Floods and Exports: An Empirical Study on
Natural Disaster Shocks in Southeast Asia

Kaori Tembata
Kenji Takeuchi

April 2018
Discussion Paper No.1817

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANFloods and Exports: An Empirical Study on
Natural Disaster Shocks in Southeast Asia
Kaori Tembata∗ and Kenji Takeuchi†
April 11, 2018

Abstract
This study analyzes the eﬀects of climate-related disasters on international
trade in Southeast Asia. We use monthly trade data to examine the relationship between disaster shocks and exports. The empirical analysis showsthat natural disasters have a significant negative eﬀect on exports. The
estimation results suggest that flooding causes immediate export losses of
USD 305–557 million. In addition, we find that the eﬀect persists in the
post-disaster period, with floods causing annual export losses of USD 2.54
billion in total. We further investigate the impact of disasters by productgroup and show that disasters are negatively associated with the exportsof agricultural and manufacturing products. The findings suggest that extreme weather events have severe repercussions on Southeast Asia, where
exports play an important role in economic development.
Keywords: Climate change; Exports; Extreme weather; Flood; Natural disaster; Southeast Asia; Storm∗

Graduate School of Economics, Kobe University. 2-1, Rokkodaicho, Nadaku, Kobe, Hyogo
657-8501 Japan. E-mail: kaoritembata@gmail.com†
Graduate School of Economics, Kobe University. 2-1, Rokkodaicho, Nadaku, Kobe, Hyogo
657-8501 Japan E-mail: takeuchi@econ.kobe-u.ac.jp1

1

Introduction

Natural disasters can cause severe destruction and devastation, leading to significant economic and human damage. According to the Emergency Events Database(EM-DAT), a total of 6,457 weather-related disasters have occurred worldwide between 1995 and 2015 (CRED and UNISDR 2015). These disasters have caused
economic damages of USD 1,891 billion and, on average, aﬀected 205 million people every year. Among such disasters, floods are the most frequent disaster type,accounting for 43% of the natural disasters. In the past two decades, 2.3 million
people have been aﬀected by floods, causing economic losses of USD 662 billion.
The Intergovernmental Panel on Climate Change (IPCC) reports growing economic losses as a result of flooding since the 1970s (IPCC 2014). This trend islikely to continue with flood events projected to increase in certain parts of theworld, especially developing nations where economic losses relative to GDP are
the most significant.
Researchers have examined the eﬀects of natural disasters on socioeconomic
activities at the aggregate and disaggregate levels (Anttila-Hughes and Hsiang
2013; El Hadri et al. 2017; Gassebner et al. 2010; Hsiang 2010; Hsiang and Narita
2012; Leiter et al. 2009; Loayza et al. 2012; Mendelsohn et al. 2011). For example,
Hsiang and Narita (2012) use global cross-sectional data for 1950 to 2008 to examine the marginal damages of tropical cyclones. The authors find that an increase incyclone exposure is associated with greater economic damages and human losses.
Loayza et al. (2012) investigate four types of natural hazards using cross-country
panel data. They find that storms negatively aﬀect agricultural growth in developing countries. However, their study shows mixed results for flood impacts.

2

While moderate flooding positively aﬀects economic growth, severe floods have anegative growth eﬀect. By using firm-level data, Leiter et al. (2009) also find apositive eﬀect of floods on the firms’ capital accumulation and employment growth
while demonstrating a negative flood impact on productivity. The eﬀects of natural disasters may not be limited to one country but involve multiple countries
through international trade. Oh and Reuveny (2010) examine the eﬀect of climatic disasters and political risk on trade. Their analysis includes bilateral trade
data and various types of weather-related disasters, showing that these climatic
events reduce imports. Gassebner et al. (2010) also use bilateral trade data toinvestigate the impact of large-scale natural and technological disasters. El Hadri
et al. (2017) focus on four types of natural disasters to examine their impacts
on export values. These studies draw attention to the economic costs of climatechange through trade channel, considering the growing importance of international
trade to the world economy. However, studies analyzing the relationship between
climate-related disasters and trade remain limited.
The objective of this study is to examine the eﬀects of climate-related disasters
on international trade. We focus on two types of extreme weather events, thatis, floods and storms, to explore the relationship between natural hazards andexports in Southeast Asian countries. Southeast Asia provides a good opportunity
to analyze the topic for two reasons. First, floods and storms in Southeast Asia
are the two most frequent and damaging natural hazards. Between 2006 and 2015,
the average annual costs incurred as a result of floods and storms were USD 5.8
billion and USD 2.7 billion, respectively (Guha-Sapir et al. 2016). Economic losses
in lower-income countries are smaller in absolute value, compared to those in highincome countries; however, these losses are relatively large as a share of GDP. In
3this sense, natural disasters can lead to severe consequences in Southeast Asia.
Second, Southeast Asia’s exports are a key factor for economic development. In
ASEAN countries, for example, the total traded goods amounted to USD 2,270
billion in 2015, accounting for 93% of the region’s GDP (ASEAN 2016). Since
more than 70% of the goods are exported to the outside of ASEAN countries,
Southeast Asia’s export losses caused by natural disasters may also aﬀect othercountries’ economy through the global supply chain.
Our study is also related to the literature that examines climate impacts on
various socioeconomic activities (see Dell et al. 2014; Heal and Park 2016). While
researchers have analyzed and found links between climate and aggregate economic outcomes, recent works emphasize broader dimensions of socioeconomic
activities and explore them as channels through which climate aﬀects macroeconomy. Such channels include agriculture, labor productivity, energy production,
migration, health and mortality, and conflicts (Anttila-Hughes and Hsiang 2013;
Barrios et al. 2006; Cachon et al. 2012; Chen et al. 2016; Di Falco et al. 2012;
Marchiori et al. 2012; McDermott and Nilsen 2014; Miguel et al. 2004). Amongthese channels, agriculture has been the primary focus of climate studies becauseof the sector’s critical dependence on weather conditions. Nevertheless, there isa growing body of literature that investigates climate impacts associated with
non-agricultural outputs. Using international trade data, Jones and Olken (2010)
examine the eﬀects of weather shocks on exports and find that a 1◦ C increase intemperature reduces export growth by 2.0–5.7 percentage points in poor countries.
They further analyze climate impacts on exports by product and show that temperature shocks in poor countries negatively aﬀect the exports of agricultural andlight manufacturing products. Dell et al. (2012) show that temperature shocks af4

fect both agricultural and industrial value-added in poor countries. Hsiang (2010)
examines the impact of temperature and cyclones and find that production losses
from non-agricultural outputs outweigh those from agricultural ones. The existing
literature suggests that economic impacts of extreme weather are not limited tothe agricultural sector but also observed in broader aspects of the economy.
This paper makes the following contributions to the literature on climate impacts. First, we focus on two types of weather shocks, that is, floods and storms,
to investigate the extent to which natural disasters aﬀect exports. While Jonesand Olken (2010) estimate the impact of temperature on trade, they do not consider weather-related disasters, which may be greatly aﬀected by climate change interms of intensity and frequency. Second, our analysis adopts export data classified
by the Harmonized System (HS) code. The breakdown of export data in our study
allows us to compare product groups and identify industries aﬀected by disastershocks. In addition, monthly data can capture the eﬀect of climate disasters more
precisely than annual data used in most studies (Gassebner et al. 2010; Oh andReuveny 2010). Third, this study uses alternative measures for disaster variables,
one of which represents the duration of disaster events. Flooding exhibits greater
variability in duration depending on its type: while flash floods occur for a shorter
duration, riverine and costal floods may last for longer periods, thereby leading togreater impacts on economic outputs. An advantage of using alternative variables
is their ability to capture such severity of disasters.
The results of the regression analysis show that floods and storms are negatively associated with export values, suggesting that the disasters reduce exports
from Southeast Asia. The findings suggest that the occurrence of floods has an immediate eﬀect on export values, as well as a persistent impact that sustains in the5

post-disaster period. To explore this possibility, we include lagged disaster variables in the estimation. We find that these variables are negatively correlated with
exports, showing that flood eﬀects persist for several months. In addition, we findthat the exports of both agricultural and manufacturing products are aﬀected bydisasters. While many studies on climatic impact focus on agriculture, this study
identifies the eﬀect of weather shocks on non-agricultural products. Our findings
support recent research findings that the negative eﬀects of weather shocks tend
to prevail in various economic outcomes, including non-agricultural outputs (e.g.,
Hsiang 2010; Jones and Olken 2010).
The remainder of this paper is organized as follows. Section 2 provides an
overview of flood and storm events in Southeast Asia. Section 3 presents theregression model and describes the data employed in the analyses. Section 4
discusses the empirical results. Finally, Section 5 concludes.

2

Floods and Storms in Southeast Asia

Southeast Asia, as part of the Asia-Pacific region, is one of the most disaster-prone
areas in the world. Figure 1 illustrates the annual number of flood events in four
Southeast Asian countries: Indonesia, Malaysia, the Philippines, and Thailand.
As shown in the figure, the number of floods has been steadily increasing over theyears. Floods, which were less frequent in the 1990s, have increased to about 10–25
times per year and intensified over the past decade. Figure 1 also depicts the extent
of damages caused by floods; the circle-shaped markers indicate damages in areas
proportional to the number of people aﬀected. The larger circles observed in recent
years denote the greater number of people aﬀected by the flooding. Similarly,

6

Figure 2 presents the annual number of storm events in Southeast Asia. While
the figure suggests only a modest increase in the number of storm events, storm
damages are likely to become larger in the subsequent years. The larger circles
in the figure indicate the severe impact of recent storms on countries in terms ofhuman damages.
Table 1 presents the five most extreme disasters that have occurred in Southeast
Asia between 1990 and 2016. These individual events caused the most costly
damages during this period. Flood and storm events are presented by country,
along with year of occurrence, economic damage, death toll, and number of people
aﬀected. As the table shows, most of the severe disasters have taken place after
2010. The most extensive flood occurred in Thailand, followed by Indonesia andthe Philippines. On the other hand, the five most damaging storms reported during
this period occurred in the Philippines.
The 2011 flooding in Thailand resulted in more than 800 deaths and aﬀected
9.5 million people across the country, causing economic damages of USD 40 billion.
The flood also reportedly destroyed 19,000 houses and damaged about 18,000 km2
of farmland (World Bank 2012). The record-breaking precipitation levels and multiple typhoons from May to September are largely responsible for the widespreadand prolonged flooding. The flood initiated in the northern region of Thailand, inundating 66 of the 77 provinces in the country and reaching the Bangkok metropolitan region by November. The primary reason for the devastating economic damageis that the flood aﬀected the central part of the country, where foreign manufacturing firms are accumulated in industrial parks (Oizumi 2012).1 Manufacturing
1

These areas are covered by the Chao Phraya River Basin that drains through the central
region to Bangkok and eventually, into the Gulf of Thailand.

7firms were forced to halt production because of the floodwaters that engulfed theseindustrial areas. According to the World Bank, the manufacturing sector suﬀered
the highest losses of approximately USD 32 billion from flood damages, followed by
the finance and banking, tourism, and housing sectors (World Bank 2012). Thailand’s GDP growth rate in 2011 was 0.84% compared to 7.51% in the previous
year. Industrial outputs are a key component of Thailand’s GDP; thus, such flood
damages are likely to hinder the country’s economic growth.
The Philippines is subject to a variety of natural hazards including floods andstorms, making it the world’s third highest country with the disaster risk (Kirch
et al. 2017). In 2013, the Philippines was hit by Typhoon Haiyan, locally known as
Yolanda, one of the strongest typhoons ever recorded in the country’s history. The
estimated damages by this category five typhoon were approximately USD 10,000
million and over 7,000 people were dead or missing. A total of 16 million people,
that is, 16% of the country’s population, were also aﬀected. Typhoon Haiyan significantly aﬀected the country’s agriculture, with more than USD 700 million indamages (FAO 2015). Crop production in the disaster-aﬀected areas was severely
damaged; for instance, the typhoon destroyed 161,400 ha of coconut farms, aﬀecting about one million farmers (Bowen 2015). Furthermore, the destruction of crop
production, such as coconut, could cause widespread damage as the Philippines is
the world’s second largest coconut producer. In addition to farmers, traders, wage
earners, processors, and other stakeholders in the industry were also aﬀected by
the typhoon (FAO 2015). Moreover, the typhoon caused severe damages to infrastructure, including electricity, roads, and seaports (World Bank 2013). Such lossesof critical infrastructure could disrupt local and global supply chains, preventing
the economy from recovering in the post-disaster period. The reconstruction of8

infrastructure, therefore, was highly emphasized in the rehabilitation process after
Haiyan. The Philippines was also struck by other storms that brought serious
damages. As Table 1 shows, subsequent typhoons have caused severe losses to thecountry in recent years.2

3

Empirical analysis

3.1

Empirical specification

Our empirical analysis aims to explore the eﬀects of disasters on international
trade. We collect data on disasters and exports to develop panel datasets for four
Southeast Asian countries: Indonesia, Malaysia, the Philippines, and Thailand.3
The dataset comprises unbalanced monthly panel data for the period from 2004
to 2016. The following specification is used to run the regressions:

Exportit = α +L∑

βj Disasterit−j + γi + δt + ϕim + εit ,

(1)

j=0

where Exportit is the export values in country i during time period t, and Disasterit−jdenotes either floods or storms. Country-specific fixed eﬀects are captured by γi ,controlling for unobserved heterogeneity across countries. δt captures month-byyear time fixed-eﬀects. These time-variant variables control for any external event
common to all countries in each time period. We also include country-by-month
fixed eﬀects denoted by ϕim , which accounts for country-specific seasonal trends
2

In addition, Myanmar was severely hit by Typhoon Nargis in 2008. More than 2.4 million
people were aﬀected and the number of fatalities exceeded 138,000 people. The typhoon caused
the second highest economic damages of USD 4,000 million; however, we do not include Myanmar
in our analysis owning to data availability.
3
The analysis includes these four countries because data on either disasters or exports areunavailable for other Southeast Asian countries.

9

aﬀecting production, such as national holidays and cultural events. εit is a disturbance term.
As shown in Equation 1, the disaster variable is subject to lags indexed by
j. We allow up to a 12-month lag for the disaster variable in the models withlag structures. It is possible that the eﬀects of disasters persist for more than one
period when the production of export goods is aﬀected by damages to factories andfarmlands. Natural disasters also cause the disruption of supply chains, leading
to a slowdown in production. Furthermore, severely damaged infrastructure, suchas roads, bridges, railways, and seaports, could create diﬃculties and delays inboth domestic and international shipping. The destruction of infrastructure may
also prevent workers from commuting to their workplaces. All of these factors can
reduce exports in the post-disaster period.

3.2

Data description

Table 2 presents the summary statistics of the variables used in the empiricalanalysis. We have three categories of export values as dependent variables. To
examine the impact on these export values, this study focuses on floods and storms
as representative of extreme weather events. For each disaster type, we introduce
alternative variables to evaluate its impact. These alternative variables can capture
the severity of the disasters.
Data on disasters are taken from the EM-DAT by the Centre for Research on
the Epidemiology of Disasters (CRED) (CRED and Guha-Sapir 2017). The EMDAT includes more than 22,000 natural and technological disasters worldwide.
The data are provided at the country level and based on information from multiple sources including international organizations, NGOs, national governments,
10and insurance companies. Each disaster in the EM-DAT meets one of the following
criteria: 10 or more people were reportedly killed, 100 or more people were reportedly aﬀected, a state of emergency was declared, or international assistance wascalled for. This database has been often utilized in previous studies exploring theeﬀects of natural disasters on socioeconomic activities (Anttila-Hughes and Hsiang
2013; El Hadri et al. 2017; Hsiang and Narita 2012; Leiter et al. 2009; Loayza et al.
2012; Oh and Reuveny 2010). For the purpose of the present research, we extract
data on floods and storms from the EM-DAT to examine their eﬀects on exports.
We first use a variable disaster event that represents the occurrence of disasters.
This dummy variable takes the value of one if a disaster has occurred at least once
during a month in a country. The summary statistics in Table 2 show that floods
are more frequent than storms in Southeast Asia during the sample period. On
average, the occurrence of floods is 0.3 per month, while that of storms is 0.1
per month. This tendency is in line with the long-term trends in Southeast Asia,
where floods are the most frequent disaster type followed by storms (Guha-Sapir
et al. 2016).
The dummy variable for disaster occurrence does not reflect the size and scaleof each disaster event. Therefore, we also introduce variables associated with adisaster event in terms of duration, human losses and damages, and economic
damages. Given that longer duration and greater damage indicate larger-scaledisasters, we use these variables to capture the severity of disasters. We constructvariable disaster duration considering the total number of disaster days in eachmonth. The duration of a disaster is calculated on the basis of its start and11end dates recorded in the EM-DAT.4 Similar to the occurrence of disasters, theduration of floods tends to be longer than that of storms; the mean duration is 3.3
days per month for floods and 0.6 days per month for storms (see Table 2). Floods
could persist for days or weeks, thus lasting longer than storms, which typicallydo not occur for more than a few days.
We also use human damages to measure the severity of disasters. Humandamages are denoted by death toll, defined as the number of deaths and missing
people, and people aﬀected, which is the number of the injured, aﬀected, and left
homeless as a result of a disaster. Contrary to the occurrence and duration ofdisasters mentioned above, storms tend to cause greater consequences in termsof damages related to mortality and injury. On average, storms lead to higher
number of deaths and people aﬀected than floods.
Moreover, we use two variables that measure economic damages from floods andstorms: economic damages and insured losses. The former is the total value of alldamages and economic losses related to the disaster and the latter denotes damages
covered by insurance companies. Unlike human damages, these two indicators
exhibit larger impacts from floods than storms.
Trade data are based on monthly export values in each country. The data are
obtained from the International Trade Centre (ITC), a joint agency by the World
Trade Organization and United Nations.5 The ITC database provides detailed
trade data that classify products into groups using the HS classification code,
thereby enabling us to investigate the eﬀects of disasters on exports by sector. We
4

In the case of disasters simultaneously occurring in diﬀerent regions of a country, we include
the durations for all such events in the calculation. This explains certain observations exhibiting
disaster duration that exceeds the number of total days in a given month.
5
Available at http://www.intracen.org/.

12

collect export values for all products and disaggregate export values at the twodigit HS code level. The latter is then aggregated to two major sectors: agriculture
and manufacturing. In this study, agricultural goods include products with HS
codes 1 to 15, whereas manufacturing goods mean products with HS codes 16 to
97.

4

Results

4.1

Main results

We run regressions to estimate the eﬀects of floods and storms on export values
using the fixed-eﬀects model. The regression models include up to a 12-monthlag in disaster variables. The models with lag structures allow us to investigate
whether a disaster eﬀect appears and persists in the post-disaster periods.
The regression results are presented in Table 3. Columns 1–4 show the results
for flood events and columns 5–8 show the results for storm events. For eachdisaster type, the models include variables with no lag, 3 lags, 6 lags, or 12 lags.
In addition, the bottom row of each column presents the total eﬀect of disasters,
calculated using the estimated coeﬃcients of the disaster variable and its lagged
variables.
According to the results in columns 1–4 in Table 3, the occurrence of floods isnegatively related to the export values for all products. In the first column, thecoeﬃcients of disaster event, that is, the immediate eﬀect of floods, are negativeand statistically significant: the occurrence of floods reduces export values by USD
557 million in a month. This implies that, on average, export losses from floods are13

approximately 4% of the total export values per month.6 The result of the modelwith no lag shows that exports are immediately aﬀected by floods. The eﬀect offloods is also observed in models with lags. In columns 2–4, the immediate impact
of floods remains significant, with export losses ranging from USD 305 million
to USD 405 million. The lagged variables are also negatively correlated with
export values. These results are mostly consistent in magnitude and statisticalsignificance, suggesting that the impact of floods appears two to three months
after the floods. When we add 12 lags, the eﬀect of floods is observed six months
after the disaster. In these lag models, the coeﬃcients of the cumulative eﬀect
at the bottom row are all negative and statistically significant. For instance, themodel with 12 lags indicates that the annual export loss from floods that occurredin a given month is USD 2.54 billion. This shows the total export losses fromfloods more than four times as large as those of USD 557 million estimated withoutlags in the first column. These results suggest that ignoring lagged eﬀects may
underestimate flood damages that persist for over a period of time.
Columns 5–8 in Table 3 present the estimation results for storm events. In
contrast to the results for floods, we find no statistically significant eﬀect of storms
on exports. While the eﬀect of floods is statistically significant both with andwithout lags, we find little evidence of storm impacts on export values from thevariable representing the disaster occurrence. One reason for the insignificancemight be the fact that the dummy variable of disaster occurrence does not fully
capture the diﬀerence in the scale of storm events. In Section 4.3, we conduct
the analysis by incorporating alternative variables that reflect the size of climate6

According to the summary statistics in Table 2, the mean export values for all products inthe sample are USD 12.68 billion.

14

related disasters.

4.2

Agricultural and manufacturing products

We estimate the eﬀect of floods and storms on the total export values in Section
4.1. The results suggest that extreme weather events contribute to reductions inexports. This leads us to further analysis on the relationship between disasters
and exports. This section investigates the eﬀect of floods and storms on exports,
with focus on specific components of exporting products. The export data areclassified into two industrial sectors using the two-digit HS code: agriculture andmanufacturing.
Table 4 presents the eﬀect of floods on exports by two product groups. Agricultural products are examined in columns 1–4 and manufacturing products arein columns 5–8. In the first column, the coeﬃcients of disaster event are negativeand statistically related to the export values of agricultural products. This immediate eﬀect is also correlated with export values when the models include lagged
variables, as shown in columns 2–4. The magnitude of these coeﬃcients is slightly
smaller than the model without lags, although the negative signs and statisticalsignificance remain consistent. The total eﬀect of flood variables suggests thatthe export losses of agricultural products from floods occurred in a given month
are USD 133 million in the model with no lag and USD 595 million in the modelwith 12 lags. Comparing these losses with the mean export values of agricultural
products in the sample (see Table 2), we find that the export losses from floods
per month are about 4–12% of the export values of agricultural products.
Similarly, we find that floods are associated with reductions in the export
values of manufacturing products. The model in the fifth column shows that15

the immediate impact of flood events is statistically related to export values ofmanufacturing products. The coeﬃcients of disaster event in columns 6–8 are
also statistically significant. Moreover, the eﬀect of floods is significant two to
three months after the disasters. The cumulative eﬀect at the bottom row ofTable 4 indicates that, in the model without lags, floods that occurred in a given
month reduce the export values of manufacturing products by USD 398 million.
In the model with 12 lags, floods are associated with export losses of USD 1.90
billion. Applying the same calculation as that for agricultural products, we findthat monthly export losses from floods are 1–3% of the mean export values for
manufacturing products.
The estimation results by product group suggest that both agricultural andmanufacturing sectors suﬀer export losses caused by floods. Related research by
Jones and Olken (2010) also investigates weather shocks on export values. Using
the two-digit SITC data, their study finds that higher temperature negatively
aﬀects agricultural and light manufacturing products in poor countries. In the
study on the relationship between temperature and economic growth, Dell et al.
(2012) find the impact of temperature on agricultural and industrial value-added
as potential channels for aggregate outputs. In line with these studies, our results
indicate that extreme weather events negatively aﬀect both sectors.
Table 5 presents the results for the impact of storms on the exports of agricultural and manufacturing products. Contrary to floods, we find no clear evidenceof storms aﬀecting exports. The coeﬃcients of storm variables are negative andstatistically significant in the first and second columns. These models suggest thatstorms are correlated with the export values of agricultural products. However,
the impact of storms does not remain when lags are added. As for manufacturing
16

products, we find no significant impact of storm variables, although the coeﬃcientsof these variables mostly show negative signs, as expected.

4.3

Alternative measures for disaster variables

We conduct additional analyses using alternative measures for disaster variables.
The five variables examined in this section depict the extent of eﬀect floods andstorms have on exports in terms of duration, human losses and damages, andeconomic damages. As described in Section 3.2, floods and storms have diﬀerent
characteristics in terms of disaster severity. Therefore, these variables may better
represent disaster impacts. We apply the same estimation strategy as used for themain results and examine the eﬀect of disasters using alternative measures.
We begin with a variable representing the duration of disasters. Table 6 shows
the results using the total days of flood and storm events during a month. Columns
1–4 show the negative impact of floods on export values for all products. In
the second column, the coeﬃcients of the immediate flood impact indicate thatan additional day of flooding increases export losses by USD 17 million. The
coeﬃcients of the immediate impact in other models also show negative signs,
but statistically insignificant. In the lag models, the flood eﬀect also appears
two months after the disaster. The coeﬃcients of these variables are all negativeand statistically significant. Columns 5–8 present the results using the total daysof storm events during a month. In these models, certain storm variables have apositive eﬀect on exports, which is contrary to our hypothesis. However, a positiveeﬀect becomes statistically insignificant when we estimate the model by productgroup (see Appendix Table A2).
Table 7 shows the eﬀect of floods and storms measured by human losses; that17

is, the number of deaths and missing people by disasters. In columns 2–4, we findthat exports are aﬀected by floods in the post-disaster periods. For instance, thecoeﬃcients of the two-month lagged variable in the second column indicate thatexport losses per death in a given month amount to USD 3 million. These flood
impacts are consistent in the remaining lag models. In the case of storms, wefind a persistent eﬀect of the disasters, as shown in columns 5–8. The coeﬃcientsof both immediate and lagged variables are negative and statistically significant,suggesting that the impact of storms lasts for five months. The cumulative eﬀectof storms shows that export losses are between USD 0.4 million (no lag) and USD
3 million (12 lags) per death. Interestingly, the magnitude of the coeﬃcients ofstorm variables is smaller than that of flood variables, whereas we find the eﬀectof storms to be more persistent than that of floods. These findings suggest thatfloods and storms may aﬀect exports diﬀerently. When measured in terms ofhuman losses, floods are associated with larger and temporary export losses, whilestorms with the same levels of severity lead to smaller and persistent export losses.
When the disaster variable is measured by the number of people aﬀected, theestimation results show similar patterns to those for the number of deaths. Table 8
presents the regression results with the number of people aﬀected by disasters. In
the lag models in columns 2–4, the coeﬃcients of the two-month lagged variable arecorrelated with export values, indicating that export losses from floods are USD
0.2–0.3 million per 1,000 people aﬀected. In columns 5–8, we find a negative andstatistically significant eﬀect of storms that persists up to four months after thedisaster. In the fifth column, for example, the coeﬃcients suggest that economic
losses from storms are USD 0.1 million per 1,000 people aﬀected. These results
show coeﬃcient patterns similar to those for the death toll variable. That is, the18coeﬃcients of each storm variable are smaller but the post-disaster eﬀect persists
for a longer period. We again find that the eﬀect of floods tends to be larger andtemporary, while the eﬀect of storms tends to be smaller and persistent. Together
with the death toll variable, these findings show that the indicators for humanlosses and damages predict reductions in export values.
Furthermore, we estimate the eﬀect of floods and storms on the basis of economic damages, presented in Table 9. The eﬀect of floods appears in lagged
variables, as shown in columns 2–4. The coeﬃcients of the two- and three-month
lagged variables are negative and significantly related to export values. On theother hand, we find a positive eﬀect of floods in the four-month lagged variable,
as shown in columns 3–4. For the storm variables, the impact on exports appears
immediately and persistently in all models. The coeﬃcients of these storm variables are negative and a significant eﬀect persists for four months. In the modelwith no lag, economic damages of USD one million by storms are associated with areduction in export values of USD 0.2 million.7 In the models with lags, the total
eﬀect of storms in the bottom row of Table 9 shows that the estimated export
losses are approximately USD 0.8–1.3 million.
We also estimate the models with the disaster variable measured by damages
covered by insurance. Table 10 presents the results using insured losses as a proxyfor disaster impacts. In columns 2–4, the flood variables with two- and three-monthlags are negatively correlated with export values. We also find a positive sign for
the four-month lagged variable in the models with more than six lags. Columns
5–8 show the immediate and persistent impact of storms on export values. These7

A response from the CRED to our inquiry suggests that economic damages and insured
losses recorded in the EM-DAT do not include losses in export values.

19models suggest that the immediate storm eﬀect causes export values to decrease byapproximately USD 3 million. When lagged variables are included, the cumulativeeﬀect of storms indicates export losses of USD 11–19 million.
In addition, we separately examine the eﬀect on the export values of agricultural and manufacturing products using alternative disaster variables discussed
in this section. The results by product category show that both agriculture andmanufacturing are aﬀected by floods and storms. This supports our findings in
the previous sections that both agricultural and manufacturing sectors account forexport losses from disasters. Furthermore, we find a significant eﬀect of lagged variables, suggesting that the eﬀect of floods and storms persists in the post-disasterperiods. These results are presented in Appendix Tables A1 to A10.

5

Conclusions

This paper examines the eﬀect of natural disaster shocks on exports in Southeast
Asia. We focus on the two most frequent weather-related disasters: floods andstorms. The panel dataset comprises monthly export data for four Southeast
Asian countries for the past two decades. In the empirical analysis, disasters aremeasured by occurrence and severity to examine the extent to which floods andstorms aﬀect exports.
The regression results show a significant eﬀect of natural disasters on exports,suggesting that flood and storm events reduce export values. In the case of floods,
we find that the occurrence of flooding is significantly correlated with total export
values. The findings suggest that the immediate eﬀect of floods leads to a reduction
in exports by USD 305–557 million. This study also analyzes models with lag

20

structures and finds flood impacts in the post-disaster periods. When considering
12-month lags, we find that the total eﬀect of floods occurred in a given monthdecreases export values by USD 2.54 billion. The eﬀect of storms is also found in
the case of alternative disaster variables. For instance, we use human losses as adisaster variable and find a negative and significant impact of storms. Moreover,
the results show that storm eﬀects appear immediately and persistently in the postdisaster periods. With 12-month lags, we find that storm impacts are associatedwith export losses of USD 3 million per death.
When disasters are measured by human losses and damages, we find that floods
and storms aﬀect diﬀerently in terms of size and length. The results show thatfloods are associated with larger export losses that appear in a relatively shortperiod of time. On the other hand, estimated export losses incurred by storms are
smaller but more persistent than those by floods. These findings suggest that floods
and storms have diﬀerent implications on trade in Southeast Asia. In addition, thismay also suggest that each disaster type should be examined individually whenevaluating their impacts.
Furthermore, our study suggests that floods and storms have a significant impact on exports across industrial sectors. We find that extreme weather events
negatively aﬀect agricultural and manufacturing products. Climatic impacts on
agriculture is in line with the findings presented in the existing literature (for example, Dell et al. 2012; Jones and Olken 2010). In addition, we also find extreme
weather eﬀects on manufacturing, which have not been largely emphasized in previous studies. This result also oﬀers support to the findings of studies examining
the eﬀect of weather shocks on firm-level labor productivity (Cachon et al. 2012;
Li et al. 2016).
21

The results of our study suggest international trade as a channel through whichclimate may aﬀect aggregate economic outputs. A better understanding of thechannels and mechanisms of the relationship between climate and economy is important to achieve economic development while facing the risks of climate change.
For developing countries, particularly those where exports play a crucial role inboosting economic growth, disaster shocks can be a great concern that needs immediate policy responses. Such issues are also related to developed countries,
whose firms are located in disaster-prone regions and who trade various products
through a global supply chain. Therefore, this study highlights the need for business entities and policymakers to pay more attention to the risks associated withextreme weather events in the context of international trade.

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26

Fig. 1 Number of flood events in Southeast Asia

25

Occurrence

20

15

10

5

0
1990

1995

2000

2005

2010

Year
Note: The areas of symbol are propotional to the number of poeple affected by floods.
The countries include Indonesia, Malaysia, the Philippines, and Thailand.
Source: CRED and Guha-Sapir (2017)

27

2015

Fig. 2 Number of storm events in Southeast Asia

25

Occurrence

20

15

10

5

0
1990

1995

2000

2005

2010

Year
Note: The areas of symbol are propotional to the number of poeple affected by storms.
The countries include Indonesia, Malaysia, the Philippines, and Thailand.
Source: CRED and Guha-Sapir (2017)

28

2015

Table 1 Severe disasters in Southeast Asia (1990–2016)

Flood

1
2
3
4
5

CountryYear

ThailandIndonesia
Philippines
ThailandIndonesia

2011–2012
2013
2013
1993
2007

Economic damage Death toll
(million USD)
40000
3000
2190
1261
971

People aﬀected

813
34
31
23
68

9500000
2488463096422393809217087People aﬀected

Storm

1
2
3
4
5

CountryYear

Economic damage Death toll
(million USD)

Philippines
Philippines
Philippines
Philippines
Philippines

2013
2015
2012
2014
2009

10000
1500
898
821
585

7354
5
1901
111
512

Note: The countries include Indonesia, Malaysia, the Philippines, and Thailand.
Source: CRED and Guha-Sapir (2017)

2916106870
14100
624666446549664478491

Table 2 Summary statistics of variables

Variable

Unit

Mean

Export values
All products
Agriculture
Manufacturing

million USD
million USD
million USD

12682.00 5421.28
1116.74 687.95
11470.72 5057.29

Flood variables
Disaster event
Disaster duration
Death toll
People aﬀected
Economic damages
Insured losses

dummy
day
people1000 people
million USD
million USD

0.34
3.32
8.48
94.29
96.30
20.59

0.47
6.88
28.69
407.43725.55177.72

0
0
0
0
0
0

Storm variables
Disaster event
Disaster duration
Death toll
People aﬀected
Economic damages
Insured losses

dummy
day
people1000 people
million USD
million USD

0.13
0.57
26.79
150.13
31.00
1.41

0.33
2.16
339.36
949.07444.31
30.52

0
1
0
17
0 73540 161070 10000
0
700

30

S.D.

Min

Max

2506 21777
95 29221756 20418
1
39
329
4452
6667
1667

Table 3 Main regression results
Export values
All products
Flood
No lag(1)

Storm
6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-557.457∗ -405.375∗∗∗(194.199) (27.940)-305.171∗∗∗(21.417)-384.119∗∗(79.775)-305.084(243.706)-191.187(221.550)-40.815(277.688)

63.646(344.284)

L.Disaster event-209.778(100.135)-115.623(123.786)-170.422(129.548)-265.623(117.323)-204.404(134.623)-153.459(231.824)

L2.Disaster event-329.010∗(105.882)-268.624∗∗(56.488)-227.486∗∗∗(34.020)-289.756(291.747)-258.790(284.033)-53.459(354.475)

L3.Disaster event-510.313∗∗(137.003)-431.765∗∗(73.970)-309.799∗(121.149)-329.845(165.792)-314.262(162.230)-20.048(301.362)

L4.Disaster event-197.135(106.083)-79.057(127.973)

31.311(139.400)

234.526(264.730)

L5.Disaster event-247.066(117.225)-148.174(96.431)50.201(67.953)304.470(152.548)

L6.Disaster event-386.569(200.050)-345.671∗∗(104.243)

273.503(288.492)

451.650(343.632)

504
0.74
Yes
Yes

480
0.73
Yes
Yes

528
0.73
Yes
Yes

504
0.71
Yes
Yes

480
0.71
Yes
Yes

-1951.953∗∗(404.555)-2538.317∗∗(663.140)-305.084 -1076.411(243.706) (723.620)

Disaster event

Observation
528
Adjusted R2
0.74
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of disaster∗3 lags
(2)

516
0.74
Yes
Yes

-557.457∗ -1454.476∗∗(194.199) (281.738)

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

31

516
0.72
Yes
Yes

-463.254
3033.941(1111.963) (3255.272)

Table 4 The eﬀect of floods on exports by product group
Export values
Agriculture

Manufacturing

No lag(1)

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

6 lags(7)

12 lags(8)-132.590∗(45.669)-93.699∗(29.942)-74.925∗(29.219)-87.857∗(34.868)-397.667∗ -312.017∗∗∗(158.497) (37.146)-262.055∗∗(46.122)-307.688∗∗(80.658)

L.Disaster event-66.396∗(22.346)-57.900∗∗(15.497)-68.578∗∗(20.817)-127.110(86.775)-56.404(115.037)-111.895(123.605)

L2.Disaster event-11.750(52.666)-1.451(53.462)3.601(63.862)-287.894∗∗(70.245)-250.523∗∗∗(36.251)-256.589∗∗(55.246)

L3.Disaster event-139.440∗∗(36.767)-104.520∗(35.684)-83.436(43.685)-345.285∗(108.653)-320.366∗∗(63.612)-274.561∗(108.972)

L4.Disaster event-73.243∗(24.717)-83.037∗(32.112)-102.097(100.068)-22.318(109.229)

L5.Disaster event-52.903∗(19.844)-44.988(20.210)-165.673(95.148)-112.246(83.206)

L6.Disaster event-91.467∗∗∗(6.090)-82.156∗∗(14.460)-267.516(189.520)-272.337(126.402)

Disaster event

3 lags(6)

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.58
Yes
Yes

516
0.59
Yes
Yes

504
0.60
Yes
Yes

480
0.58
Yes
Yes

528
0.75
Yes
Yes

516
0.74
Yes
Yes

504
0.73
Yes
Yes

480
0.71
Yes
Yes

Total eﬀect of flood-132.590∗(45.669)-311.285∗(103.543)-456.408∗∗(124.503)-594.562∗(209.736)-397.667∗(158.497)-1072.306∗∗(244.549)-1424.634∗∗(408.957)-1902.437∗(724.798)∗

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

32

Table 5 The eﬀect of storms on exports by product group
Export values
Agriculture

Manufacturing

No lag(1)

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)-65.822∗∗(11.685)-56.719∗∗(10.905)-22.375(14.997)

2.055(14.503)-245.341 -153.623(231.567) (223.914)-83.752
25.030(265.555) (323.562)

L.Disaster event-60.193(29.445)-44.553(35.135)-17.956(39.920)-241.056(119.259)-221.483 -195.409(132.473) (216.165)

L2.Disaster event

28.419(99.055)

35.940(99.217)90.321(113.357)-349.256(199.712)-334.170 -220.404(195.151) (251.423)

L3.Disaster event-43.349∗(17.300)-45.646(19.505)

5.759(23.485)-318.764(148.680)-309.450 -132.371(143.256) (261.776)

L4.Disaster event-51.776(56.461)0.152(58.097)28.763(78.753)

119.240(178.900)

L5.Disaster event

33.074(39.573)

71.464(44.057)-15.785(63.942)114.679(119.415)

L6.Disaster event

112.282(51.428)138.476∗(50.917)148.327249.578(264.044) (316.389)

516
0.55
Yes
Yes

504
0.54
Yes
Yes

480
0.54
Yes
Yes

-131.843(108.268)

16.946(155.716)

821.381-245.341 -1062.699(371.173) (231.567) (619.940)

Disaster event

Observation
528
Adjusted R2
0.57
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of storm∗-65.822∗∗(11.685)

528
0.74
Yes
Yes

3 lags
(6)

516
0.73
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

33

6 lags
(7)

504
0.71
Yes
Yes

12 lags
(8)

480
0.69
Yes
Yes

-787.549 1601.853(971.417) (2811.712)

Table 6 The eﬀect of disasters on exports, measured by duration
Export values
All products
Flood
No lag(1)

Storm
6 lags(3)

12 lags(4)

No lag(5)-22.264 -16.924∗(9.800) (6.530)-12.926(6.384)-6.584(10.618)

22.81624.74535.635∗ 5.934(18.138) (12.761) (14.937) (21.778)

L.Disaster duration-5.242(9.547)-3.945(11.224)-6.540(7.912)

4.636
9.949
(15.771) (16.275)-35.758(26.086)

L2.Disaster duration-24.533∗∗∗(0.998)-22.615∗∗∗(2.511)-19.653∗(7.334)

6.951
5.640
(23.362) (22.308)-7.685(14.223)

L3.Disaster duration-31.914(18.008)-26.902(15.276)-21.737(14.286)-38.977 -47.537(20.569) (21.262)-46.026(28.164)

L4.Disaster duration-10.135(12.507)-7.772(11.898)

23.965∗∗(6.742)17.896**(4.399)L5.Disaster duration-21.660(10.440)-18.438(7.987)23.41241.181**(16.155) (12.371)

L6.Disaster duration-26.159(16.925)-19.167(12.070)

16.24119.832(37.425) (41.575)

504
0.74
Yes
Yes

480
0.72
Yes
Yes

-124.344(59.027)-167.415 22.816-2.64667.30559.592(105.264) (18.138) (52.419) (53.734) (148.479)

Disaster duration

Observation
528
Adjusted R2
0.73
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of disaster∗3 lags
(2)

516
0.74
Yes
Yes

-22.264 -78.613∗(9.800) (31.627)

528
0.73
Yes
Yes

3 lags
(6)

516
0.72
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

34

6 lags
(7)

504
0.71
Yes
Yes

12 lags
(8)

480
0.70
Yes
Yes

Table 7 The eﬀect of disasters on exports, measured by death toll
Export values
All products
Flood
No lag(1)

Storm
3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-5.852 -4.076(2.563) (2.800)-3.623(2.977)-2.606(3.118)-0.380∗∗(0.074)-0.400∗∗(0.081)-0.401∗∗(0.083)-0.328**(0.082)

L.Death toll

-1.206(1.375)-0.857(1.346)-1.946(1.834)-0.363∗(0.126)-0.367∗(0.128)-0.330*(0.135)L2.Death toll

-3.338∗∗(0.582)-3.264∗∗(0.702)-2.941(1.294)-0.393∗∗(0.083)-0.398∗∗(0.085)-0.391**(0.089)L3.Death toll

-5.038(2.733)-4.121∗∗(0.972)-3.929∗∗(1.219)-0.350∗∗(0.063)-0.364∗∗(0.065)-0.358**(0.069)L4.Death toll

0.345
(3.520)0.375(4.203)-0.216∗∗∗(0.024)-0.217***(0.026)L5.Death toll

-2.148(2.010)-1.653(1.063)-0.301∗(0.096)-0.304*(0.102)L6.Death toll

-0.118(3.445)-0.062(1.581)-0.186(0.110)-0.196(0.116)

504
0.72
Yes
Yes

480
0.70
Yes
Yes

528
0.73
Yes
Yes

516
0.73
Yes
Yes

504
0.73
Yes
Yes

480
0.71
Yes
Yes

-13.786(11.285)-5.920(15.553)-0.380∗∗(0.074)-1.507∗∗(0.338)-2.233∗∗(0.505)-2.919∗∗(0.785)

Death toll

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.73
Yes
Yes

Total eﬀect of disaster-5.852 -13.657(2.563) (5.899)

∗

516
0.73
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

35

Table 8 The eﬀect of disasters on exports, measured by people aﬀectedExport values
All products
Flood
No lag(1)

Storm
3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)

People aﬀected-0.434 -0.264 -0.261 -0.231(0.276) (0.181) (0.182) (0.169)-0.141∗∗(0.026)-0.153∗∗(0.029)-0.152∗∗(0.033)-0.119**(0.037)

L.People aﬀected-0.228 -0.203(0.159) (0.149)-0.160(0.149)-0.157∗(0.055)-0.161∗(0.058)-0.155*(0.057)

L2.People aﬀected-0.295∗ -0.290∗(0.110) (0.098)-0.233∗(0.094)-0.138∗∗(0.037)-0.145∗∗(0.041)-0.129*(0.041)L3.People aﬀected-0.432 -0.391(0.338) (0.212)-0.368(0.189)-0.156∗∗(0.031)-0.162∗∗(0.034)-0.143**(0.043)L4.People aﬀected

0.049
0.070
(0.239) (0.208)-0.086∗∗∗(0.013)-0.087***(0.014)L5.People aﬀected-0.202 -0.243(0.210) (0.173)-0.095(0.042)-0.079(0.049)L6.People aﬀected-0.204 -0.163(0.390) (0.267)-0.053(0.052)-0.039(0.060)Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.73
Yes
Yes

Total eﬀect of disaster-0.434 -1.219 -1.501 -1.639(0.276) (0.690) (1.312) (2.013)

∗

516
0.74
Yes
Yes

504
0.73
Yes
Yes

480
0.71
Yes
Yes

528
0.73
Yes
Yes

516
0.73
Yes
Yes

504
0.73
Yes
Yes

480
0.71
Yes
Yes

-0.141∗∗(0.026)-0.603∗∗(0.151)-0.856∗∗(0.254)-0.618(0.338)p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

36

Table 9 The eﬀect of disasters on exports, measured by economic damages
Export values
All products
Flood
No lag(1)

Storm
3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.116 -0.017(0.223) (0.198)

0.034(0.239)0.072(0.236)-0.225∗∗∗(0.025)-0.230∗∗∗(0.026)-0.231∗∗∗(0.026)-0.189***(0.030)L.Economic damages

0.077(0.051)

0.073(0.051)0.040(0.024)-0.189∗(0.075)-0.193∗(0.076)-0.160(0.075)

L2.Economic damages-0.273∗∗(0.049)-0.284∗∗∗(0.043)-0.295∗∗∗(0.037)-0.214∗∗(0.042)-0.217∗∗(0.044)-0.202**(0.046)

L3.Economic damages

0.014(0.106)-0.187∗∗∗(0.028)-0.180∗∗∗(0.024)-0.190∗∗∗(0.029)-0.193∗∗∗(0.030)-0.180**(0.033)L4.Economic damages

0.178∗∗∗(0.029)0.196∗∗∗(0.032)-0.087∗∗(0.024)-0.084**(0.025)L5.Economic damages-0.021(0.047)0.007(0.058)-0.169(0.083)-0.164(0.089)L6.Economic damages

0.130(0.163)0.161(0.129)-0.088(0.076)-0.089(0.079)

504
0.72
Yes
Yes

480
0.71
Yes
Yes

528
0.73
Yes
Yes

516
0.73
Yes
Yes

504
0.72
Yes
Yes

480
0.70
Yes
Yes

-0.077(0.366)0.092(0.525)-0.225∗∗∗(0.025)-0.823∗∗(0.159)-1.180∗∗(0.284)-1.265∗(0.505)

Economic damages

Observation
528
Adjusted R2
0.73
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of disaster
∗

516
0.73
Yes
Yes

-0.116 -0.198(0.223) (0.273)

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

37

Table 10 The eﬀect of disasters on exports, measured by insured losses
Export values
All products
Flood
No lag(1)

Storm
3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.510 -0.088(0.976) (0.966)

0.146(1.174)0.333(1.210)-3.289∗∗(0.566)-3.289∗∗(0.569)-3.270∗∗(0.579)-3.085**(0.551)

L.Insured losses

0.307(0.361)

0.261(0.307)0.131(0.204)-2.677∗(1.017)-2.685∗(1.026)-2.305(1.024)L2.Insured losses-1.202∗∗∗(0.130)-1.148∗∗∗(0.147)-1.249∗∗∗(0.130)-2.844∗∗(0.712)-2.844∗∗(0.717)-2.827**(0.743)L3.Insured losses

0.208(0.496)-0.753∗∗(0.185)-0.710∗∗(0.156)-2.277∗∗(0.587)-2.277∗∗(0.591)-2.254**(0.615)L4.Insured losses

0.817∗∗∗(0.064)0.922∗∗(0.169)-1.218∗∗(0.215)-1.206**(0.232)

L5.Insured losses-0.157(0.310)-0.158(0.290)-2.230(1.217)-2.221(1.258)L6.Insured losses

0.542(0.836)0.789(0.931)-1.071(1.076)-1.056(1.102)

504
0.72
Yes
Yes

480
0.70
Yes
Yes

528
0.73
Yes
Yes

516
0.73
Yes
Yes

504
0.72
Yes
Yes

480
0.70
Yes
Yes

-0.292(1.623)0.430(2.299)-3.289∗∗(0.566)-11.088∗∗(2.646)-15.595∗∗(4.590)-18.585∗(7.828)Insured losses

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.73
Yes
Yes

Total eﬀect of disaster-0.510 -0.775(0.976) (1.182)

∗

516
0.72
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

38

Appendix

Table A1 The eﬀect of floods on exports, measured by duration: agricultural and manufacturing
products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-5.228 -3.875(3.413) (2.522)-2.931(2.944)-2.381(3.549)-11.991(11.405)-9.606(8.267)-7.725(8.498)-4.216(11.168)

L.Disaster duration-1.976(1.652)-2.044(1.632)-2.006(1.738)-1.349(8.215)-0.257(9.739)-4.231(7.639)L2.Disaster duration-0.177(3.171)

0.134(3.522)

1.017(4.428)-20.998∗∗∗(3.247)-19.879∗∗(4.343)-21.074∗(7.356)

L3.Disaster duration-7.281∗∗(1.863)-5.283∗(1.761)-4.172∗(1.689)-20.417(14.821)-18.885(12.628)-16.976(13.393)

L4.Disaster duration-3.663(1.584)-3.752(1.977)-3.891(10.990)-2.563(11.522)

L5.Disaster duration-4.379∗∗(0.763)-4.364∗∗∗(0.716)-14.259(6.795)-12.851(6.024)L6.Disaster duration-5.600(3.320)-3.187(4.017)-17.523(13.032)-14.554(9.497)

504
0.57
Yes
Yes

480
0.55
Yes
Yes

504
0.72
Yes
Yes

480
0.70
Yes
Yes

-82.419(48.605)-118.343(90.775)

Disaster duration

Observation
528
Adjusted R2
0.57
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of flood
∗

516
0.57
Yes
Yes

-5.228 -13.309(3.413) (7.693)-23.767 -30.126(11.730) (18.162)

528
0.74
Yes
Yes

516
0.74
Yes
Yes

-11.991 -52.370(11.405) (27.968)

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

39

Table A2 The eﬀect of storms on exports, measured by duration: agricultural and manufacturing
products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag
(5)

2.789
2.768
(7.491) (5.841)

4.574(6.881)-0.520(7.731)

5.568
10.599(12.632) (9.417)

L.Disaster duration

0.172(3.544)0.829(4.052)-0.329(6.821)-4.822-2.035(13.149) (13.531)-34.460(26.046)

L2.Disaster duration

4.593(7.122)4.358(7.440)

4.918(10.176)-5.404-6.745(17.222) (15.709)-16.788(7.425)L3.Disaster duration-4.885∗∗(1.313)-4.839∗∗(1.037)-4.805∗(1.576)-46.810∗ -49.558∗(18.635) (19.492)-52.485(27.772)

L4.Disaster duration

0.804(3.129)3.980(7.517)9.883(9.382)

0.039(11.109)

L5.Disaster duration

0.267(4.948)1.696(5.041)9.728
18.501(20.243) (16.101)

L6.Disaster duration

1.949(6.544)3.682(6.725)-3.732
1.814(34.986) (38.346)

504
0.54
Yes
Yes

480
0.51
Yes
Yes

Disaster duration

Observation
528
Adjusted R2
0.56
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of storm
∗

516
0.55
Yes
Yes

528
0.74
Yes
Yes

2.789
2.648
7.942
31.0975.568(7.491) (16.899) (29.672) (74.360) (12.632)

3 lags
(6)

516
0.73
Yes
Yes

6 lags(7)

14.781-0.584(11.346) (23.214)

504
0.71
Yes
Yes

480
0.69
Yes
Yes

-46.436 -27.678 -137.004(38.274) (51.454) (132.982)

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

40

12 lags(8)

Table A3 The eﬀect of floods on exports, measured by death toll: agricultural and manufacturing products
Export values
Agriculture

Manufacturing

No lag(1)

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-1.793∗∗(0.431)-1.154(0.518)-0.741(0.517)-0.651(0.339)-3.485 -2.832(2.160) (2.386)-3.003(2.790)-2.188(2.795)

L.Death toll

-0.288(0.363)-0.175(0.361)-0.325(0.411)-0.736(1.353)-0.543(1.487)-1.406(2.107)L2.Death toll

-1.124∗(0.419)-1.015∗(0.392)-0.747(0.560)-1.953∗∗(0.397)-2.111∗(0.721)-2.186∗(0.794)L3.Death toll

-1.101∗∗(0.298)-0.248(0.456)0.189(0.415)-3.371(1.896)-3.666∗∗∗(0.425)-4.205∗∗(0.901)L4.Death toll

-1.112(0.651)-1.383(0.856)1.621(2.698)1.654(3.389)L5.Death toll

-1.875∗∗(0.485)-1.703∗∗∗(0.147)-0.096(1.278)-0.077(0.926)L6.Death toll

-0.456(0.302)0.513∗(0.185)0.824(2.727)-0.574(1.302)

516
0.58
Yes
Yes

504
0.59
Yes
Yes

480
0.57
Yes
Yes

528
0.75
Yes
Yes

504
0.71
Yes
Yes

480
0.69
Yes
Yes

-3.667∗(1.449)-5.622(2.724)-7.112(3.785)-3.485 -8.891(2.160) (4.133)-6.973(7.474)2.313(10.746)

Death toll

Observation
528
Adjusted R2
0.58
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of flood∗-1.793∗∗(0.431)

516
0.73
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

41

Table A4 The eﬀect of storms on exports, measured by death toll: agricultural and manufacturing products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.036 -0.037(0.027) (0.027)-0.035(0.027)-0.017(0.018)-0.305∗∗∗(0.050)-0.323∗∗(0.057)-0.326∗∗(0.059)-0.301∗∗(0.064)

L.Death toll

-0.012(0.007)-0.012(0.007)-0.015(0.007)-0.316∗(0.125)-0.319∗(0.127)-0.308(0.134)L2.Death toll

-0.019∗∗(0.005)-0.019∗∗(0.005)-0.016∗∗(0.005)-0.349∗∗(0.078)-0.354∗∗(0.080)-0.354∗∗(0.084)L3.Death toll

-0.027(0.032)-0.028(0.034)-0.026(0.034)-0.298∗∗(0.056)-0.310∗∗(0.058)-0.310∗∗(0.065)L4.Death toll

-0.021(0.040)-0.021(0.041)-0.167∗∗(0.034)-0.171∗∗(0.039)L5.Death toll

0.018
(0.013)0.019(0.013)-0.293∗∗(0.086)-0.297∗∗(0.092)L6.Death toll

0.007
(0.016)0.008(0.018)-0.162(0.115)-0.172(0.124)

504
0.54
Yes
Yes

480
0.51
Yes
Yes

528
0.75
Yes
Yes

516
0.74
Yes
Yes

504
0.72
Yes
Yes

480
0.70
Yes
Yes

-0.089(0.099)-0.090(0.193)-0.305∗∗∗(0.050)-1.286∗∗(0.296)-1.930∗∗(0.466)-2.443∗(0.855)

Death toll

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of storm-0.036 -0.094(0.027) (0.054)

∗

516
0.55
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

42

Table A5 The eﬀect of floods on exports, measured by people aﬀected: agricultural and manufacturing products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)-0.087 -0.056(0.055) (0.036)-0.054(0.034)-0.041(0.029)-0.314(0.248)-0.194 -0.195 -0.186(0.176) (0.177) (0.173)

L.People aﬀected-0.043 -0.035(0.032) (0.033)-0.028(0.029)-0.174 -0.155 -0.127(0.116) (0.106) (0.120)

L2.People aﬀected-0.032 -0.030(0.042) (0.044)-0.033(0.041)-0.244 -0.243∗(0.112) (0.098)-0.197(0.095)L3.People aﬀected-0.087 -0.071(0.053) (0.049)-0.058(0.045)-0.320 -0.309(0.260) (0.148)-0.308(0.141)L4.People aﬀected

0.001(0.034)-0.002(0.033)

0.063
0.067
(0.216) (0.189)

L5.People aﬀected-0.065∗∗∗(0.010)-0.068∗∗(0.013)-0.139 -0.170(0.196) (0.164)

L6.People aﬀected-0.064(0.045)-0.046(0.031)-0.129 -0.118(0.330) (0.231)

504
0.56
Yes
Yes

480
0.53
Yes
Yes

528
0.75
Yes
Yes

516
0.74
Yes
Yes

-0.317(0.149)-0.379(0.231)-0.314(0.248)-0.932 -1.108 -1.194(0.579) (1.118) (1.784)

People aﬀected

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of flood-0.087 -0.217(0.055) (0.104)

∗

516
0.57
Yes
Yes

6 lags
(7)

504
0.72
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

43

12 lags
(8)

480
0.70
Yes
Yes

Table A6 The eﬀect of storms on exports, measured by people aﬀected: agricultural and manufacturing products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

3 lags(6)

6 lags(7)

12 lags(8)-0.020∗ -0.020∗(0.008) (0.008)-0.019(0.009)-0.013 -0.101∗∗(0.006) (0.020)-0.112∗∗(0.024)-0.114∗∗(0.028)-0.104∗(0.036)

L.People aﬀected-0.007(0.008)-0.006(0.008)-0.006(0.009)-0.134(0.060)-0.139(0.063)-0.150∗(0.062)L2.People aﬀected-0.011∗∗(0.002)-0.011∗∗(0.002)-0.006(0.005)-0.117∗(0.037)-0.124∗(0.041)-0.121∗(0.041)L3.People aﬀected-0.014(0.010)-0.014(0.010)-0.011(0.010)-0.132∗∗(0.029)-0.138∗∗(0.032)-0.128∗(0.042)L4.People aﬀected-0.008(0.011)-0.007(0.012)-0.070∗∗∗(0.011)-0.073∗∗(0.012)L5.People aﬀected

0.006(0.008)0.009(0.008)-0.092(0.043)-0.079(0.049)L6.People aﬀected

0.003(0.007)0.006(0.008)-0.043(0.052)-0.031(0.061)

504
0.54
Yes
Yes

480
0.51
Yes
Yes

516
0.74
Yes
Yes

504
0.72
Yes
Yes

480
0.70
Yes
Yes

-0.049(0.021)

0.004-0.101∗∗(0.065) (0.020)-0.495∗∗(0.145)-0.720∗(0.247)-0.429(0.377)People aﬀected

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of storm-0.020∗ -0.053∗∗(0.008) (0.009)

∗

516
0.55
Yes
Yes

No lag
(5)

528
0.75
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

44

Table A7 The eﬀect of floods on exports, measured by economic damages: agricultural andmanufacturing products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.029 -0.020(0.035) (0.035)-0.026(0.041)-0.021(0.044)-0.097 -0.006(0.192) (0.172)

0.049(0.207)0.085(0.209)L.Economic damages

0.040(0.028)0.038(0.028)0.033(0.026)0.034(0.030)0.033(0.029)0.008(0.023)L2.Economic damages-0.033∗∗(0.009)-0.033∗∗(0.010)-0.035∗∗(0.009)-0.238∗∗(0.043)-0.250∗∗∗(0.035)-0.261∗∗∗(0.034)L3.Economic damages-0.038(0.029)-0.040(0.048)-0.038(0.042)0.051(0.073)-0.146∗∗∗(0.023)-0.141∗∗(0.026)

L4.Economic damages

0.030(0.022)0.031(0.018)0.147∗(0.049)0.161∗∗(0.049)L5.Economic damages-0.010(0.026)-0.018(0.025)-0.009(0.022)0.025(0.040)L6.Economic damages-0.021(0.028)0.001(0.024)0.144(0.126)0.157(0.110)

504
0.55
Yes
Yes

480
0.52
Yes
Yes

528
0.74
Yes
Yes

504
0.72
Yes
Yes

480
0.69
Yes
Yes

-0.061(0.052)-0.080(0.072)-0.097 -0.159(0.192) (0.228)-0.031(0.308)0.156(0.443)

Economic damages

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of flood-0.029 -0.051(0.035) (0.040)

∗

516
0.56
Yes
Yes

516
0.73
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

45

Table A8 The eﬀect of storms on exports, measured by economic damages: agricultural andmanufacturing products
Export values
Agriculture
No lag(1)
Economic damages

3 lags(2)

Manufacturing
6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.163∗∗∗(0.023)-0.167∗∗∗(0.024)-0.168∗∗∗(0.026)-0.157∗∗(0.040)-0.026 -0.026(0.027) (0.027)-0.026 -0.014(0.028) (0.021)

L.Economic damages

0.002(0.004)

0.002
0.004
(0.005) (0.006)-0.158(0.078)-0.162(0.079)-0.153(0.078)L2.Economic damages-0.008∗∗(0.002)-0.008∗(0.003)-0.005(0.002)-0.179∗∗(0.044)-0.182∗∗(0.047)-0.178∗∗(0.048)L3.Economic damages-0.017(0.023)-0.017(0.023)-0.015(0.023)-0.149∗∗(0.042)-0.151∗∗(0.044)-0.146∗(0.047)L4.Economic damages-0.010 -0.010(0.032) (0.031)-0.050(0.055)-0.052(0.056)

L5.Economic damages

0.012
0.013
(0.009) (0.010)-0.157(0.080)-0.156(0.087)

L6.Economic damages

0.001
0.001
(0.018) (0.019)-0.065(0.093)-0.065(0.097)

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of storm-0.026 -0.049(0.027) (0.046)

∗

516
0.55
Yes
Yes

504
0.54
Yes
Yes

480
0.50
Yes
Yes

528
0.74
Yes
Yes

516
0.73
Yes
Yes

504
0.72
Yes
Yes

480
0.69
Yes
Yes

-0.046(0.090)-0.030(0.187)-0.163∗∗∗(0.023)-0.653∗∗(0.158)-0.936∗(0.344)-0.944(0.685)p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

46

Table A9 The eﬀect of floods on exports, measured by insured losses: agricultural and manufacturing products
Export values
Agriculture
No lag(1)

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)

3 lags(6)

6 lags(7)

12 lags(8)-0.147 -0.144(0.137) (0.143)-0.173(0.167)-0.156(0.181)-0.407 0.015(0.822) (0.815)

0.268(0.989)0.462(1.041)

L.Insured losses

0.218(0.155)0.208(0.146)

0.198(0.154)0.071(0.200)0.052(0.172)-0.066(0.056)

L2.Insured losses-0.141∗∗(0.043)-0.145∗∗(0.043)-0.165∗∗(0.039)-1.053∗∗∗(0.096)-1.003∗∗∗(0.112)-1.085∗∗∗(0.133)L3.Insured losses-0.161(0.154)-0.135(0.227)-0.113(0.192)0.368(0.315)-0.613∗∗∗(0.088)-0.594∗∗(0.112)L4.Insured losses

0.098(0.103)0.072(0.080)0.707∗∗∗(0.119)0.843∗∗(0.167)

L5.Insured losses-0.022(0.140)-0.037(0.112)-0.115(0.170)-0.122(0.202)

L6.Insured losses-0.116(0.161)-0.042(0.146)0.628(0.621)0.818(0.773)

504
0.55
Yes
Yes

480
0.52
Yes
Yes

528
0.74
Yes
Yes

504
0.72
Yes
Yes

480
0.69
Yes
Yes

-0.286(0.221)-0.369(0.312)-0.407 -0.599(0.822) (0.971)-0.077(1.341)0.730(1.919)Insured losses

Observation
528
Adjusted R2
0.57
Month-by-year fixed eﬀects
Yes
Country-by-month fixed eﬀects Yes
Total eﬀect of flood
∗

516
0.56
Yes
Yes

-0.147 -0.227(0.137) (0.171)

516
0.73
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

47

Table A10 The eﬀect of storms on exports, measured by insured losses: agricultural and manufacturing products
Export values
Agriculture
No lag(1)
Insured losses

Manufacturing

3 lags(2)

6 lags(3)

12 lags(4)

No lag(5)-0.370 -0.370(0.360) (0.363)-0.363(0.363)-0.250 -2.658∗∗∗(0.310) (0.449)

3 lags(6)

6 lags(7)

12 lags(8)-2.658∗∗∗(0.452)-2.657∗∗(0.468)-2.718∗∗(0.629)L.Insured losses

0.035
0.035
0.055
(0.118) (0.117) (0.117)-2.490(1.063)-2.496(1.071)-2.340(1.049)L2.Insured losses-0.071 -0.071 -0.064(0.047) (0.047) (0.050)-2.652∗∗(0.696)-2.652∗∗(0.701)-2.651∗∗(0.727)

L3.Insured losses-0.230 -0.230 -0.228(0.293) (0.295) (0.299)-1.922∗(0.632)-1.922∗(0.637)-1.910∗(0.663)L4.Insured losses-0.170 -0.170(0.441) (0.452)-0.883(0.629)-0.881(0.657)

L5.Insured losses

0.186
0.192
(0.110) (0.116)-2.276(1.159)-2.282(1.195)L6.Insured losses

0.003
0.001
(0.204) (0.210)-0.890(1.248)-0.887(1.279)

516
0.73
Yes
Yes

504
0.72
Yes
Yes

480
0.69
Yes
Yes

-9.723∗∗(2.567)-13.777∗(4.983)-15.606(9.461)

Observation
Adjusted R2Month-by-year fixed eﬀects
Country-by-month fixed eﬀects

528
0.57
Yes
Yes

Total eﬀect of storm-0.370 -0.636 -0.611 -0.678 -2.658∗∗∗(0.360) (0.489) (1.024) (2.138) (0.449)

∗

516
0.55
Yes
Yes

504
0.54
Yes
Yes

480
0.51
Yes
Yes

528
0.74
Yes
Yes

p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. Robust standard errors are clustered by country and reported in parentheses.

48