UC Irvine

This dissertation examines consumer decision-making in two distinct economically significantmarkets: vehicle choice and organic food demand. The first study investigates the factors influencing the adoption of electric vehicles (EVs) using data from the 2017 California Vehicle Survey. A Bayesian sample selection model is employed to account for differences between current EV owners and non-owners, ensuring a more accurate prediction of future purchase behavior. The results indicate that while both groups respond to price and operating costs, EV owners exhibit greater price sensitivity. Demographic characteristics such as education, housing situation, and gender play a significant role in EV adoption. Policy simulations reveal that reducing EV purchase prices is a more effective strategy for increasing EV adoption, providing practical insights for policymakers and industry stakeholders. The second study contributes to the understanding of consumer demand for organic food by examining cross-category purchasing behavior and the impact of relative price changes. Using a Multivariate Ordered Probit Model, the analysis captures substitution patterns and the role of certification labels in shaping consumer choices. The findings provide insights relevant to retailers, policymakers, and certification bodies. By applying advanced econometric modeling techniques to both vehicle and food markets, this dissertation enhances our understanding of consumer preferences, price sensitivity, and policy implications in markets with differentiated product choices.