ESSAYS ON DIGITAL ECONOMICS

Abstract

This dissertation studies economic questions in the digital environment. Specifically, it examines whether the design of a seller reward program on a livestreaming platform is optimal from the platform's revenue perspective, and how consumers' privacy concerns affect their behavior. In the first chapter, I present an empirical framework for assessing the impact of seller rewards programs on platform revenue. The context is a Korean livestreaming platform, where sellers (called streamers) broadcast content and receive tips from viewers to generate revenue. Platform revenue comes from commission charged on this revenue, and the reward is a permanent commission discount provided through performance-based monthly tournaments. I initially collect individual streamer-time level data, including efforts (measured by streaming hours), tipping revenue, and reward program acceptance. The collected data, along with anecdotal evidence, indicate that streamers exhibit heterogeneity in profitability, measured by tipping revenue per watch time. Furthermore, they tend to compete within specific broadcasting categories (e.g., within the Game category) to attract viewers. I then estimate a dynamic model to describe the effect of program design on streamers' behavior. The key trade-off for the livestreaming platform is that offering more commission discount rewards may increase the total tipping revenue by encouraging streamers---especially more profitable ones---to stream more, but it results in the platform taking a substantially smaller share of the generated tipping revenue. Counterfactual simulations reveal that the last platform share effect quantitatively dominates. This suggests that reducing the reward program by providing the reward to a smaller number of streamers or decreasing the commission discount rate would raise platform revenue. Additionally, these simulations identify opportunities to raise platform revenue by reallocating approval slots more granularly, at different broadcasting category levels instead of the entire platform level. In the second chapter, I empirically study how consumers' privacy concerns affect their behavior. Using panel survey data from South Korea that followed 5,328 individuals for four years, I find that privacy concern has a significant negative effect on their Facebook and Twitter usage. I additionally find that such concern has heterogeneous effects on online shopping behavior, while cloud storage services remain unaffected. When privacy-related events such as the Facebook-Cambridge Analytica data scandal in 2018 increases privacy concern, it appears to harm not only Facebook but also other firms in the industry (e.g., Twitter). Because a private firm does not internalize such negative spillovers, the privacy protection level determined in a free market could be different from the social optimum.