Kohei Hayashida

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.

Food waste represents a significant profit and environmental concern for grocery retailers. However, reducing waste is complex because it requires accurate local demand forecasting on 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 for every unique combination of store-product. 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- 60.1%-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- 46.9% 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.

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.

Dynamic pricing has the potential to increase grocery supermarket profit and reduce food waste. However, many retail chains do not adopt dynamic pricing strategies for perishables, highlighting a gap between academic insights and practical implementation. This study investigates the effectiveness of perishable dynamic pricing, leveraging a novel dataset of vintage-level transaction data from a Japanese grocery chain. By exploiting quasi-experimental variations in the chain's dynamic pricing policy, I identify and estimate consumer heterogeneity in price sensitivities and freshness preferences-key primitives of perishable dynamic pricing effectiveness. I use the model to evaluate alternative dynamic pricing policies achieving higher profit while potentially reducing planned waste. To validate the proposed policies, I conduct a field experiment. This study contributes to the understanding of dynamic pricing strategies for perishables and offers actionable insights for retailers aiming to improve profitability and sustainability.