Detecting Episodes of Mildly Explosive Behavior in the Hurricane Resiliency Index to Examine Community Resilience to Hurricanes
Publication: Natural Hazards Review
Volume 24, Issue 1
Abstract
Natural disasters such as hurricanes, earthquakes, and floods cause massive damage and losses around the world. Investigating their impact on affected communities and delineating them from nondisaster forces have major scientific and policy implications. To that end, a series of econometric tests were applied to the Hurricane Resiliency Index (HRI) for six selected study areas to identify and date-stamp periods of mildly explosive behavior (MEB). Evidence of MEB signals a structural break or nonlinearity in an otherwise stationary time series. The starting and ending points of multiple MEB episodes indicate the extent to which a community recovers toward normalcy. It is found that the recovery time associated with hurricanes is much shorter than that with economic recessions. The overall severity of MBE, when considering both duration and amplitude, is most pronounced when a major hurricane strikes dense populations. The findings also highlight that the compounding effect of economic recession and hurricane poses the most serious threat to a local economy. Finally, the Hurricane Resiliency Index is shown to outperform the Federal Reserve Bank of Dallas’s Metro Business-Cycle Index in capturing such behaviors.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The Hurricane Resilience Index in six MSAs (Houston, Cape Coral, McAllen, Pensacola, New Orleans, and Tampa) is available.
Acknowledgments
This material is based on work supported by the National Natural Science Foundation of China (Grant No. 52008110), Natural Science Foundation of Fujian Province (Grant No. 2020J05195), the National Institute of Standards and Technology (under Subaward through Colorado State University No. G-99042-14 Center for Risk-Based Community Resilience Planning), the National Science Foundation (under CMMI-CRISP Award No. 1735499 Collaborative Research Type 2: Defining and Optimizing Societal Objectives for the Earthquake Risk Management of Critical Infrastructure), and Economic Development Administration of US Department of Commerce (under Project No. 08-79-05280 Innovative Financing Strategies for Small Businesses in Hurricane Prone Regions). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
References
Abel, N., D. M. Cumming, and J. M. Anderies. 2006. “Collapse and reorganization in social-ecological systems: Questions, some ideas, and policy implications.” Ecol. Soc. 111 (1): 1–8.
Avila, L. 2017. Tropical Storm Harvey Advisory No. 37. Washington, DC: National Hurricane Center.
Berg, R. 2009. Hurricane Ike tropical cyclone report. Washington, DC: US Census Bureau, Population Division.
Berlemann, M., and D. Wenzel. 2016. “Long-term growth effects of natural disasters—Empirical evidence for droughts.” CESifo Working Paper Series 5598. Munich, Germany: Center for Economic Studies and Ifo Institute.
Beven, J. L. 2004. Hurricane Frances tropical cyclone report. Miami: National Hurricane Center.
Beven, J. L. 2017. Tropical Strom Harvey. Miami: National Hurricane Center.
Cagil, E. C., and P. E. Mandaci. 2017. “Detecting bubbles in the US stock market : A new evidence from the bootstrap cointegration test in ESTAR error correction model.” Emprical Econ. Lett. 16: 941–950.
Cashin, P., and S. Sosa. 2013. “Macroeconomic fluctuations in the Caribbean: The role of climatic and external shocks.” J. Int. Trade Econ. Dev. 22 (5): 729–748. https://doi.org/10.1080/09638199.2011.599854.
Caspi, I. 2016. “Rtadf: Testing for bubbles with EViews.” J. Stat. Software 81 (1): 1–16. https://doi.org/10.18637/jss.v081.c01.
Chhibber, A., and R. Laajaj. 2013. “The interlinkages between natural disasters and economic development.” In Economic impacts of natural disasters, 28–56. New York: Oxford University Press.
Chu, H. R., and J. Z. Yang. 2020. “Building disaster resilience using social messaging networks: The WeChat community in Houston, Texas, during Hurricane Harvey.” Disasters 44 (4): 726–752. https://doi.org/10.1111/disa.12388.
Cobb, H. D., III, and D. P. Roberts. 2006. Hurricane Wilma tropical cyclone report. Miami: National Hurricane Center.
Cui, Y., D. Liang, and B. T. Ewing. 2015. “Empirical analysis of building permits in response to hurricane landfalls.” Nat. Hazard. Rev. 16 (4): 04015009. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000185.
Cui, Y., D. Liang, B. T. Ewing, and A. Nejat. 2016. “Development, specification and validation of hurricane resiliency index.” Nat. Hazards 82 (3): 2149–2165. https://doi.org/10.1007/s11069-016-2299-8.
Dhakal, S. P. 2018. “Analyzing news media coverage of the 2015 Nepal earthquake using a community capitals lens: Implications for disaster resilience.” Disasters 42 (2): 294–313. https://doi.org/10.1111/disa.12244.
Eid, M. S., and I. H. EI-adaway. 2017. “Sustainable disaster recovery: Multiagent-based model for integrating environmental vulnerability into decision-making processes of the associated stakeholders.” J. Urban Plann. Dev. 143 (1): 04016022. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000349.
Enoksen, F. A., C. J. Landsnes, K. Lučivjanská, and P. Molnár. 2020. “Understanding risk of bubbles in cryptocurrencies.” J. Econ. Behav. Organ. 176 (Aug): 129–144. https://doi.org/10.1016/j.jebo.2020.05.005.
Evans, G. 1991. “Pitfalls in testing for explosive bubbles in asset prices.” Am. Econ. Rev. 81 (4): 922–930.
Ewing, B. T., D. Liang, and Y. P. Cui. 2014. “A time series approach to examining regional economic resiliency to hurricanes.” Am. J. Econ. Sociol. 73 (2): 369–391.
FEMA. n.d. “Open FEMA data sets.” Accessed February 21, 2022. https://www.fema.gov/about/openfema/data-sets.
Finn, K. 2011. “New Orleans braces for Tropical Storm Lee.” Accessed January 4, 2022. https://www.reuters.com/article/uk-storm-usa-gulf/new-orleans-braces-for-tropical-storm-lee-idUKTRE7821VF20110903.
Fomby, T., Y. Ikeda, and N. V. Loayza. 2013. “The growth aftermath of natural disasters.” J. Appl. Econom. 28 (3): 412–434. https://doi.org/10.1002/jae.1273.
GFDRR (Global Facility for Disaster Reduction and Recovery). 2005. “Understanding risk: The evolution of disaster risk assessment since 2005.” Accessed March 12, 2022. https://www.preventionweb.net/english/hyogo/gar/2015/en/bgdocs/GFDRR,%202014a.pdf.
Gourio, F. 2012. “Disaster risk and business cycles.” Am. Econ. Rev. 102 (6): 2734–2766. https://doi.org/10.1257/aer.102.6.2734.
Hayes, F. 2006. Elderly left homeless by FEMA deadline. New York: Global Action on Aging.
Herrmann-Lunecke, M. G., and P. Villagra. 2020. “Community resilience and urban planning in tsunami-prone settlements in Chile.” Disasters 44 (1): 103–124. https://doi.org/10.1111/disa.12369.
Hsiang, S. M., and A. S. Jina. 2014. “The causal effect of environmental catastrophe on long- run economic growth: Evidence from 6,700 cyclones.” National Bureau of Economic Research Working Paper Series No. 20352. Cambridge, MA: National Bureau of Economic Research.
International Recovery Platform. 2007. “Beyond lessons learned from post-disaster recovery.” Accessed February 5, 2022. https://www.recoveryplatform.org/assets/publication/irp_regional_recovery_forum_nov2007.pdf.
Ji, H. J., and D. Lee. 2019. “Disaster risk reduction, community resilience, and policy effectiveness: The case of the Hazard Mitigation Grant Program in the United States.” Disasters 45 (2): 378–402. https://doi.org/10.1111/disa.12424.
Kendra, J., L. Clay, K. Gill, and J. Trivedi. 2021. “Community resilience: Toward a framework for an integrated, interdisciplinary model of disaster.” Nat. Hazard. Rev. 22 (4): 04021049. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000495.
Khan, M., Q. Wu, S. Yan, and J. Peng. 2021. “Factors affecting urban resilience and sustainability: Case of slum dwellers in Islamabad, Pakistan.” J. Urban Plann. Dev. 147 (4): 04021053. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000749.
Klomp, J. 2015. “Economic development and natural disasters: A satellite data analysis.” Global Environ. Change 36 (Jan): 67–88. https://doi.org/10.1016/j.gloenvcha.2015.11.001.
Loayza, N. V., E. Olaberría, J. Rigolini, and L. Christiaensen. 2012. “Natural disasters and growth: Going beyond the averages.” World Dev. 40 (7): 1317–1336. https://doi.org/10.1016/j.worlddev.2012.03.002.
Longstaff, P. H., and S. U. Yang. 2008. “Communication management and trust: Their role in building resilience to “surprises” such as natural disasters, pandemic flu, and terrorism.” Ecol. Soc. 13 (1): 3. https://doi.org/10.5751/ES-02232-130103.
Lopez, R., V. Thomas, and P. Troncoso. 2016. Economic growth, natural disasters and climate change: New empirical estimates. No. WP434. Santigo, Chile: Univ. of Chile.
Mark, L. 2004. Hurricane Charley preliminary storm summary. Wilmington, NC: National Weather Service.
Mejia, S. A. 2014. Debt, growth and natural disasters a Caribbean trilogy. Washington, DC: International Monetary Fund.
Nkhata, A. B., C. Breen, and W. Freimund. 2008. “Resilient social relationships and collaboration in the management of social–ecological systems.” Ecol. Soc. 13 (1): 2. https://doi.org/10.5751/ES-02164-130102.
Noy, I., and W. DuPont. 2016. “The long-term consequences of natural disasters—A summary of the literature.” Accessed March 12, 2022. http://researcharchive.vuw.ac.nz/xmlui/bitstream/handle/10063/4981/working%20paper.pdf?sequence=1.
Phillips, P. C. B., S. Shi, and J. Yu. 2015a. “Testing for multiple bubbles: Limit theory of dating algorithms.” Int. Econ. Rev. 56 (4): 1043–1078. https://doi.org/10.1111/iere.12132.
Phillips, P. C. B., S.-P. Shi, and J. Yu. 2015b. “Testing for multiple bubbles: Limit theory of real-time detectors.” Int. Econ. Rev. 56 (4): 1079–1134. https://doi.org/10.1111/iere.12131.
Phillips, P. C. B., and J. Yu. 2011. “Dating the timeline of financial bubbles during the Subprime Crisis.” Quant. Econ. 2 (3): 455–491. https://doi.org/10.3982/QE82.
Rose, A. 2004. “Defining and measuring economic resilience to disasters.” Disaster Prev. Manage. 13 (4): 307–314.
Staff, W. 2005. Gulf Coast hurricanes situation report. Washington, DC: DOE.
The Conference Board. 2001. “Business cycle indicators handbook.” Accessed April 23, 2022. http://docplayer.net/8351005-Business-cycle-indicators-handbook.html.
UNDRR (United Nations Office for Disaster Risk Reduction). 2015. “Global assessment report on disaster risk reduction.” Accessed May 21, 2022. https://digitallibrary.un.org/record/3825375?ln=en.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 1 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 18, 2021
Accepted: Aug 19, 2022
Published online: Oct 26, 2022
Published in print: Feb 1, 2023
Discussion open until: Mar 26, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yanfang Lyu, Yun Xiang, Dong Wang, Evaluating Indirect Economic Losses from Flooding Using Input–Output Analysis: An Application to China’s Jiangxi Province, International Journal of Environmental Research and Public Health, 10.3390/ijerph20054509, 20, 5, (4509), (2023).