Chapter 1: Steering Consumers’ Learning: Evidence from Stockout Substitutions in Curbside Pickup Items ordered for curbside pickup sometimes go out of stock, obliging the store to choose substitutes on consumers’ behalf. Using novel data from a supermarket chain, I show these “stockout substitutions” influence consumers’ future purchases through the mechanism of learning. This presents the store with the following opportunity to increase its future profits: if the store selects substitutes from profitable brands that consumers have never tried before, some consumers will learn that they like the brands of their substitutes and purchase these brands’ products in the future. However, consumers are less likely to accept such substitutes than they are to accept substitutes from brands they have previously purchased. To quantify the trade-off between steering consumers’ learning and maximizing the probability of substitutes' acceptance, I estimate a learning-based model of differentiated products demand. Although steering consumers’ learning proves an unprofitable strategy, the store can still increase profits---and consumer welfare---by individualizing substitutions according to consumers’ past purchases and demographics.Chapter 2: Demand Estimation When Consumers’ Preferences Vary over TimeThis paper shows that workhorse demand systems fail to reproduce important substitution patterns when individual consumers’ preferences vary over time. This failure is rooted in the independence of preferred alternatives (IPA) properties of conditional and mixed logit, which restrict the relationship between consumers’ purchases and their preferences among unpurchased goods. To assess the empirical relevance of the IPA properties, I employ novel data from stockout substitutions in curbside pickup. For the two product categories that I study, I document substitution patterns that are inconsistent with the IPA property of conditional logit. As for mixed logit, its IPA property proves consistent with the substitution patterns in one of the two product categories. To quantify the benefits of relaxing the IPA property of mixed logit, I compare the model’s goodness of fit with that of mixed probit (which does not display an IPA property). In keeping with the descriptive evidence, the results of this comparison vary by product category.
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