How Big Data Confers Market Power to Big Tech: Leveraging the Perspective of Data Science

Abstract

Data-hungry applications such as targeted advertising, information filtering systems, web search, and virtual assistants have become central to the business models of the largest online platforms. We demonstrate how data acts as a source of market power in these applications using a novel approach that leverages the perspective of data science to inform the economic analysis. We highlight the importance of data heterogeneity, which implies that minor feature differences engender substantial value differences that insulate online platforms from competition. In addition, we call attention to the notion of concept drift, that often in these settings there exists a non-stationary relationship between the predictive variables and the target variable of interest. This signifies that having access to a continuous stream of data is essential to being a viable competitor. Further, we establish that these applications suffer from (i) an information bottleneck, which makes incremental data valuable and at times increasingly so, and (ii) high sample complexity, which mandates the need for vast amounts of clean and rich training data. Finally, we show that online platforms engage in user interaction control that enables the creation of a personalized suite of features that raises switching costs for consumers. The combined effect is to generate significant data barriers to entry that endow the large online platforms with market power. This suggests that policies to facilitate a more competitive landscape in markets involving these applications must focus on enabling access to a continuous stream of high-quality data.

Keywords: Big Data, Big Tech, Market Power, Competition Policy, Antitrust, Machine Learning, Data Science, Artificial Intelligence

JEL Classification: D4, D8, L1, L4, L5, L86, L88, O3