Economic impact of natural disasters' fatalities

International Journal of Social Economics Economic impact of natural disasters' fatalities Jaharudin Padli Muzafar Shah Habibullah A.H. Baharom

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Economic impact of natural disasters’ fatalities

Economic impact of natural disasters

Jaharudin Padli, Muzafar Shah Habibullah and A.H. Baharom Department of Economics, Faculty of Economics and Management, Universiti Putra Malaysia, Serdang, Malaysia

429


Received November 2009 Accepted December 2009

Abstract Purpose – The purpose of this paper is to find the meaningful relationship between the economic impact of the natural disaster and economic condition. Design/methodology/approach – The paper employed cross-sectional analysis to investigate the relationship between economic condition namely, gross domestic product per capita (GDPpc); gross domestic product per capita squared (GDPpc2); government consumption ratio to GDP (gc); ratio of M2 over GDP(M2); years of schooling attainment (sc); land area and finally; population and the economic impact of natural disasters, whereby ten types of natural disasters were chosen. The degree to which the human and economic losses due to these ten natural disasters were measured by, the variables selected are, number of killed; total affected; and ratio of total damage to GDP. Three different points of time were regressed, namely, 1985, 1995, and 2005 covering 73 countries. Findings – Results clearly indicate that there seems to be meaningful relationship between the economic impact of natural disasters and economic conditions. Practical implications – The paper provides some evidence on the important role of economic condition in minimizing the impact of natural disasters. Originality/value – The paper incorporates a comprehensive list of explanatory variables in accounting for natural disaster fatalities. Keywords Natural disasters, Economic conditions Paper type Research paper

Introduction The relationship between natural disasters and macroeconomic variables has enticed and excited many studies lately. Reduction of the impact of disasters in response to the needs of countries is very vital. The nature of the natural disaster, which is very unpredictable and the compelling massive losses suffered due to these disasters have further fueled these studies. There is a great variety in the disaster experiences of countries in the region. For example, some countries are frequently hit by cyclones whereas landslides are the most common disaster in other countries. The burden of natural disasters falls most heavily upon developing nations where over 95 per cent of disaster-related deaths occurred (IFRC, 2001). Arguably, the seminal works of Dacy and Kunreuther (1969) is considered as the one of the earliest document on the study of natural disaster. Other notable studies are by Wildavsky (1988), Raschky (2008), Horwich (2000), Rasmussen (2004), Albala-Bertrand (1993), Kahn (2005), Toya and Skidmore (2007) and Noy (2008). Natural disasters indeed have a profound impact on the quality of life through their destruction of food crops and livestock, and forced dislocation of households and communities. Their toll on lives and the instant poverty cause are among their most devastating impacts (United Nations Economics and Social Commission for Asia and the Pacific (UNESCAP), 2007, p. 175). The tragedy of natural

International Journal of Social Economics Vol. 37 No. 6, 2010 pp. 429-441 q Emerald Group Publishing Limited 0306-8293 DOI 10.1108/03068291011042319

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disasters shatters the livelihoods of people causing widespread trauma and distress pushing the countries to the need for national level strategies for disaster prevention, recognition, and preparedness. Economic condition measurement in this study namely, income level, education attainment, government size, and financial development were chosen after extensive literature reviews and also based on some theoretical aspects. As mentioned by Toya and Skidmore (2007), there are two components of the disaster-income-safety relationship. First, increases in income increases the private demand for safety. Higher income enables individual (and by extension countries) to respond to the risks by employing additional costly precautionary methods. However, distinct from this private disaster-income-safety relationship is the existence of an underlying social/economic fabric that increases safety for all the society. Sustainable development is undermined by the occurrence or threat of disasters. The Director General of United Nations Educational, Scientific and Cultural Organization (UNESCO), Koı¨chiro Matsuura, highlighted the significant role of education in improving the capacity of individuals and communities to reduce the risk of disasters: “anticipating, educating and informing are the keys to reducing the deadly effect of such natural disasters” ( January 3, 2005, UNESCO Press Release). Thus, because of the strong link between schools and the wider community, schools can be an ideal starting point for the formulation and implementation of disaster preparedness policies, dissemination of disaster preparedness information and establishing emergency procedures. Financial deepening was chosen as per the argument of Horwich (2000), that a critical underlying factor in any economy’s response to a natural disaster is its level of wealth. A wealthy or richer country relate to safer country. The purpose of the present study is to investigate the significant relationship between economic condition and the economic impact of impact natural disasters. This paper is organized as follows. In the following section, some related literatures are reviewed. In the following section, we discuss on the method of estimation while in the preceding section, we discuss on the data used in the study. In the concluding two sections, empirical results are discussed followed by our conclusion. Literature review Owing to the seminal work by Dacy and Kunreuther (1969), a plethora of researches on socio-economic determinants of natural disasters has been conducted in recent years. Raschky (2008) pointed out that socio-economic factors have been found to be the key determinants of a society’s response to disasters, apart from other factors such as climatic and topographic factors. In his study that consists of 2,792 events for the period 1984-2004, he found that economic development (measured by GDP per capita) is an important factor in determining a society’s vulnerability against natural hazards in which higher income countries experience a lower death toll from natural disasters. On the other hand, the institutional factors such as government stability and investment climate reduce the adverse effects on both, the death toll and the overall economic losses from natural disasters. Padli and Habibullah (2009) investigated the relationship between disaster fatalities with the level of economic development, years of schooling, land area and population for a panel of 15 Asian countries over the sample period over 1970-2005 and found that the relationship between disaster losses and the level of economic development is nonlinear in


nature suggesting that at lower income level, a country is more disaster resilience but at higher income level, an economy become less-disaster resistant. Other disaster determinants of interest, is the level of education which suggests that educational attainment reduces human fatalities as a result of disaster; larger population will increase death toll and larger land area will reduce disaster fatalities. Wildavsky (1988) interprets safety as a natural product of a growing market economy. Since the demand for safety rises with income, a nation’s per capita income is a good first approximation of the degree of safety it enjoys. Furthermore, a rise in income will provide not only general safety but, at high enough income levels, protection specific to disasters (Horwich, 2000). Rasmussen (2004) also found out the negative relationship between income and the number or persons affected by natural disasters. His cross-country regression results for the Eastern Caribbean Currency Union countries suggest that the capacity of countries to avoid the human cost of disasters improves as income levels increases. In another study, Albala-Bertrand (1993) argues that the higher the level of development, the smaller the number of deaths, injured and deprived and the relative material losses. The level of development includes income per capital and income distribution, economic diversification and social inclusion, institutionalization and participation, education and health, choice and protection. Kahn (2005) shows that countries with higher per capita income experience a similar amount of catastrophic events but suffer less death from these events. He points out that though richer nations do not experience fewer natural disasters than poorer nations, richer nations do suffer less death from disaster. Thus, economic development provides implicit insurance against nature’s shocks. Richer nations will have the resources to make investment to preempt such events. Further in his study, Kahn (2005) shows that better institutional quality insulates against death from earthquakes. Countries with better institutions, lower income inequality and higher levels of democracy experience fewer earthquake fatalities. A study by Toya and Skidmore (2007) using annual data for 151 countries over the 1960-2003 period tested several measures of social/economic variables that includes income, education, openness, financial development, and the size of the government as determinants of disaster. They found out that economic development and economic losses from disasters are inversely related. Nation with higher levels of educational attainment and greater openness for trade are less vulnerable to disasters. A stronger financial sector and a smaller size of government are associated with a lower disaster death toll. In a more recent study, Noy (2008) found out that countries with a higher literacy rate, better institutions, higher per capita income, and higher degree of openness to trade and higher levels of government spending are better able to withstand the initial disaster shock and prevent further spillovers into the macro-economy. He also points out that countries with more foreign exchange reserves and higher levels of domestic credit but with less-open capital accounts appear more robust and better able to endure natural disasters, with less-adverse spillover into domestic production. Method of estimation To determine the relationship between the economic impact of natural disaster and the major economic condition variables, we follow Toya and Skidmore (2007), and estimated three sets of cross-sectional regressions for the corresponding period 1985, 1995, and 2005:

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(1) Model A: deathsit ¼ b1 ðGDPpcit Þ þ b2 ðGDPpcit Þ2 þ b2 ðgcit Þ þ b4 ðM 2it Þ þ b5 ðscit Þ þ 1it (2) Model B: tafit ¼ a1 ðGDPpcit Þ þ a2 ðGDPpcit Þ2 þ a2 ðgcit Þ þ a4 ðM 2it Þ þ a5 ðscit Þ þ mit


432

(3) Model C: damage ¼ d1 ðGDPpcit Þ þ d2 ðGDPpcit Þ2 þ d2 ðgcit Þ þ d4 ðM 2it Þ þ d5 ðscit Þ þ vit GDPit Whereby deathsit, tafit, and damage/GDPit are the logarithm of the total number of deaths, total affected, the ratio of total damage due to these disasters to GDP, respectively, in country i during period t. GDPpcit is the natural logarithm of real gross domestic product per capita, gcit is government consumption/GDP, M2it represents M2/GDP, scit is total years of schooling attainment in population aged 15 and over, 1it, mit and vit are the error term. According to Raschky (2008), higher income does not necessarily lead to better protection against natural disaster. Thus, Raschky suspects that the process of economic development is nonlinear, the regression will be more appropriate by incorporating both GDPpc and (GDPpc)2 in the equation. Raschky contends that economic development partly reduces disaster fatalities and losses, but increasing wealth inverts this relationship and thus causes relatively higher losses in high-income countries. In this study, we tested the assumption that the relationship between disaster losses and the level of development is nonlinear. Higher income people can self-protect through a number of strategies to reduce their natural disaster risk exposure. The reason behind it was, with higher income, it enables the individual responds to the risk around them by employing additional costly precautionary measures. Besides, after a disaster has struck, richer economies are able to provide high-quality emergency care to protect the population against death from disaster. Nevertheless, a nonlinear relationship would imply that economic development provides protection but with a diminishing rate. Because of the strong link between schools and the wider community, therefore enabling schools to be the nucleus of disaster preparedness policies, years of schooling (scit) was included in the analysis. Financial deepening (M2it) was chosen as per the argument of Horwich (2000), that a critical underlying factor in any economy’s response to a natural disaster is its level of wealth. A wealthy or richer country relate to safer country, while the government size was proxied by the ratio of the government consumption to the GDP (gcit). Sources of data Owing to the difficulties in getting a continuous time-series data on economic disaster losses, we analyze the disaster-economic development relationship for selected non-Organisation for Economic Co-operation and Development (non-OECD) (developing) and OECD countries totaling 73 countries (see Appendix) by employing cross-sectional analysis. The three period of study are 1985, 1995, and 2005, respectively. We used data on natural disasters from the Centre for Research on the Epidemiology of Disasters (CRED). Since 1988, CRED has maintained the emergency events database EM-DAT (2004), accessible at: www.cred.be/emdat/. In the raw data, the unit of analysis is the number of disasters.


In this study, we focus on eight types of natural disasters: (1) Drought is an extended period of time characterized by a deficiency in a region’s water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plant, and cause a lack of drinking water and famine. (2) An earthquake is the result of a sudden release of stored energy in the earth’s crust that creates seismic waves. At the earth’s surface, they are felt as a shaking or displacement of the ground. (3) Epidemic is the cases of an infectious disease, which already exist or previously absent in the region or population concerned. (4) Extreme temperature events are heat waves and cold waves. (5) Floods are significant rise of water level in the stream, lake, reservoir, or coastal region. Mass movement is divided into two categories: . wet; and . dry.


Wet mass movement is such as avalanche, landslide, and subsidence. Meanwhile, rock-fall is categorized as dry mass movement. (6) Storm is referring to local windstorm and typical cyclone; strong winds caused by regional atmospheric phenomena which are typical for a certain area. (7) Volcanic activity describes activity like rock-fall, ash fall, lava streams, and emissions of gases which can result in pyretic eruptions. (8) Wildfire is described as uncontrolled burning fire, usually in wild lands, which can cause damage to forestry, agriculture, infrastructure, and buildings. Macroeconomic data were obtained from several sources such as Barro and Lee (1996), World Development Indicator (WDI, 2008) Database and International Financial Statistics (IFS, 2008). Meanwhile, for year of schooling data, we refer to data set taken from the World Bank Research Department’s web page as per April 21, 2009 (www. worldbank.org). Please refer Table I for the full details of the sources of data. Empirical analysis, results, and discussion All regressions were estimated using cross-sectional analysis with heteroskedasticityconsistent standard error (White, 1980). Table II presents regression estimates for the No.

Variable

Source

1 2 3 4 5 6 7 8 9

GDP per capita Government consumption/GDP M2/GDP Year of schooling attainment Land area Population Number of killed Total affected Total damage

WDI (2008)/IFS (2008) WDI (2008) WDI (2008) Barro and Lee (1996) WDI (2008) WDI (2008)/IFS (2008) EM-DAT (www.emdat.be/) EM-DAT (www.emdat.be/) EM-DAT (www.emdat.be/)

Table I. Sources of data

Table II. Natural disaster losses and economic condition 1985 (2 2.6274) (0.6188) (0.4051)

22.0339 * * 0.2902 0.3286 73 0.0444

(2.005)

73

73 0.3610

0.6641 1.2492

Log (damage/GDP)

(1.8497)

(0.616) (0.6276) 73

(4.212)

(2.69) (1.1241)

(22.0361)

73 0.1234

1.8707 * * * 1.6260

22.4617 * *

0.3888 * * *

(21.7096) 2 1.7702 * * * (221.304) 26.4426 * * * (24.043)

23.8254 * * (22.5371)

0.2225 *

(2 1.7249) 0.6026 * * * (7.1222) 23.5561 *

0.1222 * *

21.7706 *

Log (total affected)

Notes: Statistically significant at *10, * *5, and * * *1 per cent levels; numbers in parentheses are t-values based on the White (1980) heteroscedasticity-consistent covariance matrix; others independent variables are not report here are log (population), Log (land area) and series of dummy variables to indicate disaster type

Log (GDP per capita) 21.8822 * * * (232.962) Log (GDP per capita)2 Log (government consumption) Log (M2/ GDP) Log (school) No. of observations 73 R2

Log (number of killed per capita)

434

Dependent variable


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number of deaths, total affected, and economic damage/GDP equations using the entire sample for the year 1985. Table III presents regression estimates for the number of deaths, total affected, and economic damage/GDP equations using the entire sample for year 1995. Table IV presents regression estimates for the number of deaths, total affected, and economic damage/GDP equations using the entire sample for year 2005. Generally, the results of this study are similar to the results of the study by Toya and Skidmore (2007) who tested several measures of social/economic infrastructure variables that includes income, education, openness, financial development, and the size of the government as determinants of disaster. They also found that a stronger financial sector and a smaller size of government are associated with a lower disaster death toll. However, their results are slightly different in the area of education, whereby in their study they were able to find meaningful inverse relationship between educational attainment and impact of natural disaster, which we failed to obtain. Noy (2008) results are also mixed with ours, he found that countries with a higher literacy rate, better institutions, higher per capita income, and higher degree of openness to trade and higher levels of government spending are better able to withstand the initial disaster shock and prevent further spillovers into the macro-economy. It can be clearly observed from Tables II-IV that income is a significant factor in lessening the impact of losses from natural disasters. This result supports the finding of Kahn (2005), who showed that countries with higher per capita income experience a similar amount of catastrophic events but suffer less death from these events. Another study in agreement with this finding is of Wildavsky (1988). He claimed that, a nation’s per capita income is a good first approximation of the degree of safety it enjoys. Rasmussen (2004) also found negative relationship between income and the number or persons affected by natural disasters. As for the schooling, the proxy variable for level of education, it is surprisingly insignificant in all three models, for all three years. Nevertheless, it cannot be denied the crucial role played by education in the pre-disaster period, the impact of and possible integration of disaster education into the post-disaster response, and the many options and new technologies available to educate people about disaster risk management. Through education, people can learn to reduce disaster risk and mitigate the impact of disasters by living in an ecologically sustainable manner, undertaking programmes to prevent and mitigate disaster risk, and preparing to manage the impact of disasters. In order to act upon the emerging need for greater awareness of natural disasters and preparedness, political commitment will be essential, hence the intended engagement with policy makers and key stakeholders. However, the true catalysts for the transformation of knowledge into action are those directly affected by natural disasters themselves for whom the means of imparting knowledge and the content must be tailored. Recognizing the fact that those with little access to education are among the most vulnerable, it is necessary to make sure that the neo-literate population will become properly informed of potential natural disasters, the importance of preparation, and the actions to be taken in time of disaster to protect themselves, their family and neighbors. Conclusion While it is true that natural disasters cannot be predicted nor prevented, the extent of the economic impact of these disasters can be lessened, or minimized. The results of


Table III. Natural disaster losses and economic condition 1995 73

(2 0.3696) (1.1340) (2 1.1507)

(1.7254)

73

Log (damage/GDP)

(0.7886)

73 0.1981

73

(3.5584) (2 0.002) (1.7947) (0.5828) 73 0.0778

2 0.3481 2.3696 * 1.1560

0.4442 * * *

(2 0.7874) 2 1.5477 * * * (2 16.638) 2 7.2704 * * * (2 3.484)

2 2.7812 * (2 1.7301) 2.6282 * (1.9332) 0.6743 (0.1987)

0.1449

(2 1.3332) 0.8825 * * * (9.6795) 2 2.6939

73 2 0.0270

2 0.2794 0.6262 2 1.9321

0.1622 *

2 1.8067 * * * (2 32.058) 2 2.2218



Log (GDP per capita) Log (GDP per capita)2 Log (government consumption) Log (M2/GDP) Log (school) No. of observations R2


436

Dependent variable


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73


(0.4274)

73 0.0871

73

Log (damage/GDP)

(0.1939)

73 0.4317

73

(2 1.8862)

2 3.2538 2.0041 0.9148

73 0.0970

(2 1.5470) (1.2621) (0.4526)

0.3382 * * (2 2.363)

(2 0.6347) 2 1.4817 * * * (15.723) 2 4.9623 *

2 3.2799 * * (2 2.1114) 1.6811 (1.4449) 0.4220 (0.2050)

0.0245

(2 0.2904) 0.9521 * * * (10.093) 2 1.5313

2 1.5283 * * (2 2.1519) 0.2402 (0.5082) 2 1.5207 * (2 1.6865)

0.0257

2 1.7341 * * * (2 38.050) 2 0.3054



Log (GDP per capita) Log (GDP per capita)2 Log (government consumption) Log (M2/GDP) Log (school) No. of observations R2

Dependent variable



Table IV. Natural disaster losses and economic condition 2005

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this study clearly indicate that there are indeed significant and meaningful relationship between economic conditions and the economic impact of natural disasters. The results of the three sets of cross-sectional estimation for 73 countries are generally robust with income playing the most significant role. The negative coefficient sign of income proves general presumption that wealthy nations and their citizens are better prepared for the natural disasters and could lessen the aftermath economic impact of natural disasters. Though the other general and popular presumption of education as a important tool could not be proved by this study, nevertheless the authors seriously believe that further researches using different tools should be conducted in the future. The size of the government is also found to be significant and inversely related, which strengthened the understanding of government intervention and consumption on minimizing the economic impact of natural disasters. References Albala-Bertrand, J. (1993), Political Economy of Large Natural Disasters, Oxford University Press, New York, NY. Barro, R.J. and Lee, J.W. (1996), “International measures of schooling years and schooling quality”, American Economic Review, Vol. 86 No. 2, pp. 218-23. Dacy, D.C. and Kunreuther, H. (1969), The Economics of Natural Disasters: Implications for Federal Policy, The Free Press, New York, NY. EM-DAT (2004), The OFDA/CRED International Disaster Database, Unversite Catholicque de Louvain, Brussels. Horwich, G. (2000), “Economic lessons from the Kobe earthquake”, Economic Development and Cultural Change, Vol. 48, pp. 521-42. IFRC (2001), available at: www.ifrc.org/publicat/wdr2001 (accessed October 20, 2009). IFS (2008), International Financial Statistics on CD-ROM, International Monetary Fund, Washington, DC. Kahn, M.E. (2005), “The death toll from natural disasters: the role of income, geography and institutions”, The Review of Economics and Statistics, Vol. 87 No. 2, pp. 271-84. Noy, I. (2008), “The macroeconomic consequences of disasters”, Journal of Development Economics, Vol. 88 No. 2, pp. 221-31. Padli, J. and Habibullah, M.S (2009), “Natural disaster death and socio-economic factors in selected Asian countries: a panel analysis”, Asian Social Science, Vol. 5 No. 4, pp. 65-71. Raschky, P.A. (2008), “Institutions and the losses from natural disasters”, Natural Hazards and Earth System Sciences, Vol. 8, pp. 627-34. Rasmussen, T.N. (2004), “Macroeconomic implications of natural disasters in the Caribbean”, IMF Working Papers, No. WP/04/224, International Monetary Fund, Washington, DC. Toya, H. and Skidmore, M. (2007), “Economic development and the impacts of natural disasters”, Economics Toya and Skidmore (2007 ) Letters, Vol. 94, pp. 20-5. UNESCAP (2007), Statistical Yearbook for Asia and the Pacific 2007, United Nation, Washington, DC. WDI (2008), World Development Indicators on CD-ROM, The World Bank, Washington, DC. White, H. (1980), “A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity”, Econometrica, Vol. 48, pp. 817-38. Wildavsky, A. (1988), Searching for Safety, Transaction, New Brunswick, NJ.


Further reading Economic Commission for Latin America and the Inter-American Development Bank (ECLAC) (2000), A Matter of Development: How to Reduce Vulnerability in the Face of Natural Disasters, Economic Commission for Latin America and the Inter-American Development Bank, Port-of-Spain. Guha-Sapir, D. (2008a), “EM-DAT’s new disaster classification”, Cred Crunch, No. 13, available at: www.emdat.be/ (accessed October 15, 2009). Guha-Sapir, D. (2008b), “Natural disaster in 2007”, Cred Crunch, No. 12, available at: www.emdat. be/ (accessed October 15, 2009). International Monetary Fund (2000), “Fund assistance for countries facing exogenous shocks”, available at: www.imf.org/external/np/pdr/sustain/2003/080803.pdf Tol, R. and Leek, F. (1999), “Economic analysis of natural disaster”, in Downing, T., Olsthoorn, A. and Tol, R. (Eds), Climate Change and Risk, Rouledge, London, pp. 308-27. Appendix. Selected countries for the study 1. Australia 2. Canada 3. China 4. Denmark 5. Iceland 6. Israel 7. Japan 8. New Zealand 9. Singapore 10. Switzerland 11. United States 12. Algeria 13. Antigua 14. Bangladesh 15. Belize 16. Bolivia 17. Botswana 18. Brazil 19. Cameroon 20. South Africa 21. Chile 22. Colombia 23. Costa Rica 24. Congo 25. Dominican Republic 26. Ecuador 27. El Salvador


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28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67.

Fiji Islands Ghana Guatemala Guyana Haiti Honduras Hungary India Indonesia Iran Jamaica Jordan Kenya Korea Lesotho Malawi Malaysia Mali Mauritius Mexico Nepal Nicaragua Niger Pakistan Panama Paraguay People’s Republic of Benin Peru Philippines Rwanda Senegal Trinidad and Tobago Central African Republic Sri-Lanka Sudan Swaziland Syria Thailand Togo Sierra Leone

68. 69. 70. 71. 72. 73.

Tunisia Turkey Uganda Uruguay Venezuela Zambia


Corresponding author A.H. Baharom can be contacted at: [email protected]

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