Discussion Paper No. 117
Economic Activities and Regional Correlation
During Economic and Natural Disasters
Jun Nagayasu

July, 2020

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Economic Activities and Regional Correlation
During Economic and Natural Disasters
Jun Nagayasu∗

Abstract
Life is characterized by risks of different features and origins. Examining the
economic and natural disasters that have occurred in Japan in the past decades,
we show that regional relationships strengthen during chaotic moments, such as
the Lehman Brothers collapse, the Great East Japan Earthquake, and the coronavirus disease (COVID-19) pandemic. Moreover, we find that business prospects
are a good predictor of labor market conditions, and employment opportunities
deteriorate more severely when regions are highly correlated. Our study indicatesthe side effect of market integration, and the relevance of regional economic centers in cushioning nationwide economic and natural shocks.
JEL classification: R1
Keywords: Lehman Brothers collapse; Great East Japan Earthquake; COVID19; regional heterogeneity; regional correlation∗

Tohoku University, Graduate School of Economics and Management, 27-1 Kawauchi, Aobaku, Sendai-city, Miyagi 980-8576 JAPAN; Tel: +81 22 795 6265; Fax: +81 22 795 6270; Email:jun.nagayasu.d8@tohoku.ac.jp. This paper has been prepared for the public seminar series on COVID19 organized by Tohoku University.

11

Introduction

Life is full of risks; however, we know that it is difficult to predict and avoid most risks
because of the differences in their characteristics and origins. Since economic and natural disasters involve many issues that significantly and adversely affect people’s lives,
there is ample research from different academic perspectives. In this regard, economic
and financial data show a tendency for economic activities to converge during and in
the aftermath of disasters. Shocks are often transmitted initially through the banking
and financial sectors and with some time lag, through the tradable sector, harmonizing
business cycles globally.1 Many countries implemented expansionary fiscal and monetary policies to boost their economies following global crises like the collapse of Lehman
Brothers and the recent coronavirus disease (COVID-19) pandemic. This increase incommonality and cross-border spillovers among countries has been accelerated by the
globalization of economies where traders can expect greater profit-earning opportunities.2 As a result, presently, the shocks originating from one country have more direct
and immediate impacts on other countries than before.
The shocks are known to transmit to other countries through several channels:
for example, trading and investors’ expectations channels (Classens, 2000; Forbes andRigobon, 2001; Nagayasu, 2001). Deterioration in an economy typically reduces external demand for other countries, resulting in economic recession and deflation offinancial assets worldwide. Moreover, during chaotic periods, investors tend to follow
a leader who is believed to possess more information than others. Such a mimicking
strategy is called herding behavior, which is argued to have triggered the collapse of
the European Exchange Rate Mechanism (ERM) in 1992. Spillovers in global markets have been a primary research focus in the past, but contagion exists in regional
economies as well.
Against this background, we study the relationship between economic conditions
and regional homogeneity during economic and natural crises using regional correlation
1

The 1997 Asian crisis that spread from Thailand to the rest of Asia is an example of a contagion
in the global financial sector. Akhtaruzzaman et al. (2020) showed that conditional correlation amongstock returns increased significantly at the onset of the COVID-19 pandemic, focusing on the channelof financial firms.
2
Economic researchers often refer to cross-border influence as “contagion” during chaotic periods,
and as “interdependence” during tranquil periods. Here, we do not make a clear distinction between
these two terms.

2

as a proxy for regional homogeneity. Regional analysis is more prone to be influenced
by common shocks than global studies, but it nevertheless provides relevant evidence
in the global context. As summarized in the next section, some crises have morehomogeneous impacts on regional economies than others, and therefore, we expect
regional correlation to vary both over time and between regional pairs. We attempt
to identify the uniqueness of the ongoing COVID-19 pandemic compared to othercrises and argue that the effects of crises have been more devastating for homogeneous
markets compared to heterogeneous markets.

2

Natural and economic disasters

Partly because of its geographical location, Japan has been confronted with many
disasters over the last two decades. Prior to a formal analysis, we summarize below
the three most notable crises in recent decades: the Lehman Brothers collapse, theGreat East Japan Earthquake (GEJE), and the COVID-19 pandemic. The collapse of
Lehman Brothers (2008) is rooted in the banking sector and thus is expected to haveadverse effects primarily in metropolitan areas such as Kanto, Tokai, and Kinki; the2011 earthquake mainly influenced the Tohoku region. In contrast, while it is not yet
fully understood how COVID-19 came to existence, it appears to have affected regional
economies more homogeneously than other disasters because of the resultant pandemic
and the government-enforced lockdown that encouraged all people to stay at home.

2.1

The bankruptcy of Lehman Brothers

The Lehman Brothers, a hedge-fund firm, collapsed on 15 September, 2008—the largest
bankruptcy in the United States (US) history. This was originally caused by a subprime mortgage crisis in the US, which became prominent in 2007. The administrationof the erstwhile President Barack Obama implemented a policy to assist people (including low-income consumers) to own houses, and as a result, the low quality sub-prime
mortgages increased to 20% in 2006. Lehman Brothers was one of the first firms to beengaged in the mortgage business. However, the problem arose when it became apparent that poor and less creditworthy households could not pay back housing loans.

3

As financial markets are globally linked and European financial firms owned a significant amount of such funds, the contagious effects spread from the US to European
countries and then Japan. Japanese firms owned a relatively small amount of problematic assets than its European counterparts; thus, direct damage through the financial
channel was minimal. However, in the face of a weak external demand and yen appreciation (the so-called safe-haven currency), this crisis hit Japan’s exporters severely.
In addition, future uncertainty discouraged the households to increase personal consumption. As a result of the weak domestic and external demand, accompanied byslow but steady increases in the unemployment rate, Japan suffered from the collapseof Lehman Brothers for a considerably longer time than other advanced countries.3

2.2

The Great East Japan Earthquake

Japan experiences earthquakes frequently, but the one that occurred on March 11, 2011,
named the Great East Japan Earthquake (GEJE), was the biggest earthquake everrecorded in Japan. This disaster is known to people not only because of the magnitudeof the earthquake, but also because it was coupled with a large wave from the ocean(tsunami) and nuclear power failures. While aftershocks of the GEJE lasted for several
months, damages from the initial earthquake can be attributed to the tsunami. The
number of deaths exceeded 15000, while 6000 were injured and 25000 were declared
missing. The tsunami had also brought about an explosion at the Fukushima nuclear
power plants. Normally, the reconstruction after earthquakes begins right after thenatural disaster. However, in this case, the nuclear power accident forced people tomove out of the affected areas, spreading out mainly in the eastern part of Japan,
and seeking jobs that could utilize their work skills in the new destinations (Kondo,
2018). The reconstruction is still in progress and there is a long way to go to deal withthe collapsed nuclear power plants. In fact, a railway line linking Kanto and Tohoku(Joban-line) reopened only in March 2020.
The regions heavily affected by the tsunami were concentrated on the Pacific coastof Tohoku region (near the epicenter of the GEJE), which witnessed the destruction of
its fisheries and other industries. The Kanto region was also affected (for example, due
3

It was called the glacial age in job markets. The unemployment rate returned to its pre-LehmanShock level in March 2013.

4

to disruption of electric supply), but witnessed fewer deaths. In contrast, the GEJE did
not cause damages in the western parts of Japan, although manufacturing firms in themetropolitan regions were affected because the factories that produced intermediate
goods were in Tohoku. The costs incurred due to the supply chain disruptions were
estimated at 0.35% of the GDP (Tokui et al., 2017).

2.3

COVID-19

The coronavirus disease (COVID-19), which spread from China, became a global pandemic in early 2020.4 Being a neighboring country, Japan was also severely affectedby COVID-19. As of July 2020, the number of deaths was reported to be around 1000
people. When this disease initially became apparent in China, a strict lockdown was
imposed in order to prevent further diffusion, but was subsequently transmitted to
the rest of Asia, in addition to the other continents of Oceania, Europe, and North
America by early 2020. By the second quarter of 2020, it was evident that COVID-19
had become a pandemic, with the increasing number of infected patients and deaths
throughout the world, including Africa and South America.
It is difficult to identify the exact date of occurrence and arrival of COVID-19. However, the Japanese government confirmed that it was transmitted to Japan in January
2020 and announced quarantine restrictions on March 6, 2020 for people coming fromthe severely affected regions in China (e.g., Wuhan) and Korea, subsequently extending
it to other countries. Moreover, a state of emergency was declared from 8 April to 6
May in metropolitan prefectures (Tokyo, Kanagawa, Saitama, Chiba, Osaka, Hyogo,
and Fukuoka) and was extended to all regions in Japan from 16 April. During this period, there was no lockdown, as was observed in other countries, due to the absence ofa law to enforce such a restriction on people’s movements. However, Japanese residents
were encouraged to stay and work at home in order to maintain a physical distancefrom others in the absence of a vaccine for COVID-19.5 As a result, the transportationindustry was severely affected, and many departmental stores, restaurants, and hotels
were forced to shut down their businesses temporarily or forever. In contrast, becauseof the increase in telework, the IT industry found a good opportunity to expand and4
5Since it was identified in Wuhan, China, in late 2019, it is called COVID-19.
The low number of the deaths in Japan without introducing a lockdown is considered a myth.

5

develop its business. Nevertheless, enterprises benefiting from COVID-19 are an exception. While the magnitude of the damage may differ, all consumers, regardless ofresidential location and income, and most industries, have been adversely affected by
COVID-19.

3

Data

For an analysis of an ongoing crises, researchers have always faced a shortage of statistical information. Therefore, researchers have typically examined high-frequency
financial data, such as stock prices and exchange rates in order to comprehend financial market conditions during and in the aftermath of crises. Our study similarly
aims to analyze the effects of the crises using a limited amount of information that isdisseminated in a timely manner. For this reason, we collected business projections(2004Q2 to 2020Q2) and unemployment data (2004Q2 to 2020Q1) for nine regions:
Hokkaido, Tohoku, Kanto, Hokuriku, Tokai, Kinki, Chugoku, Shikoku, and Kyushu.6
Regions refer to a subdivision of the country, consisting of administrative areas called
prefectures. Figure 1 illustrates the geographical location and the definition of theseregions.
[Figure 1]
The Business Survey Index (BSI) contains information on future business prospects
indicated by the firms in Japan. This survey is conducted quarterly by the Cabinet
Office and the Ministry of Finance and covers a whole sector (approximately 12000 to
15000 firms in each survey). Figure 2 plots the contemporaneous business projections(i.e., business prospects for the present quarter), which reveals that the respondents
have been most pessimistic about business performance during COVID-19. Thesebusiness prospects were even worse than those after the Lehman Brothers collapse or
the 2011 earthquakes.
Moreover, Table 1 summarizes changes in projections about business performance.
NextQ is the difference between the business prospects for the succeeding quarters
6

Okinawa is excluded from our analysis due to missing data during our sample periods. Furthermore, the 2016Q2 data for Kyushu are missing due to severe earthquakes witnessed in Kyushu, andthus, have been treated the same as the value in the previous quarter.

6

(t + 1) made in t and that for contemporaneous quarters made in t + 1. NextQQ is thedifference between business prospects for t+2 made in t and contemporaneous prospects
at t + 2. It is interesting to note that the average (and median) of the projection errors
is positive, indicating that the initial projections were often optimistic. Furthermore,
there is significant heterogeneity among regional economies, despite Japan often being
considered very homogeneous by international standards.7[Figure 2 & Table 1]
Quarterly unemployment data were collected from the Statistics Bureau of Japanof the Ministry of Internal Affairs and Communications, which are available for eachprefecture. Regional data were compiled on the basis of prefectural data from theLabor Force Survey between 2004 and 2020. The unemployment data for Japan arecompiled and disseminated more frequently than the regional GDP data, which areoften used by researchers to measure the overall economic activities (but are available
with a considerable time lag). Moreover, because the effects of the crises are not limited
to the financial sector, the unemployment rates are more appropriate to understandthe real economy that is more directly linked with the living standards in a country
where consumers prefer to possess safe financial assets. Table 1 also summarizes thebasic statistics of regional unemployment rates and shows that the rates, while beingaround 3 to 4%, varies among regions.
A poor business prospect may become a factor that triggers a layoff. Therefore, anegative relationship is expected between the BSI and unemployment rates, although it
may take some time for this relationship to be effective, given the contracts and rigidity
in the labor market. In order to verify this relationship with limited observations, we
conduct panel data analyses using the standard estimation approaches: the pooled
ordinary least square (POLS) and the within and random effects estimation.
Table 2 reports the results of the unemployment rate equations for three approaches.
We also use a lagged explanatory variable (lagged BSI) to take into account the time
lag for making employment decisions. Consistent with our expectation, the data show aclear negative relationship between these variables. A drop in the BSI by one point leads
to an increase in unemployment rates by roughly 0.03%. While the size of the changes
7

Regional disparities in Japan have been pointed out by Nagayasu (2017) with respect to inflation.

7

in unemployment rates appears very subtle, the influence of the BSI is statisticallysignificant. Moreover, a slightly stronger impact is obtained from the lagged BSI,
which is also shown to be a better fit to the data according to the R2 and the adjusted
R2 values, thereby confirming that a time lag is required for business prospects to
impact the implementation of employment decisions.
[Table 2]

4

Empirics

4.1

Regional correlation during crises

Regional homogeneity, measured by correlation, is likely to be time-varying; likewise,a reaction of firms to crises likely differs by the type of firm. Therefore, we classify
our samples into the following: 1) manufacturing and non-manufacturing firms, and2) large and small & medium firms. Large firms are those corporations with capital ofmore than three billion yen and generally possess significant internal reserves to protect
themselves against crises. Among the different types of firms, the non-manufacturingindustry was directly affected by the COVID-19 pandemic, which forced people toreduce economic activities.
We initially calculated time-varying correlation using the rolling estimation method
to examine regional homogeneity. With nine regions under investigation, we obtained
36 pairs of regional correlation for the BSI and unemployment rates, with a window
size of 3 and 4, respectively.8 Figure 3 depicts the national average of time-varying
regional correlation based on BSI, and the crisis periods are denoted by the shaded areas, which will be defined shortly. Regional correlation is very volatile and approaches
one on a number of occasions. A sharp correlation curve during the GEJE indicatesthat the shocks from the earthquakes were initially scattered across the country, but
regional heterogeneity dominates soon after the eruption of the earthquakes. In contrast, the shocks from the Lehman Brothers collapse were more pervasive throughout
the country. While a formal analysis is required, regional correlation seems to be highin the aftermath of the crises.
8

We attempt to set a short window size that allows us to estimate such a model.

8[Figure 3]
Using the estimates of the time-varying regional correlation (Corrit ), we first determine if regional correlation, which can be interpreted as regional homogeneity, ishigher during economic and financial disasters. The sensitivity of correlation to crises
at time t is estimated against the constant term (α) and dummy variables to capture
the effects of the Lehman Brothers collapse (Lehman), the GEJE (Earthquake), andthe COVID-19 pandemic (COV ID).
Corrit = α + β1 Lehmant + β2 Earthquaket + β3 COV IDt + t(1)

where Corr is a matrix of 36 pairs of regional correlation, and  ∼ N (0, σ 2 ). The
constant term, α, represents the average level of regional correlation, and βs becomessignificantly positive when regional heterogeneity weakens during crises. The effects ofcrises are captured by the dummy variables defined below. The definition may not be
exactly consistent with the crisis periods due to the low frequency and availability ofthe data. Although COVID-19 was prevalent in early 2020, we assume that its effectbegan in April, given that life was almost normal until March 2020.
Lehman=1 for 2008Q4-2009Q1,

otherwise 0;

Earthquake=1 for 2011Q2-Q3,otherwise 0;

COV ID = 1 for 2020Q2,

otherwise 0.

Table 3 reports the correlation analysis for all firms based on the POLS and thewithin (fixed effects) estimation methods. The average BSI correlation is approximately
0.65, and the regional correlation is highest for COVID-19, which suggests that its
effects prevailed across the country. Moreover, there are little differences between theestimates for the effect of the crises from the POLS and Within models, confirmed bya similarity in the average value of regional correlation. These results are interesting
when compared with those of the regional correlation of unemployment rates. This
analysis does not consider COVID-19 because of the absence of data, but the results
show that regional correlation in the labor market is relatively more stable over time.
[Table 3]
The BSI correlation analysis for different sectors has been summarized in Table 4.
There is evidence of an increase in regional correlation during crisis periods; however,9the regional correlation for the manufacturing sector drops in response to the GEJE.
This may be due to some regions that are not popular locations for manufacturing firms.
In this case, regional discrepancies may arise because of the supply chain linkages with
Tohoku and the geographical concentration of manufacturing firms in metropolitan
areas, which is said to increase knowledge spillovers and improve productivity (Fujita
and Thisse, 2013). Among the three crises, the COVID-19 pandemic has the largest
positive impact on regional correlation for the non-manufacturing firms, thereby confirming that non-manufacturing firms have been universally and severely affected by
the pandemic; their correlation increased significantly in response to the government
policy to close businesses in the service sector.
[Table 4]
Table 5 shows the results by the size of firms, which also reveals a tendency forregional correlation to increase during the crises, regardless of the firm size. However,the collapse of Lehman Brothers did not affect regional correlation for small & medium
firms. This seems to confirm that large manufacturing firms (that are often global
enterprises and are concentrated in metropolitan regions) suffered severely from thefailure of Lehman Brothers, whereas small & medium non-manufacturing firms weremore evenly affected by this crisis.
[Table 5]

4.2

Unemployment rates and regional BSI correlation

We investigate the relationship between regional BSI and economic conditions andseek to understand if the increases in regional correlation would explain a deterioration in economic activities. Table 6 presents a preliminary analysis of the dynamic
relationship between regional unemployment rates, regional correlation, and economic
variables. Here, the dynamic analysis is conducted using lagged economic variables
such as the BSI correlation, sales, and profits of regional firms obtained from the Ministry of Finance. The lagged variables were used not only to account for the timerequired for making employment decisions by firms, but also to deal with potential
endogeneity in the specification. In order to check the robustness of our findings to themodel specification, we use the economic variables with lags from 0 to 2.
10

We discovered a negative relationship between them; the unemployment rates increase along with a fall in sales and profits of firms, consistent with the standard
economic theory. Some variables, particularly the BSI correlation, require time to havea statistically significant impact on unemployment rates. According to the model with
two lags, an increase in regional correlation will increase unemployment rates by 0.24%.
This positive relationship of regional BSI correlation is in line with the increasing unemployment rates during times of natural and economic disasters, when regional ties
tend to strengthen.
[Table 6]
The previous analysis is dynamic, but is limited to a time span of one or two quarters. In order to conduct a more dynamic analysis, we estimate the relationship between
unemployment rates and BSI using impulse response functions. These functions canbe estimated from the panel vector autoregression (PVAR) model, which is a popularstatistical approach in economic and financial studies. The VAR model is a convenientstatistical model to summarize a dynamic relation in time-series data and is a useful
method to deal with a potential endogeneity issue in economic variables. PVAR is anextension of VAR designed for panel data analyses. Therefore, the standard PVAR(p)for a m × 1 vector of endogenous variables (y) can be stated as:

yi,t =

Im −

p
X

!
Al

µi +

l=1

p
X

Al yi,t−l + i,t

(2)

l=1

where I is a m × m identity matrix, µi is a fixed effect, and  is a random error. We
estimate the generalized impulse response functions or GIRFs (Pesaran and Shin, 1998)
from PVAR(6).9 This model specification appears to be appropriate for the relationshipbetween unemployment rates and BSI correlation as well as between unemployment
rates and BSI. The Hansen test of over-identification restrictions examines the validityof the parameter identification under the null hypothesis, which cannot be rejected forboth cases. The GIRF for a shock to variable j (δj ) can be obtained as:
GIRF (j) = E[yi,t+h |i,t,j = δj , Σ ] − E[yi,t+h |Σ ] = Ah Σ (σj,j )−1/2
9(3)

The PVAR was estimated using the panelvar function in R (Sigmund and Ferstl, 2017). We used
forward orthogonal data transformation.

11

where σj,j is the jth element in Σ,j,j . Figure 4 depicts that a dynamic responseof unemployment rates to the BSI is consistent with the conventional expectations.
We calculate the GIRFs of h = 6, extended from the time-series model, which are
invariant to the level of exogeneity (i.e., the order) of variables in the PVAR. Figure 4(a) shows the positive relationship between unemployment rates and BSI correlation.
A high regional correlation would worsen the labor market conditions by increasing
unemployment rates. Figure 4 (b) shows the negative relationship between unemployment rates and the BSI (in levels). Thus, the worsening business prospects of firms areassociated with the increases in unemployment rates. These two graphs suggest thata certain amount of time is required for the employment condition to change after the
projections are made, with BSI being a good predictor of employment conditions.
[Figure 4]

5

Conclusion

As proposed in standard economic theory, market globalization has had many positive
impacts in our life, and presently, it is difficult to imagine life without any imported
goods. We do not intend to refute this assertion but argue that market integration has
a side effect, which becomes prominent during nationwide crises and disasters.
Our findings imply that regional homogeneity would hinder economic recovery, sinceno other regions can become an engine of economic growth in the country. This finding
has relevant economic policy implications for the future of Japan. Japan is one of themost aged countries in the world. The proportion of people who are older than 65 years
increased from 4.9% to 28.4% between 1950 and 2020. Along with this demographic
change, the population as well as the labor force has been declining. Moreover, people
tend to relocate to Kanto region (Tokyo prefecture). A concentration of populationand firms (economic agglomeration) may bring economic benefits through increasing
returns, thus widening regional heterogeneity, which, according to our results, acts as acushion against various disasters. However, this cushioning effect may not function in
the absence of a modest scale of regional economic centers. Therefore, we caution thatthe disappearing local economic centers in Japan, along with changes in demography

12and lifestyle, could be problematic during disasters and provide a justification against
the geographical over-concentration of population and economic activities in a country.
Similarly, our conclusion may contradict the conventional economic theory that
advocates a very high level of (or perfect) regional homogeneity in a single currency
market. One very notable example is the Maastricht convergence criteria to ensure thehomogeneous Euro-wide area. In tranquil periods, heterogeneity in member countries
is often considered a negative factor for a single currency area, but diversified markets
with a certain level of economic centers in member countries buffer Euro-wide shocks
during chaotic times.

13References[1] Akhtaruzzaman, M., S. Boubaker, and A. Sensoy (2020). Financial contagion during
COVID–19 crisis. Finance Research Letters, 101604.
[2] Claessens, S., R. Dornbusch, R. Dornbusch, and Y. Park (2000). Contagion: Understanding how it spreads. World Bank Research Observer 15, 177–97.
[3] Forbes, K. and R. Rigobon (2001). Measuring Contagion: Conceptual and Empirical Issues, pp. 43–66. Boston, MA: Springer US.
[4] Fujita, M. and J.-F. Thisse (2013). Economics of Agglomeration. Cambridge Books.
Cambridge University Press.
[5] Kondo, A. (2018). The effects of supply chain disruptions caused by the Great East
Japan Earthquake on workers. Japan and the World Economy 47, 40 – 50.
[6] Nagayasu, J. (2001). Currency crisis and contagion: evidence from exchange rates
and sectoral stock indices of the Philippines and Thailand. Journal of Asian Economics 12 (4), 529 – 546.
[7] Nagayasu, J. (2017). Regional inflation, spatial locations and the BalassaSamuelson effect: Evidence from Japan. Urban Studies 54 (6), 1482–1499.
[8] Pesaran, H. and Y. Shin (1998). Generalized impulse response analysis in linear
multivariate models. Economics Letters 58 (1), 17 – 29.
[9] Sigmund, M. and R. Ferstl (2017). Panel vector autoregression in R with the
package panelvar. SSRN .
[10] Tokui, J., K. Kawasaki, and T. Miyagawa (2017). The economic impact of supply
chain disruptions from the Great East-Japan earthquake. Japan and the World
Economy 41, 59 – 70.

14

Table 1: Errors in business projections and regional unemployment rates
RegionMean
SD Median
Min Max
BSI (NextQ)
Hokkaido
7.05 7.28
5.25 -1.40 46.30
Tohoku6.91 7.70
5.65 -7.20 46.70
Kanto
6.27 7.55
4.00 -1.70 43.80
Hokuriku
6.02 9.29
4.15 -6.50 52.30
Takai
5.82 9.37
3.65 -12.20 52.10
Kinki
7.02 7.83
5.65 -1.70 50.00
Chugoku6.31 8.01
4.30 -3.70 51.30
Shikoku
6.40 7.37
4.85 -5.00 43.10
Kyushu5.92 8.01
4.10 -12.00 45.00
BSI (NextQQ)
Hokkaido
9.04 8.82
7.10 -4.50 48.90
Tohoku9.59 9.32
8.10 -3.20 49.60
Kanto
8.98 9.96
6.70 -4.40 49.10
Hokuriku
9.59 11.24
7.80 -5.10 57.70
Takai
9.24 11.34
8.00 -6.00 58.20
Kinki
9.88 9.96
8.10 -4.40 55.90
Chugoku
9.07 10.44
8.20 -5.30 57.80
Shikoku
8.78 9.21
6.60 -5.50 46.70
Kyushu7.98 9.80
6.80 -9.70 53.00
Unemployment rates
Hokkaido
4.73 1.14
5.00
2.10 8.30
Tohoku4.18 1.14
4.30
2.10 6.30
Kanto
3.91 0.84
4.00
2.10 5.40
Hokuriku
2.92 0.73
3.00
1.50 4.20
Tokai
3.12 0.79
3.10
1.50 4.70
Kinki
4.72 1.22
4.80
2.30 7.20
Chugoku3.38 0.72
3.50
1.90 4.50
Shikoku
3.73 0.90
3.90
1.50 5.20
Kyushu4.35 0.98
4.50
2.30 6.00

Skew

Kurtosis SE

2.67
2.37
2.77
2.24
2.36
2.96
3.17
2.25
1.83

11.07
9.48
9.24
8.04
8.31
12.46
13.97
8.06
7.07

0.91
0.96
0.94
1.16
1.17
0.98
1.00
0.92
1.00

1.61
1.83
2.31
1.84
2.23
2.44
2.32
1.72
2.00

4.77
4.52
6.28
4.43
6.10
7.69
7.64
4.25
6.29

1.11
1.17
1.25
1.42
1.43
1.25
1.32
1.16
1.23

-0.12
-0.19
-0.46
-0.40
-0.13
-0.12
-0.40
-0.58
-0.54

0.00
-1.03
-0.70
-0.78
-0.85
-0.82
-0.82
-0.47
-0.71

0.12
0.12
0.09
0.08
0.08
0.13
0.07
0.09
0.10

Note: The BSI Statistics. The BSI data are from 2004Q2 to 2020Q2, and unemployment rates from
2004Q2 to 2020Q1. NextQ refers to projection differences between t and t + 1, and NextQQ to thosebetween t and t + 2. Regions comprises the following prefectures stated in parentheses. Hokkaido(Hokkaido), Tohoku (Aomori, Akita, Iwate, Miyagi, Yamagata, Fukushima), Kanto (Tokyo, Kanagawa, Saitama, Chiba, Ibaraki, Tochigi, and Gunma), Hokuriku (Toyama, Ishikawa, Fukui), Tokai(Gifu, Shizuoka, Aichi, Mie), Kinki (Shiga, Kyoto, Osaka, Hyogo, Nara, Wakayama), Chuugoku (Tottori, Shimane, Okayama, Hiroshima, Yamaguchi), Shikoku (Kumamoto, Oita, Miyazaki, Kagoshima).

Table 2: Panel estimations for unemployment rates
Estimation methods
Intercept
BSI

POLS
3.344∗∗∗(0.054)−0.031∗∗∗(0.004)

Within−0.032∗∗∗(0.003)

Random3.331∗∗∗(0.216)−0.032∗∗∗(0.003)lag(BSI)
R2
Adj. R2
Num. obs.

0.112
0.110
567

0.164
0.151
567

0.162
0.161
567

POLS
3.287∗∗∗(0.053)

WithinRandom
3.273∗∗∗(0.215)−0.036∗∗∗(0.004)
0.154
0.152
558

−0.038∗∗∗(0.003)
0.224
0.212
558

−0.038∗∗∗(0.003)
0.222
0.221
558

Note: Unemployment rates are endogenous variables. The POLS stands for the pooled ordinary least square.
The sample period is from 2004Q2 to 2020Q1.

15

Table 3: Regional correlation levels during crises
POLS
Within
BSI correlation (All sectors)
0.656∗∗∗ 0.647∗∗∗ 0.635∗∗∗(0.010) (0.010) (0.010)
0.236∗∗∗ 0.245∗∗∗ 0.257∗∗∗ 0.257∗∗∗(0.058) (0.057) (0.057) (0.056)
Earthquake
0.277∗∗∗ 0.288∗∗∗ 0.288∗∗∗(0.057) (0.057) (0.056)
COVID
0.349∗∗∗ 0.349∗∗∗(0.057) (0.056)
R2
0.000
0.007
0.017
0.033
0.035
Adj. R2
0.000
0.007
0.017
0.032
0.019
Num. obs.
2268
2268
2268
2268
2268
End. variable
Unemployment rate correlation
Intercept
0.440∗∗∗ 0.441∗∗∗ 0.443∗∗∗(0.011) (0.011) (0.011)
Lehman−0.022 −0.024−0.024(0.061) (0.061)(0.058)Earthquake−0.075−0.074(0.061)(0.058)
R2
0.000
0.000
0.001
0.001
Adj. R2
0.000-0.000-0.000-0.016
Num. obs.
2188
2188
2188
2188
End. variable
Intercept
0.663∗∗∗(0.010)
LehmanNote: ∗∗∗ p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05. Explanatory variables are dummies and are expected to capture crisis effects.

Table 4: Regional correlation during crises II
POLSWithinEnd. variable
BSI correlation (Manufacturing sector)
Intercept
0.642∗∗∗ 0.631∗∗∗ 0.636∗∗∗ 0.625∗∗∗(0.011) (0.011) (0.011) (0.011)
Lehman0.337∗∗∗ 0.332∗∗∗ 0.343∗∗∗ 0.343∗∗∗(0.060) (0.060) (0.059) (0.058)
Earthquake−0.152∗ −0.140∗ −0.140∗(0.060) (0.059) (0.058)
COVID
0.340∗∗∗ 0.340∗∗∗(0.059) (0.058)
R2
0.000
0.014
0.017
0.031
0.033
Adj. R2
0.000
0.013
0.016
0.029
0.016
Num. obs.
2268
2268
2268
2268
2268
End. variable
BSI correlation (Non-manufacturing sector)(Intercept)
0.566∗∗∗ 0.561∗∗∗ 0.549∗∗∗ 0.534∗∗∗(0.012) (0.012) (0.012) (0.012)
Lehman0.136∗
0.149∗
0.164∗
0.164∗(0.068) (0.067) (0.067) (0.066)
Earthquake
0.392∗∗∗ 0.407∗∗∗ 0.407∗∗∗(0.067) (0.067) (0.066)
COVID
0.439∗∗∗ 0.439∗∗∗(0.067) (0.066)
R2
0.000
0.002
0.016
0.035
0.037
Adj. R2
0.000
0.001
0.016
0.033
0.020
Num. obs.
2268
2268
2268
2268
2268
Note: ∗∗∗ p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05. Explanatory variables are dummies and are expected to capture crisis effects.

16

Table 5: Regional correlation during crises III
POLS
WithinEnd. variable
BSI correlation (Large firms)
Intercept
0.505∗∗∗ 0.492∗∗∗ 0.476∗∗∗ 0.463∗∗∗(0.013) (0.013) (0.013) (0.013)
Lehman0.415∗∗∗ 0.431∗∗∗ 0.443∗∗∗ 0.443∗∗∗(0.073) (0.072) (0.072) (0.070)
Earthquake
0.478∗∗∗ 0.490∗∗∗ 0.490∗∗∗(0.072) (0.072) (0.070)
COVID
0.376∗∗∗ 0.376∗∗∗(0.072) (0.070)
R2
0.000
0.014
0.033
0.045
0.048
Adj. R2
0.000
0.014
0.032
0.044
0.032
Num. obs.
2260
2260
2260
2260
2260
End. variable
BSI correlation (Small & medium firms)
Intercept
0.615∗∗∗ 0.614∗∗∗ 0.609∗∗∗ 0.596∗∗∗(0.011) (0.011) (0.011) (0.011)
Lehman
0.021
0.027
0.039
0.039
(0.062) (0.062) (0.062) (0.060)
Earthquake
0.178∗∗ 0.191∗∗ 0.191∗∗(0.062) (0.062) (0.060)
COVID
0.371∗∗∗ 0.371∗∗∗(0.062) (0.060)
R2
0.000
0.000
0.004
0.019
0.021
Adj. R2
0.000-0.000
0.003
0.018
0.004
Num. obs.
2268
2268
2268
2268
2268
Note: ∗∗∗ p < 0.001, ∗∗ p < 0.01, ∗ p < 0.05. Explanatory variables are dummies and are expected to capture crisis effects.

17

Table 6: Unemployment rate equations
POLS
2.866∗∗∗(0.107)
BSI corr−0.070(0.138)Sales
0.031(0.022)
Profits−0.038∗∗∗(0.008)R2
0.084
Adj. R2
0.073
Num. obs.
261
Intercept
2.784∗∗∗(0.101)lag(BSI corr, 1)
0.022(0.132)lag(Sales, 1)
0.021(0.021)lag(Profits, 1)−0.041∗∗∗(0.008)R2
0.112
Adj. R2
0.101
Num. obs.
252
Intercept
2.500∗∗∗(0.132)lag(BSI corr, 1)
0.236∗(0.137)lag(BSI corr, 2)0.136(0.132)lag(Sales, 1)
0.055(0.029)lag(Sales, 2)−0.018(0.027)lag(Profits, 1)−0.040∗∗∗(0.013)lag(Profits, 2)−0.015∗(0.009)R2
0.120
Adj. R2
0.097
Num. obs.
243

RandomIntercept−0.063(0.119)0.007(0.020)−0.024∗∗∗(0.008)0.038-0.005261

0.027(0.113)−0.003(0.019)−0.028∗∗∗(0.008)
0.070
0.027
252

0.250∗∗(0.117)0.159(0.112)0.027(0.025)−0.033(0.023)−0.020∗(0.012)−0.006(0.008)
0.077
0.020
243

Within2.894∗∗∗(0.160)−0.065(0.119)0.009(0.019)−0.025∗∗∗(0.008)
0.042
0.031
261
2.814∗∗∗(0.156)0.025(0.113)−0.001(0.018)−0.029∗∗∗(0.008)
0.073
0.062
252
2.536∗∗∗(0.184)0.244∗∗(0.116)0.154(0.111)0.029(0.025)−0.032(0.022)−0.022∗(0.012)−0.007(0.008)
0.078
0.055
243

Note: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.010. BSI corr presents
regional correlation.

18

Figure 1: Japanese regions

Note: See Table 1 about the definition of regions.

Figure 2: Business Survey Index

Note: The sample from 2004Q2-2020Q2. Increases in the BSI shows that businessman see a bright
future.

19

Figure 3: Regional correlation

Note: The national average of regional BSI correlation.

Figure 4: Impulse response functions from the PVAR(a) Unemployment rates in response to a shock in (b) Unemployment rates in respose to a shock in
BSI correlation
the BSI

Note: The generalized impulse response functions are obtained from the PVAR(6). Lines with circles
show a 95% confidence interval, which is obtained by the bootstrap method with 1000 draws. Hansen
test of overidentification restrictions: χ2 (1076) = 0.001 for (a) and χ2 (1116) = 0.001 for (b).

20