Assessing the Vulnerability of Building Exterior Construction’s Labor Wages to Weather-Related Disasters in the United States

Ahmadi, Navid; Shahandashti, Mohsen

doi:10.1061/9780784481288.058

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The construction sector, as an important sector in the economy of the United States (Ahmadi and Shahandashti, 2017), plays a vital role in the recovery phase of natural hazards (Lloyd-Jones, 2006; Jayaraj, 2006; Benson et al , 2007; Owen and Dumashie, 2007; Amaratunga and Haigh, 2008; UN/ISDR, 2009; Hallegatte, 2014; Ahmadi and Shahandashti, 2018). Moreover, most indirect economic losses in the aftermath of natural hazards are construction-related losses (Hallegatte, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The increased construction costs following large-scale natural hazards, also referred to as “demand surge,” has been discussed in the literature (Kuzak and Larsen, 2005; EQECAT, 2005; Munich-Re, 2007; Olsen and Porter, 2011a, 2011b; Olsen and Porter, 2013; Kirchberger, 2017; Ahmadi and Shahandashti, 2018). Demand surge occurs due to the heightened demand for labor and materials after large-scale hazards; however, the consensus is that the construction labor wage escalation is the driving factor in demand surge measurement.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Post-hazard labor wage fluctuations: a comparative empirical analysis among different sub-sectors of the U.S. construction sector

Esfahani

Shahandashti

2020

JFMPC

View full text Add to dashboard Cite

Purpose The primary objectives of this study are to (1) highlight subsectors and industry groups of the construction sector that are most vulnerable to weather-related disasters (with highest labor cost escalation) and (2) analyze how immediate this labor wage escalation happens in different subsector of the construction sector. Design/methodology/approach The research methodology consists of three steps: (i) integrating various data sources to enable measurement of the county-level labor wage changes following large-scale weather-related disasters; (ii) measuring postdisaster labor wage changes at the county level; and (iii) comparing amount and timing of postdisaster labor wage changes among all sub-sectors (and industry groups) of the construction sector. Findings The results show that among the three construction subsectors (Heavy and Civil Engineering Construction subsector, Construction of Buildings subsector, and Specialty Trade Contractors sub-sector), Heavy and Civil Engineering Construction subsector is the most vulnerable to weather-related disasters. The industry groups under the Heavy and Civil Engineering Construction subsector showed the same vulnerability level; however, under the Construction of Buildings subsector, Industrial Building Construction industry group showed to be the most vulnerable; and under the Specialty Trade Contractors subsector, the Building Foundation and Exterior Contractors industry group is the most vulnerable. The results also showed that in approximately 75% of the damaged counties, there were increases in wages of all construction labors, over the following three quarter after the disasters. In average, labor wages in Construction of Buildings subsector and the Specialty Trade Contractors subsector decreased by 0.6% and 0.8%, respectively, in the quarter of disaster and gradually increased by 4.4% and 4.6%, respectively, in the following three quarters. On the other hand, Heavy and Civil Engineering Construction’s labor wages did not experience this decrease right after the disasters; wages increased immediately after disasters hit the counties and continually increased by 8.6% in three quarters after the disasters. It is expected that the results of this study will help policy makers, cost estimators and insurers to have a better understanding of the post-disaster construction labor wage fluctuations. Originality/value This study is unique in the way it used construction labor wage data. All data are location quotient, which makes the comparison among the affected counties (with different construction size) feasible.

show abstract