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Labor market matching has significant economic and social impacts since a low matching efficiency/quality reduces aggregated gains in productivity and wages and may lead to unemployment and job vacancy. IT has played a crucial role in influencing labor markets matching by reducing search costs, lowering enter barriers, and promoting flexibility. In this dissertation, I explore one antecedent (i.e., digital labor markets) and two consequences of labor market matching (i.e., local employment and wage). The first essay examines the role of project descriptions (i.e., codifiability, flexibility, outcome standards) in influencing the matching efficiency in the digital labor markets. The results find that an appropriate project description could improve the matching efficiency by 15% between employers and service providers. The second essay studies the impact of an extension in the Optional Practical Training (OPT) program (STEM OPT), an immigration policy that matches local demand with global supply, on local labor markets. I found that the STEM OPT extension boosts employment for domestic IT professionals by promoting innovative and entrepreneurial activities. The third essay studies the impact of an emerging gig platform (i.e., TaskRabbit), a new matching mechanism, on the employment of workers in the housekeeping industry. The results suggest that the platform mostly impacted middle-level management (e.g., first-line supervisors), while the manual workers, such as cleaners and janitors, were not as affected. The contributions and implications of each essay are discussed. / Business Administration/Management Information Systems