Examining the Effect of Weather-Related Natural Disasters on Labor Wage Fluctuations in Transportation Construction
Publication: International Conference on Transportation and Development 2021
ABSTRACT
Weather-related hazards have a significant impact on labor cost fluctuations. These fluctuations lead to construction cost under- or over-estimation that impacts the overall success of transportation projects. However, the effect of weather-related natural disasters on the post-disaster labor wage fluctuations in the transportation infrastructure sector has not been studied. The objective of this research is to examine the spatiotemporal effect of natural disasters on the fluctuation of the labor weekly wages in the transportation construction sector. In this research, the required construction data of 254 counties in Texas (from 2014 to 2018) were collected to create the panel data models. Multiple spatial Durbin models (SDM) were developed to examine the effect of natural disasters on labor wage fluctuations in the transportation infrastructure sector. The results obtained from the SDM models combined with the multiple imputation method to address the missing data indicate that natural disasters significantly affect increasing weekly labor wage in the transportation infrastructure sector in counties affected by weather-related disasters. It is expected that this study helps cities, the Departments of Transportation (DOTs), Metropolitan Planning Organizations (MPOs), and infrastructure construction companies to have a better understanding of post-disaster construction cost fluctuations resulting from natural disasters.
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REFERENCES
Abediniangerabi, B., Ahmadi, N., and Shahandashti, M. (2018). “Is Architectural Billing Index Helpful to Explain Fluctuations in Non-Residential Construction Spending in the United States?.” In Construction Research Congress 2018 (pp. 19-28).
Abediniangerabi, B., Shahandashti, S. M., Ahmadi, N., and Ashuri, B. (2017). “Empirical investigation of temporal association between architecture billings index and construction spending using time-series methods.” Journal of Construction Engineering and Management, 143(10), 04017080.
Ahmadi, N., and Shahandashti, M. (2020b). “Characterizing Construction Demand Surge Using Spatial Panel Data Models.” Natural Hazards Review, 21(2), 04020008.
Ahmadi, N., and Shahandashti, S. M. (2018a). “Role of Predisaster Construction Market Conditions in Influencing Postdisaster Demand Surge.” Natural Hazards Review, 19(3), 04018010.
Ahmadi, N., and Shahandashti, S. M. (2018b). “Assessing the Vulnerability of Building Exterior Construction’s Labor Wages to Weather-Related Disasters in the United States.” In Construction Research Congress, 594-603.
Ashuri, B., and Shahandashti, S. M. (2012). Quantifying the relationship between construction cost index (CCI) and macroeconomic factors in the United States. In Proceedings of the 48th ASC Annual International Conference, Birmingham City University, Birmingham, April 11 (Vol. 14).
Ashuri, B., Shahandashti, S. M., and Lu, J. (2012a). “Is the information available from historical time series data on economic, energy, and construction market variables useful to explain variations in ENR construction cost index?.” In Construction Research Congress 2012: Construction Challenges in a Flat World, 457-464.
Ashuri, B., Shahandashti, S. M., and Lu, J. (2012b). “Empirical tests for identifying leading indicators of ENR construction cost index.” Construction Management and Economics, 30(11), 917-927.
Brown, J. T. (2014). The hurricane sandy rebuilding strategy: in brief. Congressional Research Service.
Bureau of Labor Statistic. (2020). “QCEW Location Quotient Details.” The United States Department of Labor, Bureau of Labor Statistic, <https://data.bls.gov/cew/doc/info/location_quotients.htm> (Jul. 22, 2019).
Chang, S. E., and Miles, S. B. (2004). The dynamics of recovery: A framework. In Modeling spatial and economic impacts of disasters Springer, Heidelberg, 181-204.
Esfahani, N. A., and Shahandashti, M. (2020a), Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the US construction sector. Journal of Financial Management of Property and Construction.
Farooghi, F., Ahmadi, N., and Shahandashti, S. M. (2020). “Quantifying Relationship Between Pre-Disaster Construction Market Conditions and Post-Disaster Construction Labor Wage Fluctuations in the Gulf Coast Construction Industry.” In Construction Research Congress.
FEMA. (2014). “Florida Severe Storms, Tornadoes, Straight-line Winds, and Flooding (DR-4177).” The department of Homeland Security, Federal Emergency Management Agency, <https://www.fema.gov/disaster/4177> (May. 5, 2019).
FEMA. (2015). “Texas Severe Storms, Tornadoes, Straight-line Winds, and Flooding (DR-4223).” The department of Homeland Security, Federal Emergency Management Agency, <https://www.fema.gov/disaster/4223> (May. 2, 2019).
FEMA. (2016). “Louisiana Severe Storms and Flooding (DR-4277).” The department of Homeland Security, Federal Emergency Management Agency, <https://www.fema.gov/disaster/4277> (May. 2, 2019).
FEMA. (2017). “Texas Hurricane Harvey (DR-4332).” The department of Homeland Security, Federal Emergency Management Agency, <https://www.fema.gov/disaster/4332> (May. 5, 2019).
FEMA. (2018). “OpenFEMA dataset: Disaster declarations summaries—V1.” Accessed July 25, 2018. https://www.fema.gov/openfema-dataset-disaster-declarations-summaries-v1.
Galbusera, L., and Giannopoulos, G. (2018). “On input-output economic models in disaster impact assessment.” International journal of disaster risk reduction, 30, 186-198.
Guha-Sapir, D., Vos, F., Below, R., and Ponserre, S. (2012). Annual disaster statistical review 2011: the numbers and trends, Centre for Research on the Epidemiology of Disasters (CRED).
Khodahemmati, N., and Shahandashti, M. (2020). Diagnosis and Quantification of Postdisaster Construction Material Cost Fluctuations. Natural Hazards Review, 21(3), 04020019.
Kim, S., Abediniangerabi, B., and Shahandashti, M. (2020). Forecasting Pipeline Construction Costs Using Time Series Methods. In Pipelines 2020 (pp. 198-209). Reston, VA: American Society of Civil Engineers.
Munich-Re. 2007. Annual review: Natural catastrophes 2006. Munich, Germany: Munich Reinsurance Group, Geoscience Research Group.
National Oceanic and Atmospheric Administration (NOAA). (Fall 2020)“ Billion-Dollar Weather and Climate Disasters: Overview.” Retrived frome https://www. https://www.ncdc.noaa.gov/billions/ (November. 2, 2020).
National Oceanic and Atmospheric Administration office for coastal Management. (2020, March 20) Retrived frome https://coast.noaa.gov/states/florida.html (March.27, 2020).
National Oceanic and Atmospheric Administration office for coastal Management. (2020, March 20) Retrived frome https://coast.noaa.gov/states/florida.html (March.27, 2020).
NOAA (National Oceanic and Atmospheric Administration) and NCEI (National Centers for Environmental Information). 2018. “U.S. billiondollar weather and climate disasters.” Accessed June 26, 2018. https://www.ncdc.noaa.gov/billions/.
NOAA (National Oceanic and Atmospheric Administration). “2019 was the 2nd wettest year on record for the U.S.” Accessed July 6, 2020. https://www.noaa.gov/news/2019-was-2nd-wettest-year-on-record-for-us.
Olsen, A. H., and Porter, K. A. (2011 a). On the Contribution of Reconstruction Labor Wages and Material Prices to Demand Surge, Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado at Boulder, Boulder, CO.
Olsen, A. H., and Porter, K. A. (2011 b). “What We Know about Demand Surge: Brief Summary.” ASCE Journal of Natural Hazards Rev., 12(2), 62–71.
Olsen, A. H., and Porter, K. A. (2013). “Storm surge to demand surge: exploratory study of hurricanes, labor wages, and material prices.” ASCE Journal of Natural Hazards Review, DOI: https://doi.org/10.1061/(ASCE)NH.1527-6996.0000111.
Shahandashti, M. (2014a). Analysis of the temporal relationships between highway construction cost and indicators representing macroeconomic and construction and energy market conditions. In Construction Research Congress 2014: Construction in a Global Network (pp. 1103-1110).
Shahandashti, S. M. (2014b). Analysis of construction cost variations using macroeconomic, energy and construction market variables (Doctoral dissertation, Georgia Institute of Technology).
Shahandashti, S. M., and Ashuri, B. (2013). Forecasting engineering news-record construction cost index using multivariate time series models. Journal of Construction Engineering and Management, 139(9), 1237-1243.
Shahandashti, S. M., and Ashuri, B. (2016). Highway construction cost forecasting using vector error correction models. Journal of management in engineering, 32(2), 04015040.
Tiefelsdorf, M. (2006). Modelling spatial processes: the identification and analysis of spatial relationships in regression residuals by means of Moran’s I (Vol. 87). Springer.
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International Conference on Transportation and Development 2021
Pages: 167 - 177
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© 2021 American Society of Civil Engineers.
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Published online: Jun 4, 2021
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