Limited skilled labor has been one of the greatest challenges facing the construction industry. The COVID-19 pandemic has further exaggerated the already strained construction labor market, leading to an additional negative impact. One of the major contributors to skilled labor shortages in construction is the issue of labor retention. Overall, this is a complex and dynamic situation that requires effective and efficient simulation-based techniques to capture the interdependent relationships that affect the performance of the construction labor market. This paper fills this knowledge gap. To this end, the authors used a multistep research methodology that involved (1) identifying factors that affect skilled labor shortages; (2) developing a one-module system dynamics model that consists of three interconnected systems (namely, construction labor market system, industry characteristics system, and economic conditions system); (3) initializing and calibrating the model to simulate the construction labor market; (4) validating the model through structural, behavioral, and calibration tests; and (5) conducting sensitivity analysis to simulate different parameters and examine their impact on skilled labor shortage. Among other findings, results indicated that all scenarios were successful in improving the conditions of the skilled labor market by increasing the workforce size and labor retention rate. Further, the model demonstrated that economic indicators have a more impactful influence on labor retention patterns compared with industry characteristics. The developed model offers industry practitioners, policymakers, business analysts, and other associated stakeholders a useful tool to test various scenarios including national-level economic policies and labor retention regulations that affect the construction skilled labor market. Consequently, this allows users to analyze the impact of variables such as fiscal policies, economic support plans, and construction spending strategies.


Civil, Architectural and Environmental Engineering

International Standard Serial Number (ISSN)

1943-7862; 0733-9364

Document Type

Article - Journal

Document Version


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© 2023 American Society of Civil Engineers, All rights reserved.

Publication Date

01 Oct 2023