Abstract

Markets, institutions, and borders shape political behavior. Geography influences why markets form, how institutions flourish, and where nations draw borders. As a consequence, empirical studies of political behavior frequently struggle to disentangle spatial effects from quantities of interest. This dissertation makes a methodological advancement in this problem area and conducts a spatial study with these concerns in mind. The dissertation studies bias in spatial regressions, how natural disasters impact political participation, and the political economy of espionage. It introduces a flexible machine learning methodology to mitigate bias in spatial regressions, it identifies spatial and demographic factors associated with changes to voter registration after widespread flooding, and it presents the first formal model of intelligence gathering and counterintelligence as strategic policy. The dissertation contributes methodologically, substantively, and theoretically to the field of political science.

Committee Chair

Keith Schnakenberg

Committee Members

Guillermo Rosas; Justin Fox; Marcus Berliant; Ted Enamorado

Degree

Doctor of Philosophy (PhD)

Author's Department

Political Science

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

4-28-2026

Language

English (en)

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