths as controls.
Standard errors are clustered at the city level, and bars represent 95% confidence intervals.
The round dots indicate the results of the estimation using a subsample that includes cities
with social capital levels greater than the median, while the square dots indicate the resultsfrom using a subsample that includes cities with social capital levels below the median.
The shaded areas indicate phases in which a state of emergency was declared. The
54

explained variables are (1) the number of people who travel outside their city of residence,(2) the distance traveled, and (3) the number of visitors to commercial areas, each ofwhich is measured as the percentage change from the corresponding values during normal
times.

55

Figure 4. Differences in mobility changes between cities with high and low social
capitalNote: Using city-by-day level data, we plot the coefficients on the cross terms between
each phase dummy and the high-social capital city dummy (in graphs (2), (4), and (6),these terms are further multiplied by the declaration dummy) from regressions that
include weather variables, pre-emergency measure dummies (indicating normal vs.
priority status), holiday dummies, day-of-week fixed effects, city fixed effects, date fixed
effects, the cross terms between the phase dummies and variables for local characteristics,and (the natural logarithm of) the previous day's COVID-19 cases and deaths as controls.
Standard errors are clustered at the city level, and bars represent 95% confidence intervals.
The darker shaded areas and lighter shaded areas indicate the differences in the percentage
56change in mobility due to social capital in cities where a state of emergency was declared
and in those where a state of emergency was not declared, respectively. The explained
variables are (1)-(2) the number of people who leave their city of residence, (3)-(4) the
distance traveled, and (5)-(6) the number visitors to commercial areas, respectively, andeach is measured as the percentage change from the corresponding value during normal
times.

57

Figure 5. Differences in mobility change by political preferences
Note: Using city-by-day level data, we plot the coefficients of each phase dummy anddummy representing high approval ratings for each party from regressions that include
weather variables, pre-emergency measure dummies (indicating normal vs. priority
status), holiday dummies, day-of-week fixed effects, city fixed effects, date fixed effects,
the cross terms between the phase dummies and variables for local characteristics, and(the natural logarithm of) the previous day's COVID-19 cases and deaths as controls.
Standard errors are clustered at the city level, and bars represent 95% confidence intervals.
The shaded areas indicate phases in which a state of emergency was declared. The
explained variables are the percentage change in the number of people who travel outside
their city of residence compared to the number who travel during normal times.
58

A. Additional Figures and Tables
Table A1. Summary statistics
N

Mean

Std. Dev.

Min.

Max.

Data source

Mobility index relative to 2019 value
Leaving the city of residence
1,205,166 -0.162
0.238-1.00088.653
Mobile Spatial Statistics (NTT Docomo)
Daily distance traveled
1,199,667 -0.229
5.464-1.000 4465.456
Mobile Spatial Statistics (NTT Docomo)
Visitors to commercial areas
1,205,182 -0.236
7.003-716.979 1386.451
Mobile Spatial Statistics (NTT Docomo)
Leaving the prefecture of residence
1,205,166 -0.423
0.824-1.000 223.133
Mobile Spatial Statistics (NTT Docomo)
People in hotspots
1,205,182 -0.108
0.534-1.000 120.634
Mobile Spatial Statistics (NTT Docomo)
People around train stations
1,205,182 -0.142
0.630-1.000 228.044
Mobile Spatial Statistics (NTT Docomo)
Policies other than the state of emergency
Pre-emergency measures: normal
1,205,182 0.062
0.242
0.000
1.000
Public materials from each municipality
Pre-emergency measures: priority
1,205,182 0.019
0.138
0.000
1.000
Public materials from each municipality
HealthNumber of confirmed cases
1,205,182 1.279
6.649
0.000
465.000
Public materials from each municipality
Number of deaths caused by COVID-19
1,205,182 0.997
2.928
0.000
129.000
Public materials from each municipality
Weather
Precipitation
1,205,182 5.190
14.9710.000
490.000
Japan Meteorological Agency
Temperature
1,205,182 15.249
8.917-22.900
34.000Japan Meteorological Agency
Wind speed
1,204,985 2.376
1.456
0.000
26.100Japan Meteorological Agency
Snowfall
1,205,182 0.168
1.631
0.000
117.000
Japan Meteorological Agency
City characteristics
Share of people aged 65 or over
1,889
31.287
7.147
12.67860.485National Census 2015
Population density
1,889 1543.180 3210.951 1.600 22380.200
National Census 2015
Share of white-collar workers
1,889
0.309
0.065
0.094
0.562
National Census 2015
Unemployment rate
1,889
0.041
0.013
0.000
0.137
National Census 2015
Share of residents with a college degree or above 1,889
0.138
0.083
0.023
0.534
National Census 2010
Number of hospital beds per capita
1,889
57.42358.8150.000
630.562
Survey of Medical Institutions 2015
Average taxable income
1,889
2.886
0.505
2.042
9.799
Ministry of Internal Affairs and Communications
Social capital
Composite civic capital index
1,889
0.000
1.508
-2.641
12.261
Voter turnout in national elections
1,889
0.593
0.076
0.363
0.943
Ministry of Internal Affairs and Communications
Number of community centers per capita
1,889
3.056
5.820
0.000
98.280Social Education Survey 2008
Number of public meeting places per capita
1,889
9.204
15.4160.000
212.121Social Education Survey 2008
Participation rate in volunteer fire brigade
1,889
0.023
0.026
0.000
0.310
Fire and Disaster Management Agency

Note: Since the number of visitors to a commercial area is calculated as the difference between thedaytime population and the permanent nighttime population, it can take on a negative value if the
daytime population is less than the nighttime population. Thus, the percentage change relative to thepre-pandemic (“normal”) period can be less than -1.

59

Figure A2. Timing of the state of emergency declarations(1) First state of emergency declaration(2) Second state of emergency declaration(3) Third state of emergency declaration(4) Fourth state of emergency declaration
Note: The color gradation indicates the duration of the state of emergency declaration; the darker thecolor, the longer the duration of the declaration. The third and fourth waves were defined according
to the number of COVID-19 cases, but in Okinawa only (a remote island off the southern tip ofJapan), the period covered by the third and fourth declarations was continuous (May 23, 2021, toSeptember 30, 2021).

60

Table A3. Differences in mobility changes between high- and low-social capital areas (full results)
1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration

(1)

(2)

(3)

(4)

(5)

(6)

-0.115***-0.003-0.274***-0.005-0.119***-0.007-0.109***-0.008-0.057***-0.005-0.098***-0.004-0.101***-0.006-0.089***-0.001-0.241***-0.003-0.074***-0.003-0.062***-0.004-0.024***-0.003-0.081***-0.003-0.091***-0.004-0.075**-0.028-0.263***-0.0450.021-0.0430.015-0.041
0.067-0.0360.061-0.0350.009-0.074-0.22-2796.9-0.354-2796.9-0.181-2796.9-0.309-2796.9-0.162-11776.9
0.022-6628.6-0.079-6628.6

0.275-1221.9
0.135-1221.9
0.297-1221.9
0.162-1221.8
2.4
-7133
0.763
-2004.4
0.665-2004.4-0.129***-0.006-0.146***-0.008-0.100***-0.004-0.120***-0.0050.170***-0.048-0.114-0.0640.309-6245.1-0.016-6245.2

1.87-3969.5
1.52-3969.5-0.158***-0.009-0.172***-0.009-0.117***-0.005-0.133***-0.0050.152**-0.0490.033-0.0630.163-8623.3
0.032-8623.3

0.813-1736.6
0.631-1736.6-0.155***-0.013-0.110***-0.007-0.017-0.097-0.003-8623.3

0.674-1736.6-0.045-254.6-0.185-254.6-0.023-254.6-0.156-254.60.597-1100.8
0.199-383.70.076-383.70.193-383.70.108-151.2-0.182-151.20.214-151.20.121-151.2-0.028-151.20.179-151.20.028-151.20.020***-0.006
0.043***-0.0080.065***-0.0070.046***-0.0050.018***-0.005-0.007-0.006-0.043*-0.018-0.025***-0.0030.001-0.0060.022***-0.0060.002-0.005-0.025***-0.005-0.051***-0.005-0.068***-0.011-0.016**-0.006-0.013-0.01
0.029-0.0180.007-0.008-0.011-0.007-0.023**-0.008-0.053**-0.018-0.015**-0.006-0.012-0.01
0.026-0.0160.007-0.008-0.011-0.007-0.022**-0.008-0.056***-0.016-0.014**-0.006-0.012-0.01
0.026-0.0160.007-0.008-0.011-0.007-0.021**-0.008-0.056***-0.016-0.127***-0.007-0.205***-0.013-0.171***-0.007-0.221***-0.011-0.074***-0.01-0.169***-0.018-0.072***-0.01-0.164***-0.017-0.072***-0.009-0.161***-0.017-0.128***-0.007-0.170***-0.015-0.168***-0.008-0.200***-0.012-0.067***-0.009-0.140***-0.019-0.062***-0.009-0.141***-0.019-0.059***-0.009-0.141***-0.019-0.113***-0.024
YES
YES
NO
NO
NO
NO
City
1204969
0.18377-0.148***-0.016
YES
NO
YES
NO
NO
NO
City
1204969
0.28993-0.057**-0.019
YES
NO
YES
NO
YES
NO
City
1204969
0.30132-0.056**-0.019
YES
NO
YES
YES
YES
NO
City
1204969
0.33553-0.061**-0.019
YES
NO
YES
YES
YES
YES
City
1204969
0.33814

Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
High social capital *1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration
Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
Controls (health, policy, date, weather)
City characteristics
City FE
Day FE
Period * City characteristics
Day * Prefecture FE
Clustered SE
Observations
R-squared-0.014*-0.006-0.012-0.01
0.026-0.0160.007-0.008-0.011-0.007-0.006-0.009-0.058***-0.016-0.029***-0.008-0.092***-0.011-0.165***-0.018-0.048***-0.009-0.089***-0.01-0.144***-0.019-0.029**-0.01-0.065***-0.019
YES
NO
YES
YES
YES
YES
City
1204969
0.33942

Table A3.1. Changes in the number of residents traveling outside their city of residence relative
to 2019 numbers

61

1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration

(1)

(2)

(3)

(4)

(5)

(6)

-0.253***-0.024-0.441***-0.043-0.293***-0.015-0.330***-0.059-0.252***-0.049-0.264***-0.016-0.333***-0.059-0.276***-0.05-0.452***-0.063-0.328***-0.042-0.262***-0.058-0.253***-0.072-0.268***-0.019-0.352***-0.072-0.158-0.114-0.577**-0.183-0.047-0.117-0.065-0.1220.042-0.158-0.167-0.088-0.27-0.2760.305-21225.9
0.012-21225.9
0.549-21226-0.311-21225.5
2.06-77735-0.101-47369.3-0.345-47369.2

0.397-7314.2
0.085-7314.2
0.662-7314.2-0.229-7314.7
6.42-80854.4
0.317-15077.1
0.118-15077.3-0.334***-0.029-0.552***-0.061-0.329***-0.049-0.505***-0.0670.002-0.154-0.071-0.2530.584-37294.3
0.503-37294.3

1.14-18548.6
1.05-18548.6-0.384***-0.027-0.460***-0.054-0.353***-0.05-0.399***-0.058-0.028-0.14-0.211-0.2060.31-47327.7
0.167-47327.7

0.295-10163.9
0.112-10163.9-0.223***-0.043-0.287***-0.047-0.123-0.1420.279-47327.8

0.26-10163.7-0.08-8510.4-0.391-8510.3
0.186-8510.5-0.703-8510
1.4-77857.9-0.797-12631.7-0.904-12631.5-0.625-12631.7-0.009-4627.4-0.098-4627.5
0.017-4627.5-0.048-4627.4-0.284-4627.4-0.193-4627.4-0.186-4627.5

0.012-0.015-0.038-0.0350.081**-0.0250.091**-0.034
0.096**-0.036-0.011-0.0230.097-0.0970.082-0.0670.034-0.0610.192**-0.062
0.106
-0.06
0.154
-0.0860.029-0.0390.139-0.116-0.078-0.057-0.037-0.06-0.037-0.061-0.08-0.056-0.113-0.073-0.071*-0.031-0.201-0.105-0.079-0.058-0.032-0.062-0.062-0.07-0.084-0.058-0.113-0.074-0.075*-0.032-0.199*-0.099-0.078-0.058-0.034-0.061-0.063-0.071-0.085-0.058-0.113-0.073-0.075*-0.032-0.197*-0.099-0.006-0.0220.122-0.0850.056-0.0690.132-0.116-0.121-0.063-0.337**-0.104-0.122-0.064-0.330**-0.101-0.122-0.065-0.324**-0.099-0.037*-0.0180.037-0.054

0.021
-0.06
0.047
-0.075-0.122*-0.061-0.162*-0.068-0.118*-0.06-0.158*-0.068-0.118*-0.06-0.159*-0.067-0.108*-0.052
YES
YES
NO
NO
NO
NO
City
11994700.002340.014-0.058
YES
NO
YES
NO
NO
NO
City
11994700.02385-0.125*-0.062
YES
NO
YES
NO
YES
NO
City
11994700.02424-0.128*-0.063
YES
NO
YES
YES
YES
NO
City
11994700.02542-0.131*-0.066
YES
NO
YES
YES
YES
YES
City
11994700.02584

Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
High social capital *1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration
Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
Controls (health, policy, date, weather)
City characteristics
City FE
Day FE
Period * City characteristics
Day * Prefecture FE
Clustered SE
Observations
R-squared

Table A3.2. Changes in daily distance traveled relative to 2019 values

62-0.078-0.058-0.034-0.061-0.063-0.071-0.085-0.058-0.113-0.073-0.056*-0.024-0.198*-0.098-0.111*-0.053-0.142-0.077-0.322***-0.097-0.097-0.052-0.159*-0.068-0.159*-0.066-0.067-0.047-0.136*-0.068
YES
NO
YES
YES
YES
YES
City
11994700.02585

1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration

(1)

(2)

(3)

(4)

(5)

(6)

-0.188**-0.06-0.438***-0.048-0.107-0.101-0.048-0.149-0.05-0.054-0.122***-0.033-0.119*-0.052-0.160***-0.005-0.480***-0.014-0.172***-0.028-0.158***-0.036-0.084***-0.022-0.141***-0.013-0.200***-0.025

-1.23
-1.3
-3.99
-3.82
-3.1
-3.08
-4.4
-4.45
-2.31
-2.35
-0.631-0.435
-2.04
-1.61

2.02
-18483.7-0.502-184820.123-18482.4-1.79-18481.6
0.906-99227.8
1.22-64925.2-0.196-64924.1

0.758-29060.5-1.83-29062.6-1.14-29062-3.04-29062.9-9.7-148535.9-0.911-35100.9-2.45-35101.6-0.019-0.104-0.029-0.16-0.110**-0.038-0.228***-0.04-0.023
-1.43
-2.35
-1.86

1.76
-68035.9-0.709-68033.5-2.43-51297.6-4.95-512980.051-0.202-0.089-0.158-0.098-0.059-0.229***-0.043-0.98-0.827
-1.97
-1.79

0.385
-93631.9-0.843-93630.2-1.48-29066.8-2.78-29067.9

0.172-0.248-0.034-0.085-1.1-1.31-0.018-93630.5-2.13-29067.3

2.07-25082.9-0.529-25085.4
0.176-25084.7-1.72-25086-0.812-535680.333-7778.1-1.3-7779.1
0.334-7777.3
0.477-2190.2-1.63-2190.6
1.45-2190.1-0.964-2190.4-1.45-2190.6
0.776-2190.4-0.716-2190.3-0.003-0.09-0.118-0.136-0.033-0.071-0.164-0.163-0.059-0.06
0.0006-0.116-0.395-0.207-0.107-0.09-0.178-0.262-0.135-0.206-0.269-0.291-0.154-0.163-0.093-0.058-0.237-0.132-0.128-0.102-0.255-0.286-0.159-0.231-0.289-0.301-0.195-0.175-0.132-0.083-0.314-0.169-0.122-0.1-0.25-0.285-0.173-0.226-0.289-0.299-0.193-0.174-0.127-0.082-0.304-0.165-0.122-0.101-0.254-0.288-0.173-0.227-0.288-0.3-0.192-0.175-0.124-0.083-0.309-0.167-0.092-0.214-0.737**-0.25-0.207*-0.1-0.622**-0.217-0.26-0.149-0.701*-0.3-0.256-0.15-0.690*-0.299-0.253-0.149-0.692*-0.304-0.072-0.15-0.979**-0.34-0.219**-0.074-0.785**-0.258-0.256**-0.084-0.839*-0.404-0.267**-0.087-0.827*-0.401-0.263**-0.087-0.832*-0.403-0.526**-0.191
YES
YES
NO
NO
NO
NO
City
12049850.00128-0.388**-0.123
YES
NO
YES
NO
NO
NO
City
12049850.09776-0.299-0.164
YES
NO
YES
NO
YES
NO
City
1204985
0.09825-0.301-0.166
YES
NO
YES
YES
YES
NO
City
1204985
0.0987-0.275-0.161
YES
NO
YES
YES
YES
YES
City
1204985
0.09879

Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
High social capital *1st interval without restrictions
1st state of emergency declaration
2nd interval without restrictions
1st domestic travel promotion
2nd domestic travel promotion
3rd interval without restrictions
2nd state of emergency declaration
Period of the 2nd state of emergency inprefectures without a declaration
4th interval without restrictions
3rd state of emergency declaration
Period of the 3rd state of emergency inprefectures without a declaration
5th interval without restrictions
4th state of emergency declaration
Period of the 4th state of emergency inprefectures without a declaration
6th interval without restrictions
Controls (health, policy, date, weather)
City characteristics
City FE
Day FE
Period * City characteristics
Day * Prefecture FE
Clustered SE
Observations
R-squared-0.122-0.101-0.256-0.288-0.173-0.227-0.288-0.3-0.192-0.175-0.073-0.075-0.312-0.171-0.163-0.095-0.351-0.207-0.696*-0.307-0.125-0.121-0.352**-0.124-0.839*-0.408-0.188*-0.076-0.289
-0.17
YES
NO
YES
YES
YES
YES
City
1204985
0.09886

Table A3.3. Changes in the number of visitors to commercial areas relative to 2019 numbers
Note: Columns (5)-(6) in Tables A3.1, A3.2, and A3.3 correspond to plots (1)-(2), (3)-(4), and (5)-(6)in Figure 4, respectively. Robust standard errors clustered at the city level are in parentheses. *, **,and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.
63

Figure A4. Nonlinear impact of social capital on changes in mobility

Note: Using city-by-day level data, we plot the coefficients on each phase dummy and the social capitalquintile dummy obtained from regressions that include weather variables, preemergency measuredummies (indicating normal vs. priority status), holiday dummies, day-of-week fixed effects, city fixed
effects, date fixed effects, cross terms between the phase dummies and local characteristic variables,and (the natural logarithm of) the previous day's COVID-19 cases and deaths as controls. Standard
errors are clustered at the city level, and bars represent 95% confidence intervals. The estimates forcities in the first quintile of the social capital distribution (0%-20%) are represented by circles, thosein the second quintile (20%-40%) by triangles, those in the fourth quintile (60-80%) by squares, and64

those in the fifth quintile (80-100%) by rhombuses. The coefficients indicate differences from thebaseline of cities in the third quintile (40-60%). The shaded areas indicate phases in which a state of
emergency was declared. The explained variables are (1) the number of people who leave their city ofresidence, (2) the distance traveled, and (3) the number of visitors to commercial areas, all measured
as the percentage change relative to their values during normal times.

65

Figure A5. Subsamples defined by being above or below the median for each characteristic66

(continued)

Note: Using city-by-day level data, we plot the coefficients on the cross terms of the phase dummies
and the high social capital city dummy from regressions that include weather variables, preemergency
measure dummies (indicating normal vs. priority status), holiday dummies, day-of-week fixed effects,
city fixed effects, date fixed effects, cross terms between the phase dummies and local characteristic
variables, and (the natural logarithm of) the previous day's COVID-19 cases and deaths as controls.
Standard errors are clustered at the city level, and bars represent 95% confidence intervals. The shaded
areas indicate phases in which a state of emergency was declared. The explained variables are thepercentage change in the number of people who leave their city of residence compared to the same
number during normal times. The left side plots the results of the estimation using the above-mediansubsample for each variable, and the right-side plots the results of the below-median subsample.

67

Figure A6. Results using alternative measures of social capital

Note: Using city-by-day level data, we plot the coefficients on the cross terms of the phase dummy
and dummies for high social capital areas based on alternative social capital measures from regressions
that include weather variables, preemergency measure dummies (indicating normal vs. priority status),
holiday dummies, day-of-week fixed effects, city fixed effects, date fixed effects, cross terms between
the phase dummies and local characteristic variables, and (the natural logarithm of) the previous day's
COVID-19 cases and deaths as controls. Standard errors are clustered at the city level, and bars
represent 95% confidence intervals. The shaded areas indicate phases in which a state of emergency
was declared. The explained variables are the percentage change in the number of people who leave
their city of residence relative to the same number during normal times. The proxy variables for social
capital are (1) voter turnout in national elections, (2) participation in volunteer fire brigades, (3) the
number of community centers per capita, and (4) the number of meeting facilities per capita.

68Figure A7. Results using alternative measures of mobility

Note: Using city-by-day level data, we plot the coefficients on the cross terms of the phase dummies
and the high social capital city dummy from regressions that include weather variables, preemergency
measure dummies (indicating normal vs. priority status), holiday dummies, day-of-week fixed effects,
city fixed effects, date fixed effects, cross terms between the phase dummies and local characteristic
variables, and (the natural logarithm of) the previous day's COVID-19 cases and deaths as controls.
Standard errors are clustered at the city level, and bars represent 95% confidence intervals. The shaded
areas indicate phases in which a state of emergency was declared. The explained variables are thepercentage change in (1) the number of people visiting from other cities, (2) the distance traveled bypeople visiting from other cities, (3) the number of people visiting commercial areas from other cities,(4) the number of people leaving the prefecture where they live, (5) the number of people in hot spots,and (6) the number of people near train stations relative to their values during normal times.
69

Table A8. Major political parties in JapanParty

Averageturnout

Share of seats in the House ofCouncillors(2012)(2014)(2017)

Liberal Democratic Party

0.336

0.613

0.613

Democratic Party of Japan(The Democratic Party)

0.178

0.119

Komeito0.136

Japan Innovation Party

Ideology

Position

0.611

Conservatism
Japanese nationalism

Right

0.154

0.118

Liberalism
Social Liberalism

0.065

0.074

0.062

Buddhist democracy
Social conservatism

Center-right

0.119

0.113

0.086

0.024

Conservatism
Neoliberalism
Populism

Right

Japanese Communist Party

0.075

0.017

0.044

0.026

Communism
Social democracy

Left

Social Democratic Party

0.025

0.004

0.004

0.004

Social democracy

Center-leftCenter-left

Note: Each party's share of the vote and number of seats are determined based on the results of national
elections (held in 2012, 2014, and 2017) as reported by the Ministry of Internal Affairs andCommunications. Descriptions of each party's ideology and standing are based on information
obtained from each party's website, election-related materials, and Taniguchi and Winkler (2020).

70

Table A9. Differences in vaccination coverage by social capital level
High social capital levels

Low social capital levels

t value

Mean Std. Dev.

Min.

Max.

Mean Std. Dev.

Min.

Max.

Vaccination coverage 30 days after start date

0.019

0.007

0.008

0.040

0.011

0.005

0.004

0.022

4.240

Vaccination coverage 60 days after start date

0.160

0.024

0.112

0.208

0.133

0.026

0.097

0.186

3.635

Vaccination coverage 90 days after start date

0.366

0.024

0.317

0.420

0.331

0.027

0.261

0.410

4.038

Vaccination coverage on September 30

0.759

0.024

0.716

0.792

0.742

0.035

0.696

0.787

2.631

Panel A: First round of vaccination

Vaccination coverage on September 30(Total across all prefectures)

0.759

0.739

Panel B: Second round of vaccination
Vaccination coverage 30 days after start date

0.020

0.007

0.008

0.036

0.011

0.006

0.005

0.024

4.521

Vaccination coverage 60 days after start date

0.163

0.025

0.122

0.203

0.133

0.036

0.089

0.185

4.051

Vaccination coverage 90 days after start date

0.361

0.018

0.301

0.401

0.318

0.025

0.249

0.397

4.744

Vaccination coverage on September 30

0.655

0.027

0.595

0.707

0.633

0.039

0.568

0.714

2.260

Vaccination coverage on September 30(Total across all prefectures)

0.652

0.629

Note: This table provides information on vaccination coverage by subsample, with subsamples definedaccording to whether the prefecture’s social capital level is above or below the median prefectural levelof social capital. Panel A shows the coverage rate for the first round of vaccination, and Panel B shows
the coverage rate for the second round of vaccination for each period after the vaccination start date.
The bottom row in each panel represents the number of vaccinations for an entire given subgroupdivided by that subgroup's population.

71

B. Detailed information on Mobile Spatial Statistics and the process for creating city-level data
B1. Mobile Spatial Statistics overview
Mobile Spatial Statistics are demographic data collected and published by NTT DOCOMO, Inc.,
Japan's largest wireless operator with over 80 million subscribers. 1 2 NTT DOCOMO, Inc. candetermine the number of subscribers located in 500 square meter units (also known as mesh units) onan hourly basis (24 hours a day, 365 days a year) through the location information from subscribers'
devices, which is continuously and automatically aggregated from the cellular phone operating
network. Based on the number of subscribers in each region, a mesh-level estimated population is
produced. There are approximately 2 million meshes throughout Japan, and of these, approximately
500,000 to 600,000 include areas with human activity, so population estimates at very finegeographical scales can be obtained.
The coverage area is the whole of Japan, and if one person stays in one mesh for 30 minutes, he or
she is counted as 0.5 people in the hourly level data. Population estimates include information ongender and age or place of residence, with age reported from 15 to 79 years in 10-year increments (1519 for teenagers) and place of residence reported at the city or prefectural level. To prevent theidentification of specific individuals, data containing both gender and age and place of residence are
not available. For example, if the estimated population of a given mesh at a given time is 100, we cansee that there are 60 men in their 20s and 40 women in their 30s or that there are 30 people who livein Osaka city and 70 people who live in Yokohama city. However, we cannot observe the number ofmen in their 20s living in Osaka city in that mesh. Additionally, if there are fewer than 10 people in acertain gender and age cell in a given mesh, it is reported as a missing value (confidential processing)1

For the official website (Japanese only), see https://mobaku.jp/

2

For a complete methodological description, see

https://www.docomo.ne.jp/english/corporate/technology/rd/technical_journal/bn/vol14_3/72

to avoid the identification of individuals. Thus, gender and age data are available for urban areas but
likely to be missing for rural areas. Our study focuses on the social capital level of places of residence,and therefore, to minimize the amount of missing data, we use only the information for place ofresidence.
The advantage of Mobile Spatial Statistics is the continuity of the data because it uses informationfrom the mobile phone operating network. GPS data can provide more detailed location information,
but if the device's GPS service is disabled, information cannot be retrieved, and the data are
discontinuous. However, Mobile Spatial Statistics have very little missing data because all mobile
phones that are turned on are included in the sample. While smartphone ownership is lower among
elderly individuals, Mobile Spatial Statistics also cover traditional cell phones, so the elderly
population can also be estimated with a high degree of accuracy. The information, including theindividual's place of residence, is highly credible because it is based on subscriber information.
Because the data are so representative and reliable, they are frequently used in Japan for policy-making
and research, notably for measuring people's mobility during the COVID-19 pandemic.

B2. Generating mobility measures from Mobile Spatial Statistics
Raw data for Mobile Spatial Statistics are generated hourly in four files (population by gender and age,by city of residence, by prefecture of residence, and fully aggregated), with each file containing meshlevel information for all of Japan. We use the fully aggregated population data to create the meshdefinitions and use the population by city of residence data to create the primary dataset used in theanalysis. Each file contains information on the date, time, mesh ID, residential city code (for the cityof residence data), and estimated population, giving us the mesh-level (by city of residence) population.
To construct the city-level dataset used in our analysis from these raw data, we proceed as follows.

73

B2.1. Creating mesh definitions
Using 2019 data, we define commercial area meshes (meshes with high day/night population ratios)and hotspot meshes (meshes that attract people from a wide area).

Commercial areas(a) The fully aggregated population data are used. For a given day for each mesh, the average nighttime
and daytime populations are calculated for the 4 hours from 00:00 to 04:00 and the 8 hours from09:00 to 17:00, respectively, to create daily mesh-level cross-sectional data.
(b) All data created by repeating step (a) for all 365 days in 2019 are merged to create the mesh-byday-level panel data.
(c) From the data created in (b), we calculate the average daytime and nighttime populations for eachmesh in 2019 to create mesh-level cross-section data.
(d) Based on the data generated in (c), we define a mesh as a commercial area if that mesh's 2019average day/night population ratio is greater than 1.5 and its nighttime population is greater than
0.1 (because the day/night population ratio is overestimated for uninhabited meshes).

Hotspots(a) In preparation, a correspondence table between meshes and cities is created using GIS software. If
a mesh contains more than one city, the mesh is defined as belonging to the city that covers thelargest share.
(b) The data on population by city of residence are used. For a given hour on a given day, the cities
are connected with meshes based on the correspondence table created in (a).
(c) From the data created in (b), mesh-level cross-sectional data are created for a given hour by
calculating the following for each mesh: (1) the percentage of people coming from other cities (i.e.,
74those whose city of residence does not match the city they are currently in) and (2) the unique
number of cities of residence for people currently in that mesh.
(d) We repeat step (c) 8 times for 09:00 to 17:00 to produce daily averages for (1) and (2), respectively.
The step is repeated for all 365 days in 2019, merging all the created data to produce the mesh-byday-level panel data.
(e) From the data produced in (d), the 2019 averages for (1) and (2) are calculated for each mesh to
produce mesh-level cross-section data.
(f) Based on the data generated in (e), a mesh is defined as a hotspot if (1) is greater than 0.2 and (2)is greater than 2.

B2.2. Creating city-level mobility data
The population data by place of residence for 2019 to September 2021 are used to create the daily andcity-level mobility variables. We use the mesh definitions described in B2.1 as appropriate.

(a) In preparation, a correspondence table between the meshes and the cities is created using GIS
software. If a mesh contains more than one city, the area for each city is prorated by the ratio of
the areas. This allows the mesh-level population to be converted to the city level. A distance matrix
for distances between the centers of gravity for each city is also created using GIS software. Basedon the station data published by the Ministry of Land, Infrastructure, Transport, and Tourism, abuffer of 150 meters around train stations is also created, and the share of the area in each meshthat is near a station is calculated.
(b) For a given time on a given day, the cities are linked to the meshes with the correspondence tablecreated in (a), and the populations are prorated by the ratio of areas. The following process is thenused to create each city-level variable for a given hour. In (1) through (3), by setting the aggregation75

criterion to the city where one is currently located rather than the city of residence, variables thatmeasure incoming visitors rather than outgoing residents can be created.
(1). The percentage of individuals leaving their city of residence is created based on whether thecity of residence and the current city are the same.
(2). The distance matrix created in (a) is used to calculate the distance between the city ofresidence and the current city, and the average distance traveled is computed.
(3). Using the definition of commercial areas for each mesh created in B2.1, only the number ofpeople in commercial areas are counted.
(4). The percentage of individuals leaving their prefecture of residence is calculated based onwhether the prefecture of residence and the current prefecture are the same.
(5). Using the station coverage rates for each mesh created in (a), the number of people in thestation area is calculated by prorating the population based on the share of the area includedin the station area.
(6). Using the definition of hotspots for each mesh created in B2.1, only the number of people inhotspots are counted.
(c) Process (b) is repeated for each hour from 09:00 to 17:00 to produce daily averages for (1) through(6). The process is repeated for 2019 through September 2021, and all data created are merged to
produce daily and city-level panel data. For commercial areas, nighttime (00:00-04:00) data arealso created.
(d) For the number of people in commercial areas at night, calculated in (c), the average for 2019 is
calculated, which gives the number of people originally living in commercial areas. The numberof people originally living in the commercial areas is subtracted from the number of people in thecommercial area during the day, which is used to define the number of people visiting commercial
areas during the daytime and is used in place of the total number of people in commercial areas.
76

(e) For each variable created in (c) (or in the case of commercial areas, the variable created in (d)),
averages for each weekday and holiday in 2019 are calculated to provide a measure of mobility
during normal times.
(f) The data from 2020 to September 2021 are merged with the data from 2019 (“normal times”)
created in (e), and the mobility measures divided by the average mobility measures during normal
times – 1 are calculated. These measures of the change in mobility relative to that in normal times
are used in the analysis.

77