Abstract

This study examines the impact of natural disasters on affected countries’ accessibility to international financial resources. We find empirical evidence that natural disasters significantly downgrade the sovereign credit rating of an affected country, an indicator of international financial accessibility. This finding is robust in developing countries, implying that they are faced with additional difficulties in financing post-disaster recovery costs compared with developed countries. Among disasters, droughts and storms display a particularly significant downgrading effect. Further results show that foreign aid from the international community helps to improve the accessibility, implying a possible acceleration of the post-disaster recovery in recipient countries.

1.  Introduction

Natural disasters such as floods, hurricanes, droughts, and earthquakes frequently incur big economic damages. For instance, Hurricane Katrina of 2005, the Kobe Earthquake of 1995, and the Great East Japan Earthquake of 2011 caused huge economic losses in the United States and Japan. The World Bank estimates that the total loss from natural disasters is on an increasing trend because of a rising disaster frequency and an increasing wealth exposed to natural hazards (Mahul et al. 2014, 10). The probability of disaster occurrence is determined by nature, but the magnitude of damage is largely affected by human activities. The role of human beings explains why a similar disaster costs a bigger economic loss in developing countries than in developed ones.1 The economic damage from a natural disaster includes more than direct or immediate loss such as destruction of human and physical assets. It may cause indirect or long-term losses by restraining long-term growth capacities or destabilizing macroeconomic fundamentals. For instance, fiscal balance may worsen because of recovery costs and decreased tax revenue; furthermore, the current account balance may deteriorate because of damaged export capacities and increased import demands. The worsening macroeconomic conditions can undermine the sovereign creditworthiness and financial accessibility of a country, consequently hindering a quick recovery from a disaster.

This paper examines the impact of natural disasters on affected countries’ accessibility to international financial resources. If a natural disaster makes it harder for an economy to get access to international capital markets beyond destroying its wealth, it would produce double-fold damage and hamper fast recovery. We focus on developing countries because they rely heavily on external financing for post-disaster reconstruction. As an indicator of a country's international financial accessibility, we consider two variables: the interest rate spread of sovereign bonds and sovereign credit rating. Both variables indicate the solvency of an economy or the ability to repay sovereign debt. When a certain factor undermines an economy's creditworthiness, it is reflected as increased bond spreads or downgraded credit ratings. Although bond spreads change more in a more flexible way than do credit ratings, the available data are very limited for developing countries. In this paper, therefore, sovereign credit ratings are used as the basic indicator of financial accessibility, with bond spreads as an auxiliary indicator.

Most of the previous studies on the economic consequences of disasters have examined the impacts on macroeconomic indicators such as gross domestic product (GDP) or its growth rate. However, this study discusses financial accessibility as an intermediary factor between disasters and GDP or its growth rate. From panel data analyses, we observe that natural disasters significantly restrain financial accessibility in disaster-hit countries. Furthermore, we examine whether external humanitarian subsidies or foreign aid can help to enhance financial accessibility. We expect that this study will deepen our understanding of the channel through which natural disasters incur economic losses.

Section 2 discusses the relationship between natural disasters and finance, particularly the accessibility to finance, through a literature review. Section 3 provides an overview of natural disaster damages; Section 4 explains the models and data sets for analysis. Section 5 provides the empirical results and their interpretations. Section 6 concludes the paper.

2.  Literature review and hypotheses development

2.1  Literature review

Most studies on the economic impacts of natural disasters focus on the effects on GDP or the growth rate. These studies find one of three conclusions: no significant, negative, or positive effects. Some studies observe that no natural disaster sustains economic damages over a long period of time (i.e., 5 or 10 years or more; Jaramillo 2009; Cavallo et al. 2013). However, many studies maintain that natural disasters incur negative impacts on economic growth in the short to medium term by destroying production capacities and harming macroeconomic stability. This view is supported by case studies and empirical analyses (Hochrainer 2009; Norman et al. 2012; An 2013). Others highlight the positive economic impacts of natural disasters, focusing on their innovation or productivity effects through Schumpeterian constructive destruction perspective (Skidmore and Toya 2002; Schultz and Elliott 2013).

Recent studies examine the significance of institutional conditions in the determination of the economic losses from natural disasters. This view sees the risk of natural disasters as a function of natural hazard, exposure, and vulnerability. Hazard is the probability of disaster occurrence, which is the naturally given or exogenous factor. Exposure is the volume of human and physical assets susceptible to these hazards. Lastly, vulnerability relates to the ability of a society to withstand the hazard and reduce its social and economic impacts.

Previous studies focused particularly on vulnerability, which is a multidimensional concept that includes various physical, economic, and institutional factors, thus differentiating the magnitude of loss among countries. For example, Kahn (2005) demonstrates that the diversity in economic structure and a higher income or development level tend to alleviate negative impacts, while others observe that more education, greater economic openness, and higher institutional quality alleviate negative impacts (Athukorala and Resosudarmo 2005; Nidhiprabha 2006; Toya and Skidmore 2007; Noy 2009).

Concerning the relationship between natural disasters and finance, previous literature largely notes that financial conditions may influence the extent of economic impacts. Early studies considered financial measures to reduce disaster-induced losses (ECLAC and IDB 2000). Later, empirical studies found that financial development tends to reduce economic losses (Noy 2009; McDermott, Barry, and Tol 2014). Micro-level empirical studies also show that easier financial access by households and enterprises strengthens their resilience from natural disasters (Sawada and Shimizutani 2008; De Mel et al. 2012).

Along the same lines, there have been international attempts to develop financial methods as means to control the risk of natural disasters. For instance, the World Bank launched the Global Facility for Disaster Reduction Recovery (GFDRR), a global funding-partnership to support disaster risk management projects that reduce developing countries' vulnerability to natural hazards and climate change. Later, the GFDRR established the Disaster Risk Financing and Insurance Program (DRFIP); DRFIP leads the dialogue on financial resilience to disasters. These moves recognize the importance of financial accessibility in the management of disaster risk.

2.2  Hypotheses development

It should be noted that previous studies considered the mitigation effects of finance, after taking financial conditions as given. However, natural disasters can change financial conditions because of the worsened macroeconomic environments in an affected country. From this perspective, this study investigates how natural disasters influence financial conditions, focusing on the accessibility to external financial resources. Because of their underdeveloped local financial markets and limited domestic savings, developing economies need to rely on international financing for post-disaster recovery. However, the disaster occurrence may hurt the confidence in the affected country's solvency, consequently restricting access to international financial markets. To test this hypothesis, we estimated the effect of natural disasters on international financial accessibility that we measured using sovereign credit ratings.2

In addition to the analysis of financial accessibility, this paper investigated whether post-disaster external support increases the financial accessibility of an affected country. To do this, we built on the empirical analysis on whether foreign aid helps upgrade the credit rating of the affected economy. Hochrainer (2009) is one of the few studies that examined the effect of foreign aid on disaster recovery; he observed the positive role of foreign aid in containing the damage to economic growth. However, while the paper noted the real impact, such as on GDP growth rate, we focused on the financial impacts using a more specific indicator (sovereign credit rating). If foreign aid helps to improve creditworthiness in a disaster-hit country, it would imply that foreign aid plays a greater role than merely providing an immediate supplement to financial resources.

3.  Economic loss and fatalities from natural disasters

In empirically examining the link between natural disasters and financial accessibility, the first step was to understand loss and damage from natural disasters. To do this, we relied on the International Disaster Database (EM-DAT) covering natural disaster events since 1900. The database reports the economic loss (the amount of damage to property, crops, and livestock) and fatalities (the number of people who lost their lives or could not be found), which can serve as proxies for the scale of natural disasters. As shown in Table 1, the data set used in this study indicates that natural disasters caused approximately 1.6 million fatalities in 193 countries in 1990–2014. By region, 62 percent of the fatalities occurred in East and South Asia and the Pacific (Asia), followed by the Americas, particularly Central and South America, which accounted for another 20 percent of the total. In terms of monetary value, direct economic losses were estimated at US$ 2.4 trillion. Asia accounted for 49 percent of the losses and the Americas, 36 percent. Asia's lower share in the economic losses, as compared with the fatalities, can be explained by the relatively smaller value of disaster-exposed assets in Asia. In contrast, North American countries such as the United States and Canada have a larger share in economic loss because of greater exposure of assets.

Table 1.
Fatalities and economic losses from natural disasters
Economic loss (billion US$)
By disaster type
Period (1990∼2014)Fatality (1,000)GEOMETHYDCLITotal
Total Region 1,598 (100%) 666 988 617 161 2,433 (100%) 
Asia and Pacific 984 (62%) (77%) (24%) (63%) (32%) 1,188 (49%) 
Europe and Central Asia 171 (11%) (9%) (11%) (20%) (20%) 317 (13%) 
America and Caribbean 319 (20%) (12%) (65%) (15%) (44%) 881 (36%) 
Africa and Middle East Income 124 (8%) (3%) (1%) (2%) (4%) 47 (2%) 
Developing countries 1,412 (88%) (29%) (22%) (67%) (41%) 884 (36%) 
Developed countries 187 (12%) (71%) (78%) (33%) (59%) 1,549 (64%) 
Economic loss (billion US$)
By disaster type
Period (1990∼2014)Fatality (1,000)GEOMETHYDCLITotal
Total Region 1,598 (100%) 666 988 617 161 2,433 (100%) 
Asia and Pacific 984 (62%) (77%) (24%) (63%) (32%) 1,188 (49%) 
Europe and Central Asia 171 (11%) (9%) (11%) (20%) (20%) 317 (13%) 
America and Caribbean 319 (20%) (12%) (65%) (15%) (44%) 881 (36%) 
Africa and Middle East Income 124 (8%) (3%) (1%) (2%) (4%) 47 (2%) 
Developing countries 1,412 (88%) (29%) (22%) (67%) (41%) 884 (36%) 
Developed countries 187 (12%) (71%) (78%) (33%) (59%) 1,549 (64%) 

Source: Data provided by EM-DAT and authors’ calculation.

Note: GEO (geophysical), MET (meteorological), HYD (hydrological), and CLI (climatological) are each disaster type. Numbers in parentheses are percentages based on the total period.

We also compared economic losses by disaster type. Natural disasters are categorized into four types: (1) geophysical, such as an earthquake; (2) meteorological, such as a storm; (3) hydrological, such as a flood or tsunami; and (4) climatological, such as a drought or a wildfire. As shown in Table 1, meteorological disasters have caused the highest economic loss (US$ 9 trillion), mainly because of the enormous cost of hurricanes in North America (65 percent). Next, geophysical disasters, mostly earthquakes, have caused losses of US$ 6.7 trillion. Asia alone accounted for 77 percent of loss from geophysical disasters, because of big earthquakes in Japan. Comparing damages by income group, we see that an overwhelming majority of fatalities occurred in developing regions, while the economic losses were in high-income economies. This difference can be explained by the larger exposure of assets in high-income countries and the greater vulnerability faced by developing regions.

Figure 1 shows that the world's economic losses and fatalities from natural disasters tend to increase in the long run. The Asia-Pacific region is leading the upward trend. Since the average economic growth in Asia is relatively fast, its exposure to natural hazards also will continue to increase rapidly. Thus, if there is no substantial improvement in vulnerability, the region will suffer greater economic losses in the future. Economic losses and fatalities do not always correspond‥ The greatest number of fatalities was recorded in 2010, largely because of the Haiti earthquake event; however, the greatest economic losses were reported in 2011 because of Japan's earthquake and tsunami events.

Figure 1.

Trends in economic loss and fatalities by natural disasters

Figure 1.

Trends in economic loss and fatalities by natural disasters

4.  Data and empirical methodology

4.1  Data

The natural disaster events are obtained from the EM-DAT. To measure the scale of disasters across countries, we employ economic losses or fatalities after standardizing them with GDP or population, respectively. As our focus is on the impact of economic losses on international financial accessibility of a disaster-hit country, we mainly report empirical results as economic losses as a percentage of the GDP (ND). Previous studies use sovereign credit ratings as a proxy for external finance accessibility (Cantor and Packer 1996; Afonso, Gomes, and Rother 2011). Sovereign credit ratings are a comprehensive evaluation of a government's capacity and willingness to repay its debt on time by rating agencies. Standard & Poor's (S&P), Moody's, and Fitch agencies report credit ratings for long-term government bonds denominated in foreign currency. When a country experiences political instability or a recession, the probability of default increases and its sovereign ratings will be degraded. A lower sovereign creditworthiness restricts the financial access of not only the government but also firms in a country.3

We use the S&P ratings, as its coverage is the largest and includes more small-sized economies. The rating ranges from Default to AAA. We assign numerical values starting from 0 to 20. Table 2 shows the S&P ratings (RATING) and the frequencies of each grade in our final sample. Most developed countries have A grades, while developing countries have in B grades.4

Table 2.
S&P sovereign rating scores and frequencies
S&P sovereign ratingsFrequencies
GradeScoresDevelopedDevelopingTotal
Investment grade 
AAA 20 249 249 
AA+ 19 80 80 
AA 18 62 62 
AA− 17 55 60 
A+ 16 51 55 
15 106 12 118 
A− 14 71 26 97 
BBB+ 13 37 36 73 
BBB 12 50 57 107 
BBB− 11 54 70 124 
Speculative grade 
BB+ 10 17 71 88 
BB 10 109 119 
BB− 96 99 
B+ 88 92 
65 68 
B− 66 72 
CCC+ 15 16 
CCC 
CCC− 
CC, C 
D, SD 11 12 
Total 862 737 1,599 
S&P sovereign ratingsFrequencies
GradeScoresDevelopedDevelopingTotal
Investment grade 
AAA 20 249 249 
AA+ 19 80 80 
AA 18 62 62 
AA− 17 55 60 
A+ 16 51 55 
15 106 12 118 
A− 14 71 26 97 
BBB+ 13 37 36 73 
BBB 12 50 57 107 
BBB− 11 54 70 124 
Speculative grade 
BB+ 10 17 71 88 
BB 10 109 119 
BB− 96 99 
B+ 88 92 
65 68 
B− 66 72 
CCC+ 15 16 
CCC 
CCC− 
CC, C 
D, SD 11 12 
Total 862 737 1,599 

Source: Data provided by Korea Center for International Finance and authors’ calculation.

Note: The table shows how S&P ratings are allocated to scores and the frequencies of the main data in each grade across all countries (Total); the developed group and developing group are based on Table A.1.

Humanitarian aid data from the OECD are included in the analysis. Studies related to the effects of aid have suggested that the form of bilateral aid or multilateral aid can have different efficiencies; we therefore employed each of them: ODAH_B, ODAH_M, and ODAH are bilateral, multilateral, and total aid, respectively.5 For the purpose of our analysis, we required that observations include values of RATING and ND. Thus, the final sample consists of the annual observations for 87 countries (45 developed countries, 42 developing countries) from 1995 to 2014. Definitions of other control variables are shown in Table A.2.

4.2  Empirical methodology

This study presents a possible link between the economic losses from natural disasters (ND) and changes of sovereign credit ratings (RATING). To test the relationship, we construct a country panel data sample and empirically set a panel regression approach as the base model.
ΔRATINGi,t=α+βNDi,t+kγkZk,i,t+ɛi,t,
(1)
where ΔRATINGi,t is the first-differences of RATING between t and t − 1 for country i, Zk,i,t is a kth control variable for all k = 1, …, n. For equation (1), the Hausman test was conducted to test whether fixed-effects or random-effects models are appropriate. As a robustness test, the level of RATING was employed as a dependent variable in equation (2). We also conducted the Hausman test for equation (2).
RATINGi,t=α'+β'NDi,t+kγk'Zk,i,t+ɛi,t'.
(2)
IMF (2016) states that climate change can affect debt sustainability or debt distress, which implies that natural disasters can lower the financial accessibility of countries with severe damages. In this regard, sovereign credit ratings, as well as government bond spread, can be proxies for financial accessibility. EMBI global bond spread was also employed in the robustness test.

Previous studies such as Cantor and Packer (1996) and Afonso, Gomes, and Rother (2011) analyzed the determinants of sovereign credit ratings. Those determinants included macroeconomic variables such as GDP per capita, GDP growth rate, current account balance, and the inflation rate. We also employed similar macroeconomic variables as control variables. They are described in Table A.2.

Finally, external support, such as emergency relief funds or humanitarian aid (ODAH), could mitigate the impacts of natural disasters on financial accessibility. To investigate the mitigation effect, the interaction term between ND and ODAH was included in the model.
ΔRATINGi,t=α+β1NDi,t+β2NDi,txODAHi,t+kγkZk,i,t+ɛi,t.
(3)

5.  Empirical results

5.1  Impacts of natural disasters on sovereign credit ratings

Table 3 displays the results of the impact that disaster-induced economic losses exert on sovereign credit ratings. Based on the result of the Hausman test, we chose the fixed-effects model as the base in the analysis, as well as the pooled-panel and random-effects models to check the sensitivity of the results. In all the specifications, the economic losses from natural disaster (ND) constantly show a significant and negative effect on the credit rating. A disaster loss of 10 percent of GDP is estimated to reduce the credit rating of an affected economy by approximately 0.5 grade. Most of the control variables that were included in our model had the expected signs, and this was consistent with the findings of previous studies. That is, higher inflation rates (INFLATION) significantly lowers credit ratings, while improvements in the economic growth rate (GDPG), fiscal balance D(GOVTBALANCE), and foreign reserve ratio D(RESERVE) help credit ratings to be upgraded. Better current account balances, D(CA), appear to lower credit ratings, but previous studies also report mixed results with respect to current account balances.

Table 3.
Effects of natural disasters: Base results
ModelPooled (1)FE (2)RE (3)FE (4)FE (5)
Country fixed effects NO YES NO YES YES 
Year fixed effects NO NO NO YES YES 
ND −0.047*** −0.048*** −0.047*** −0.049*** −0.052*** 
 (0.006) (0.006) (0.006) (0.006) (0.007) 
GDPG 8.288*** 8.642*** 8.288*** 9.384*** 10.669*** 
 (0.712) (0.808) (0.712) (0.942) (1.267) 
D(CA) −0.020*** −0.019*** −0.020*** −0.020*** −0.016 
 (0.006) (0.006) (0.006) (0.006) (0.010) 
D(RESERVE) 0.015** 0.013** 0.015** 0.008 0.006 
 (0.006) (0.006) (0.006) (0.006) (0.008) 
INFLATION −0.011*** −0.017*** −0.011*** −0.014*** −0.035*** 
 (0.003) (0.004) (0.003) (0.004) (0.008) 
D(GOVTBALANCE)     0.035** 
     (0.017) 
CONSTANT −0.124*** −0.098*** −0.124*** −0.357** −0.550*** 
 (0.033) (0.038) (0.033) (0.144) (0.192) 
Observations 1,368 1,368 1,368 1,368 984 
R2 0.156 0.159 0.156 0.187 0.211 
No. of countries 86 86 86 86 67 
Hausman test: Coefficients of RE (3) and FE (2) are the same: 13.16 
Hausman prob > Ch2: 0.0211 
ModelPooled (1)FE (2)RE (3)FE (4)FE (5)
Country fixed effects NO YES NO YES YES 
Year fixed effects NO NO NO YES YES 
ND −0.047*** −0.048*** −0.047*** −0.049*** −0.052*** 
 (0.006) (0.006) (0.006) (0.006) (0.007) 
GDPG 8.288*** 8.642*** 8.288*** 9.384*** 10.669*** 
 (0.712) (0.808) (0.712) (0.942) (1.267) 
D(CA) −0.020*** −0.019*** −0.020*** −0.020*** −0.016 
 (0.006) (0.006) (0.006) (0.006) (0.010) 
D(RESERVE) 0.015** 0.013** 0.015** 0.008 0.006 
 (0.006) (0.006) (0.006) (0.006) (0.008) 
INFLATION −0.011*** −0.017*** −0.011*** −0.014*** −0.035*** 
 (0.003) (0.004) (0.003) (0.004) (0.008) 
D(GOVTBALANCE)     0.035** 
     (0.017) 
CONSTANT −0.124*** −0.098*** −0.124*** −0.357** −0.550*** 
 (0.033) (0.038) (0.033) (0.144) (0.192) 
Observations 1,368 1,368 1,368 1,368 984 
R2 0.156 0.159 0.156 0.187 0.211 
No. of countries 86 86 86 86 67 
Hausman test: Coefficients of RE (3) and FE (2) are the same: 13.16 
Hausman prob > Ch2: 0.0211 

Note: Dependent variable is the change in S&P sovereign ratings, D(RATING). Numbers in parentheses are standard errors. ***p < 0.01, **p < 0.05, *p < 0.10.

The results indicate that natural disasters not only cause direct losses such asset destruction, but also constrain the availability of external financing by downgrading sovereign credit ratings. Consequently, an affected country's recovery can be delayed because of the lack of capital. In that sense, natural disasters induce multifold losses. Furthermore, the effects of disasters remain significant after controlling for many macroeconomic indicators. This implies that natural disasters have additional negative financial impacts, besides their immediate destabilizing influences largely expressed through worsening economic indicators. The additional downgrading effect on credit ratings is likely due to the prospect for further economic worsening, continuing uncertainties, and undermined confidence in governments after the disaster damage.

Although credit rating agencies rarely downgrade ratings directly because of a natural disaster, as noted by Kraemer et al. (2015), it is obvious that natural disasters negatively affect credit ratings for multiple reasons, explicit or implicit. And unlike the Kraemer et al.’s (2015) presumption that natural disasters will change credit ratings through the channel of worsening economic indicators, our study suggests the existence of other different channels.

To determine whether the degree of this effect is different among income groups, we divided the sample into developed and developing economy groups. As shown in Table 4, the developed group does not suffer significant downgrading impacts from disasters, whereas the developing-economy group does. After conducting the Hausman test, a fixed-effects model was chosen. The coefficients of natural disasters for developing countries were almost the same as for the whole sample, which means that the effects in the whole sample are dominated by those in developing countries. Therefore, the following analyses were conducted only for developing countries. The existence of the negative impacts only in the case of developing countries can probably be explained by their greater economic or institutional vulnerability in the prevention and management of natural disasters.

Table 4.
Effects of natural disasters: By income group
ModelFE (1)FE (2)FE (3)FE (4)FE (5)
Country fixed effects YES YES YES YES YES 
Year fixed effects YES YES YES YES YES 
Samples Developed Developing 
ND 0.005 −0.027 −0.051*** −0.054*** −0.049*** 
 (0.032) (0.055) (0.006) (0.007) (0.006) 
GDPG 9.303*** 11.825*** 10.282*** 9.950*** 8.838*** 
 (1.360) (1.808) (1.404) (1.892) (1.448) 
D(CA) −0.013 −0.003 −0.019* −0.018 −0.023** 
 (0.009) (0.015) (0.010) (0.014) (0.010) 
D(RESERVE) 0.011 0.009 0.005 0.005 0.014 
 (0.008) (0.010) (0.010) (0.015) (0.010) 
INFLATION −0.035*** −0.033*** −0.011** −0.038*** −0.011** 
 (0.010) (0.012) (0.004) (0.011) (0.004) 
D(GOVTBALANCE)  −0.001  0.095***  
  (0.024)  (0.025)  
D(EXTERNALDEBT)     −0.015*** 
     (0.004) 
CONSTANT −0.458** 0.033 −0.192 −0.151 −0.159 
 (0.181) (0.230) (0.266) (0.552) (0.265) 
Observations 742 607 626 377 599 
Adjusted R2 0.051 0.054 0.199 0.255 0.225 
No. of countries 45 38 41 29 39 
ModelFE (1)FE (2)FE (3)FE (4)FE (5)
Country fixed effects YES YES YES YES YES 
Year fixed effects YES YES YES YES YES 
Samples Developed Developing 
ND 0.005 −0.027 −0.051*** −0.054*** −0.049*** 
 (0.032) (0.055) (0.006) (0.007) (0.006) 
GDPG 9.303*** 11.825*** 10.282*** 9.950*** 8.838*** 
 (1.360) (1.808) (1.404) (1.892) (1.448) 
D(CA) −0.013 −0.003 −0.019* −0.018 −0.023** 
 (0.009) (0.015) (0.010) (0.014) (0.010) 
D(RESERVE) 0.011 0.009 0.005 0.005 0.014 
 (0.008) (0.010) (0.010) (0.015) (0.010) 
INFLATION −0.035*** −0.033*** −0.011** −0.038*** −0.011** 
 (0.010) (0.012) (0.004) (0.011) (0.004) 
D(GOVTBALANCE)  −0.001  0.095***  
  (0.024)  (0.025)  
D(EXTERNALDEBT)     −0.015*** 
     (0.004) 
CONSTANT −0.458** 0.033 −0.192 −0.151 −0.159 
 (0.181) (0.230) (0.266) (0.552) (0.265) 
Observations 742 607 626 377 599 
Adjusted R2 0.051 0.054 0.199 0.255 0.225 
No. of countries 45 38 41 29 39 

Note: Dependent variable is the change in S&P sovereign ratings, D(RATING). Numbers in parentheses are standard errors. ***p < 0.01, **p < 0.05, *p < 0.10.

Next, we investigated the possibility of different impacts among the four disaster types mentioned previously. Table 5 shows that meteorological and climatological disasters have significantly negative impacts on credit ratings, whereas geophysical and hydrological disasters do not. Meteorological disasters include storms like hurricanes. Climatological disasters include droughts. Particularly, climatological disasters are the most damaging to credit ratings; for instance, the loss of 10 percent of GDP from the disasters is estimated to lower the rating by approximately 3.5 grades. Possibly, it is because the occurrence of severe droughts is concentrated in the poorest sub-Saharan countries near the equator. These findings are in line with Norman et al.’s (2012) empirical results that droughts and storms are negative for industry or agriculture, whereas floods and earthquakes are rather positive for industry.6

Table 5.
Effects of natural disasters: By disaster type
Country/year fixed effects Disaster typeFE (1) TotalFE (2) GEOFE (3) METFE (4) HYDFE (5) CLI
ND −0.049***     
 (0.006)     
ND_type  0.030 −0.050*** −0.055 −0.337* 
  (0.058) (0.006) (0.069) (0.194) 
GDPG 8.838*** 9.037*** 8.948*** 8.937*** 8.968*** 
 (1.448) (1.529) (1.448) (1.531) (1.525) 
D(CA) −0.023** −0.035*** −0.023** −0.035*** −0.035*** 
 (0.010) (0.011) (0.010) (0.011) (0.011) 
D(RESERVE) 0.014 0.011 0.014 0.011 0.012 
 (0.010) (0.011) (0.010) (0.011) (0.011) 
INFLATION −0.011** −0.011** −0.011*** −0.010** −0.011** 
 (0.004) (0.004) (0.004) (0.004) (0.004) 
D(EXTERNALDEBT) −0.015*** −0.017*** −0.016*** −0.017*** −0.016*** 
 (0.004) (0.004) (0.004) (0.004) (0.004) 
CONSTANT −0.159 −0.181 −0.155 −0.186 −0.165 
 (0.265) (0.280) (0.265) (0.280) (0.279) 
Observations 599 599 599 599 599 
Adjusted R2 0.225 0.136 0.225 0.136 0.140 
No. of countries 39 39 39 39 39 
Country/year fixed effects Disaster typeFE (1) TotalFE (2) GEOFE (3) METFE (4) HYDFE (5) CLI
ND −0.049***     
 (0.006)     
ND_type  0.030 −0.050*** −0.055 −0.337* 
  (0.058) (0.006) (0.069) (0.194) 
GDPG 8.838*** 9.037*** 8.948*** 8.937*** 8.968*** 
 (1.448) (1.529) (1.448) (1.531) (1.525) 
D(CA) −0.023** −0.035*** −0.023** −0.035*** −0.035*** 
 (0.010) (0.011) (0.010) (0.011) (0.011) 
D(RESERVE) 0.014 0.011 0.014 0.011 0.012 
 (0.010) (0.011) (0.010) (0.011) (0.011) 
INFLATION −0.011** −0.011** −0.011*** −0.010** −0.011** 
 (0.004) (0.004) (0.004) (0.004) (0.004) 
D(EXTERNALDEBT) −0.015*** −0.017*** −0.016*** −0.017*** −0.016*** 
 (0.004) (0.004) (0.004) (0.004) (0.004) 
CONSTANT −0.159 −0.181 −0.155 −0.186 −0.165 
 (0.265) (0.280) (0.265) (0.280) (0.279) 
Observations 599 599 599 599 599 
Adjusted R2 0.225 0.136 0.225 0.136 0.140 
No. of countries 39 39 39 39 39 

Note: Dependent variable is the change in S&P sovereign ratings, D(RATING), and the sample is developing countries group. ND_GEO is the economic loss caused by geophysical disaster events. In similar ways, ND_MET is for meteorological, ND_HYD is for hydrological, and ND_CLI is for climatological. Numbers in parentheses are standard errors. ***p < 0.01, **p < 0.05, *p < 0.10.

Table 6.
Effects of natural disasters with humanitarian aid
Country/year fixed effects ODAH typeFE (1) TotalFE (2) BilateralFE (3) Multilateral
ND −0.063*** −0.053*** −0.126*** 
 (0.008) (0.007) (0.022) 
NDxODAH 0.031*** 0.033*** 0.264*** 
 (0.004) (0.004) (0.048) 
ODAH 0.083 0.049 1.028 
 (0.161) (0.190) (0.782) 
GDPG 9.487*** 9.533*** 8.963*** 
 (1.546) (1.545) (1.596) 
D(CA) −0.020* −0.019* −0.030*** 
 (0.011) (0.011) (0.011) 
D(RESERVE) 0.015 0.015 0.013 
 (0.010) (0.010) (0.011) 
INFLATION −0.013** −0.014** −0.010* 
 (0.006) (0.006) (0.006) 
D(EXTERNALDEBT) −0.015*** −0.015*** −0.015*** 
 (0.004) (0.004) (0.004) 
CONSTANT −0.791*** −0.784*** −0.809*** 
 (0.255) (0.254) (0.263) 
Observations 563 563 563 
Adjusted R2 0.249 0.250 0.202 
No. of countries 39 39 39 
Country/year fixed effects ODAH typeFE (1) TotalFE (2) BilateralFE (3) Multilateral
ND −0.063*** −0.053*** −0.126*** 
 (0.008) (0.007) (0.022) 
NDxODAH 0.031*** 0.033*** 0.264*** 
 (0.004) (0.004) (0.048) 
ODAH 0.083 0.049 1.028 
 (0.161) (0.190) (0.782) 
GDPG 9.487*** 9.533*** 8.963*** 
 (1.546) (1.545) (1.596) 
D(CA) −0.020* −0.019* −0.030*** 
 (0.011) (0.011) (0.011) 
D(RESERVE) 0.015 0.015 0.013 
 (0.010) (0.010) (0.011) 
INFLATION −0.013** −0.014** −0.010* 
 (0.006) (0.006) (0.006) 
D(EXTERNALDEBT) −0.015*** −0.015*** −0.015*** 
 (0.004) (0.004) (0.004) 
CONSTANT −0.791*** −0.784*** −0.809*** 
 (0.255) (0.254) (0.263) 
Observations 563 563 563 
Adjusted R2 0.249 0.250 0.202 
No. of countries 39 39 39 

Note: Dependent variable is the change in S&P sovereign ratings, D(RATING), and the sample is the developing countries group. NDxODAH is an interaction term between ND and ODAH. ODAH types are bilateral, multilateral, and total (bilateral + multilateral aid). Numbers in parentheses are standard errors. ***p < 0.01, **p < 0.05, *p < 0.10.

5.2  Effects of external support

Next, we examine whether external financial subsidies after disasters help affected countries to obtain better financial accessibility. The most common post-disaster subsidy is emergency relief or humanitarian aid from donor countries or multinational organizations such as the United Nations. Such aids may prevent the expansion of damage and supplement capital needed to recover from disasters. Also, administrative and technical support can compensate for the institutional vulnerability of damaged poor countries. These effects, consequently, may lead to the prevention or mitigation of a decrease in credit rating.

To check this possibility, we added to the model the volume of humanitarian aid that each country received. In addition, we included its interaction term with the natural disaster variable to determine whether foreign aid exerts a mitigating effect under the condition of disaster losses. The model FE (1) considers the effect of total humanitarian aid. Its interaction term with natural disasters (ND) shows a significant and positive sign, indicating that foreign aid under the situation of disaster occurrence contains the decrease in the sovereign credit rating. We further divided humanitarian aid into bilateral aid provided by donors individually and multilateral aid provided through international organizations such as the United Nations. Both types of aid significantly reduce the decline in the credit rating in the event of a disaster. However, the magnitude of the coefficients shows a substantial difference. Assume, for example, that 1 percent of GDP is provided as humanitarian aid. If this is provided as bilateral aid, the marginal effect of disaster losses on credit rating is −0.020 (−0.053 + 0.033 × 1); however, if it is supported through multilateral mechanisms, the marginal effect is +0.139 (−0.126 + 0.265 × 1). Therefore, even when the same amount of aid is provided, coordinated multilateral delivery is more effective than bilateral aid. It is believed that the provision of individual assistance causes a lot of transaction costs and fragmentation problems, while the transfer through multilateral institutions can reduce such costs (Djankov, Montalvo, and Reynal-Querol 2009; Bigsten and Tengstam 2015).

5.3  Robustness test

We checked the robustness of the basic results in two ways. First, we used level values for most of the variables including credit ratings, instead of the first-differences. Additionally, new control variables like GDP per capita (LOGGDPCAP) and a dummy for default history (DEFAULT_H), which were not used in the base model, were introduced. These two variables are significant determinants of credit ratings in previous studies. As shown in FE (1) and (2) of Table 7, the change in model specification keeps negative impacts of natural disasters unchanged. That is, an income increase leads to upgrading in credit ratings, whereas the existence of a default history within the last 10 years lowers the ratings by approximately one grade.

Table 7.
Robustness tests
Country/year fixed effectsFE (1)FE (2)Country/year fixed effectsFE (3)FE (4)
ND −0.029*** −0.028*** ND_DUMMY 1.277** 1.203** 
 (0.008) (0.008)  (0.537) (0.590) 
LOGGDPCAP 5.675*** 6.651*** USTB3MS −0.553*** −0.566*** 
 (0.600) (0.995)  (0.122) (0.130) 
GDPG 3.977** 5.889** LOGGDPCAP −5.100*** −2.640 
 (1.765) (2.308)  (1.448) (1.819) 
CA −0.030*** −0.049*** GDPG −20.587*** −13.240* 
 (0.011) (0.016)  (6.098) (6.735) 
RESERVE 0.042*** 0.050*** CA −0.026 0.064 
 (0.009) (0.016)  (0.046) (0.076) 
INFLATION −0.029*** −0.063*** RESERVE −0.037 −0.042 
 (0.005) (0.012)  (0.030) (0.058) 
DEFAULT_H −1.130*** −0.789** DEFAULT_H 5.283*** 5.482*** 
 (0.275) (0.354)  (1.104) (1.173) 
GOVTBALANCE  −0.024 CRISIS  2.571*** 
  (0.027)   (0.566) 
EXTERNALDEBT −0.006**  EXTERNALDEBT  0.049*** 
 (0.003)    (0.012) 
CONSTANT −34.329*** −42.408*** CONSTANT 47.363*** 24.253* 
 (4.546) (7.775)  (11.680) (14.479) 
Observations 635 403 Observations 434 323 
Adjusted R2 0.355 0.324 Adjusted R2 0.171 0.308 
No. of countries 39 29 No. of countries 34 23 
Country/year fixed effectsFE (1)FE (2)Country/year fixed effectsFE (3)FE (4)
ND −0.029*** −0.028*** ND_DUMMY 1.277** 1.203** 
 (0.008) (0.008)  (0.537) (0.590) 
LOGGDPCAP 5.675*** 6.651*** USTB3MS −0.553*** −0.566*** 
 (0.600) (0.995)  (0.122) (0.130) 
GDPG 3.977** 5.889** LOGGDPCAP −5.100*** −2.640 
 (1.765) (2.308)  (1.448) (1.819) 
CA −0.030*** −0.049*** GDPG −20.587*** −13.240* 
 (0.011) (0.016)  (6.098) (6.735) 
RESERVE 0.042*** 0.050*** CA −0.026 0.064 
 (0.009) (0.016)  (0.046) (0.076) 
INFLATION −0.029*** −0.063*** RESERVE −0.037 −0.042 
 (0.005) (0.012)  (0.030) (0.058) 
DEFAULT_H −1.130*** −0.789** DEFAULT_H 5.283*** 5.482*** 
 (0.275) (0.354)  (1.104) (1.173) 
GOVTBALANCE  −0.024 CRISIS  2.571*** 
  (0.027)   (0.566) 
EXTERNALDEBT −0.006**  EXTERNALDEBT  0.049*** 
 (0.003)    (0.012) 
CONSTANT −34.329*** −42.408*** CONSTANT 47.363*** 24.253* 
 (4.546) (7.775)  (11.680) (14.479) 
Observations 635 403 Observations 434 323 
Adjusted R2 0.355 0.324 Adjusted R2 0.171 0.308 
No. of countries 39 29 No. of countries 34 23 

Note: FE (1) and FE (2)’s dependent variables are RATING, the numerical values of S&P sovereign ratings. FE (3) and FE (4)’s dependent variable is EMBISP/100 as of the last day of December. Control variables vary due to different dependent variables. The sample is the developing countries group. Numbers in parentheses are standard errors. ***p < 0.01, **p < 0.05, *p < 0.10.

Another test employed the interest rate spread of sovereign bonds as an indicator of financial accessibility, instead of credit ratings. The spread has the disadvantage of narrower data coverage; however, it has the feature of more frequent and sensitive changes. It was noted, in unreported results, that the coefficient of natural disasters had a positive sign as expected but was not significant. This may be because the spread does not precisely respond in proportion to the size of disaster losses. Therefore, we changed the disaster loss variable into a dummy. That is, if the loss is 0.1 percent of GDP or more, the dummy was assigned a value of 1. In our sample, less than 20 percent of observations were below the critical value. In this specification, the model FE (3)∼(4) shown in Table 7 had consistently significant and positive signs for the disaster dummy. The coefficient estimates indicate that a disaster incurring damage of 0.1 percent of GDP or more widens the interest rate spread by approximately 120 basis points. This implies that disaster-hit countries need to pay higher financing costs, which increases their burden.

6.  Conclusion

Economic losses from natural disasters have been increasing continuously. This trend is especially evident in emerging and developing countries, including Asian countries, which are particularly vulnerable to disaster shocks. Previous studies have focused on the impact of these disasters on the real sector, such as production. However, our study is novel because it points out that disasters can affect the financial condition of impacted countries, especially financial accessibility to reduce damage.

The statistical analysis showed that the damage caused by natural disasters is another obstacle for post-disaster recovery because it lowers the credit rating of an affected country. This effect is very significant because it does not occur in high-income countries but is apparent in developing countries. Although credit rating agencies say that the occurrence of a disaster is rarely a direct cause of credit rating adjustments, disasters in developing countries are the cause of the decline in credit rating, either directly or indirectly. Disasters cause multifold harm to developing countries: the direct costs of the destruction of human and physical assets, deteriorated macroeconomic stability, and restricted access to international finance.

The effects of disasters such as droughts and storms are particularly significant. This suggests that future climate change could pose a risk for developing countries, particularly low-income and small-island countries, by isolating them from international financial markets. To curb these adverse effects, these countries must strengthen disaster prevention and mitigation programs, and enhance resilience through international support. In terms of finance, it is theoretically possible to transfer risk through disaster insurance; however, this is not easy considering the poor economic situations of low-income countries. Fortunately, the results of this study show that emergency assistance from the international community after a disaster helps to improve access to capital. Therefore, it is desirable that aid donors expand their post-disaster support. Furthermore, affected countries can be more effectively supported in a coordinated effort through international organizations rather than through individual bilateral support.

Notes

*

We would like to thank two discussants and seminar participants from the 2018 Asian Economic Panel meeting in Seoul for their comments. All errors and omissions are our own. The authors gratefully acknowledge financial support from Kyung Hee University (Project No. KHU-20140293). Bokyeong Park is the corresponding author.

1 

As a percentage of GDP, the annual loss from natural disasters is less than 1 percent on average in high-income countries, whereas it was 2.8 percent and 1.3 percent in middle-income and low-income countries, respectively (Munich Re 2013).

2 

The interest rate spread can be a better measurement, but the spread data cover only a limited number of countries and a relatively short period. Therefore, this study uses sovereign credit ratings as a proxy for financial accessibility. The use of this proxy is verified by a strong correlation between interest rate spreads and credit ratings.

3 

The negative impacts at the firm level are severe particularly in developing economies (Ferri and Liu 2003).

4 

The interest rate spread of sovereign bonds can be also used as a proxy for sovereign credit or external finance accessibility. However, it has been difficult to obtain the spread for emerging and developing countries. Kennedy and Palerm (2014) used the J.P. Morgan emerging market bond index (EMBI) global spreads as the interest rate spread. The EMBI global spread (EMBISP) is the yield difference over U.S. Treasuries of 65 countries, denominated in U.S. dollars. Because the EMBISP covers only emerging markets, we report results using EMBISP in the robustness tests section.

5 

ODAH is the three-year average of humanitarian aid over the three-year average of GDP in percentage.

6 

It is because floods and earthquakes likely boost the demand for reconstruction of infrastructure and housing.

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Appendix A.  Appendix Tables

Table A.1
List of 87 countries in the main analysis
ES Asia and Pacific (14)Europe and Central Asia (33)America and the Caribbean (23)Africa (17)
Developed (5) Developed (27) Developed (6) Developed (7) 
Australia, Japan, Korea, Rep., New Zealand, Singapore Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Russian Federation, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom Bahamas, Canada, Chile, Trinidad and Tobago, United States, Uruguay Bahrain, Israel, Kuwait, Malta, Oman, Qatar, Saudi Arabia 
Developing (9) Developing (6) Developing (17) Developing (10) 
China, India, Indonesia, Malaysia, Mongolia, Pakistan, Philippines, Thailand, Vietnam Bulgaria, Hungary, Kazakhstan, Romania, Turkey, Ukraine Argentina, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Jamaica, Mexico, Panama, Paraguay, Peru, Suriname, Venezuela Botswana, Egypt, Ghana, Jordan, Kenya, Lebanon, Morocco, Senegal, South Africa, Tunisia 
ES Asia and Pacific (14)Europe and Central Asia (33)America and the Caribbean (23)Africa (17)
Developed (5) Developed (27) Developed (6) Developed (7) 
Australia, Japan, Korea, Rep., New Zealand, Singapore Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Russian Federation, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom Bahamas, Canada, Chile, Trinidad and Tobago, United States, Uruguay Bahrain, Israel, Kuwait, Malta, Oman, Qatar, Saudi Arabia 
Developing (9) Developing (6) Developing (17) Developing (10) 
China, India, Indonesia, Malaysia, Mongolia, Pakistan, Philippines, Thailand, Vietnam Bulgaria, Hungary, Kazakhstan, Romania, Turkey, Ukraine Argentina, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Jamaica, Mexico, Panama, Paraguay, Peru, Suriname, Venezuela Botswana, Egypt, Ghana, Jordan, Kenya, Lebanon, Morocco, Senegal, South Africa, Tunisia 

Note: ES Asia = East and South Asia. Numbers in parentheses are the number of countries. Developed countries are in the high-income group and developing countries are in a non-high-income group based on the World Bank country classification as of July 2013.

Table A.2
List of control variables
Variable nameDescription
LOGGDPCAP The log of real GDP per capita 
GDPG The annual growth rate of real GDP per capita (%) 
CA Current account to GDP (%) 
RESERVE Total reserves (includes gold) to GDP (%) 
INFLATION Inflation, consumer prices (annual %) 
GOVTBALANCE General government's structural balance to GDP (%) 
EXTERNALDEBT Total external debt stocks over GDP (%) 
DEFAULT_H 1 for sovereign debt default experience over the last 10 years, 0 otherwise Source: Valencia and Laeven (2012
CRISIS 1 for financial crisis (currency, inflation, debt), 0 otherwise Source: Reinhart and Rogoff (2009
Variable nameDescription
LOGGDPCAP The log of real GDP per capita 
GDPG The annual growth rate of real GDP per capita (%) 
CA Current account to GDP (%) 
RESERVE Total reserves (includes gold) to GDP (%) 
INFLATION Inflation, consumer prices (annual %) 
GOVTBALANCE General government's structural balance to GDP (%) 
EXTERNALDEBT Total external debt stocks over GDP (%) 
DEFAULT_H 1 for sovereign debt default experience over the last 10 years, 0 otherwise Source: Valencia and Laeven (2012
CRISIS 1 for financial crisis (currency, inflation, debt), 0 otherwise Source: Reinhart and Rogoff (2009

Source: World Bank.

Note: D(variable name) indicates the change between t and t − 1 year.