Date of Award

Spring 2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Forestry and Environmental Studies

First Advisor

Gillingham, Kenneth

Abstract

This dissertation contributes developments in modeling and policy analysis in environmental and energy economics. All three chapters are useful to ongoing debates in climate change policy and the regulation of greenhouse gas emissions. My first chapter develops a model of consumer decision-making in an analysis of the electricity retail choice market in Texas. This project explores (1) the limitations of consumer decision-making in a setting with large choice sets and (2) the relationship between competition and product variety after deregulation. I find strong evidence of inattention and search costs as explanations for consumers' widespread failure to choose cost-minimizing contracts. These findings suggest that policymakers could improve welfare with interventions that reduce search costs and inattention, such as removing the legal obstacles to concierge services or introducing a web-based tool to find consumers' cost-minimizing contract based on their consumption history. My findings also suggest that these interventions could lead to higher adoption of time-varying rates, which could lead to more efficient allocation of grid resources and lower emissions levels. My other main finding is that consumers are constrained in the monopoly setting from expressing their heterogeneous preferences for contract variety. This insight may guide regulators in monopoly settings to consider increasing variety. Of course, the possible benefits of increased variety face a trade-off with the costs of search and inattention. My second chapter is co-authored with Robert Mendelsohn and Paula Pereda. We propose a model to estimate the economic damages from weather shocks and climate change. We contrast our model with the models used in previous literature, and we show that our model estimates substantially different effects than this earlier work, a finding the emphasizes the importance of model selection and careful consideration of the implicit assumptions. We demonstrate our method in the contexts of both agricultural profits and GDP, but this model could be easily transported to a variety of other settings and sectors in the climate change damages literature. My final chapter is co-authored with Kenneth Gillingham and James Stock. We compare several time series models to estimate the price elasticity of new vehicle sales, addressing the classic challenges of price and sales endogeneity and simultaneity in time series analysis with aggregate data. Correctly identifying the price elasticity of new vehicle sales is especially important for estimating the impacts of fuel economy standards because changing fuel economy stringency is assumed to cause a shock to new vehicle prices. The resulting effect on new vehicle sales has broad implications beyond the immediate impact on the vehicle industry. In particular, new vehicles generally have the best safety features and pollution controls, so reducing replacement of used vehicles has consequences for public safety and pollution levels. This project is also a novel application of a structural vector autoregression with instrumental variables (SVAR-IV), a relatively new methodology borrowed from the monetary policy literature. We compare the SVAR-IV with other time series approaches, some of which have been considered in policymaking for fuel economy standards.

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