A Novel Methodological Approach to Estimate the Impact of Natural Hazard-Induced Disasters on Country/Region-Level Economic Growth

Abstract

With the increased frequency of extreme weather events and large-scale disasters, extensive societal and economic losses incur every year due to damage of infrastructure and private properties, business disruptions, fatalities, homelessness, and severe health-related issues. In this article, we analyze the economic and disaster data from 1970 through 2010 to investigate the impact of disasters on country/region-level economic growth. We leveraged a random parameter modeling approach to develop the growth-econometrics model that identifies risk factors significantly influencing the country/region-level economic growth in the face of natural hazard-induced disasters, while controlling for country/region- and time-specific unobserved heterogeneities. We found that disaster intensity in terms of fatalities and homelessness, and economic characteristics such as openness to trade and a government’s consumption share of purchasing power parity (PPP), are the significant risk factors that randomly vary for different countries/regions. Other significant factors found to be significant include population, real gross domestic product (GDP), and investment share of PPP converted GDP per capita. We also found that flood is the most devastating disaster to affect country/region-level economic growth. This growth-econometrics model will help in the policy and decision making of governments related to the investment needs for pre- and post-disaster risk mitigation and response planning strategies, to better protect nations and minimize disaster-induced economic impacts.

Keywords

Disaster risk reduction / Economic growth / Growth econometrics / Impact of natural hazard-induced disasters / Panel data analysis / Random parameter modeling

References

[1] Aisen A, Veiga FJ. How does political instability affect economic growth?. European Journal of Political Economy, 2013, 29(C): 151-167

[2] Albala-Bertrand JM. Natural disaster situations and growth: A macroeconomic model for sudden disaster impacts. World Development, 1993, 21(9): 1417-1434

[3] Anastasopoulos PCh, Mannering FL. A note on modeling vehicle accident frequencies with random-parameters count models. Accident Analysis & Prevention, 2009, 41(1): 153-159

[4] Anastasopoulos PCh, Mannering FL. An empirical assessment of fixed and random parameter logit models using crash- and non-crash-specific injury data. Accident Analysis and Prevention, 2011, 43(3): 1140-1147

[5] Baltagi BH. Econometric analysis of panel data, 2013 5 Hoboken: Wiley

[6] Cadwalladr, C. 2015. Nepal earthquake: The village wiped off the map in a few terrifying seconds. The Guardian, 17 May 2015. https://www.theguardian.com/world/2015/may/17/nepal-earthquake-langtang-village-everyone-was-dead. Accessed 20 Nov 2017.

[7] Carlsson F, Martinsson P. Willingness to pay among Swedish households to avoid power outages: A random parameter Tobit model approach. The Energy Journal, 2007, 28(1): 75-89

[8] Cavallo, E., S. Galiani, I. Noy, and J. Pantano. 2010. Catatrophic natural disasters and economic growth. Inter-American Development Bank working paper series No. IDB-WP-183. http://www.iadb.org/res/publications/pubfiles/pubidb-wp-183.pdf. Accessed 5 Dec 2017.

[9] CES (Center for Economic Studies) ifo Munich Group. 2017. Ifo GAME—The geological and meteorological events database. https://www.cesifo-group.de/ifoHome/research/Departments/International-Trade/Ifo_GAME.html. Accessed 20 Sept 2017.

[10] Cunado, J., and S. Ferreira. 2011. The macroeconomic impacts of natural disasters: New evidence from floods. Paper prepared for Agricultural and Applied Economics Association’s 2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, 24–26 July 2011.

[11] Dinu RR, Veeraragavan A. Random parameter models for accident prediction on two-lane undivided highways in India. Journal of Safety Research, 2011, 42(1): 39-42

[12] Dowrick S, Rogers M. Classical and technological convergence: Beyond the Solow-Swan growth model. Oxford Economic Papers, 2002, 54(3): 369-385

[13] Econometric Software Inc. 2016. Panel data models: Random parameters—mixed models. http://www.limdep.com/features/capabilities/panel_data/random_parameters_multilevel_6.php. Accessed 1 Jan 2016.

[14] Gassebner M, Keck A, Teh R. Shaken, not stirred: The impact of disasters on international trade. Review of International Economics, 2010, 18(2): 351-368

[15] Guha-Sapir, D., and R. Below. 2002. The quality and accuracy of disaster data: A comparative analysis of three global data sets. The Disaster Management Facility, The World Bank, Working Paper No. 191. http://www.cred.be/sites/default/files/Quality_accuracy_disaster_data.pdf. Accessed 5 Dec 2017.

[16] Guha-Sapir, D., R. Below, and Ph. Hoyois. 2015. EM-DAT: International disaster database. Brussels: Centre for Research on the Epidemiology of Disasters (CRED), Catholique University of Louvain.

[17] Hasan S, Mesa-Arango R, Ukkusuri S. A random-parameter hazard-based model to understand household evacuation timing behavior. Transportation Research Part C: Emerging Technologies, 2013, 27: 108-116

[18] Headey DD, Hodge A. The effect of population growth on economic growth: A meta-regression analysis of the macroeconomic literature. Population and Development Review, 2009, 35(2): 221-248

[19] Heston, A., R. Summers, and B. Aten. 2012. Penn world table version 7.1, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. https://pwt.sas.upenn.edu/php_site/pwt_index.php. Accessed 20 Sept 2017.

[20] Hochrainer, S. 2009. Assessing the macroeconomic impacts of natural disasters: Are there any? Policy research working paper, No. WPS 4968. The World Bank. https://openknowledge.worldbank.org/handle/10986/4162 License: CC BY 3.0 IGO. Accessed 20 Nov 2017.

[21] Hoyois, P., and D. Guha-Sapir. 2003. Three decades of floods in Europe: A preliminary analysis of EMDAT data. Brussels: Centre for Research on the Epidemiology of Disasters (CRED), Catholique University of Louvain.

[22] Hoyois, P., and D. Guha-Sapir. 2005. Flood disasters in Europe: A short analysis of EMDAT data for years 1985–2004. Brussels: Centre for Research on the Epidemiology of Disasters (CRED), Catholique University of Louvain.

[23] Jong-A-Pin R. On the measurement of political instability and its impact on economic growth. European Journal of Political Economy, 2009, 25(1): 15-29

[24] Kahn ME, Costa D, Gerking S, Glaeser E, Levinson A, Shapiro J, Shimshack J, Timmins C. The death toll from natural disasters: The role of income, geography, and institutions. The Review of Economics and Statistics, 2005, 87(2): 271-284

[25] Katz, M. 2013. Hurricane Sandy flood damage—Toxic mold invading NYC homes and businesses. ReleaseWire, 31 January 2013. http://www.releasewire.com/press-releases/hurricane-sandy-flood-damage-toxic-mold-invading-nyc-homes-and-businesses-200065.htm. Accessed 20 Nov 2017.

[26] Kousky C. Informing climate adaptation: A review of the economic costs of natural disasters, their determinants, and risk reduction and options, 2012, Discussion Paper: Resources for the Future

[27] Mannering FL, Shankar V, Bhat CR. Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research, 2016, 11(Supplement C): 1-16

[28] Mohammadian, A., M. Haider, and P.S. Kanaroglou. 2005. Incorporating spatial dependencies in random parameter discrete choice models. Paper prepared for the 84th Annual Meeting of the Transportation Research Board, Washington, DC, January 2005. http://milute.mcgill.ca/Research/Senior/Spatial-Mixed-TRB_rev.pdf. Accessed 5 Dec 2017.

[29] Momota A. A population-macroeconomic growth model for currently developing countries. Journal of Economic Dynamics and Control, 2009, 33(2): 431-453

[30] Mukherjee, S. 2017. Towards a resilient grid: A risk-based decision analysis incorporating the impacts of severe weather-induced power outages. Ph.D. dissertation, Purdue University, West Lafayette, Indiana, USA. https://www.researchgate.net/publication/321528706_Towards_a_resilient_grid_A_risk-based_decision_analysis_incorporating_the_impacts_of_severe_weather-induced_power_outages. Accessed 5 Dec 2017.

[31] Mukherjee, S., and M. Hastak. 2016. Risk of natural disasters on economic growth of a country: A random parameter panel-data analysis. Paper prepared for the 12th International Conference of the International Institute for Infrastructure Resilience and Reconstruction, University of Peradeniya, Kandy, Sri Lanka, 5–7 August 2016, 29–37. https://www.researchgate.net/publication/306548252. Accessed 20 Nov 2017.

[32] Mukherjee S, Nateghi R. Climate, weather, socio-economic and electricity usage data for the residential and commercial sectors in FL, U.S. Data in Brief, 2017, 13: 192-195

[33] Mukherjee S, Nateghi R. Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States. Energy, 2017, 128: 688-700

[34] Mukherjee, S., and R. Nateghi. 2017c. Estimating climate-demand nexus to support long-term adequacy planning in the energy sector. In 2017 IEEE PES General Meeting, Chicago, 16–20 July 2017, IEEE Xplore. https://www.researchgate.net/publication/319851722. Accessed 20 Nov 2017.

[35] Munich RE. 2012. Review of natural catastrophes in 2011: Earthquakes result in record loss year. Press Release. https://www.munichre.com/en/media-relations/publications/press-releases/2012/2012-01-04-press-release/index.html. Accessed 20 Nov 2017.

[36] NASA (National Aeronautics and Space Administration). 2016. Fort McMurray wildfire continues to burn. Fire and Smoke. 9 May 2016. https://www.nasa.gov/feature/goddard/2016/fort-mcmurray-wildfire-continues-to-burn. Accessed 9 Dec 2016.

[37] Nateghi R, Mukherjee S. A multi-paradigm framework to assess the impacts of climate change on end-use energy demand. PloS One, 2017

[38] Noy I. The macroeconomic consequences of disasters. Journal of Development Economics, 2009, 88(2): 221-231

[39] Noy, I., and W. DuPont. 2017. Long-term consequences of natural disasters. Oxford research encyclopedia of natural hazard science. http://hdl.handle.net/10063/4981. Accessed 20 Nov 2017.

[40] Plyer, A. 2016. Facts for features: Katrina impact. The Data Center. http://www.datacenterresearch.org/data-resources/katrina/facts-for-impact/. Accessed 30 Dec 2016.

[41] Qayyum U, Haider A. Foreign aid, external debt and economic growth nexus in low-income countries: The role of institutional quality. The Pakistan Development Review, 2012, 51(4): 97-116.

[42] Riebeek, H. 2005. The rising cost of natural disasters. Earth Observatory NASA. http://earthobservatory.nasa.gov/Features/RisingCost/rising_cost5.php. Accessed 12 Nov 2014.

[43] Sadia B, Faheem M, Nawaz M, Khan K. Impact of disaster related mortality on gross domestic product: A case study of Pakistan. International Journal of Economics and Management, 2012, 6(1): 128-149.

[44] Shabnam N. Natural disasters and economic growth: A review. International Journal of Disaster Risk Science, 2014, 5(2): 157-163

[45] Skidmore M, Toya H. Do natural disasters promote long-run growth?. Economic Inquiry, 2002, 40(4): 664-687

[46] Tang CF, Abosedra S. The impacts of tourism, energy consumption and political instability on economic growth in the MENA countries. Energy Policy, 2014, 68: 458-464

[47] Tapia, J. 2013. From the oil crisis to the great recession: Five crises of the world economy. https://marxismocritico.com/2013/05/08/from-the-oil-crisis-to-the-great-recession-five-crises-of-the-world-economy/. Accessed 20 Nov 2017.

[48] Toya H, Skidmore M. Economic development and the impacts of natural disasters. Economics Letters, 2007, 94: 20-25

[49] Tumusiime E, Wade BB, Mosali J, Johnson J, Locke J, Biermacher JT. Determining optimal levels of nitrogen fertilizer using random parameter models. Journal of Agricultural and Applied Economics, 2011, 43(4): 541-552

[50] Venkataraman N, Ulfarsson GF, Shankar VN. Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. Accident Analysis & Prevention, 2013, 59: 309-318

[51] Wirtz A, Kron W, Löw P, Steuer M. The need for data: Natural disasters and the challenges of database management. Natural Hazards, 2014, 70(1): 135-157

[52] Wooldridge JM. Introductory economics: A modern approach, 2002 2 Mason, OH: South-Western

[53] World Bank. 2016a. GNI per capita, atlas method (current US$). World Bank national accounts data, and OECD national accounts data files. http://data.worldbank.org/indicator/NY.GNP.PCAP.CD?contextual=default&end=2010&locations=XL-ET-SD-BH-MV-US-AG-SZ-BZ-DJ&start=1987&view=chart. Accessed 28 Nov 2017.

[54] World Bank. 2016b. What is an “international dollar”? Currencies. https://datahelpdesk.worldbank.org/knowledgebase/articles/114944-what-is-an-international-dollar. Accessed 20 Nov 2017.