Kohei Hayashida

Although perishable foods contribute significantly to grocery retailers' profit, estimating demand for perishables is challenging because freshness–a critical factor unique to these products–is typically unobservable. Using novel loyalty-card transaction data that records time-to-expiration for each purchase in a Japanese grocery chain's prepared fried food category, I study consumer preference for freshness and examine how markdowns influence these choices. I first reveal substantial consumer heterogeneity in freshness preferences and discount responsiveness. Then, I develop a structural model to quantify trade-offs between freshness and price, allowing consumer heterogeneity. Results show that (1) ignoring freshness significantly biases price elasticity estimates, and (2) while a large portion of consumers highly value freshness, a smaller segment is primarily price-sensitive and indifferent to freshness. Counterfactual simulations show that, given these consumer preferences, dynamic pricing underperforms static pricing due to margin erosion. Furthermore, I validate this counterfactual prediction in the field, demonstrating an 8.2% revenue improvement under static pricing. These findings suggest that measuring freshness is essential for accurate demand estimation and pricing for perishables.

Food waste represents a significant profit and environmental concern for grocery retailers. Reducing waste is complex, however, because it requires accurate local demand forecasting at a very large scale. In this paper, we analyze transaction and waste records from 112 new store openings of a Japanese grocery chain. We show model-free evidence of demand learning across every cohort of new store openings and every perishable product category. We derive empirical predictions from a Bayesian-learning model in which a store solves the Newsvendor problem under parameter uncertainty. We document three main findings: (1) Waste rates significantly decline—62.6%—after store openings and new product introductions, but this reduction takes time—about two years. (2) Stores learn not just about how much inventory to stock, but also about which products to stock—37.6% of products are dropped within two years, indicating the chain aggressively tailors its perishables assortments to local demand conditions. (3) The waste decrease cannot be fully explained by other coincident mechanisms, such as chain- or store-level operational learning, demand-side learning, or changes in profit margins or dynamic markdowns.

We revisit the nascent literature on algorithmic collusion (Calvano et al. 2020; Hansen et al. 2021) which considers settings where single-product firms compete by setting prices via algorithm, and establishes that supra-competitive prices may arise in such settings. Our key point of departure is that we consider multi-product firms. We show evidence that despite selling multiple products, in practice, firms often price each item via independent algorithms to mitigate the curse of dimensionality. In other words, the algorithms in use optimize each product individually rather than jointly optimizing over the entire product assortment. We show that in such settings, the risk of supra-competitive outcomes is reduced and can even result in sub-competitive prices. Conversely, we show that if firms were able to solve the dimensionality and use algorithms that priced jointly, this may increase the mechanisms by which collusive prices are reached, including multi-market contact.

This paper studies the effects of taxation and regulation on addictive alcohol consumption. Exploiting the changes in tax policies and sales regulation in the Japanese beer market, we first show some descriptive evidence that consumers (i) are addicted to alcohol, (ii) are forward-looking and stockpile, but potentially present-biased, and (iii) substitute across categories in response to policy changes. To quantify the impacts of policy changes, we then estimate a dynamic structural model of alcohol purchase and consumption where consumers can be present-biased. A series of counterfactual simulations show that the current Japanese alcohol tax system is suboptimal in that alternative policies can increase tax revenues while keeping alcohol addiction lower. Finally, we derive the optimal alcohol tax policy, taking both externalities and internalities into account.