Mun, Byungki2025-06-062025-06-062025-06-05vt_gsexam:44231https://hdl.handle.net/10919/135098This dissertation employs a quasi-experimental design to examine how major economic shocks—the COVID-19 pandemic, immigration restrictions, and the U.S.-China tariff war (2018–2020)—reshaped labor demand and employer skill requirements in the United States. Using econometric methods on panel data from online job postings, the Current Population Survey, and trade exposure measures, the analysis provides empirical evidence on how firms adjust hiring criteria in response to disruptions in labor supply, market conditions, and global trade. The first chapter shows that, in contrast to the upskilling trend following the Great Recession, the COVID-19 pandemic led to widespread downskilling in education requirements, driven by labor market tightness and accelerated technological adoption, especially in tradable industries and routine-manual occupations. The second chapter applies a shift-share difference-in-differences (DiD), finding that visa bans reduced immigrant employment but increased native employment, particularly among less-educated workers. The immigration shock also induced firms to adopt more automation and broadband technologies, raising demand for technical and digital skills. The third chapter uses a staggered DiD with Shift-Share IV and CSDiD models to analyze the tariff war, revealing export tariffs reduced high-skill job postings and wages while increasing low-skill roles, and import tariffs boosted engineering skills but lowered wages. These studies underscore how firms dynamically adjust skill demand under uncertainty, highlighting the role of labor market tightness, technological advancements, and trade policy in shaping hiring strategies.ETDenIn Copyrightlabor marketeconomic shocksCOVID-19 pandemicimmigration restrictionsU.S.-China trade wartariffsemploymentwagesskill demandjob postingsautomationshift-share instrumental variablestaggered Difference-in-DifferencesTWFECSDiD.Dissertation