In current antitrust policy debates, it is almost a foregone conclusion that digital platforms’ collection and use of “big data” is a barrier to entry. In this article, we argue that big data should properly be considered a two-stage process. The reason why this classification matters is because it allows us to link big data to concepts that antitrust is already familiar with: economies of scale, learning by doing, and research & development. By linking big data with the familiar, we hope to avoid a common tendency in antitrust to condemn the strange.

Alexander Krzepicki, Joshua Wright, John Yun1


An emerging refrain in antitrust dialog is that the accumulation and use of big data is a unique and particularly troublesome entry barrier, worthy of antitrust scrutiny. Yet, it seems that both the concept of big data and entry barriers continue to be used in a highly casual and superficial manner. Antitrust is a fact-intensive area of law, given the necessity to both under-stand a business practice (including its potential harms and benefits) and make forecasts of market performance. While antitrust jurisprudence has developed reasonable measures to facilitate such analyses — such as condemning price fixing as a per se violation — conduct such as vertical integration, resale price maintenance, and exclusive deals rightly require substantive inquiries to determine the ultimate competitive impact. Though some would


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