Essays in Empirical Industrial Organization

Citation

Kobayashi, Shunto Jerry (2024) Essays in Empirical Industrial Organization. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/bt6y-ax30. https://resolver.caltech.edu/CaltechTHESIS:05072024-200701988

Abstract

This dissertation comprises three essays related to the field of Empirical Industrial Organization. Chapter 1 and 2 contribute to the economic literature on online advertising auctions, and Chapter 3 contributes to the study of decision-making under risk using structural methods.

In Chapter 1, co-authored with Miguel Alcobendas, we provide a novel empirical analysis of a large-scale sequential market employing auctions to allocate objects to firms with budget constraints. Leveraging a unique proprietary dataset of online ad auctions, we examine the trade-off participants face due to short-run budget constraints. We develop and estimate a finite-horizon dynamic game among bidders with heterogeneous budgets, and we find that dynamic incentives significantly influence their participation and bidding strategies. We conduct a counterfactual simulation comparing first-price and second-price formats, illustrating how dynamics lead to significant disparities in competitive outcomes.

In Chapter 2, co-authored with Miguel Alcobendas, Matthew Shum, and Ke Shi, we investigate the impact of removing third-party cookies on the online advertising market. Utilizing a proprietary dataset of online ad auctions, we document stylized facts about the value of third-party cookies to advertisers. Adopting a structural approach, we simulate counterfactual scenarios to quantify the impact of Google's plan to phase out third-party cookies from Chrome. Our analysis suggests a 54\% reduction in publisher revenue and a 40\% reduction in advertiser surplus under an outright ban. Introduction of alternative tracking technologies under Google's Privacy Sandbox initiative would mitigate some of the loss. We find big tech firms can leverage their informational advantage to gain a larger surplus from the ban.

In Chapter 3, co-authored with Aldo Lucia, we explore the limited ability of prominent economic models in explaining multiple behavioral patterns. Conducting an experiment with 500 participants, we study two classical behaviors inconsistent with Expected Utility: the common ratio effect and preferences for randomization. We illustrate the lack of generalizability of existing models across these behaviors. Motivated by this, we introduce a novel empirical approach that does not commit on specific decision models. Our method offers more accurate out-of-sample predictions about behaviors under risk, both inside and outside laboratory settings, compared to leading economic models and machine learning algorithms.

Thesis Files