Essays on the Economics of Algorithms, Markets, and Organizations
Author(s)
Raymond, Lindsey
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Advisor
Li, Daniellie
Mullainathan, Sendhil
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This dissertation contains three chapters that study how digitization and increasingly reliance on algorithms shapes workers, organizations, and markets. In the first chapter, I show how the digitization of public housing records leads to the entry of investors using algorithms. Digitization and entry lead to changes in equilibrium prices and allocation in the US residential real estate market. Consistent with a theoretical model of comparative advantage, I observe shifts in investment patterns for both humans and algorithmic investors and changing house prices, particularly for minority homeowners. In the second chapter, I study how hiring algorithm design shapes the effects of algorithms in the labor market. Using data from a professional services firm, I show that incorporating exploration can improve the quality of the interview screening process (as measured by eventual hiring rates), while also increasing demographic diversity, relative to the firm's existing practices. While the adoption of automated approaches to hiring is often associated with decreasing access to opportunity, we show the impact on efficiency and equity depends on algorithm design choices. In the third chapter, joint with Danielle Li and Erik Brynjolfsson, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,000 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14\% on average, including a 34\% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers, helps newer workers move down the experience curve, and improves worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers. Together, these chapters highlight how the increasing prevalence of algorithmic decision making impacts workers, firms, and markets.
Date issued
2024-05Department
Sloan School of ManagementPublisher
Massachusetts Institute of Technology