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

Ubiquitous access to the Internet and supporting technologies gave birth to online labor markets (Malone and Laubacher 1998). Online labor markets enable employers (employers) to contract with professionals (service providers) from anywhere in the world. Firms now are able to greatly expand their workforce and bring a large arsenal of labor to bear on IT jobs, such as software or web development using Internet-enabled procurement platforms such as Freelancer. These markets serve as intermediaries for IT services (outsourcers post Call for Bids (CFBs) for services and providers offer bids for IT services) that help match employers with service providers across the globe. In my dissertation, I try to comprehensively study this Internet-enabled phenomenon from the perspectives of these three entities on global online markets with three separate yet related essays. The first essay focuses on the "global" nature of the market, and assess the effect of global frictions and global labor arbitrage on both provider bidding and employer selection. The second essay focuses on the effect of auction mechanism - sealed versus open bid auction - on providers' bidding dynamics, and the market performance. The third essay focuses on estimating true consumer (employer) surplus of online labor markets with a quality-adjusted measure. I also test its robustness by comparing its effects on consumers' subsequent transactions. I also find that market immaturity, consumers' lack of experience in the market, and consumers' lack of familiarity with IT service providers lead to the difference between the traditional measure and the quality-adjusted consumer surplus. / Business Administration/Management Information Systems

Over the past decade, the explosive growth of the Internet has led to a surge of interest to understand and predict aggregate behavior of large number of people or agents, particularly when they are connected through an underlying network structure. Numerous Internet-based applications have emerged that are as diverse as getting micro-tasks executed through online labor markets (also known as crowd sourcing) to acquiring new skills through massively open online courses (also known as MOOCs). However, there has been a major inadequacy in existing studies with respect to evaluating the impact of strategic behavior of the agents participating in such networks, crowds, and classrooms. The primary focus of this doctoral work is to understand the equilibrium behaviour emerging from these real-world, strategic environments by blending ideas from the areas of game theory, graph theory, and optimization, to derive novel solutions to these new-age economic models. In particular, we investigate the following three research challenges: (1) How do strategic agents form connections with one another? Will it ever happen that strategically stable networks are social welfare maximizing as well? (2) How do we design mechanisms for eliciting truthful feedback about an object (perhaps a new product or service or person) from a crowd of strategic raters? What can we tell about these mechanisms when the raters are connected through a social network? (3) How do we incentivize better participation of instructors and students in online edu-cation forums? Can we recommend optimal strategies to students and instructors to get the best out of these forums?