Tohoku University Policy Design Lab Discussion Paper

TUPD-2021-007

Natural Disasters and Firm Selection:
Heterogeneous Effects of Flooding Events on Manufacturing
Sectors in JapanJun Yoshida
Graduate School of Engineering, Kyushu University
Policy Design Lab, Tohoku University

Shinsuke Uchida
Graduate School of Economics, Nagoya City University
Policy Design Lab, Tohoku University

Katsuhito Nohara
School of Economics, Hokusei Gakuen University
Policy Design Lab, Tohoku University

Aki ra Hi biki
Policy Design Lab and Graduate School of Economics and Management
Tohoku University

September 2021

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Discussion Papers are a series of manuscripts in their draft form and are circulated for discussion
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Natural Disasters and Firm Selection:
Heterogeneous Effects of Flooding Events on Manufacturing
Sectors in Japan*Jun Yoshida , Shinsuke Uchida, Katsuhito Nohara, and Akira Hibiki

Abstract
Recently, natural disasters and extreme weather events have been occurring more frequently.
This study examines how large floods affect the value of manufacturing product shipments
and the number of facilities in the long run using municipality-level data in Japan. We
considered the impacts of flooding depending on the size of the facilities and past flood
experiences (leading to flood preparedness in advance). We found “build back better”
dynamics, in which the value of manufacturing product shipments grew in cities affected byfloods. We also found that large facilities increased, while small and mid-sized facilities
decreased following floods. These results suggest two important mechanisms characterizing
the damage and recovery processes of floods. First, large facilities were more resilient toflooding, while small and mid-sized facilities were more vulnerable to flooding. Economies
of scale resulting from small facilities exit, and an increase in large facilities may increase thenumber of shipments of manufactured goods per facility. Experience with past floods did notaffect the activities of large facilities. In frequently flooded cities, the activity levels of small
and mid-sized facilities recovered to predisaster trends. In rarely flooded cities, a long-term
decline was observed in the business activities of small and mid-sized facilities because they
likely needed to revise their supply chains due to unexpected events. In addition, unexpectedflooding had devastating effects on employment.
JEL classification: Q54, O18, R11
Keywords: Flood, climate change, manufacturing sectors, firm selection, past experience offloods*E-mail address: j-yoshida@doc.kyushu-u.ac.jp
This research was performed by the Environment Research and Technology Development Fund(JPMEERF20S11821) of the Environmental Restoration and Conservation Agency of Japan.

11. Introduction
Recently, natural disasters and extreme weather events have been occurring more frequently.
Many studies that examined the impact of disasters on the economy have tended to focus on
the extent of the damage using country-level data (e.g., Dell et al., 2012; Hsiang and Jina,
2014; Kocornik-Mina and McDermott, 2020). However, the results of these studies differ fromstudy to study: some disasters reportedly have a positive impact on economic growth whileothers have a negative impact, some disasters cause long-lasting damage while others cause
short-term damage, and so on. The mechanism of how disasters affect the economy is not wellunderstood. To understand this mechanism, some studies have focused on the spatial
heterogeneity of damage and sector heterogeneity using plant-level data, but the number ofsuch studies is limited (e.g., Cole et al., 2019; Okubo and Strobl, 2021). This study aims tounderstand the mechanism of how floods caused by extreme weather events affect production
in manufacturing sectors using municipality-level data in Japan.
Previous studies mainly used country-level data to estimate the impacts of disasters on theeconomy and compared the results between developing and developed countries. Skidmore
and Toya (2002) focused on historical natural disasters, including earthquakes, tsunamis,
volcanic eruptions, and floods. They found that climatic events were positively related to longterm economic growth. Cunado and Ferreira (2014) found that flood shocks had a direct
positive impact on the agricultural sector and an indirect positive impact on the nonagricultural
sector, yet these effects were limited to developing countries and moderate floods. However,Hsiang and Jina (2014) obtained different results by using the exposure of each country to all
tropical cyclones from 1950–2008. They found that national incomes were reduced bycyclones incidences and that the incomes do not recover within twenty years. Dell et al. (2012)
found that higher temperatures substantially reduced economic growth in poor countries andthat the effects persisted in the medium run.
Other have studies focused on postdisaster economic activities using nighttime light data
(Elliott et al. 2015; Kocornik-Mina and McDermott, 2020). Kocornik-Mina and McDermott(2020) found that low-elevation areas recovered as rapidly as areas located at higher elevations
when cities flooded, and no permanent movement of economic activities in response to floods
was found. This result suggested that there was no significant adaptation, at least in the senseof the relocation of economic activities away from the most vulnerable locations. Testa (2021)
focused on the role of institutions in postearthquake population recovery and found sustained
negative effects of earthquakes on city population growth, with effects driven by cities locatedoutside of stable democracies.

2

These macrolevel studies were insufficient to provide useful information for constructing
local disaster prevention policies because they lacked insights into microlevel heterogeneity,such as municipalities, households, and firms. Some studies used plant-level data to consider
the spatial heterogeneity in damage. Tanaka (2015) showed that the 1995 Kobe earthquake’s
negative impact on plant growth persisted for three years. Elliott et al. (2019) found that the
impact of typhoons on the plant sales of Chinese manufacturers was substantial, but this effect
was relatively short-lived.
One of the important mechanisms by which natural disasters can impact firms is throughthe “natural selection” of firms. Uchida et al. (2014) found that the rate of closure of firms due
to bankruptcy decreased after the Tohoku earthquake in Japan, perhaps due to aid, and thatfirm exits following the earthquake were predominantly voluntary closures, with firms seizing
the moment to leave an aging market. Cole et al. (2019) focused on the 1995 Kobe earthquake
in Japan as a natural experiment. They used ex post damage measurements of the earthquake
to show that damaged plants were more likely to fail than undamaged plants and that this effectpersisted for up to 7 years. They also found a ‘build back better’ effect in which continuing
plants experienced a temporary increase in productivity following the earthquake. Craioveanu
and Terrell (2016) considered the impact of Hurricane Katrina on firm survival using a spatial
Bayesian analysis and showed that firms with less flooding and firms with larger chain stores
were more likely to survive. They also found that locally owned businesses opened faster thanlarge chain stores. Okubo and Strobl (2021) considered heterogeneity among manufacturing,
wholesale, retail, and construction industries. They found that the storm surge caused by Ise
Bay Typhoon increased the probability of firm closures in some industries, but in otherindustries, it either had no effect or reduced firm exit. They also found that the impact of thestorm surge on firm performance after the event varied from firm to firm.
In terms of understanding the mechanisms by which natural disasters affect the economy,
the contributions of this study are as follows. The first contribution is the experience of past
floods. Cities that experienced significant flooding in the past are likely to differ from thosethat did not experience significant flooding in relevant ways. For example, they may be better
prepared for floods, with better infrastructure and more funds allocated for postfloodreconstruction, which may mitigate the impact. Neumayer et al. (2014) found that past flood
experience drives current cities to take proactive measures. If so, firms may relocate theirfacilities to cities with better flood protection. In other words, the number of facilities may
tend to increase in cities with more experience with past floods, while it may tend to decreasein cities with less experience with past floods.
Second, the effect of floods on facilities may differ depending on the size of the facility.
For example, smaller facilities have less money to spend on flood protection in advance. If so,
3such facilities would be unable to continue their business due to heavy damage. As a result,the number of small facilities would be likely to decrease after a major flood. The number oflarger facilities may not decrease even after the flood if large facilities can afford to takeprecautionary measures. To focus on the size of facilities, facilities were classified into three
categories based on the number of employees: small facilities with fewer than 10 employees,
mid-sized facilities with 10–299 employees, and large facilities with more than 300 employees.
To identify the immediate and long-run impacts of large floods on manufacturing sectors,
we adopt a finite distributed lag model of order ten periods. The flood data are obtained fromthe Dartmouth Flood Observatory (DFO), which provides detailed information on flood events
such as location, timing, duration, severity indicator, causes, and other information for
thousands of flood events worldwide from 1985 to 2015 (Brakenridge, n.d.). Not all floods arerecorded in this database; it contains only those that have caused severe damage, for example,
deaths or severe damage to structures and agriculture. Therefore, it is possible to exclude fixed
effects associated with different vulnerabilities to flooding in different locations and consider
what the consequences would be if the same level of damage occurred. Using this database,this study obtained the number of severe floods each city has experienced in the past. Since
the interest of this study is in the effects of past flood experience on the speed of recovery andthe postdisaster change in the number of facilities, a dummy variable was created to distinguishbetween cities with more and fewer frequent floods in the past.
The primary findings are as follows. 1) Last year floods have a negative and statisticallysignificant effect on the value of shipment of manufactured goods and the number of facilities.
2) A “build back better” effect, in which the shipment of manufactured goods grows in cities
affected by floods, is found. 3) Large facilities increase following floods, while small and midsized facilities decrease. These results suggest two important insights for the adaptive behaviorof manufacturing sectors. First, larger facilities are more resilient to flooding, while smaller
facilities are vulnerable to flooding. Second, economies of scale resulting from an increase in
the number of larger facilities may cause the “build back better” dynamic, which increases thevalue of manufactured products shipped per facility. In terms of the experience of severe
flooding in the past, it was found that 4) in frequently flooded cities, the activity levels of small
and mid-sized facilities recover to predisaster levels; in rarely flooded cities, a long-term
decline occurs in the business activities of small and mid-sized facilities. However, it was also
found that 5) the experience of past floods does not affect the activities of large facilities.
Finally, it was found that 6) unexpected flooding has devastating effects on employment,
which is a serious issue for the regional economy.

4The remainder of this publication is organized as follows. Section 2 introduces the data andprovides descriptive statistics. Section 3 describes the estimation strategy. Section 4 presents
the main results. Section 5 concludes.

2. Data
A balanced panel was compiled at the city level in Japan with annual data on the number andphysical intensity of floods and economic variables of manufacturing sectors. We selected
1729 cities during the 2002–2010 period. There are two reasons why the sample period was
extended from 2002 to 2010. The first reason is that floods with a severity of 1.5 or higher
occurred somewhere in Japan every year during this period except for 2003, and the second
reason is to remove the effect of the Great East Japan Earthquake in 2011. Since a finite
distributed-lag model was used with 10-year lags on the explanatory variable (i.e., a flood
variable), the number and physical intensity of floods was added in the period 1992–2001 to
the panel data. The data were drawn from some sources as detailed below.
Floods
The flood variables used in this study come from the Dartmouth Flood Observatory (DFO)
archive (Brakenridge, n.d.). The DFO database includes location, timing, duration, severity
indicator, and other information for thousands of flood events worldwide from 1985 to 2015.
These data were compiled from a wide variety of media estimates, governmental sources, andsatellite images based on the MODIS (Moderate Resolution Imaging Spectroradiometer,
http://modis.gsfc.nasa.gov) and optical remote sensing and passive microwave remotesensing1, which provide frequent updates of water conditions worldwide to detect and locate
flood events. For a flood event to be considered “large” and recorded in the dataset, it mustmeet at least one of the following criteria: “Significant damage to structures or agriculture,long reporting interval (decades) since the last similar event, and/or fatalities”. Floods are
divided into three severity classes depending on their estimated recurrence interval. Class 1
floods have a 10- to 20-year-long reported interval between similar events, class 1.5 floods
have a 20- to 100-year recurrence interval, and class 2 floods have a recurrence interval greaterthan 100 years.
Economic data of manufacturing sectors

1

AMSR-E and TRMM sensors monitoring approximately 10,000 areas(http://old.gdacs.org/flooddetection/)
5

Data on the value of shipments of manufactured goods, the number of facilities, and thenumber of employees at the end of the year was used for each of the approximately 1,800
cities from the Census of Manufacture Achieves by Ministry of Economy, Trade, and Industry(METI), Japan. The census was conducted to clarify the actual conditions of Japan’s
manufacturing industry and to obtain basic data for industrial policies. The data were available
annually from 1979 to 2015. Facilities were divided into four groups in terms of the size offacilities: facilities of any size; small facilities with fewer than 10 employees; mid-sizedfacilities with 10–299 employees; and large facilities with more than 300 employees. In thisdataset, if the number of facilities was equal to one (i.e., there was only one firm in themunicipality), the numerical value of shipments and employees was not listed.

3. Empirical strategies
To identify the immediate (i.e., following year) and longer-run impact of large floods on
manufacturing sectors, data from city i in year t (t = 2002–2010) were used, and a distributedlag approach that controls for a finite number of lags on the explanatory variable was adopted(Dell et al. 2012; Testa, 2021):10ln(Yit ) = å b s Floodi ,t -s + Cityi + Yeart + eit ,

(1)

s =1

where Yit is our set of dependent variables including the value of shipment of manufactured
goods, the number of facilities, and the number of employees for city i in year t, Floodi,t-s is adummy equal to one if at least one flood with a severity indicator of 1.5 or higher occurred inyear t – s for s = 1, 2, … up to 10; cityt is city fixed effect, and Yeart is year fixed effects; eit iserror term. To account for spatial correlation, the standard errors were clustered by city, which
is a more conservative approach than that taken in most of the literature.
Since the interest of this study is in how past flood experience influences the change in thenumber of facilities after a flood, a dummy variable was created, denoted as Experiencei, todistinguish between cities with more and fewer frequent floods between 1985 and 2001.
Experiencei equals one if city i experienced more than six floods with a severity indicator of
1 or higher between 1985 and 2001. Figure 1 shows that the number of flood events with aseverity indicator is more than one that each city experienced between 1985 and 2001. Table
1 shows the frequency distribution for large floods. Regressions with this variable were
estimated, which we interact with Floodi,t-s for all s:

610ln(Yit ) = b0 + å b1s floodi ,t -s ´ experiencei
s =1

10

+ å b 2 s floodi ,t -s + experiencei + cityi + yeart + eit .

(2)

s =1

Table 2 summarizes the description of variables and data sources. Table 3 shows thedescriptive statistics.

4. Results and discussion
4.1 The effects of floods on manufacturing sectors
Estimation without lags
Table 4 presents the results estimated using Equation (1) without lags of the flood dummy
variable. Column 1 shows that when there was at least one flood with a severity indicator of
1.5 or higher, the value of shipment of manufactured goods decreases by 2.1%. Columns 2
and 3 show that the effects on the value of shipment per facility and the number of employees
are not statistically significant. Column 4 shows that the total number of facilities decreasedby 2.4%. The effects of floods vary depending on the size of facilities. Column 5 shows thatthe number of small-sized facilities decreased by 3.0%, while Column 6 shows that the effecton the number of mid-sized facilities was not statistically significant. Column 7 shows that the
number of large-sized facilities increases by 3.1%.
Estimation with lags
Next, more flexible models with ten-year flood lags were considered to better understand how
long the effect of floods persists. Table 5 presents the results from estimating Equation (1)—that is, testing the effects of large floods on manufacturing sectors up to ten years after thefloods. Table 6 presents the cumulative effect of floods on flow variables such as shipments,
shipments per facility, and the number of employees, calculated by summing the respective
flood dummy variable and its lags.2
Column 1 of Table 5 shows that last year’s negative effects on the value of shipment of
manufactured goods disappear at t – 2. Interestingly, after t – 3, the coefficient was positiveand statistically significant, which means that the value of shipments in flooded cities washigher than that in nonflooded cities. The cumulative effect, as shown in Column 1 of Table
6, is an increase of 21.3% and statistically significant. This illustrates the “buildback better”
2

The number of facilities is a stock variable. The coefficient of a 10-year lagged flood variable indicates thelong-run effect of floods.

7dynamics, in which the shipment of manufactured products grows in cities affected by floods.
Column 2 in Table 5 shows that the value of shipment per facility also increases after thefloods and that the cumulative effect in Table 6 is an increase of 25.9% and statisticallysignificant. Column 3 shows that there was no negative effect on the number of employees,
and the cumulative effect in Table 6 was not statistically significant. This means that
employment does not grow beyond the baseline.
Column 4 shows that the negative impact of flooding on the total number of facilities
persists for two years. The impacts almost vanished three years after the events. Column 5shows that the negative effect on the number of small-sized facilities persists up to five years
after the floods. Column 6 shows that the effect on the number of mid-sized facilities isnegative at t – 2, t – 3, t – 4, and t – 9. Column 7 shows that the effect on the number of largesized facilities is positive and statistically significant at t – 1 and t – 7 to t – 10.
The interpretation of these results is as follows. The small and mid-sized facilities
disappeared, and the number of large-sized facilities increased due to new entry or absorption
or merging into larger ones. Economies of scale resulting from an increase in large-sizedfacilities may increase the value of manufactured goods shipped per facility. That is why wecan see the “build back better” dynamic.
4.2 The role of past flood experiences
Estimation without lags
To examine how past experiences of flooding affect the magnitude of damage, the speed ofrecovery, and the number of facilities, the flood dummy could interact with a dummy
distinguishing between “frequently flooded cities” and “rarely flooded cities”. The dummy
variable equals 1 if a city has experienced more than six floods with a severity indicator of 1
or higher between 1985 and 2001. Table 7 presents the results where the flood dummy variableinteracts with the experience dummy variable without lags.
Column 1 shows that the coefficient of the interaction between the experience dummy andthe flood dummy on the value of shipments was negative, but Column 2 shows that thecoefficient on the value of shipments per facility was not statistically significant. Column 3shows that the interaction effect on employment was negative. Columns 4–7 show that theinteraction effect on the number of facilities was negative because floods have a negative
impact on the total number of facilities.
Columns 6 and 7 show that the numbers of mid-sized and large facilities increased in rarely
flooded cities, while they decreased in frequently flooded cities. This suggests that there is afirm’s adaptive behavior to flooding, where firms tend to move their facilities to rarely flooded
cities and restart the business or new firms start their business in rarely flooded cities because8they think that the frequently flooded cities in the past are more likely to flood again. Column
5 shows that the number of small-sized facilities decreases in both frequently and rarely
flooded cities, indicating that there was no entry of new facilities in either frequently or rarely
flooded cities one year after the floods. This result suggests that small facilities are more
vulnerable to flooding than larger facilities regardless of past flood experience.
Estimation with lags
How long the damage persists in cities that experienced more than six floods during 1985–
2001 was examined. Figure 2 illustrates the long-run effect of flooding on the total number offacilities in frequently and rarely flooded cities. Figure 3 compares the long-run effect on small,
mid-sized, and large facilities between both frequently and rarely flooded cities. Figure 4
compares the long-run effect of flooding on shipments, shipments per facility, and the numberof employees between both frequently and rarely flooded cities. The effect in frequently andrarely flooded cities is the same as β1s + β2s and β2s in Eq. (2), respectively. Both figures display
95% confidential intervals. Table 8 shows the cumulative effect of the flow variables(shipment, shipment per facilities, and employment). Table A-1 in the Appendix presents theresults obtained when the flood dummy variable interacted with the experience dummy
variable.
Figure 2 shows that in frequently flooded cities, the last year’s floods adversely affect the
total number of facilities, and the effect persists up to eight years after the floods. Nine years
after the floods, the total number of facilities recovers. In rarely flooded cities, the negative
effects persist for two years. Three years after the floods, the number of facilities recovers
temporally, but it decreases again, and the negative trend continues even ten years after thefloods. Frequently flooded cities take flood protection measures based on lessons learned in
the past (Neumayer et al. (2014)), and thus, the number of facilities increases. This result is
consistent with this mechanism.
The next step was to analyze whether the effects vary depending on the size of the facilities.
The following hypothesis was formulated. Smaller facilities that cannot afford flood protection
may go bankrupt due to flood damage. Even if they survive, it may be more efficient to movetheir facilities to less flooded areas rather than take flood protection measures. Conversely,,
the number of large facilities may not change because they can afford to take flood
countermeasures.
The upper part of Figure 3 shows that in the frequently flooded cities in the past, thenumber of small and mid-sized facilities decreases due to the last year’s floods. The negative
effects disappear until ten years after the floods in both small and mid-sized facilities. In otherwords, the numbers of small and mid-sized facilities recovers in the long run. On the other9

hand, the number of large facilities does not change due to the last year’s floods and increased
beginning seven years after the floods. The lower part of Figure 3 shows that the number ofsmall facilities decreases, and the number of mid-sized and large facilities increases due to thelast year’s floods. Three years after flooding, the number of small facilities temporally
recovered, but it decreased again at t – 8. The number of large facilities increased temporarily
one year after the floods. After the second year, the effects are not statistically significant.
In summary, in frequently flooded cities, the numbers of small and mid-sized facilities
recovers in the long run. In rarely flooded cities, these facilities temporarily recovers but
decrease in the long run. This suggests that small and mid-sized facilities prefer to be locatedin frequently flooded cities because flood control measures would be well developed in thosecities. Large facilities survived flooding even in rarely flooded cities where flood control
measures were not well installed. This suggests that large facilities are resilient to flooding.
Next, the effect of shipments and employment was evaluated. The upper part of Figure 4
shows that in frequently flooded cities, shipments of manufactured goods and employment
decrease from t – 1 to t – 4 because the number of small and mid-sized facilities decreased
during that period. Five years after the floods, the effect disappears. On the other hand,
shipments per facility do not change from t – 1 to t – 6; they increase from t – 7 to t – 10 (thecoefficient was positive and statistically significant at the more than 90% confidence level).
The interpretation of this result is as follows. As shown in the upper part of Figure 3, large
facilities increase seven years after flooding. Thus, because economies of scale result from anincrease in large-sized facilities, the value of manufactured goods shipped per facility
increases. The lower part of Figure 4 shows that in rarely flooded cities, the last year's floods
do not affect shipments. Three years after the floods, shipments and employment temporally
increase. Then, they decrease ten years after the floods.
Table 8 shows that the cumulative effect on shipments and shipments per facility was notstatistically significant in either frequently or rarely flooded cities. The cumulative effect on
employment is – 31.3% and statistically significant in frequently flooded cities.
The experience of past floods did not affect the business activities of large-sized facilities,
but it affected the business activities of small- and mid-sized facilities. In frequently flooded
cities, their activity recovered to predisaster levels. In the rarely flooded cities, there was along-term decline in their activities because small and mid-sized facilities probably need torevise their supply chains due to unexpected events. In addition, unexpected flooding haddevastating effects on employment even ten years after the floods, which is a serious issue forthe regional economy.

10

4.3 Alternative specifications of panel results
Equation (1) was estimated using the prefecture-specific year fixed effect instead of the year
fixed effect. The result are presented in Table A-1 in the Appendix. Comparing the alternative
specification result with the baseline results presented in Table 5, we can obtain the following
three results. First, the results obtained for the small facilities were similar to the baseline
results: the number of small facilities decreases one year after the floods, and the negative
effects persist up to five years after the floods. Second, the results for the mid-sized facilities
are slightly different from the baseline results: they decrease even 10 years after the floods
using a year×prefecture fixed effect. Third, floods do not affect the number of large facilities,
which was different from the baseline result that the number of large facilities increases sevenyears after floods. These results suggest that floods may have devastating effects on small andmid-sized facilities in the long run, while large facilities were resilient to flooding.

5. Conclusion
This study examined how large floods affect the value of shipments, employment, and thenumber of facilities in manufacturing sectors in the long run. To do this, a finite distributedlag model of 10-year order was used with municipal-level data in Japan. The impact offlooding depending on the size of facilities and past flood experience was estimated. It was
found the “build back better” dynamic in which shipments of manufactured goods grow incities affected by floods, and we found that the number of small and mid-sized facilities
decreases, while the number of large facilities increased after floods.
These results suggest two important insights for the adaptation behavior of manufacturingsectors. First, large facilities are resilient to flooding, while small and mid-sized facilities arevulnerable to flooding. Therefore, small facilities exit the affected areas and/or may be
absorbed or merged into larger facilities after large floods. As a result, economies of scale
resulting from small facility exit and an increase in large-sized facilities may increase theshipments of manufactured goods per facility. That was why the build back better dynamics
were found.
Considering the experience of past flooding, the following results were obtained.
Experience with past floods did not affect the business activities of large facilities. However,it affects the activities of small and mid-sized facilities. In frequently flooded cities, thebusiness activity of small and mid-sized facilities recovered to predisaster levels. In the rarely
flooded cities, however, there was a long-term decline in the business activities of small andmid-sized facilities. In addition, unexpected flooding had devastating effects on employment,
which was a serious problem for the regional economy. Unexpected flooding may force firms
11

to rethink their supply chains, delaying decisions on business continuity and delaying
government responses such as the introduction of subsidies. The task of future research is tounderstand the mechanism by which the business activities of small and mid-sized facilities
are negatively affected for an extended period following flooding in rarely flooded cities.

References
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Observatory, University of Colorado, USA. http://floodobservatory.colorado.edu/
Archives/ (Accessed 1 October 2020).
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heterogeneity in damages: the birth, life and death of manufacturing plants. Journal of
Economic Geography, 19(2), 373-408.
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econometric model for firm survival. In Spatial Econometrics: Qualitative and Limited
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Cunado, J., & Ferreira, S. (2014). The macroeconomic impacts of natural disasters: The caseof floods. Land economics, 90(1), 149-168.
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Hsiang, S. M., & Jina, A. S. (2014). The causal effect of environmental catastrophe on longrun economic growth: Evidence from 6,700 cyclones (No. w20352). National Bureau of
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Kocornik-Mina, A., McDermott, T. K., Michaels, G., & Rauch, F. (2020). Flooded cities.
American Economic Journal: Applied Economics, 12(2), 35-66.
Ministry of Economy, Trade and Industry (METI), Japan. Census of Manufacture Achieves.
https://www.meti.go.jp/statistics/tyo/kougyo/archives/index.html (Accessed 1 October2020).
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Neumayer, E., Plümper, T., & Barthel, F. (2014). The political economy of natural disasterdamage. Global Environmental Change, 24, 8-19.
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Testa, P. A. (2021). Shocks and the spatial distribution of economic activity: The role ofinstitutions. Journal of Economic Behavior & Organization, 183, 791-810.
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13

Figure 1. Flood frequency in 1985–2001

Figure 2. Long-run effects on the number of facilities: the role of experience14Figure 3. Heterogeneous effects of floods on facilities in frequently and rarely flooded cities

15

Figure 4. Long-run effects on shipments and employment in frequently and rarely flooded cities

16

Table 1. Flood frequencies and distribution of cities in 1985–2001
Number of

Number offloods
0
1
2
3
4
5
6
7
8
9
11
Total

cities
53
74
210
344
441
247
155
152
119
50
1
1846

Cumulative
Percentage

percentage

2.87
4.01
11.38
18.63
23.89
13.38
8.40
8.23
6.45
2.71
0.05
100.00

2.87
6.88
18.26
36.89
60.78
74.16
82.56
90.79
97.24
99.95
100.00

Table 2. Descriptions of variables and data sources
Name
Shipmentit
Shipment per facilityit

Employee
Total number of facilitiesit
Number of small-sizedfacilitiesit
Number of mid-sized facilitiesit
Number of large-sized facilitiesit

Floodi,t-s

Description
The value of shipment of
manufactured goods in a city
The value of shipment of
manufactured goods per
facility
The number of employees in acity
The total number of facilities ina cityThe number of facilities withless than 10 employees in acity
The number of facilities with
10–299 employees in a city
The number of facilities with
300 or more employees in acity
Dummy variable representing
city i’s experience offlooding in year t – s
Dummy variable representing
city i’s past experience oflarge floods

Sources
METI

Notes
Flow variable

METI

Obtained by dividing shipment by
the total number of facilities.
Flow variable

METI

Flow variable

METI

Stock variable

METI

Stock variable

METI

Stock variable

METI

Stock variable

DFO

1 if at least more than one floodwith severity indicator of 1.5 orhigher occurred in year t – s

1 if city i experienced more thansix floods with severity
indicator of 1 or higher
between 1985 and 2001
Note: DFO, Dartmouth Flood Observatory; METI, Ministry of Economy, Trade and Industry, Japan
Experiencei

17

DFO

Table 3. Descriptive statistics
Observations

Mean

S.D.

Min

Max

ln(Shipment)

15,561

15.22

1.89

8.64

21.00

ln(Shipment per facilities)

15,561

10.99

1.02

7.05

16.72

ln(Employee)

15,561

7.52

1.50

2.94

11.60

ln(Total number of facilities)

15,561

4.23

1.27

1.10

8.25

ln(Number of small-sized facilities)

15,525

3.95

1.28

0

8.17

ln(Number of mid-sized facilities)

14,355

2.73

1.25

0

7.28

ln(Number of large-sized facilities)

6,912

1.02

0.82

0

3.89

Floodi,t-s

15,561

0.05

0.22

0

1

Experiencei

15,561

0.27

0.44

0

1

ln(Shipment)

4,176

15.67

1.65

9.24

20.16

ln(Shipment per facilities)

4,176

11.15

0.98

7.85

14.41

ln(Employee)

4,176

7.87

1.29

3.56

10.68

ln(Total number of facilities)

4,176

4.52

1.13

1.39

7.83

ln(Number of small-sized facilities)

4,176

4.23

1.17

0

7.76

ln(Number of mid-sized facilities)

4,014

2.93

1.12

0

6.76

ln(Number of large-sized facilities)

2,106

1.04

0.806

0

3.09

Floodi,t-s

4,176

0.04

0.20

0

1

ln(Shipment)

11,385

15.06

1.95

8.64

21.00

ln(Shipment per facilities)

11,385

10.94

1.03

7.05

16.72

ln(Employee)

11,385

7.39

1.56

2.94

11.60

ln(Total number of facilities)

11,385

4.12

1.31

1.10

8.25

ln(Number of small-sized facilities)

11,349

3.86

1.31

0

8.17

ln(Number of mid-sized facilities)

10,341

2.65

1.28

0

7.28

ln(Number of large-sized facilities)

4,806

1.01

0.82

0

3.89

Floodi,t-s

11,385

0.06

0.23

0

1

Full sample

Experiencei = 1

Experiencei = 0

Note: Observations in small, mid, and large-sized facilities are smaller than that in the total facilities because there
are some cities that have no small, mid, and large-sized facilities.

18

Table 4. Effects of floods(1)
Shipment(2)(3)

Shipment

Employee(4)

per facility

(5)

(6)

(7)

Total

Number of

Number of

Number of

number ofsmall-sized

mid-sized

large-sized

Floodi,t-1-0.0214*(0.0111)0.00272(0.0103)-0.00706(0.00644)

facilities-0.0241***(0.00480)

facilities-0.0290***(0.00595)

facilities
0.0114(0.00993)

facilities
0.0322**(0.0152)

Year FE
City FE
Observations
Adjusted
within R2

Yes
Yes
15,5610.000393

Yes
Yes
15,561
6.43×10-5

Yes
Yes
15,561
7.65×10-5

Yes
Yes
15,5610.00287

Yes
Yes
15,5250.00183

Yes
Yes
14,355
6.07×10-5

Yes
Yes
6,912
0.000761

Notes: Robust standard errors, clustered by city, in parentheses. *** denotes p<0.01, ** denotes p<0.05, and * denotes
p<0.1. Observations in columns of (5)-(7) are smaller than the others because some cities have no small-sized, or midsized, or large-sized facilities.

19

Table 5. Model with lags
(1)
Shipment(2)(3)

Shipment

Employee(4)

per facility
Floodi,t-1
Floodi,t-2
Floodi,t-3
Floodi,t-4
Floodi,t-5
Floodi,t-6
Floodi,t-7
Floodi,t-8
Floodi,t-9
Floodi,t-10
Year FE
City FE
Observations
Adjusted
within R2-0.0232**(0.0109)-0.0117(0.0122)0.0370*(0.0210)0.0360(0.0236)0.0385*(0.0206)
0.0483**(0.0225)
0.0289*(0.0150)
0.0148(0.0148)0.0279**(0.0137)
0.0164(0.0121)
Yes
Yes
15,5610.00166

0.000721(0.0101)
0.00803(0.0111)0.0292(0.0190)0.0403*(0.0218)0.0475**(0.0193)0.0450**(0.0206)
0.0291**(0.0134)0.0209(0.0135)0.0248**(0.0121)0.0134(0.0107)
Yes
Yes
15,5610.00128-0.00792(0.00629)-0.00721(0.00727)
0.0122(0.0105)
0.0153(0.0126)
0.0222**(0.0107)0.0259**(0.0117)0.00626(0.00938)-0.000667(0.00860)
0.00560(0.00833)
0.000492(0.00706)
Yes
Yes
15,5610.000649

(5)

(6)

(7)

Total

Number of

Number of

Number of

number ofsmall-sized

mid-sized

large-sizedfacilities-0.0239***(0.00471)-0.0197***(0.00517)
0.00781(0.00706)-0.00434(0.00773)-0.00899(0.00576)
0.00337(0.00776)-0.000181(0.00641)-0.00613(0.00559)
0.00313(0.00551)
0.00301(0.00490)
Yes
Yes
15,5610.00415

facilities-0.0264***(0.00570)-0.0416***(0.00642)-0.0207**(0.00894)-0.0279***(0.00896)-0.0189***(0.00659)
0.000188(0.00831)-0.00482(0.00858)-0.000716(0.00684)-0.00316(0.00576)
0.0108(0.00931)

facilities
0.0171*(0.00953)-0.0342***(0.0103)-0.0537***(0.0179)-0.0438**(0.0186)-0.0108(0.0154)0.0222(0.0167)
0.0129(0.0135)
0.0184(0.0139)-0.0268**(0.0109)
0.00712(0.0164)

facilities
0.0312**(0.0153)-0.0157(0.0203)
0.0119(0.0345)0.0350(0.0352)
0.0303(0.0286)
0.0242(0.0288)0.0548**(0.0236)0.0505**(0.0218)0.0501**(0.0233)
0.0590***(0.0199)

Yes
Yes
15,5250.00480

Yes
Yes
14,3550.00294

Yes
Yes
6,912
0.00403

Notes: Robust standard errors, clustered by city, in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * denotes
p < 0.1.

Table 6. Cumulative effects of floods on shipments and employment

Cumulative effects

(1)

(2)

(3)

Shipment
0.213**(0.103)

Shipment per facility
0.259***(0.0918)

Employee
0.0722(0.0618)

Notes: Cumulative effects are calculated by summing the coefficient of flood variable and its lags.
Robust standard errors, clustered by city, in parentheses. *** denotes p<0.01, ** denotes p<0.05, and *denotes p<0.1.

20

Table 7. Effects of floods: the role of past flood experiences

Floodi,t-1
Floodi,t-1×Experiencei
Year FE
City FE
Observations
Adjusted
within R2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Shipment

Shipment

Employee

Total

Number of

Number of

Number of

per firm

number ofsmall-sized

mid-sized

large-sized-0.00865(0.0128)-0.0616***(0.0222)0.00551(0.0123)-0.0135(0.0176)

0.00841(0.00695)-0.0748***(0.0154)

facilities-0.0142***(0.00528)-0.0481***(0.0108)

facilities-0.0164**(0.00656)-0.0610***(0.0124)

facilities
0.0384***(0.0104)-0.129***(0.0228)

facilities
0.0440**(0.0171)-0.0641*(0.0342)

Yes
Yes
15,5610.00103

Yes
Yes
15,5610.0001

Yes
Yes
15,5610.00306

Yes
Yes
15,5610.00494

Yes
Yes
15,5250.00329

Yes
Yes
14,3550.00333

Yes
Yes
6,912
0.00123

Notes: Robust standard errors, clustered by city, in parentheses. *** denotes p<0.01, ** denotes p<0.05, and * denotes
p<0.1. Experiencei = 1 if the total number of floods with a severity indicator of 1 or higher each city experienced during
1985–2001 is more than six.

Table 8. Cumulative effects of floods on shipment and employment
in the frequently and rarely flooded cities

Frequently flooded cities
Rarely flooded cities

(1)

(2)

(3)

Shipment-0.196(0.174)0.0650(0.148)

Shipment per facility
0.109(0.161)
0.152(0.131)

Employee-0.313***(0.0998)-0.0388(0.0995)

Notes: Cumulative effects are calculated by summing β1s + β2s or β2s. Robust standard errors, clustered bycity, in parentheses. *** denotes p<0.01, ** denotes p<0.05, and * denotes p<0.1.

21

Appendix
Table A-1. Long-run effects of floods: the role of past flood experiences

Floodi,t-1
× Experiencei
Floodi,t-2
× Experiencei
Floodi,t-3
× Experiencei
Floodi,t-4
× Experiencei
Floodi,t-5
× Experiencei
Floodi,t-6
× Experiencei
Floodi,t-7
× Experiencei
Floodi,t-8
× Experiencei
Floodi,t-9
× Experiencei
Floodi,t-10
× Experiencei
Floodi,t-1
Floodi,t-2
Floodi,t-3
Floodi,t-4
Floodi,t-5
Floodi,t-6
Floodi,t-7
Floodi,t-8
Floodi,t-9
Floodi,t-10
Year FE
City FE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Shipment

Shipment

Employee

Total

Number of

Number of

Number of

per firm

number ofsmall-sized

mid-sized

large-sized

0.0163(0.0203)-0.0330(0.0292)-0.0808*(0.0446)-0.0715*(0.0421)-0.0655*(0.0367)-0.0302(0.0364)0.0355(0.0287)
0.0475(0.0305)
0.0676**(0.0284)
0.0714**(0.0280)-0.0886***(0.0160)-0.0977***(0.0227)-0.104***(0.0274)-0.0836***(0.0246)-0.0349(0.0218)0.0308(0.0223)0.000661(0.0203)
0.0124(0.0203)
0.0456**(0.0179)
0.0448***(0.0162)

facilities-0.0605***(0.0111)-0.0577***(0.0146)-0.0671***(0.0175)-0.0624***(0.0169)-0.0239**(0.0117)0.0264*(0.0135)-0.0126(0.0136)0.00442(0.0131)0.0179(0.0111)0.0171(0.0105)-0.000670(0.0120)0.0184(0.0124)0.0521**(0.0250)
0.0573*(0.0295)
0.0708**(0.0305)
0.0395(0.0292)
0.00294(0.0215)-0.0146(0.0250)-0.0306(0.0244)-0.0430*(0.0257)
Yes
Yes

0.0112*(0.00678)
0.00688(0.00741)
0.0202(0.0142)0.0401**(0.0167)
0.0334**(0.0157)-0.00992(0.0157)-0.0141(0.0166)-0.0292*(0.0172)-0.0461***(0.0151)-0.0512***(0.0143)
Yes
Yes-0.0109**(0.00528)-0.0125**(0.00542)
0.0135(0.00910)
0.0157(0.00996)-0.00112(0.00904)-0.0198*(0.0111)-0.00433(0.0111)-0.0224**(0.0110)-0.0219**(0.00909)-0.0233***(0.00894)
Yes
Yes

facilities-0.0788***(0.0135)-0.0667***(0.0177)-0.0865***(0.0227)-0.0568***(0.0203)-0.0351**(0.0139)-0.0440**(0.0174)-0.0284(0.0216)0.0209(0.0149)
0.00367(0.0128)
0.00775(0.0186)-0.0118*(0.00671)-0.0351***(0.00698)-0.0102(0.0119)-0.00931(0.0120)-0.00681(0.0107)0.000794(0.0135)-7.19e-05(0.0194)-0.0260**(0.0125)-0.0194*(0.0105)-0.00907(0.0133)
Yes
Yes

facilities-0.144***(0.0248)-0.120***(0.0328)-0.122***(0.0443)-0.0916**(0.0382)-0.0137(0.0312)-0.106***(0.0346)-0.0556(0.0344)0.0675**(0.0287)
0.0481*(0.0257)
0.0693**(0.0352)0.0490***(0.0109)-0.0271**(0.0113)-0.0424*(0.0247)-0.0115(0.0258)-0.00756(0.0254)0.0259(0.0248)0.0234(0.0298)-0.0454*(0.0233)-0.0841***(0.0212)-0.0615**(0.0277)
Yes
Yes

facilities-0.0183(0.0405)-0.0257(0.0581)0.0107(0.0790)0.0230(0.0780)-0.0129(0.0590)0.0933(0.0640)0.132**(0.0540)0.0839(0.0519)0.0109(0.0486)0.0204(0.0456)
0.0339**(0.0170)-0.00659(0.0213)-0.00705(0.0450)
0.0148(0.0434)
0.0307(0.0421)-0.0273(0.0414)-0.0435(0.0456)-0.0159(0.0458)
0.0379(0.0413)0.0350(0.0404)
Yes
Yes-0.0442*(0.0233)-0.0907***(0.0335)-0.148***(0.0487)-0.134***(0.0453)-0.0894**(0.0399)-0.00376(0.0397)
0.0230(0.0311)0.0519(0.0333)
0.0855***(0.0306)
0.0885***(0.0304)-0.0116(0.0124)0.00588(0.0132)
0.0656**(0.0280)
0.0730**(0.0313)0.0697**(0.0319)0.0196(0.0310)-0.00139(0.0235)-0.0370(0.0277)-0.0525**(0.0264)-0.0663**(0.0281)
Yes
Yes

22

Observations
Adjusted
within R2

15,5610.00800

15,5610.00381

15,5610.011815,5610.0113

15,5250.00839

14,3550.01096,912
0.00556

Notes: Robust standard errors, clustered by city, in parentheses. *** denotes p<0.01, ** denotes p<0.05, and * denotes
p<0.1. Experiencei = 1 if the total number of floods with a severity indicator of 1 or higher each city experienced during
1985–2001 is more than six.

Table A-2. Long-run effects of floods: year*prefecture FE
(1)

(2)

(3)

(4)

(5)

(6)

(7)

Shipment

Shipment

Employee

Total

Number of

Number of

Number of

number ofsmall-sized

mid-sized

large-sizedfacilities

facilities-0.0110(0.00793)-0.0299***(0.0107)-0.0297**(0.0118)-0.0332***(0.0112)-0.0162*(0.00851)-0.00330(0.00994)-0.00769(0.0109)-0.00463(0.0117)-0.00905(0.00869)-0.00356(0.0106)
Yes
Yes
15,5250.0005facilities
0.0250*(0.0143)-0.0383**(0.0171)-0.0577**(0.0240)-0.0623***(0.0231)-0.0158(0.0190)-0.0187(0.0230)-0.0334(0.0217)0.00288(0.0224)-0.0746***(0.0175)-0.0603***(0.0209)
Yes
Yes
14,3550.0028facilities
0.000596(0.0225)-0.0307(0.0347)-0.0179(0.0438)
0.00918(0.0423)0.00199(0.0330)-0.0351(0.0388)-0.0136(0.0423)0.0202(0.0410)0.0458(0.0368)
0.0454(0.0310)
Yes
Yes
6,912
0.000

per facility
Floodi,t-1
Floodi,t-2
Floodi,t-3
Floodi,t-4
Floodi,t-5
Floodi,t-6
Floodi,t-7
Floodi,t-8
Floodi,t-9
Floodi,t-10
Year*pref. FE
City FE
Observations
Adjusted
Within R2-0.0258*(0.0145)-0.0152(0.0211)0.0151(0.0264)0.0115(0.0277)
0.0160(0.0247)-0.00867(0.0295)-0.0205(0.0246)-0.0339(0.0228)-0.0340(0.0210)-0.0322*(0.0173)
Yes
Yes
15,5610.0006-0.0101(0.0145)0.00678(0.0198)
0.0249(0.0239)0.0310(0.0252)
0.0306(0.0232)-0.00487(0.0281)-0.0104(0.0233)-0.0109(0.0221)-0.0276(0.0207)-0.0252(0.0165)
Yes
Yes
15,5610.0004-0.0130(0.00884)-0.0172(0.0130)-0.00531(0.0149)-0.0154(0.0155)-0.00192(0.0133)-0.00702(0.0159)-0.0223(0.0151)-0.0297**(0.0131)-0.0106(0.0120)-0.0181*(0.00997)
Yes
Yes
15,5610.0005-0.0156**(0.00660)-0.0220**(0.00906)-0.00988(0.00968)-0.0195**(0.00979)-0.0146*(0.00765)-0.00380(0.00917)-0.0101(0.00923)-0.0231***(0.00821)-0.00639(0.00777)-0.00698(0.00651)
Yes
Yes
15,5610.001

Notes: Robust standard errors, clustered by city, in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, and * denotes
p < 0.1.

23