Dissertations, Theses, and Capstone Projects

Date of Degree

6-2025

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy

Program

Economics

Advisor

Matthew J. Baker

Committee Members

Wim Vijverberg

Suleyman Taspinar

Subject Categories

Behavioral Economics | Econometrics | Economic Theory | Regional Economics

Keywords

Information Diffusion, Misinformation, Social Learning, Information Coordination, Spatial Econometrics, Regional Economic Convergence

Abstract

This dissertation consists of three chapters on information and economic interaction. It investigates how information shapes economic behavior across individual, institutional, and regional levels by developing distinct yet interconnected frameworks to study belief formation, decision-making under uncertainty, and economic interaction. The chapters span theoretical modeling and empirical analysis, exploring the mechanisms of information diffusion, the strategic dynamics of information coordination, and the spatial dynamics of economic interaction. In doing so, the dissertation contributes to a growing literature on how information flows influence not only micro-level behavior and strategic environments, but also aggregate outcomes such as political stability and regional income convergence. By integrating insights from social learning, global games, and spatial econometrics, it provides a unified perspective on how information and economic interaction shape both individual incentives and systemic disparities.

Chapter 1 - Misinformation vs. Truth: Information Diffusion Dynamics on Social Media

Social media has been a core information distribution center in recent decades. With more and more people sharing information and attitudes on social media, people are exposed to other people's (even strangers') points of view much more readily than before. This paper develops a social learning model of incomplete information with bounded rational Bayesian agents that can observe only their private information and their predecessors' choices in a sequential network. Through the comparative analysis of the model, this article identifies how the diffusion dynamics of true news and misinformation differ and how the characteristics of news, information timeliness, and the informativeness of the private and public beliefs of truthfulness determine these differences. Using high-dimensional Twitter data related to COVID-19 misinformation, the empirical analysis confirms the theoretical findings and quantifies the impact of signal informativeness on diffusion patterns. Furthermore, the findings of the paper provide valuable insights into policy implications for mitigating the spread of misinformation and enhancing user welfare in social media environments.

Chapter 2 - Revolutions in Uncertainty: A Structural Approach to Global Games and Coups (with Matthew J. Baker)

We develop a general empirical estimation framework for global games, designed to analyze large-scale coordination under incomplete information. Applying this framework to the context of coups d'état, we bridge the theory of information coordination with empirical models of political instability. The model distinguishes between the feasibility and desirability of coups, enabling a structural interpretation of observed outcomes. Estimation proceeds via simulated maximum likelihood applied to a nested ordered probit structure, accommodating unobserved heterogeneity across regimes and periods. The results show that distinct macroeconomic factors separately influence regime strength and the benefits citizens derive from regime change. By integrating theoretical insights from global games with a tractable empirical strategy, the study advances the understanding of information coordination and the foundations of regime stability.

Chapter 3 - Economic Interaction: Regional Dependency and Spatial Converging Clubs in Mainland China

This paper revisits the spatial dynamics of regional income growth in China in the 21st century by examining whether interprovincial economic activity exhibits persistent spatial dependencies and converging club structures. Leveraging a quarterly panel dataset spanning 31 provincial-level regions from 2005Q1 to 2020Q4, I integrate spatial analysis, Spatial Vector Autoregressions (SpVAR), within-club Vector Error Correction Models (VECM), and a high-dimensional Global Vector Autoregression (GVAR) framework to trace the evolution and transmission of regional growth dynamics. The results reveal robust evidence of spatial income converging clubs, consisting of a coastal High-Income Club and an inland Low-Income Club. A consistent pattern of within-club convergence and between-club divergence emerges across all methodologies: leading regions reinforce growth among proximate high-income neighbors, while their spillovers to low-income regions are weak, asymmetric, or even negative. Conversely, some underdeveloped regions lack the structural capacity to absorb positive shocks, generating system-wide inefficiencies. These findings challenge the continued validity of the “get rich first” development paradigm and underscore the need for differentiated, regionally coordinated policy strategies to promote inclusive and sustainable growth.

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