The effect of natural disasters on construction labor wage fluctuations: A spatial difference-in-difference analysis

The United States is one of the top five countries in the world prone to natural disasters. Natural disasters could have a significant impact on the construction industry. In a large-scale disaster, labor cost fluctuation is known to be an important driving factor in the construction cost increases. Labor cost fluctuation could increase the reconstruction cost by 20 to 50 percent after a large-scale disaster. In the literature, the effect of a disaster on the construction market condition has been calculated through two stages, measurement and quantification. Merging two stages, measurement and quantification, in one stage, provides an opportunity to decrease the amount of error in the quantification step due to measurement error. Merging two stages, measurement and quantification, in one stage using an appropriate regression model has not been studied in the literature for the construction market indices. This research has two main objectives. The first objective of this research is to estimate the spatio-temporal effect of natural disasters on the fluctuation of the labor weekly wages in the residential construction sector, using the difference-in-difference technique. This technique is capable of eliminating the need for measurement in this analysis and can directly quantify the effect of natural disasters on the labor wage fluctuations. This technique has not been used in this context before. The second objective in this research is to use a spatial multiple imputation method to tackle the missing data problem. This spatial imputation method has not been used in this context before. In this research, the required construction county-level data of 67 counties in Florida State has been collected from the Bureau of Labor Statistics (BLS) to create the county-level panel data models for Florida State from 2014 to 2018. Historical county-level data of those counties impacted by weather-related disasters (flood, tornado, and storm) from the Federal Emergency Management Agency (FEMA) from 2014 to 2018 were also collected to conduct the analysis. Three commonly used construction market exogenous variables are used within spatial panel data models to explore natural disasters’ effect on labor weekly wage fluctuations in the residential construction market. Also, a disaster dummy variable is used to capture these fluctuations in the county level dataset. To have less biased results and increase the efficiency of our spatial model, four strategies were used to tackle the missing data problem. Thus, in this research, multiple spatial panel data models (Spatial Autoregressive Model (SAR), Spatial Autocorrelation Model (SAC), Spatial Error Model (SEM), and Spatial Durbin Model (SDM) models) have been developed to investigate the effect of natural disasters on labor wage fluctuations. Based on the Breusch–Pagan LM test and Hausman test results, the fixed-effect Spatial Durbin Model (SDM) using a multiple imputation method is identified to be a more appropriate model in this research. The total effect obtained from SDM using the multiple imputation methods indicates that labor weekly wage increases by 7. 5 percent in counties affected by natural disasters compared to those that are not affected. This study helps risk managers, cost engineers, city policymakers, construction companies, property owners, and insurers to have a better understanding of post-disaster construction cost fluctuations aftermath of a natural disaster.