Discussion Paper No.93
Impacts of constructing flood control dams on
industrial investments in downstream regions in
the case of Shiga Prefecture
Takeru Sugasawa

January, 2019

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Impacts of constructing flood control dams on industrial investments in downstream
regions in the case of Shiga Prefecture
Takeru Sugasawa†

Abstract
There is the possibility that disaster risks prevent regions from developments through decreases inincentive to investments. We investigate the impacts of constructing flood control dams on location
decision of industries in their downstream areas. To analyze the relationship, we focus on four flood
control dams and 50 municipalities in Shiga prefecture, a suburban area located in middle-westernJapanese main island, between 1965 and 2000. We conduct event study analyses, and find that
constructing the flood control dams significantly increased the number of manufacturing plants ofindustries requiring large amount of physical capital in downstream areas. We consider that sincephysical capital requires long periods to recover their establishment costs, manufacturing categorystrongly depending on physical capital would sensitively react to the reduction of flood risks. We alsofind that the number of commerce stores increased in the dams’ downstream areas. However, the effects
required more than 10 years to be observed after the completion of flood control dams. We interpret thatcommerce industry could receive positive effects only after increases in manufacturing plants, because
commerce industry receive the effects of decreases in flood risks through increases in manufacturing
plants’ demand for materials or tools to do their production activities.

1. Introduction
Flood disasters have caused serious damages to human economic activities. For instance, Toyota
plants in Thailand experienced a large-scale flood in July 2011, and they lost production opportunities ofapproximately 260,000 cars.

1

In June 2018, floods in Guanajuato State in middle Mexico hit Honda

plants, and monetary damages accumulated of more than 50 billion yen.

2

Ways to reduce natural

disaster risk and damages remain an important theme.

†

Graduate School of Economics and Management, Tohoku University (tel: 090-8478-3247, e-mail:takeru.sugasawa@gmail.com)

1
2

According to Toyota’s company history, 75 years of TOYOTA.
We refer to the article by Asahi Shimbun in August 2018.

1

Disaster risk is known to affect industrial investment activities. Gourio (2012) focuses on the impactsof natural disasters on regional economic activities using the real business cycle model and suggests thatlarger disaster risk reduce the investment amount.
In terms of capital structure, disaster risk decrease expectations for physical capital’s return andprevent firms from investing in physical capital with low mobility (Skidmore and Toya: 2002). To
determine the reason for the decreases in investments in physical capital, Kahn (2005) focuses on 73
countries between 1980 and 2002. He finds that physical capital received larger damages than humancapital from natural disasters, especially in developed countries. Since physical capital requires long
periods to recover establishment costs, higher disaster risk would decrease industrial agents’ incentives
for investments in physical capital. From the above discussions, public investments decreasing disasterrisk may affect regional investment activities and industrial locations.
In a study focusing on the effects of public investments aiming to reduce regional disaster risk,
Boustan, Kahn and Rhode (2012) investigate the impacts of floods and hurricanes in the United States
between the 1920s and the 1930s. They find that the areas where the New Deal’s public investments wereconducted to reduce flood risk achieved higher population growth than the areas hit by hurricanes. From
the discussion, we consider that economic agents consider the reduction in disaster risk when deciding
their locations. However, they do not focus on the impacts of flood control dams on the regional
industrial location and investment activities.
In this study, we investigate the impacts of constructing flood control dams on industrial location
decisions in their downstream areas by using Shiga Prefecture’s municipality-level data between 1965
and 2000. In the period, four flood control dams were constructed in Shiga Prefecture, and the industrial
locations in their downstream areas received the effects of decreases in flood risk.
Municipalities in Shiga Prefecture are appropriate for our empirical study for two reasons. First,
since the prefecture is landlocked and almost all of its border is composed of mountains, municipality
geographical characteristics have only small differences. Second, almost of the municipalities arecontained in downstream areas of completed or suspended control flood dams. From the characteristics,
we assume that the municipalities in Shiga Prefecture are different only in the periods of the constructionof dams located in their upstream area. Utilizing the difference in their periods of constructions, we
conduct event study analyses.
From our analyses, we find that the reduction in flood risk increased the number of manufacturing
plants in downstream areas, and the magnitude is larger in the basic materials as well as the processing
and assembling categories, both of which require large amounts of physical capital. For this reason, we
consider that manufacturing plants in categories requiring larger amounts of capital tend to have higher2

price elasticity in response to disaster risk and more sensitively react to reduction in flood risk thancategories requiring smaller amounts of capital.
We also find that plants in the basic materials category, requiring the largest amounts of physicalcapital of the three categories focused on in the study, increased investments in tangible capital after
constructing flood control dams. However, their number of employees was not affected by the reductionin flood risk. For this reason, we interpret that since human capital has higher mobility than physicalcapital, manufacturing plants tend to invest in human capital compared to physical capital even with thedisaster risk. Therefore, constructing flood control dams would not have significant effects on the numberof employees in the downstream areas. Our analyses suggest that economic agents decide industrial
locations considering disaster risk, and public investments reducing disaster risk would attract industries.
The structure of this paper is as follows. Section 2 describes the previous studies relating to ourinvestigation. Section 3 explains the characteristics of Shiga Prefecture, our empirical target, and theflood control dams in it. Section 4 describes our estimation models and variables. Section 5 describes theresults of the event study and interpretation. Section 6 is the conclusion.

2. Previous Study
This chapter describes previous studies relating to the impacts of disaster risk on investment activities
and industrial location decisions. Skidmore and Toya (2002) examine the impacts of natural disasters on
countries’ capital, productivity and economic growth by focusing on 89 countries and find that countries
frequently experiencing natural disasters tend to increase investments more in human capital than inphysical capital. In terms of impacts of flood disasters, Leiter, Oberhofer and Raschky (2009) focus on
the impacts of European flood disasters occurring between 1950 and 2007 on capital structure. They findthat firms located in the hit areas tend to prefer intangible assets to tangible ones.
Although many studies investigate the relationship between natural disasters and industrial investment
activities, almost all of them focus only on the effects of happened disasters, not on the effects ofcountermeasures to natural disasters. Public investments decreasing disaster risk may affect industrial
investment activities and locations. As a previous study focusing on the effects of public investments
aiming at reducing regional flood risk, Boustan, Kahn and Rhode (2012) investigate the impacts of floods
and typhoons in the United States between the 1920s and the 1930s. They find that the areas where the
New Deal’s public investments were conducted to reduce flood risk achieved higher population growththan the areas hit by typhoons, and the effects are observed in long-term.

3From the discussion, economic agents would decide their locations or investment activities
considering the regional disaster risk. However, there are no previous studies focusing on the impacts offlood control dams on regional industrial locations and investment activities.

3. Shiga Prefecture and Flood Control Dams
To investigate the relationship between reduction in flood risk and manufacturing activities, we focus
on the case of Shiga Prefecture and four flood control dams in it. In this chapter, we describe thecharacteristics of Shiga Prefecture relating to flood risk.

3.1. Shiga prefecture and Lake Biwa
As the estimation sample, we focus on municipalities in Shiga Prefecture between 1965 and 2000. In
this section, we describe the geographical and industrial characteristics of Shiga Prefecture.
Shiga Prefecture is a landlocked prefecture in the Kinki region located in central/western of Japan’s
main island. As an important geographical characteristic, Shiga Prefecture contains Lake Biwa, thelargest freshwater lake in Japan. One hundred nineteen rivers flow into Lake Biwa, and only one river,
Yodo River, flows out from it. Figure 1 is a map of Japan showing the location of Shiga Prefecture in red
and Lake Biwa in blue.

4

From those geographical characteristics, Shiga Prefecture has experienced many and serious flood
disasters. Since only the Yodo River flows out from Lake Biwa, flood disasters easily occur if sedimentaccumulates along the river’s bottom. In terms of the seriousness of the flood disasters, the large-scale
flood in 1896 overwhelmed all of the central area of Otsu City and approximately 80% of Hikone City,and the flood water did not recede until approximately three months later according to the report by Shiga
Prefecture. Table 1 shows the human and industrial damages as well as main cities hit by each flood in
Shiga Prefecture between 1895 and 2013. The period included 34 floods with 2,264 human casualties and
227,401 damaged buildings.

3

Figure 2 shows the number of floods each municipality experienced

between 1895 and 2013. From the map, we can observe that sites hit by floods are distributed all over
Shiga Prefecture. The large risk of flood disasters would have prevented industries in Shiga Prefecturefrom investments in capital weak in disasters, such as physical capital.
Shiga Prefecture is suitable for our analyses because of the uniform geographic characteristics amongits municipalities. As discussed above, Shiga Prefecture is landlocked, and almost all of the prefecturalborder consists of mountains. These conditions mean industrial structures differ little as a result of thenatural characteristics shared among municipalities, which enables us to clearly observe the impacts ofconstructed flood control dams on their downstream areas.
In terms of industrial characteristics, manufacturing industries have prospered in Shiga Prefecture. In
2015, products from manufacturing industries comprised 41.1% of GDP of Shiga Prefecture, the largest
share in Japan’s 47 prefectures.

4

Lake Biwa plays a role as a large-scale source of industrial water, andmunicipalities located in surrounding regions receive its advantages. Since manufacturing industries tend
to require large amounts of water resources for cooling and cleaning machines, manufacturing plants are
attracted to surrounding (or downstream) areas of Lake Biwa to receive the benefits of better access to therich industrial water.

3

Human casualties consist of 208 deaths, 2,045 injured and 11 missing. Building damages are divided
by 2,687 complete destructions, 15,829 partial destructions, and 116,458 inundations.

4

Using data from the Report on the Prefectural Account by the Cabinet Office (2015).

5

6

3.2. The flood control dams in Shiga prefectureThis section describes the flood control dams in Shiga Prefecture. An important public work
conducted in Shiga Prefecture is the Lake Biwa Comprehensive Development (LBCD) project. It is alarge-scale and multipurpose project led by the national and Shiga Prefecture’s governments to develop
surrounding regions of Lake Biwa between 1972 and 1997. The project contained many kinds of works,
such as constructing dams to reduce floods damages, promoting agriculture and manufacture in
surrounding regions, and protecting the natural environment of Lake Biwa. The total monetary costs ofthe project were approximately 1.9 trillion yen. Of that, Shiga prefectural and municipal governments
bore approximately 1.3 trillion yen, the national government bore approximately 430 billion yen, and therest was borne by organizations receiving benefits from the project.
As the works aiming to reduce disaster damages, the LBCD project included the construction of sevenflood control dams and 59 small barriers against landslides. In terms of flood control dams, however, only7

four of the seven dams were completed, and the rests were suspended in the 21th century. Therefore,
some municipalities are not yet protected by flood control dams, and we can observe the impacts of thedams by comparing the downstream areas of the completed and the suspended dams. The small barriers
against landslides are not the focus of this study, because we focus on the effects of reducing the risk offlood disasters. Table 2 describes the period of construction of each flood control dam and the numbers
and names of municipalities located in the downstream areas.

Figure 3 shows the locations of flood control dams in Shiga Prefecture and their downstream areas.
The red areas are municipalities contained in downstream areas of flood control dams. We can observethat municipalities in downstream areas of completed or suspended flood control dams distribute
throughout Shiga Prefecture. We consider that no selection occurred for the public investment’s decisionin any planned or completed construction of flood control dams, only in regions frequently hit by flood
disasters. From Figures 2 and 3, we can observe many municipalities have experienced many flood
disasters, but these disasters are contained in downstream areas of suspended flood control dams. From
the above discussions, we assume that the location of each dam and the timing of its construction orcompletion were not selected.

8

The flood control dams have reduced flood damages in the downstream areas. For instance,
Ishidagawa Dam, which began operation in 1969, prevented floods in the case of the season’s 23rd
typhoon in 2004 by controlling for inflows of water downstream. In this case, the dam controlled
approximately 1,133,000 tons of water, and were the Ishidagawa Dam not in existence, approximately108
tons of water would have overflowed into the downstream areas. 5 In addition, according to Table 1,
which describes damages from each flood in Shiga Prefecture, we can observe the strong decreases inhuman casualties and building damages by flood disasters after 1960s, the period when flood control
dams began to be operational. In terms of numerical value, approximately 99.2% of human casualties and5

The reference is from the report by Shiga Prefecture in 2004.

9

97.9% of building damages concentrate in the period before 1970. From this data, we consider that theflood control dams in Shiga Prefecture decreased the risk of flood disasters in their downstream areas.
We consider that manufacturing industries requiring large amounts of physical capital sensitively
react to a change of the disaster risk. Manufacturing industries have invest heavily in physical capital withlow mobility to develop new products and improve productivity. Therefore, manufacturing industries
would be affected by the constructed flood control dams in Shiga Prefecture.
Figure 4 shows the transition of the number of manufacturing plants across Japan, prefectures in the
Kinki region without Shiga Prefecture, and Shiga Prefecture.6 Each value is divided by the value in 1980.
From the figure, we can observe that although the number of manufacturing plants in Shiga Prefectureappears similar to Japan as whole and the other prefectures in the Kinki region before 1990, its growth
rates become larger than the other two categories between 1990 and 2000. Therefore, we consider that the
public investments aimed at reducing disaster risk activated manufacturing industries in Shiga Prefecture,and they received an advantage compared to the other regions. That result would contribute to theprosperity of manufacture in Shiga Prefecture, with the largest share of manufacturing products to total
GDP among Japanese prefectures.

Regional governments may decide locations of manufacturing plants through establishing industrial
parks. We check whether regional governments in Shiga Prefecture established industrial parks especiallyin downstream areas of completed flood control dams. If manufactural plants location are decided by

6

The Kinki region comprises Mie, Shiga, Osaka, Kyoto, Hyogo, Nara and Wakayama prefectures, andwe refer to the Census of Manufactures by the MEIT of each year to observe the number of their
manufacturing plants.

10

prefectural government, we cannot observe clear effects of flood risk reduction on economic agents’locations. Shiga Prefecture Land Development Corporation is a public agent aiming to expand public
lands through establishing industrial parks in Shiga Prefecture. Figure 5 shows the locations andestablishment periods of industrial parks in Shiga Prefecture by the corporation between 1960 and 2000.
We observe that industrial parks were established in not-downstream areas of flood control dams and theestablishment pace did not largely change even after completion of the dams. From the fact, we consider
that Shiga Prefecture did not attempt to especially develop the downstream areas of flood control dams.

From the above discussions, the municipalities in Shiga Prefecture are desirable for our event study
analyses from the perspectives of a uniform geography, the non-selection of location and completion
timing of control flood dams, and the holding of prosperous manufacturing industries requiring large
amounts of physical capital weak in disasters. In the next chapter, we describe our estimation model ofthe event study and data we use.

4. Empirical Strategy and Data
4.1. Empirical strategy
We create panel data and estimate the impacts of constructing flood control dams on industrial
locations in downstream regions, controlling for the time invariant characteristics of municipalities. Since
the timing of constructions differs by dam, we adopt an event study model. The estimation model is asfollows:

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NumberPlantsit = α𝑖𝑡 + β𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕𝒊+ η𝑖 + σ𝑡 + ε𝑖𝑡 .

(1)

NumberPlantsit is a variable indicating the number of manufacturing plants in municipality i in year t.
We define 𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 is the vector of dummy variables that become one if municipality i iscontained in the downstream area of each flood control dam. 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕𝒊 is the vector of year
dummies which becomes one if the construction of each flood control dam is completed in year t.
Therefore, the product of 𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 and 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 becomes one only when the constructionof the dam located upstream of municipality i is completed. Since no municipalities in Shiga Prefecture
are contained in multiple downstream areas of dams, the product is only zero or one. η𝑖 indicates themunicipality fixed effects. σ𝑡 indicates the year fixed effects. ε𝑖𝑡 is the error term, which comes fromthe time variant characteristics of municipality i.

4.2. Data
This section describes the data used in our analysis. First, we clarify each dam’s downstream area.
The definition of municipality by the Japanese government is the smallest unit of administrative
districts, and it is composed of cities, towns, villages, and specified districts.

7

Shiga Prefecture has 50

municipalities composed of 7 cities, 42 towns and one village in 2000. In 1960, Shiga Prefecture had an
additional three towns, but they were merged with other cities. We control for the merges by adding eachvariable of merged municipalities before the merges.
We define the downstream areas of flood control dams as municipalities through which the riverpasses where the flood control dams were constructed. Between 1965 and 2000, Shiga Prefecture saw theconstruction of seven flood control dams, three of which were completed in the period.
As NumberPlantsit , we adopt the natural logarithm of the total number of manufacturing plants, thenatural logarithm of the number of manufacturing plants with fewer than 299 employees, and the natural
logarithm of the number of manufacturing plants with more than 300 employees. To obtain data relating
to the number of manufacturing plants in each municipality and year, we refer to the Census of
Manufacturers released by the Ministry of Economy, Trade and Industry.
Table 3 represents the summary statistics and describes the number of manufacturing plants divided
by the scale of employees they hire. The sample is classified by municipalities that are downstream areasof the flood control dams completed between 1965 and 2000, that are not downstream areas and that were7

In 2012, 1,747 municipalities existed in Japan. These comprised 786 cities, 754 towns, 184 villages,
and 23 districts in the Tokyo metropolitan area.

12intended to be downstream areas but the project was suspended.

8

From the table, we can observe that themean and standard deviation of each variable show similar values between downstream areas ofcompleted and suspended flood control dams. Therefore, we consider that flood control dams locatedonly in upstream areas with evolved manufacturing industries had no tendency to be completed.

5. Results
This section describes the results of our estimation by the event study. Table 4 shows the estimation
results.
In the table, columns (1) – (3) use all of the municipalities in Shiga Prefecture as the estimation
sample, and columns (4) – (6) are the results of estimations adopting only the municipalities contained indownstream areas of completed flood control dams and downstream areas of suspended dams as the
sample. If we compare the municipalities located in downstream and not downstream areas of flood
control dams, the results may reflect heterogeneities other than the existence of the dams in their
upstream areas. For instance, a project constructing dams could target areas of regional importance forpolitical or economic reasons. To control for the heterogeneities, we conduct an event study analysis only
using municipalities in downstream areas of completed and suspended flood control dams. As statedabove, Table 3 shows that downstream areas of suspended dams have more similar values of eachdependent variable than municipalities that are not in downstream areas of the dams.
First, we describe the results of columns (1) – (3) using all of the municipalities in Shiga Prefecture as
the estimation sample. Column (1) adopts the natural logarithm of the number of manufacturing plants as
the dependent variable, and the coefficient is positive with one percent significance. The magnitude isthat constructing flood control dams increased the number of manufacturing plants by approximately 24.1

8

The downstream areas of suspended flood control dams are also contained in areas that are notdownstream.

13

percentage points in the downstream areas. Column (2) uses the natural logarithm of the number ofmanufacturing plants with fewer than 299 employees as NumberPlantsit . The coefficient is positive
with one percent significance. The magnitude is that flood control dams increased manufacturing plants
with fewer than 299 employees by approximately 23.8 percentage points in the downstream areas.
Column (3) shows the result of the estimation using the logarithm of the number of manufacturing plants
with more than 300 employees, and its coefficient is positive and insignificant. This result suggests thatthe construction of flood control dams does not have significant effects on the number of manufacturing
plants with more than 300 employees in the downstream area.
Columns (1) and (2) are consistent with our hypothesis that constructing flood control dams reduces
the risk of flood and promotes the entry of new plants in downstream regions. On the other hand, column(3) does not support the hypothesis. According to the Survey of Factory Location Trends by the MEIT
(2001), the average number of employees of newly established manufacturing plants was approximately
52 in 2000. From the fact, we consider that the establishment of manufacturing plants with more than 300
employees occurs rarely even in regions with small disaster risk, and they were not significantly affectedby constructed flood control dams.
Furthermore, we focus on the results of columns (4) – (6). Column (4) adopts the natural logarithm of
the number of manufacturing plants as the dependent variable, and the coefficient is positive with one
percent significance. The magnitude is that constructing flood control dams increased the number ofmanufacturing plants by approximately 29.0 percentage points in the downstream areas. Column (5) uses
the natural logarithm of the number of manufacturing plants with fewer than 299 employees as
NumberPlantsit . The coefficient is positive with one percent significance. The magnitude is that flood
control dams increased manufacturing plants with fewer than 299 employees by approximately 28.8
percentage points in the downstream areas. Column (6) shows the result of the estimation using thelogarithm of the number of manufacturing plants with more than 300 employees, and its coefficient is
positive and insignificant. From the results, we consider that constructing flood control dams had positive
impacts on the number of manufacturing plants in the downstream areas.

14

As a way of investigating the impacts of flood risk’ reduction on manufacturing industries, theamount of the impacts on industries may differ by their investment tendencies. To investigate the effects
of manufacturing industries’ investment activities on industries’ reactions to the constructed flood control
dams, we divide manufacturing industries and compare the effects they received from reduction in floodrisk.
The Japanese Census of Manufactures classifies 21 types of manufacturing into three categories: thebasic materials, the processing and assembling, and the life-related industries.

9

The basic materials

industry is composed of industries manufacturing products used by other manufacturers such as iron, coal,
lumber and paper. The processing and assembling industry is composed of industries manufacturing
processing products such as cars, televisions and clocks. The life-related industry is composed ofindustries related with necessities of human living such as food, clothes and furniture. The amount of
capital required to do their production activities differs among manufacturing categories. Table 5 shows
the number and share of manufacturing plants categorized by their manufacturing categories and amountsof capital in 1998 in all of Japan.

10

From the table, we can observe the tendency for the basic material

category to consistently occupy the largest share among classes of manufacturing plants holding capital

9

The Census of Manufactures by the MEIT classifies manufacturing types into three categories: basic
materials industry (manufacture of lumber and wood products; manufacture of pulp, paper and paper
products; manufacture of chemical and allied products; manufacture of petroleum and coal products;
manufacture of plastic products; manufacture of rubber products; manufacture of ceramic, stone and clay
products; manufacture of iron and steel; manufacture of non-ferrous metals and products; andmanufacture of fabricated metal products), processing and assembling industry (manufacture of general
purpose machinery; manufacture of electrical machinery; manufacture of transportation equipment; andmanufacture of high precision machinery), and life-related industry (manufacture of food; manufacture ofbeverages, tobacco and feed; manufacture of textile mill products; manufacture of furniture; printing;manufacture of leather tanning and leather products; and miscellaneous manufacture).

10

We refer to the Census of Manufactures (1999) by the MEIT to obtain the data.

15

more than 30,000,000 yen, and the share of the life-related category is the smallest among the three
categories. We consider that manufacturing plants in categories requiring larger amounts of capital havehigher price elasticity to disaster risk and more sensitively react to reduction in flood risk than categories
requiring smaller amounts of capital.

To investigate the difference of effects from constructing flood control dams among industrial
categories, we conduct an event study using an estimation model as follows:
NumberPlantsijt= α𝑖𝑡 + β𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊
× 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕+ γ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × Basic𝑗+ θ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × Processing j+ φ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × LifeRelatedj + ηi + σ𝑡 + ε𝑖𝑡 .
(2)
NumberPlantsijt is a variable indicating the number of manufacturing plants in industry category j,
in municipality i, in year t. Basicj is a dummy variable that becomes one if industry j is a basic materials
industry. Processing j is a dummy variable that is one when industry j is a processing and assemblingindustry. LifeRelatedj is a dummy variable that becomes one if industry j is a life-related industry. We
omit miscellaneous manufacturing industries from the life-related category and regard it as the baseline of
our results, and we represent the impacts of constructing flood control dams on miscellaneous
manufacturing industries by β in the estimation model. Table 6 shows the summary statistics of thesample.

16

Table 7 describes the results of the estimation analyzing the difference in the impacts of constructing
flood control dams on the number of downstream areas’ manufacturing plants among manufacturingcategories. As the dependent variables, columns (1) – (2) adopt the natural logarithm of the number ofmanufacturing plants, columns (3) – (4) use the natural logarithm of the number of manufacturing plants
with fewer than 299 employees, and columns (5) – (6) use the logarithm of the number of manufacturing
plants with more than 300 employees. Columns (1), (3) and (5) show the results of estimations that do not17control for the heterogeneities coming from manufacturing types, and columns (2), (4) and (6) control forthe heterogeneities by using cross-terms.

11

The coefficient of column (1) is negative and insignificant. The result suggests that constructing
flood control dams does not affect the number of manufacturing plants. In column (2), the coefficient of
the cross-term is negative with five percent significance, that of the cross-term using the basic materials
category dummy is positive with one percent significance, that of the coefficient of the cross-term using
the processing and assembling category dummy is positive with 10% significance, and that of thecross-term using the life-related category dummy is positive and insignificant. The results suggest that the
number of manufacturing plants in the basic materials and in the processing and assembling categories
increased after completion of the dams, but those in the life-related category did not see any effects fromdam completion. The magnitude is that the construction of flood control dams increased the number ofmanufacturing plants in the basic materials category by approximately 26 percentage points indownstream areas. In the processing and assembling category, reduction in the risk from flood increased
the number of plants by approximately 48.5 percentage points. In addition, the number of plants inmiscellaneous manufacturing industries decreased by approximately 30.0 percentage points after
constructing flood control dams in downstream areas.
Columns (3) and (4) show basically the same results as columns (1) and (2). In column (3), thecoefficient of column (3) is negative and insignificant. The coefficients of column (4) show that thecoefficient of the cross-term is negative with five percent significance, that of the basic materials category
dummy is positive and one percent significant, and that of the cross-term using Processing j is positive
with 10% significance. The coefficient of the cross-term with the life-related category dummy is positiveand insignificant. In basic materials manufacturing, the magnitude suggests that flood control dams
increased the number of manufacturing plants with fewer than 299 employees by approximately 26.0
percentage points in downstream areas. On the other hand, the number of plants with fewer than 299
employees in the processing and assembling category increased by approximately 51.6 percentage points
after the completion of flood control dams. The number of plants in miscellaneous manufacturingindustries with fewer than 299 employees decreased by approximately 31.2 percentage points after
constructing flood control dams in downstream areas.
The coefficient of column (5) is positive and insignificant. The result suggests that constructing flood
control dams does not affect the number of manufacturing plants with more than 300 employees. The

11

In columns (1), (3) and (5), manufacturing category dummy variables, Basicj , Processing j ,LifeRelatedj become zero in estimation model (3).

18

result suggests that constructing flood control dams tends to increase the number of large-scale
manufacturing plants. In column (6), the coefficient of the cross-term containing Basic𝑗 is positive andinsignificant, and that of the cross-term with the processing and assembling category dummy is positive
with 10% significance. In addition, the coefficient of the cross-term with LifeRelatedj is negative andinsignificant. The results suggest that the construction of flood control dams increased the number ofmanufacturing plants with more than 300 employees in the processing and assembling category. In thebasic material and life-related categories, the number of manufacturing plants with more than 300
employees was not affected by the flood control dams. The magnitude is that flood control dams
increased the number of manufacturing plants with more than 300 employees of processing andassembling category by approximately 2.72 percentage point in downstream areas.

The results in Table 7 consistently suggest that the flood control dams had positive effects on thenumber of plants in the processing and assembling category in downstream regions, and it did not affectthe number of plants in the life-related category. We consider that the different reaction to the reductionin flood risk comes from the heterogeneity of their capital holdings. From Table 5 we can observe that the19tendency of a life-related category’s share of plants to have a large amount of capital is smaller than it isfor the other manufacturing categories. Only in the class of plants with capital less than 30,000,000 yen is
the share of the life-related category higher than the others. From the data, we consider that life-related
manufacturing plants tend to demand smaller amounts of capital than plants in the other manufacturingcategories and have lower price elasticity for flood risk than the other types.
In Table 7, the total number of plants and the number of plants with fewer than 299 employees
receive significantly positive effects from constructing flood control dams, but the number of plants withmore than 300 employees in the basic material category was not affected. In terms of these plant types,
we consider that since basic material manufacturers tend to require large amount of capital, they
sensitively react to the reduction in flood risk. Our interpretation is consistent with Table 5, suggesting
the tendency that basic material manufacturers hold larger amounts of capital than plants in the othercategories.
On the other hand, regarding plants with more than 300 employees, each municipality has only
approximately 0.15 plants with more than 300 employees in the basic material category on average, andthe point is noticeably small compared to the other categories according to Table 6. From this fact, weconsider that basic material manufacturing plants do not require as many employees, and the number oflarge-scale plants in the category did not increase even after the reduction in flood risk.
We consider that flood control dams can also affect industrial location which are not downstream
areas. Increases in manufacturing plants in downstream areas of flood control dams might be resulted
from decreases in ones in not downstream areas. Figure 5 shows the transition of the number ofmanufacturing plants in a whole of Japan, downstream areas of flood control dams in Shiga Prefecture,and not downstream areas of flood control dams in Shiga Prefecture. We observe that the number ofplants increased after beginning of the LBCD project in downstream areas of the flood control dams. On
the other hand, the number of manufacturing plants in not downstream areas of the flood control dams did
not increase after 1975. In addition, the level suggests similar level to one of as whole of Japan even after
the flood control dams were completed. From the data, we consider that the increases of manufacturing
plants in downstream areas of flood control dams were not resulted from decreases in one in surrounding
areas.

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A question remains whether the amount of investments by manufacturing plants increased after
constructing flood control dams in the downstream areas. To investigate the point, we conduct an event
study focusing on the impacts of constructing flood control dams on the amount of investment. The
estimation model is as follows:
Capitalijt= α𝑖𝑡 + β𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊
× 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕+ γ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × Basicj+ θ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × Processing j+ φ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 × LifeRelatedj + ηi + σ𝑡 +
ε𝑖𝑡 .

(3)

Capitalijt contains two types of capital: physical and human. As the indicator of physical investment, we
use the natural logarithm of the amount of average investment by plants. To measure the amount of
human capital held by plants, we adopt the natural logarithm of the number of employees, and the natural
logarithm of the amount of income per employees. By the constraint of data, the indicator of physical21

capital is limited to manufacturing plants with more than 20 employees, and the measures of humancapital archive only manufacturing plants with more than four employees. We obtain data from the
Census of Manufactures from 1965 to 2000.
Table 8 describes the estimation results. Column (1) uses the natural logarithm of the amount of
average investment as the dependent variable. In column (1), the coefficient of the cross-term between
𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 and 𝐂𝐨𝐧𝐬𝐭𝐃𝐚𝐦𝐬𝒕 is negative and insignificant. The coefficient of the cross-term
containing the basic materials category dummy is positive with five percent significance. The magnitudeis that the amount of average investment by plant in the basic materials category increased by 69.4
percentage points. The coefficients of the cross-terms using the processing and assembling category as
well as the life-related category dummies are positive and insignificant. The results suggest that
constructing the flood control dams did not affect the amount of investment by plants in the processing
and assembling as well as the life-related categories.
From the results, we can observe that constructing flood control dams increased physical investments
only for plants in the basic material category. The result is consistent with our above discussions,suggesting that flood control dams have larger impacts on basic material manufacture, which requirelarge amounts of capital to produce. We consider that since plants in the processing and assembling aswell as the life-related categories sufficiently held physical capital even before completed the flood
control dams, they did not receive significant effects from the reduction in flood risk
Column (2) adopts the natural logarithm of the number of employees in plants with more than four
employees as the explained variable. The coefficient of the cross-term between the downstream area
dummy and the after dams’ completion dummy is positive and insignificant. The result suggests that the
number of employees in miscellaneous manufacture was not affected by the flood control dams. The
coefficient of the cross-term containing the basic materials category dummy is negative andinsignificance, and that of the cross-term using the processing and assembling category dummy ispositive and insignificant. The result suggests that constructing flood control dams did not have any
impacts on the number of employees in the basic materials or the processing and assembling categories.
The coefficient of the cross-term containing the life-related category is negative with 10% significance.
The magnitude suggests that employees in life-related manufacture decreased by 22.6 percentage points
after the completion of flood control dams.
The results show that constructing flood control dams does not have positive and significant effects
on the number of manufacturing employees. For this reason, we consider that since human capital haslarger mobility than physical capital, manufacturing plants tend to invest in human capital compared tophysical capital even with the risk of disaster, and the number was not affected by flood control dams. A

22

confusing point remains that the number of employees in the life-related category significantly decreased
after constructing the flood control dams. Since the amount of investments by plants in the life-related
category received positive but insignificant effects, we interpret that some plants in the life-related
category decreased their employees and shifted to be capital-intensive after the reduction in flood risk.
Column (3) uses the natural logarithm of the amount of wage per employee as Investment ijt . The
coefficients of the four cross-terms are positive and insignificant. The result suggests that the flood
control dams did not affect the amount of wage per employee of manufacturing plants.
From the discussions, we conclude that reduction in disaster risk increases the amount of investment
in plants requiring large amounts of physical capital to produce. The result is consistent with our above
results suggesting that plants in the basic materials as well as the processing and assembling categories
increased their number of plants after the construction of flood control dams.

In addition, the increases in manufacturing plants and investment may have impacts on
non-manufacturing industries in surrounding areas, because other industries could receive positive effects

23

through increases in manufacturing industries’ demand for materials or tools to do their production
activities. To investigate the point, we focus on commerce industry located in the downstream areas.
We investigate the impacts of constructing flood control dams on commerce industries in downstream
areas. The effects would require a time lag to appear. For this reason, we consider that the reduction inflood risk indirectly affects commerce industry, and the effects would occur only after increases inmanufacturing plants or investment in downstream areas. To examine the time lag, we conduct anestimation using cross-terms between each year dummy and each downstream area dummy. The
estimation model is as follows:
Storeit = α𝑖𝑡 + β𝐘𝐞𝐚𝐫𝐭 + γ𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 × 𝐘𝐞𝐚𝐫𝒕 + ηi + σ𝑡 + ε𝑖𝑡 .

(4)

As the dependent variable, we use the natural logarithm of the number of stores in municipality i in year t.
We obtain data for the number of stores in each municipality by referring to the Statistical Handbook of
Shiga Prefecture between 1960 and 2000. Table 9 shows the summary statistics of commerce industries in
Shiga Prefecture, and they are classified into each downstream area of flood control dams.
𝐘𝐞𝐚𝐫𝒕 is the vector of year dummy variables every five years between 1965 and 2000, and the value
in 1960 is omitted and regarded as the baseline. As the sample, we adopt municipalities that are
downstream areas of the flood control dams completed or suspended after 1965.

Table 10 describes the results of analyses using estimation model s (4) and (5). Columns classified in(1) show the result of estimation model (5). The left area of column (1) describes the coefficients ofcross-terms between year dummy variables and Ishidagawa Dam’s downstream area dummy. The
coefficients are consistently positive and become significant after 1975 without 1985 and 1995. The
magnitudes show that the number of stores in Ishidagawa Dam’s downstream area increases by between
15.9-39.1 percentage points compared to the other areas. The central area of column (1) shows the24coefficients of cross-terms between year dummies and the downstream area dummy of Odsuchi Dam.
The coefficients are positive, and become five percent significant after 1985. The magnitudes are that the
number of commerce plants increases by between 60.3-64.2 percentage points in downstream area of
Odsuchi Dam compared to the other areas. The right area of column (1) represents the coefficients ofcross-terms between

𝐘𝐞𝐚𝐫𝒕 and Usogawa Dam’s downstream area dummy. The coefficients are

consistently positive but not significant. The result suggests that the number of stores in the downstream
area of Usogawa Dam were not affected significantly by constructing flood control dams.
Regarding the results of column (1), we consider the relationship between the coefficients and theperiods of flood control dam construction. As described in Table 2, Ishidagawa Dam was constructed
between 1963 and 1969, Odsuchi Dam was constructed between 1966 and 1987, and Usogawa Dam wasconstructed between 1971 and 1979. We compare the periods to the results of column (1) in Table 10 andfind that the impacts of constructing flood control dams on the number of stores in downstream areas
becomes statistical significance approximately five years after the completion of the dam (in the case of
Ishidagawa Dam), or later in the construction phase (in the case of Odsuchi Dam). The case of
Ishidagawa Dam is consistent with our hypothesis that the impacts of flood risk’ reduction on commerceindustries in downstream areas requires a time lag to appear because the effects occur only after increases
in manufacturing plants or investment in surrounding areas. However, the positive impacts ofconstructing Odsuchi Dam on the number of stores in downstream area appeared in 1985, before thecompletion of the dam. For this reason, we consider that since the dam’s construction took a long time, 21
years between 1966 and 1987, people in the downstream area would expect the decreases in flood risk,
and the effects appeared even before the completion.
On the other hand, the right area of column (1) consistently shows positive and insignificant effects offlood control dams on downstream regions. For this reason, we interpret that the downstream area of
Usogawa Dam contains Hikone City, the core city of East Shiga Prefecture, and many commerce stores
were present even before the construction of Usogawa Dam. From Table 9, we can observe the tendency
that the average number of stores of Usogawa Dam’s downstream area is larger than the other areas, but
the value did not change greatly between 1960 and 2000. From this fact, we consider that the commerceindustry of Usogawa Dam’s downstream area was developed even before the construction of Usogawa
Dam, and additional demand resulted from increases in manufacturing plants or investments.
In addition, to check our hypothesis that the impacts of the reduction in flood risk on commerceindustries in downstream areas appear after the increases of manufacturing plants in downstream areas,
we conduct estimation model (5) using the natural logarithm of the number of manufacturing plants as thedependent variable. We can observe the number of plants in each municipality between 1965 and 2000,

25

and we regard the 1965 values as our results’ baseline. Since Ishidagawa Dam was completed in 1969, wecannot observe the changes in the number of manufacturing plants in the downstream area between
before and after the completion. Therefore, we conduct the estimation using only the downstream areas of
Odsuchi and Usogawa Dams as 𝐃𝐨𝐰𝐧𝐒𝐭𝐫𝐞𝐚𝐦𝒊 .
Column (2) in Table 10 shows the estimation results. The left area of the column describes thecoefficients of cross-terms between year dummy variables and Odsuchi Dam’s downstream area dummy.
The coefficients are consistently positive with one percent significance. The magnitudes show that the
number of manufacturing plants in Odsuchi Dam’s downstream area increases by between 37.6-88.7
percentage points compared to the other areas, and the magnitude becomes larger over time. The rightcolumn shows the coefficients of cross-terms between the year dummy variable and the downstream area
dummy of Usogawa Dam. The coefficients are positive, and become 10% significant after 1990. The
magnitudes are that the number of manufacturing plants increases by between 37.7-42.5 percentage
points in the downstream area of Odsuchi Dam.
From the results in the central area of column (1) and the left area of column (2), we can observe thatthe increases in the stores in Odsuchi Dam’s downstream area occurred after the appearance of the
impacts of the construction of Odsuchi Dam on downstream areas’ manufacturing plants. This result is
consistent with our hypothesis that the reduction in flood risk affects commerce industries in downstream
areas after the appearance of the impacts of the flood risk’ reduction on the manufacturing industry.
Regarding Usogawa Dam, from the right area of column (2), although the coefficients become significant
after 1990, the magnitude becomes large in 1975 compared to 1970, and the statistical significance isapproximately 12%. We consider that the manufacturing industry in Usogawa Dam’s downstream area
expected a forthcoming reduction in flood risk between the periods of Usogawa Dam’s construction,
leading to a tendency to increase their plants or investment, but the amount was not sufficiently large to
be statistically significant. From the right area of column (1), we can observe that the coefficients becomelarge after 1985, from approximately 0.085 in 1980 to 0.244 in 1985. We interpret the change ofcoefficient in the commerce industry as the result of spillover effects from the manufacturing industry:
the spillover effects required approximately 10 years to be observed, but the magnitude was not
sufficiently large to be significant.
From the above discussions, we conclude that the reduction in flood risk activates non-manufacturingindustries in downstream areas through increases in manufacturing plants and investment. Public
investments reducing regional disaster risk could improve the regions’ economic activities of many kindsof industries in the long term.

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6. Conclusion
We empirically investigate the relationship between constructing flood control dams andmanufacturing agglomeration in downstream areas by focusing on the case of Shiga Prefecture between
1965 and 2000 using municipality-level data. From the results of our event study, we find that the
reduction in flood risk increased the number of manufacturing plants in downstream areas, but the impact
is limited to manufacturing types requiring large amounts of physical capital to do their production
activities: the basic materials as well as the processing and assembling categories.
We also find that plants in the basic materials category, requiring the largest amount of capital among
the three categories focused on in the study, increased their investments in physical capital after
constructing flood control dams, but the number of their employees did not increase. From the result, weconclude that manufacturing plants exposed to flood risk tend not to invest in physical capital with lower
mobilities than human resources.
In addition, reduction in flood risk affected not only the manufacturing industry but also thecommerce industry in downstream areas, and the effects required 10 years or more to be observed. For

27

this reason, we consider that the commerce industry could receive positive effects through increases inmanufacturing industries’ demand for materials or tools to do their production activities, but that becomes
clear only after manufacturing plants or investments increase.
Our estimation results show that constructing flood control dams promotes manufacturing
agglomeration in downstream areas, and the magnitude becomes large in industries needing larger
physical capital. Developments of flood control dams could stimulate downstream areas’ economic
activities, in spite of governments not anticipating the effects. In the long term, large-scale public
investments aiming to reduce regional disaster risk could improve the regions’ economic activities.

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