IMPACTS OF SOCIAL INFLUENCE, ORGANIC, AND PLANT-BASED MILKS ON THE U.S. DAIRY MARKET

S. Badruddoza

This dissertation investigates retail markets for dairy in three stand-alone but related studies. The U.S. dairy market is going through a period of major transition. Dairy productivity has multiplied globally in the last few decades due to innovation, automation, and vertical integration. Moreover, consumers who are concerned about their health, the environment, and animal welfare often purchase organic and/or plant-based alternatives instead of conventional dairy. Consequently, historically low prices and public interventions have been observed in recent years. The first study seeks to understand if (and how) social influence has changed the market for dairy. I evaluate consumer valuations of cow’s milk, goat’s milk, almond milk, soymilk, rice milk and coconut milk using national retail scanner data and test if the valuations change due to social influences. The model specifies that consumers receive greater utility from milk types as they become fashionable—the “bandwagon effect”— up to a threshold point. Consumers’ utility decreases when popularity exceeds the point, leading to an “anti-bandwagon effect”. My results suggest that the social influence affects both the mean and variance of milk preferences, willingness to pay, own- and cross-price elasticities, and price-cost markups. The second examines the organic dairy price premiums and their determinants over 11 years using national retail scanner data. The monthly price premiums for organic whole milk, other fluid milks, yogurt, and eggs were 53%, 47%, 21%, and 44%, respectively, with the annual growth rates of 3.77%, 5.22%, 2.53%, and -1.77%. Agricultural services, feed, fuel, and transportation costs have positive associations with organic price premiums, whereas supercenter sales have a negative association. The first two studies use retail scanner data under a structural framework, which can be computationally expensive when market data are generated in real-time. The third chapter proposes a model to uncover consumers’ priority of product attributes without employing a structural model. Using simulations, I found that Random Forests (RF)—a machine-learning algorithm—can detect consumers’ sensitivity to product attributes similar to the structural framework of demand estimation. The two estimates correlate in the ranking (68%) and magnitude (79%), and the rates increase with the sample size.

IMPACTS OF SOCIAL INFLUENCE, ORGANIC, AND PLANT-BASED MILKS ON THE U.S. DAIRY MARKET

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