Essays on Consumer Preferences and Demand for Aquaculture and Wild Fish

Abstract

This dissertation comprises three empirical chapters investigating U.S. consumers' perceptions and demand for aquaculture products within the broader animal protein market. Using both revealed and stated preference methods, I analyze how environmental concerns, production methods, and ecological shocks influence consumer behavior, while developing improved demand forecasting tools for perishable protein products. \textbf{Chapter 1} addresses a fundamental gap in the animal protein literature by examining consumer choices across fish, chicken, and beef simultaneously, reflecting the real-world choices set for consumer. Based on survey data from 1,274 consumers across seven Seattle farmers markets (June–September 2019), we estimate willingness-to-pay (WTP) for key product attributes using a mixed logit model. The results reveal a clear asymmetry in production method preferences: free-range terrestrial proteins command a premium of \$9.21 per pound relative to conventional alternatives, and wild-caught fish secure an additional \$3.49 premium, whereas farmed fish suffers a distinctive \$2.29 discount. This anti-aquaculture bias is particularly pronounced for whitefish (–\$5.03), while wild steelhead trout accounts for the majority of the wild fish premium. Further heterogeneity analysis indicates that environmentally conscious consumers penalize wild fish due to overfishing concerns, while health-motivated segments disproportionately avoid beef. These findings highlight aquaculture's unique market challenges. \textbf{Chapter 2} leverages grocery scanner data(2016 -2020) from Puget Sound retailer and event study methodology to examine how high profiled salmon related ecological disruptions—a 2017 farmed salmon spill and 2018 orca mourning event—impact Seattle-area salmon demand. While the 2017 Atlantic salmon escape generated no measurable market response, the 2018 orca mourning event triggered immediate and persistent demand shifts, with consumers substituting toward wild-caught alternatives ($\beta = 0.019$, $p<0.001$) and away from farmed salmon ($\beta = -0.014$, $p\approx0$). Counterfactual analysis reveals a 25-40\% sustained reduction in farmed salmon market share, demonstrating how ecological disruptions can reveal latent consumer preferences and reconfigure market structure through aquaculture externality narratives. \textbf{Chapter 3} focuses on accurate demand forecasting for perishable protein products such as meat and fish, where even small errors can lead to spoilage, revenue losses, and environmental costs. This chapter examines the use of machine learning applied to weekly retail scanner data to improve demand prediction for animal protein products. A systematic comparison is made between classical parametric approaches (e.g., SARIMA, regression) and modern non-parametric ensemble methods (e.g., Random Forest, LightGBM, XGBoost), with the objective of capturing nonlinear dynamics and heterogeneity in consumer behavior. The results show that tuned gradient boosting models substantially outperform traditional benchmarks: the best XGBoost specification achieved an RMSE of 119.2 and an $R^2$ of 0.82, compared to SARIMA with an RMSE of 185.3 and $R^2$ of 0.23. In percentage terms, this corresponds to a reduction of forecast error by nearly 70\% relative to baseline seasonal naïve models. An anomalous finding is that a simple median forecast produced the lowest MAPE (2.24\%), outperforming even tuned XGBoost (2.96\%), reflecting the bias of percentage-based metrics in intermittent demand settings. Feature importance analysis confirms the economic relevance of predictors: price and promotions drive short-term fluctuations, lagged sales capture stockpiling and purchase regularity, brand and store identifiers reflect consumer heterogeneity, and seasonal indicators align with calendar-driven consumption cycles. These findings advance the forecasting literature by linking machine learning outcomes with economic interpretation, while also providing actionable insights for retail managers and policy makers. Improved forecasts can enhance pricing and promotional strategies, reduce food waste through better inventory management, and support more sustainable supply chains. The study concludes by noting key limitations—including missing causal variables, evaluation metric biases, and context-specific scope—and outlines future directions involving richer data integration, SHAP-based interpretability, and pipeline approaches to study cross-product substitution effects. Collectively, this research advances understanding of aquaculture demand dynamics, quantifies the market impacts of ecological narratives, and delivers practical forecasting improvements for perishable animal protein products supply chains.