1) Ecosystems. Along with Jidong Zhou (Yale) and Junjie Zhou (Tsinghua) I have developed a framework of cross-product data usage by an ecosystem. In our working paper “Digital Ecosystems and Data Regulation” we consider an ecosystem that is present in many different markets, and which competes in each market with a different specialised firm; competition is in both price and quality. Data collected by the ecosystem in one market enables it to improve its offering in other markets. We use our framework to evaluate policies that limit data collection by the ecosystem (e.g. the GDPR), or restrict its ability to leverage data from one market into another, or force it to share data with specialised firms (e.g. the DMA). We show these policies are beneficial to consumers if and only if the specialised firms are relatively good at improving their quality; we also show that mandating data sharing is better than restricting the ecosystem’s data usage. Here we fix the set of firms, but in “Optimal Merger Remedies” with Volker Nocke (Mannheim) we explore the role of data sharing in remedying otherwise anti-competitive mergers. We show that the non-rivalrous nature of data makes it an ideal part of a merger remedies package.
2) Personalisation. In “Personalized Pricing and Competition” (AER, 2024), joint with Jidong Zhou, we consider a discrete choice model where firms sell differentiated products. We show that personalised pricing---meaning firms observe each consumer’s tastes and tailor prices accordingly---is anti-competitive when market coverage is relatively low (e.g. when products are niche). We also demonstrate that a situation where only some firms can personalise prices (e.g. because only some firms have access to the relevant data) can be worse for consumers than when either all or no firms can do personalised pricing. Meanwhile in “Platform Disintermediation with Repeated Transactions” (Management Science, forthcoming), joint with Andreea Enache (Stockholm), we consider a model where buyers and sellers use a platform to meet. They can then transact on the platform or disintermediate and transact off it; they differ in how convenient they find transacting on the platform. We show that if the platform can learn users’ convenience benefits and offer personalised fees, users neither gain nor lose from the ability to disintermediate.
3) Search. The paper “Personalization and Privacy Choice” (RAND, forthcoming), joint with Jidong Zhou, bridges the gap between this and the previous workstream. We consider a setting where a buyer needs to search for a suitable seller, but if she shares her data a platform can offer a personalized recommendation about which seller is most suitable for her. Even when the recommendation is unbiased, buyers can be harmed due to a novel “privacy-choice externality” whereby sellers raise their prices. Along the same lines, in “Mobile Payments and Interoperability: Insights from the Academic Literature” (Information Economics and Policy, 2023), with several coauthors we discuss, amongst other things, how access to financial data can change the products offered to consumers. Meanwhile, in a working paper “Dynamic Consumer Search”, joint with Alexei Parakhonyak (Oxford), we consider a model in which consumers wish to buy products repeatedly over time, but need to search initially to find a good match. We show that when firms can observe a consumer’s search and purchase history, and tailor prices accordingly, consumers can be better or worse off depending on how forward-looking they are.
4) Algorithms. A preliminary project entitled “Algorithmic Steering and Advertising on Platforms”, joint with Justin Johnson (Cornell) and Matthijs Wildenbeest (Arizona), considers how a platform’s ranking algorithm uses data on a seller’s price to decide how prominently to display it to consumers. The plan is to have both a theoretical part and a simulation part where sellers and the platform use Q-learning algorithms to decide their actions.
