自然災害科学 J. JSNDS 38 特別号 185 -199（2019 ）Estimation of the economic impact of urban flood through theuse of big data on inter-branch
office transactions
Shaofeng YANG1, Yoshiki OGAWA2, Koji IKEUCHI3, Yuki AKIYAMA4 and Ryosuke SHIBASAKI4

Estimation of the economic impact of urban ﬂood through theuse of big data on inter-branch ofﬁce transactions
Shaofeng YANG1, Yoshiki OGAWA2, Koji IKEUCHI3,
Yuki AKIYAMA4 and Ryosuke SHIBASAKI4
Abstract
This paper quantitatively examines the economic impact of urban flood disasters in Tokyoon the supply chain. It accomplishes this task by using geographic information system (GIS)
data on inter-firm transactions. First, we use flood simulation analysis data to identify firms thatwere unable to continue business operations due to flood damage. Second, we employ inter-firmtransaction data to detect businesses that are related to affected firms. Subsequently, we discussand analyze the industrial structure of the identified companies that have business associations
with affected firms. Finally, we focus on the indirect economic damage caused to these companies
and estimate the affected transaction amount in consideration of the magnitude of their linkages
with directly affected firms. The present investigation reveals several interesting results. First,
19-29% of companies in Japan are associated with affected firms through business interests. This
figure increases to nearly 50% when fifth-order firms are included: those companies connected toaffected firms through the five shortest-link paths in the trading network. Many firms could thus
be affected by a natural catastrophe even when they are actually located outside the core disasterarea. In addition, the economic damage is estimated to be nearly 1.3% of Japanʼs gross domestic
product (GDP).
Key words：inter-ﬁrm transaction data, supply chain, spillover effects, urban ﬂood disaster, ﬂood simulation

1

2

3

Graduate School of Frontier Sciences, The University ofTokyo
Institute of Industrial Science, The University of Tokyo

4

185

Graduate School of Engineering, The University of Tokyo
Center for Spatial Information Science, The University of
Tokyo

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Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions

1. INTRODUCTION

models typically use a dataset of statistical data

In recent years, natural disasters have become

between wide areas, such as input-output (I/O) ta-

large scale, diversiﬁed, and frequent in Japan. Nat-bles, which does not cover supply chain networks

ural calamities such as the torrential rainfall in July

between ﬁrms. Carvalho et al. (2016) developed a2018 in Heisei and the enormous earthquake inmodel based on the role of input-output to quanti-

east Japan in 2011 have caused enormous damage

fy the spillover effects of damage to estimate theand loss of human lives. The river ﬂooded duringharm caused by the Great East Japan Earthquake.

the Heavy Rain of July, Heisei 30 (2018). Large

This model incorporated the notion that damage

areas were inundated and 237 people died (Cabinet

would be propagated through transactional re-

Ofﬁce, 2019). Examples in other countries include

lationships of firms. Japanʼs total production de-

Hurricane Katrina (2005) and the Thailand ﬂoods

creased 1.2% in 2012 as a result of the earthquake(2011). On the economic side, many businesses inof 2011. Tokui et al. (2012) estimated the damage

locations other than the affected areas have suf-by the Great East Japan Earthquake by approxi-

fered indirect costs such as the halt of production

mating the gross output and damage rate of eachbecause of interruptions in the inter-firm trans-

region using statistical data such as the net capitalaction network. It is thought that the incidence ofstock of each industry sector and the total grossvarious natural disasters may increase worldwide

output. According to their calculation, the total

in the future (Dercon, 2005; Fachamps et al., 2003).

damage exceeded 1.35% of Japanʼs annual GDP in

To reduce the economic impact of large-scale

2011. Ogawa et al. (2017) applied sparse modelingcatastrophes, it is crucial for a stable inter-firmby combining inter-firm transaction data and GIS

transaction network to be established so that busi-

microdata, and examined the sales growth andness activities are able to continue. According tobusiness relationships of firms outside the area

Tokyo Shoko Research Ltd. (2018), 27.7% of Japa-

affected by the tsunami before and after the Great

nese ﬁrms have formulated a business continuity

East Japan Earthquake. The study revealed thatplanning (BCP) methodology that takes natural di-

the disaster had a significant negative impact onsasters into account. However, only 2.3% of compa-

directly and indirectly related ﬁrms; the businessnies are prepared for large-scale ﬂoods because thepartners of firms in affected areas continued toestimation of economic damage caused by ﬂooding

conduct new transactions. However, the inter-ﬁrm

is not sufﬁcient for the formulation of BCP, and be-transactional data used in these studies refer tocause the magnitude of the damage has not beentransactions between the corporate headquarters.

calculated. Therefore, it is critical to estimate the

In reality, since firms run many branch of fices

potential economic impact of large-scale ﬂooding.

throughout the country, the locations of such re-

Numerous extant studies have used inter-ﬁrm

gional divisions must also be considered along withtransaction and statistical data (Noy, 2009; Raddatz,

their transactions. In addition, Ogawa et al. (2019)2009; Strobl, 2011) to investigate the economic

used varied statistics such as the transaction data

impact of large-scale natural disasters. Kajitani andof inter-branch of fices and tsunami records toTatano (2018) estimated ﬂow damage of the Great

estimate the recover y process of firms after the

East Japan Earthquake by using the spatial com-

Nankai Megathrust Earthquakes in Japan. Studies

putable general equilibrium (SCGE) model, whichsuch as this one may be used to model other in-comprises comparative static equilibrium models

vestigations pertaining to the economic impact ofof interregional trade and location. However, SCGE

large-scale natural disasters that are predicted in187

自然災害科学 J. JSNDS 38 特別号（2019 ）the future. Webdawatta et al. (2012) estimated theeach region and industry at the branch ofﬁce level,

entrepreneurial and regional ‐ economic impacts

which is more micro; second, the drainage condi-

of a specific production site in Austria by using

tions of inundation are taken as a time series, andlight detection and ranging data as a source, based

more accurate damage estimation can be made as

on ﬂood modeling and expert judgement.

the inundation and recovery periods of the affect-

There are also many examinations of floods

ed firms are taken into account; third, a method

caused by hurricanes, heavy rain, and so on, e.g.,of quantitatively identifying the firms affected by

Risk Management Solutions (2005) and Congres-the ﬂood and the ﬁrms that are inﬂuenced by thesional Budget Ofﬁce (2005). The Bureau of Labor

inter-firm transaction network is posited by thisStatistics (2006) analyzed the decline in production

study. This proposed model also considers their

due to Hurricane Katrina by using statistical data

degree of inﬂuence by combining the ﬂood simula-from the Current Employment Statistics programtion analysis data and the transaction data of ﬁrms

and from the Quarterly Census of Employment andacross the country.

Wages. However, there are very few studies on the

The rest of this paper is organized as follows.

analysis of the wide-area economic impact caused

Section 2 describes the data used in this paper.

by flooding, and those few investigations are lim-

Section 3 presents the analysis and Section 4. theited to evaluations using statistical data such as

results. Section 5 concludes the paper.

input-output (I/O tables) tables. Further, only prefectural-level I/O tables are available throughout

2. DATA

Japan, and these are not suitable for the analysis

2. 1of the impact of narrow river basin areas that may
also suffer extensive ﬂood damage.

Flood simulation analysis data

The ﬂooding area investigated in this study is
the Arakawa basin located in the Tokyo metropol-

On the other hand, there are several examples

itan area. The Arakawa River flows through met-of micro-analysis of damage caused by large-scaleropolitan Tokyo, the central city of the Japaneseﬂoods. For instance, Ikeuchi et al. (2011) estimatedeconomy. It is a first-class river with a maximum

the potential human damage by floods in the To-

width of 2,537 m in Japan. Because it is close to the

kyo metropolitan area by using ﬂooding simulation

central part of the city, many ﬁrms are located indata at the building level using a death estimation

its basin. If this river ﬂoods, it is likely to cause im-model. This dataset was simulated on a 100-meter

mense damage. Further, since lowlands are widelymesh basis, and had the advantage of being able

distributed in the lower reaches of the Arakawa

to identify the location of affected firms within aRiver, areas that are inundated would take time tonarrower range and to accomplish a micro-level

drain, and many firms are expected to be forced

analysis.

to suspend operations for a long period of time

Based on the above context, this study evalu-

in such a situation of flooding. Thus, the initialates the economic impact on the supply chain bydamage caused by the primary disaster is likely tousing ﬂood analysis data and transaction data at theincrease manifold.

level of branch ofﬁces of ﬁrms. The following three

The present investigation employs data for apoints can be mentioned as the distinctive features

ﬂood on a scale of 0.001 AEP (annual exceedanceof this paper: first, the use of flood analysis dataprobability) to analyze potential damage to ﬁrms.

and inter-branch offices transaction data makes

The data were sourced from the results of ﬂooding

it possible to scrutinize the economic impact ofsimulations of the Arakawa River area provided by

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Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions

the Arakawa Downstream River Office, Ministr ynear the break point such that the Soryu ﬂows intoof Land, Infrastructure, Transport and Tourism

Itabashi, Arakawa, Otemachi, Marunouchi, Ginza,(MLIT). The ﬂooding simulation derived from thisand other central areas. The Koto Delta scenario

data is being conducted along the assumed Floodenvisions that the zero-meter zone is widely distrib-

Inundation Area Diagram Drawing Manual (2017)

uted and that there is an area with an inundation

created by the MLIT.

depth of more than 5 m.

This dataset contains 335 scenarios, eachwith a different levee breakdown location. These
data represent 100-meter-mesh figures contain-

2. 2 CORPORATE HEADQUARTERS AND
BRANCH OFFICE DATA

ing information such as breakdown location and

These data were compiled by a major credit

time-series (10 minutes data for one month after

reporting agency of Japan, Teikoku Databank Ltd.

the levee breakdown) data on inundation depth.

(TDB). The dataset encompassed nearly 1.65 mil-This paper applies three scenarios with high dam-lion corporate headquarters and 580,000 branch

age identiﬁed by Ikeuchi et al. (2011) (Fig. 1). To

offices in 2015 and accounted for nearly 90% of

deﬁne the features of each case, scenario Saitama

the 1.87 million corporate enterprises in Japan in-encompasses the largest population in the inunda-

cluding 1.62 million incorporated companies andtion area. Scenario Tokyo comprises the possibility

240,000 other ﬁrms (2016 Economic Census) in allof inundation reaching 5 m or more in some areas

47 Japanese prefectures and spanning all sectors of

Fig. 1

Flood area and maximum inundation depth in the three scenarios

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自然災害科学 J. JSNDS 38 特別号（2019 ）the economy. The resulting information is highly

on I/O tables evinces a high correlation of R=0.9,

comprehensive and includes the ﬁrmsʼ codes, ad-indicating that it is adequate for a comprehensive

dresses, industry type, and sales amounts.

grasp of production activities. In addition, there isa ﬂow of goods and money in the transaction data

2. 3 INTER-BRANCH OFFICE TRANSACTION DATA

between ﬁrms, and the transaction network is evaluated as a directed graph.

Many firms have multiple branch of fices.
Since these data were compiled from transactions

3. METHOD

among headquarters, it was not possible to ascer-First, latitudinal and longitudinal information

tain transactions between branch offices. There-

was used from the Arakawa River ﬂood simulation

fore, the authors of this paper used nearly 332 mil-analysis data and the data on firms and branchlion transaction observations at both headquartersofﬁces to identify the affected ﬁrms based on theand branch offices as developed by Ogawa et al.

spatial join. The deﬁnition of an affected ﬁrm in the(2018). Transaction amounts were distributed tocase of a large-scale flood is an enterprise whose

each branch ofﬁce based on the gravity model thatbusiness activities may be suspended for one ofuses the distances between ofﬁces and number ofmany reasons including power outage and stagna-

employees. Nearly 5 million inter-ﬁrm transaction

tion of supplies.

observations were compiled in 2016 from the TDBThis paper focuses on employees and assumes

headquarters transaction data. These data include

that business activities cannot be continued if em-

transaction information between headquar ters,ployees cannot go to work. However, we do notsuch as the company codes of suppliers and cli-take into account the inundation situation of em-

ents, transaction items, and estimated transaction

ployees homes or the roads used for commuting.

amounts. As shown in Fig. 2, the firmʼs code

According to the Guidance on Analysis of Floodenables network analysis to be performed in asso-

Damage Index (2013) of the MLIT Water and Di-

ciation with data pertaining to the company and its

saster Management Bureau, it is difﬁcult for people

branch ofﬁces.

to walk on foot if the ﬂooding depth exceeds 0.5 m.

A comparison of the transaction amounts ofIf the ﬂooding depth reaches 0.3 m, cars will also

these data with inter-regional transactions based

be unable to move. Therefore, we deﬁne the ﬁrms
in areas with a maximum inundation depth exceeding 0.3 m as affected ﬁrms. Next, we identify thebusiness partners of the affected firms throughthe transaction data on inter-branch ofﬁces and thecompiled data on ﬁrms and branch ofﬁces.
3. 1

SPILLOVER EFFECTS

According to Alber t and Barabasi (2002),
many networks exhibit complex structures with
distinguishing features that define them as scalefree or small-world networks. It is said that everyone in the world is connected by a maximum ofFig. 2Attribute of inter-firm transaction data

six degrees of separation. If the structure of this190

Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions“small-world” network known in sociology is also

it does not become a Tier n＋1 ﬁrm or beyond.

valid for trading networks between companies, theNext, the proportion of Tier 0 to Tier 5 ﬁrms

disaster area would actually extend to include oth-is sought for all ﬁrms in each prefecture and indus-

er regions that are not directly affected and many

try, respectively. The number of ﬁrms in industry pﬁrms are likely to be affected through their busi-

in prefecture q shall be Fpq. The number of ﬁrms inness relationships. Ohnishi et al. (2010) demon-

Tier n in industry p and prefecture q can be written

strate that the inter-ﬁrm transaction network also

as Fpq, Tier and the proportion of Tier n in the ﬁrms

indicates the structure of the small-world network,of industry p, prefecture q, Rpq, Tier is given by equa-

implying that economic impact will indirectlytion (1):nnspread throughout the country through inter-ﬁrm
transaction networks. This paper assumes that all(1)

ﬁrms are linked to each other at the sixth transaction based on the concept of six degrees of separation.

Moreover, there is a flow of objects in thetransaction. Each company can thus be consideredFirst, let the affected ﬁrms be zero-order ﬁrms

as a node and the transaction network can be re-(Tier 0), and the business partners of the affected

garded as a directed graph if the transaction is a

firms be first-order firms (Tier 1). Similarly, thelink. For the purposes of this paper, the direction inbusiness partners of nth-order firms (Tier n) are

which goods and services ﬂow is downstream, andset as (n＋1)th-order ﬁrms (Tier n＋1) (Fig. 3).

the opposite direction is upstream.

The number of zero-order firms will be thenumber of directly affected ﬁrms, and the number

3. 2

AFFECTED TRANSACTION AMOUNTof ﬁrst-order ﬁrms will be the number of business

Estimation of the amount of damage caused

partners of those directly affected ﬁrms. Similarly,

by disasters is important in determining the bud-the second to ﬁfth orders are obtained by sequen-

get for disaster recovery and expediting damage

tial calculation. However, in order to avoid duplica-

insurance payments. In general, damage is calcu-

tion in the identiﬁcation of ﬁrms, Tier n cannot be-lated by assessing direct and indirect monetar ycome Tier n＋1 or beyond. For example, if a Tierloss. This study attends to indirect damage andn−1 customer is Tier n, then, even if it has a busi-

uses inter-branch office transaction data to esti-

ness relationship with a Tier n＋1 ﬁrm or beyond,mate the transaction amounts of affected ﬁrms. In
addition, the affected amount is considered to be
the quantum of transactions lost during the period
when the economic activities of affected ﬁrms are
suspended.
Yang et al. (2018) attempted to calculate theaffected transaction amount using the sales of oneof the annual transactions between branch ofﬁces.
However, it is also necessary to consider the frequency of transactions conducted during the period of business outage due to disasters. Therefore,

Fig. 3

Inter-fir m transaction network ofaffected firms

the average annual transaction frequency of firm
i is determined by dividing total sales Si for one

191

自然災害科学 J. JSNDS 38 特別号（2019 ）year by the sum of the amount of one transaction

time supposes the loss of half the quantum of thethat the ﬁrm has traded with each customer. Next,transactions within the period.

assume that the transaction between ﬁrm i (Tier n)

Next, the affected time shall be the sum of theand its downstream ﬁrms i’ is Lii, and ﬁrms iʼ arebusiness interruption time and half the businessdenoted by {1, 2, 3, ..., Iʼ}. One year has 365 days,

stagnation time and the inundation time (depth

and the average daily transaction amount withof inundation exceeds 0.3 m). Let Ti(iundation) be thedownstream ﬁrms i’ of the ﬁrms i, yii’ is given by

inundation time of affected ﬁrm i, Ti(interruption) be theequation (2):business interruption time, and T i(stagnation) be thebusiness stagnation time. Period of economic dam(2)age Ti(affected) is referred to as affected time, which
is given by equation (3):

In addition, the inundation depth changes inthe time series, and the degree of impact on the(3)company also differs depending on the degree ofinundation. The inundation period is referenced asa daily unit and termed inundation time.

In addition, indirect damage spreads throughAfter being flooded, the company will carr y

the trading network but, as the degree of tradingout recovery activities until it can resume its busi-

increases, the impact is considered to be lessness. During this period, the company loses 100%significant. For Tier 0 firms, it is assumed thatof its trade because it cannot conduct any produc-

the transaction amount loss is multiplied by thetion activities. This period is called business inter-average daily transaction amount by the number

ruption time. Even when the companies are readyof s days. However, for Tier 1 and later, the impact

to resume their business and can engage in pro-is smaller, so spillover effect coefﬁcient K is set as

duction activities, the production capacity is onlya factor that indicates how much production is de-

50% of that before the disaster. This period is called

pendent on the affected ﬁrms. The value obtained

business stagnation time. However, the ﬂooding

by multiplying this amount by the economic dam-analysis data only lasts 28 days. Scenario Tokyoage can be calculated by multiplying the produc-

and Koto Delta do not envisage draining even aftertion amount by K. For example, affected companies

one month, and there are areas where ﬂooding iswill undergo 100% impact, as 100% production will

likely to continue even longer. The inundation peri-stop during the affected time. Spillover effect coef-

od of those ﬁrms is set as 28 days for the purposesﬁcient of affected ﬁrm i, Ki,Tier is given by equationof the present computation.

(4):

In this paper, the affected time is calculated
according to the business interruption time andbusiness stagnation time of the establishments
according to the flooding depth as seen in Table
1 based on the Flood Control Economic Sur vey
Manual (2005).
In addition, business interr uption time istaken to be the period in which the entirety of dealings of the period is lost. The business stagnationo

Table 1 Impact on firms by inundation depthclassification
Inundation
depth (m)Businessinterruption
time (days)

Businessstagnation time(days)

0-0.5

4.4

8.8

1.5-0.99

6.3

12.6

1-1.9910.3

20.6

2-2.99

16.8

33.6

3-

22.6

45.2

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Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions

by spillover effect coefﬁcient Ki,Tiern－1 of Tier n−1(4)
The impact on Tier 1 ﬁrms is the loss in theﬁrm iʼʼ to the total transaction value of Tier n ﬁrm i
be ﬁrm iʼs spillover effect coefﬁcient Ki,Tiern, which
can be expressed by equation (6):share of purchase transactions with affected ﬁrms;
so, for Tier 1 ﬁrms, the spillover effect coefﬁcient(6)is the ratio of the transaction amount with affected

Based on the above, af fected transaction

ﬁrms to total purchasing transactions.
For Tier 1 ﬁrms, let K be the ratio of the transaction amount of a particular company vis-à-vis theamount Δyi of ﬁrm i can be expressed by equation(7):

affected ﬁrms to the total transaction amount. As-(7)sume that the supplier ﬁrm of ﬁrm i is iʼʼ and thatits transaction with ﬁrm i is yii’’ , and ﬁrms iʼʼ are denoted by {1, 2, 3, ..., Iʼʼ}. The affected time of ﬁrm i’’is Ti’’(affected), and spillover effect coefﬁcient Ki,Tier can
1

4. RESULTS
4. 1 COMPARISON OF THE THREE SCENARIOS

be expressed by equation (5):Fig. 4 shows the number of affected ﬁrms in(5).

each scenario, the inundation time for each industry, the business stagnation time, and the business

For Tier 2 and beyond, the impact on ﬁrm i ininterruption time. A total of 44,210 ﬁrms are affect-

Tier n is its transaction with Tier n-1, which has a

ed in the Tokyo scenario and the average affectedbusiness relationship with the affected ﬁrms. The

time is 57 days. The service industry accounts forspillover effect coefficient is the ratio of transac-

the major percentage in comparison to the othertions with Tier 1, which is affected by damage toscenarios. A total of 20,218 affected ﬁrms are cal-all purchasing transactions. For Tier 2 and beyond,culated for the Koto Delta simulation, with an aver-Tier n ﬁrm i, let the ratio of the sum of the value ofage of 50 days of being affected. Scenario Saitamatransactions with each Tier n−1 ﬁrm iʼʼ multiplied

estimates 16,001 affected ﬁrms, with an average of

Fig. 4Situation of affected firms in each scenario

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自然災害科学 J. JSNDS 38 特別号（2019 ）41 days of impact.

sellers than suppliers.

The results reveal similar trends for all threeFig. 6 shows the af fected amount in eachscenarios (Fig. 5). The propor tion of af fectedscenario. Scenario Tokyo shows the highest af-

ﬁrms up to Tier 1 is 20-30% of the total. The ﬁgure

fected transaction amount at 7,424 billion JPY

for scenario Saitama is the lowest at 20% and thewhen companies up to Tier 4 are included. A major

number for scenario Tokyo is the highest at 32%. It

difference in the affected transaction amount is re-is assumed that the majority of business enterpris-corded between the other scenarios and the Tokyoes are located on the Tokyo side of the Arakawa

calculations. For the Saitama and Koto Delta sce-

River.

narios, the affected amount is about 4,000 billion

Additionally, although there is a significant

JPY. However, the difference between the Saitama

dif ference between the three scenarios up to

and Koto Delta projections is small. In terms ofTier 1, the figure for all scenarios after Tier 3 istier, the Tokyo scenario posits a heavier amount ofapproximately 51%. Similar results were obtained

Tier 0 to 1 compared to the other two scenarios.

for distribution by region and industry. Therefore,

However, for the other two scenarios, the weightthe impact caused by the flooding of Arakawa

of Tier 0 to 1 is small, and Tier 2 is found to be thediffers depending on the scenario for companies

most affected.

with direct dealings with affected firms, but theThis projection seems valid because manydifference is not as signiﬁcant for indirectly related

large ﬁrms would be affected by the Tokyo scenar-

companies.

io and their transactions are many. Their transac-

Also, the evaluation of the spillover effect bytion amount is also very sizeable. In the other two

transaction direction yields the outcome whereby

scenarios, the affected ﬁrms are mostly small andthe ﬁgure is between 11% and 20% when ﬁrms up

medium-sized, and they do not have as many busi-

to Tier 1 are included. Once again, scenario Tokyo

ness partners as the larger companies. A rise inreports the largest effect at 20%. The number in-

the number of transactions also signals an increase

creases to 31-36% up to Tier 2 and to 40-41% whenin the volume of money that is transacted by enter-companies up to Tier 5 are included. In addition,prises.

the downstream impact is estimated to be greater

With regard to industries (Fig. 7), the whole-than upstream because firms tend to have moresale, retail, food, and manufacturing sectors are
signiﬁcant for the Saitama and the Koto Delta sce-

Fig. 5

Propor tion of af fected firms in eachscenario

Fig. 6Affected amount for each tier in eachscenario

194

Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions

Fig. 7

Fig. 8Affected amount for each industry in each scenario

Proportion of firms by prefecture in each tier in the Tokyo scenario

narios and account for the major part of the trans-

at prefectures (Fig. 8), Tier 0 accounts for about

actions. However, the proportion of the ser vice

2% of the total, but the inclusion of Tier 1 takes

industry is also observed to be high for the Tokyo

the figure to 31%. It can be seen that the ratio of

scenario because the service industry is concen-

firms up to Tier 1 is higher in locations closer totrated in the downtown Tokyo area and more trans-

the projected disaster area such as Chiba, Saitama,actions are associated with them here compared toand Gunma Prefectures. In addition, the percent-scenario Saitama and Koto Delta.

age of impact in major cities in each area such as
Osaka, Aichi, Miyagi Prefectures, etc. is high, and

4. 2

SCENARIO TOKYOthe influence on these areas is considered to be

Of the three scenarios, the impact is the larg-

larger than in other locations. When connections

est in the Tokyo simulation. A detailed analysis of

up to Tier 3 are included, the figure increases toscenario Tokyo is presented below. First, looking

51% of the total, but changes only minimally after

195

自然災害科学 J. JSNDS 38 特別号（2019 ）Fig. 9Affected transaction amount in each prefecture in the Tokyo scenario

this tier, with about 51% of ﬁrms and branch ofﬁces
being affected up to the inclusion of Tier 5 transactions. The remaining ﬁrms, or 49% of the total, arethought to form a large network that is external tothis inter-ﬁrm transaction network.
Fig. 9 shows the af fected amount in eachprefecture. The af fected transaction amount is
7,424 billion JPY when companies up to Tier 5 areincluded in the Tokyo scenario. It can be seen thatamounts are relatively high in prefectures locatedin areas close to the affected areas such as Chiba,

Fig. 10 Propor tion of af fected firms in eachindustry in the Tokyo scenario

Saitama, and in other major cities in other areas
such as Osaka, Aichi, and Miyagi Prefectures.

small.

Next, in terms of industr y, over 50% of theﬁrms up to Tier 2 are engaged in the ﬁnance, insurance, electricity, gas, water, or heating sectors

4. 3 COMPARISON WITH THE RESULTS
OF PREVIOUS STUDIES(Fig. 10). It is noteworthy that these two types

The Japan Society of Civil Engineers (2018)of businesses are prevalent across regions. The

estimates the economic damage of a large-scale

next-highest sectors are transportation, commu-

flood in metropolitan Tokyo using the flood anal-

nication, and manufacturing, with the number ofysis data simulated from the conditions of floodﬁrms up to Tier 5 in these industries representing

hazard maps (2016). The levee break point is as-nearly 70% of the total. The real estate industry is

sumed to be the same as the one presumed for thisthe lowest, with the number of ﬁrms up to Tier 5

research: the 21-kilometer Arakawa right bank.

representing less than 30% of the total. In this case,

The scenario envisaged by the Japan Society of

it is pertinent that the real estate industr y often

Civil Engineers is also similar to the circumstancestransacts with local ﬁrms. The agriculture, forest-

described in this paper, and it also targets the ﬂood

ry, and ﬁsheries sectors account for less than 40%

with a scale of 0.001 AEP.

of the total number of ﬁrms up to Tier 5, indicating

The above investigation empirically estimat-that the impact on these industries is relatively

ed the economic damage caused by stagnation of

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Estimation of the economic impact of urban flood through the use of big data on inter-branch office transactions

economic activity based on the damage situation

ed period is determined according to the Floodof past floods and computed the damage to theControl Economic Sur vey Manual (2005). It is

production facilities and the transportation infra-

regarded as beginning from the actual time of thestructure. Specifically, using the spatial comput-

direct effect on companies up to the full resump-able general equilibrium (SCGE) model, previous

tion of work processes. The affected period for thestudies estimated the amount of decrease in GDP

three scenarios considered in this paper is about

in the affected area due to disasters. The results

two months on average up to approximately threeprojected that economic damage of 26 trillion JPY

months.

would be incurred in 14 months.
The amount is very large compared to the es-

5. CONCLUSION

timated amount in this study. The reasons for theThis paper used GIS data on inter-branchfollowing differences between the research meth-office transactions and flooding analysis data to

ods used by the present study and previous studies

estimate the economic impact of urban flooding

are considered. The first is that the dataset usedin Tokyo on the supply chain. The investigation

is different. Previous studies have used the SCGE

revealed the following results. First, many firms

model, which uses a social accounting matrix withand branch offices in areas close to the affected

two regions and two household-income levels as

areas have business relationships with the affected

the database. It is built to estimate the benefit ofﬁrms, so the impact on the area is signiﬁcant. Sec-

each region and household level from traffic in-ond, ﬁrms connected to the directly affected ﬁrms

frastructure investment. In other words, the dataaccount for 51% of the total if business partners up

used are statistical data between wide areas. In

to Tier 5 are included; thus, many ﬁrms might bethis paper, inter-branch ofﬁce transaction data are

indirectly affected even if they do not report anused, and analysis is conducted on a branch-ofﬁce

apparent business relationship with the directly

basis. Flood damage is different depending on theaffected ﬁrms; also, in industries such as ﬁnance,

location of the damage; by grasping the damage

insurance, electricity, gas, etc., nearly 70% of ﬁrms

amount for each company, it can be accurately esti-

are related to affected firms, suggesting that themated.

impact on the entire industr y is significant. In

Second, in previous studies, the directly af-

addition, the impact is projected to be greater on

fected area was considered as the area of analysis.

downstream ﬁrms than on upstream ﬁrms in terms

In this paper, not only the directly affected area butof transactional direction; further, among the three

the whole of Japan is taken as the affected area.

scenarios, the Tokyo scenario is the most affected

Thus, the indirectly affected branch ofﬁces in Ja-

in economic terms at 7,428 billion JPY, which cor-

pan are taken into consideration up to the ﬁfth lev-

responds to about 1.3% of the Japanese GDP.

el of business partners of directly affected firms,

Future scholarly work by the authors of thismaking it possible for the present investigation topaper will incorporate three additional consider-determine indirect damage more accurately. In ad-ations. First, the roles of indirectly affected ﬁrms

dition, in previous studies, the period of economic

in each area will be examined by using the concept

damage is set to 14 months with reference to theof centrality, including betweenness centrality.

past disaster. However, it does not specify the basis

Second, only human factors were set as a condition

for the choice of this time span, and no further

for the cessation of business activities in this paper.

references are provided. In this paper, the affect-

However, the halting of work may depend on a

自然災害科学 J. JSNDS 38 特別号（2019 ）variety of factors in reality. In order to better estimate the economic impact, it is thus necessary toconsider disaster conditions based on past ﬂoods.
For example, physical distribution of goods may be
severely affected by the damage or congestion ofa road or a lifeline. Third, the impact of the return
rate of employees on the production value will be
evaluated using population distribution data.
The present study contributes to extant scholarship by presenting a method of estimating the
economic damage of a natural disaster at the microlevel by combining inter-branch ofﬁce transaction
data and ﬂood simulation analysis data. This model
may also be successfully applied to cases otherthan the Arakawa scenario described in this paper.
By establishing a method of analyzing the spillover
effects of the economic damage caused by ﬂoods
using micro-level transaction data between firms
and branch offices, the present investigation enables a detailed scrutiny that takes regional characteristics into consideration. Firms and branch ofﬁces may use the calculations made by this paper
as basic data when considering their BCP for ﬂood
damage.

ACKNOWLEDGEMENTS
We would like to thank Teikoku Databank,Ltd. and the Arakawa Downstream River Office,Ministr y of Land, Infrastructure, Transport andTourism for providing the data, and Mr. Yuki
Okuma, Mitsubishi Research Institute, Inc. for his
valuable advice. We also would like to thank Enago(www.enago.jp) for English language editing.

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（投 稿 受 理：2019年 4 月 5 日
訂正稿受理：2019年 7 月 3 日）旨

本稿では，東京都に位置する荒川の流域において大規模水害が発生した場合に，大規模な企業間取引の GIS データを用いて，水害がサプライチェーンに与える経済的影響を定量的に推定した。まず，荒川流域の氾濫シミュレーション解析データを用いて，被害を受けて事業継続ができない被災企業を特定した。次に，大規模取引ネットワークデータを用いて被災企業と取引関係によって繋がっている企業を特定し，その産業構造についてネットワーク分析した。最後に，被災区域及び被災区域外の取引先の影響される取引金額について，被災企業の浸水期間（被災期間）や，被災企業の取引先の次数が増加するにつれ影響が小さくなることを考慮し，推計を行った。分析の結果，被災地の企業の取引先は全体の20〜30 ％であり， 5 次取引先までを含めると全体の約50 ％であり，被災地以外でも多くの企業が影響を受ける可能性があることがわかった。

自然災害科学 J. JSNDS 38 特別号（2019 ）

地域別では，東京や大阪などの大都市の受ける影響が大きく，産業別では，金融，運輸業への影響が大きく，農林水産業への影響が小さいことが明らかになった。また，生産額に関しては全国で最大で GDP の約1.3 ％の取引金額に影響を与える可能性があるとわかった。

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