Research

Economic thinking and analysis lie at the heart of the objectives and the design of the EU Digital Markets Act. However, the design of the DMA reflects a very deliberate—and reasonable—intention to ensure clarity, speed, administrability, and enforceability. In doing so, this procompetitive regulation omits several elements of standard competition law where economics has typically played a key role. Nonetheless, we believe that economic insights and analysis—including behavioural economic thinking—will continue to play an important role in enabling the DMA to achieve its ambitious and laudable goals, albeit in a somewhat different way.

The Commission is charged with implementing the Digital Markets Act (DMA). Based on economic and legal reasoning, this paper asks how the Commission can fulfil this challenging task effectively. We make recommendations about how the Commission might prioritize cases, design optimal internal work structures, maximize the compliance mechanism’s effectiveness, avoid reinventing at least some wheels by leaning on antitrust tools and knowledge, and leveraging the Commission’s concurrent antitrust and regulatory powers to ensure the speedy and effective resolution of current and future investigations.

The ability to make accurate predictions relating to consumer preferences is a key factor of a digital firm’s success. Examples include targeted advertisements and, more broadly, business models relying on capturing consumers’ attention. The prediction technologies used to learn consumer preferences rely on consumer generated data. Despite the importance of data-driven technologies, there is a lack of knowledge about the precise role that data-scale plays for prediction accuracy. From a policy perspective, a better understanding about the role of data is needed to assess the risks that “big data” might pose for competition. This article highlights potential complementarities in algorithmic learning, which suggest data-scale advantages might be substantial. We analyze our hypothesis using search engine data from Yahoo! and provide evidence consistent with locally increasing returns to scale. The ability to make accurate predictions relating to consumer preferences is a key factor of a digital firm’s success. Examples include targeted advertisements and, more broadly, business models relying on capturing consumers’ attention. The prediction technologies used to learn consumer preferences rely on consumer generated data. Despite the importance of data-driven technologies, there is a lack of knowledge about the precise role that data-scale plays for prediction accuracy. From a policy perspective, a better understanding about the role of data is needed to assess the risks that “big data” might pose for competition. This article highlights potential complementarities in algorithmic learning, which suggest data-scale advantages might be substantial. We analyze our hypothesis using search engine data from Yahoo! and provide evidence consistent with locally increasing returns to scale..

A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer's data are predictive of others' behavior—generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.

We characterize the revenue-maximizing information structure in the second price auction. The seller faces a classic economic trade-o§: providing more information improves the e¢ - ciency of the allocation but also creates higher information rents for bidders. The information disclosure policy that maximizes the revenue of the seller is to fully reveal low values (where competition will be high) but to pool high values (where competition will be low). The size of the pool is determined by a critical quantile that is independent of the distribution of values and only dependent on the number of bidders. We discuss how this policy provides a rationale for conáation in digital advertising.

We analyze nonlinear pricing with finite information. We consider a multi-product environment where each buyer has preferences over a d-dimensional variety of goods. The seller is limited to offering a finite number n of d-dimensional choices. The limited menu reflects a finite communication capacity between the buyer and seller.
We identify necessary conditions that the optimal finite menu must satisfy, for either the socially efficient or the revenue-maximizing mechanism. These conditions require that information be bundled, or "quantized," optimally. We introduce vector quantization and establish that the losses due to finite menus converge to zero at a rate of 1/n2/d_ In the canonical model with one-dimensional products and preferences, this establishes that the loss resulting from using the n-item menu converges to zero at a rate proportional to 1 /n2 . 

This is the fifth in a series of papers prepared by a collection of economists and policy experts in the United States, the UK, and the European Union who have studied, and are committed to the improvement of, competition in digital markets. Previous papers addressed consumer protection in online markets, regulating the market for general search services, the concepts of “fairness” and “contestability” as used in the Digital Markets Act, and the use of “equitable interoperability” as a “super tool” to restore and encourage competition in online markets.

Consider a market with identical firms offering a homogeneous good. For any given ex ante distribution of the price count (the number of firms from which a consumer obtains a quote), we derive a tight upper bound on the equilibrium distribution of sales prices. The bound holds across all models of firms’ common-prior higher-order beliefs about the price count, including the extreme cases of full information and no information. One implication of our results is that a small ex ante probability that the price count is equal to one can lead to a large increase in the expected price. The bound also applies in a large class of models where the price count distribution is endogenously determined.