Essays in Econometrics and Political Economy

Citation

Montero, Sergio (2016) Essays in Econometrics and Political Economy. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9MS3QQZ. https://resolver.caltech.edu/CaltechTHESIS:05272016-213755050

Abstract

This dissertation comprises three essays in Econometrics and Political Economy offering both methodological and substantive contributions to the study of electoral coalitions (Chapter 2), the effectiveness of campaign expenditures (Chapter 3), and the general practice of experimentation (Chapter 4).

Chapter 2 presents an empirical investigation of coalition formation in elections. Despite its prevalence in most democracies, there is little evidence documenting the impact of electoral coalition formation on election outcomes. To address this imbalance, I develop and estimate a structural model of electoral competition that enables me to conduct counterfactual analyses of election outcomes under alternative coalitional scenarios. The results uncover substantial equilibrium savings in campaign expenditures from coalition formation, as well as significant electoral gains benefitting electorally weaker partners.

Chapter 3, co-authored with Benjamin J. Gillen, Hyungsik Roger Moon, and Matthew Shum, proposes a novel data-driven approach to the problem of variable selection in econometric models of discrete choice estimated using aggregate data. Our approach applies penalized estimation algorithms imported from the machine learning literature along with confidence intervals that are robust to variable selection. We illustrate our approach with an application that explores the effect of campaign expenditures on candidate vote shares in data from Mexican elections.

Chapter 4, co-authored with Abhijit Banerjee, Sylvain Chassang, and Erik Snowberg, provides a decision-theoretic framework in which to study the question of optimal experiment design. We model experimenters as ambiguity-averse decision makers who trade off their own subjective expected payoff against that of an adversarial audience. We establish that ambiguity aversion is required for randomized controlled trials to be optimal. We also use this framework to shed light on the important practical questions of rerandomization and resampling.

Thesis Files