Research

Brave is an open-source, privacy-focused browser that blocks third-party ads and is able to load pages twice as fast as Chrome or Firefox. It was founded in 2015 by the creator of Javascript and the Mozilla Project. This summary explains how Brave operates a decentralized digital ad-exchange using its own cryptocurrency, the Basic Attention Token (BAT). The BAT ecosystem rewards users for viewing ads, funds advertisers, and helps make the user attention market more transparent and efficient.

The more information a monopolist seller has about consumer preferences, the more power it has to price selectively and maximize profits. This paper analyzes the welfare consequences of price discrimination via market segmentation. The authors use formal mathematical arguments to show that a market can be segmented to produce all feasible surplus combinations. They conclude that consumer welfare can be prioritized through the proper structuring of information transmission and data collection.

For search engine giants like Google, advertising is their primary source of profits. This paper explores what happens to these profits when advertisers let intermediary companies bid in ad auctions on their behalf. The authors discuss strategies that intermediaries employ to distort and coordinate bids. Using a novel market definition and approach to analyzing intermediary concentration, the authors show that intermediaries may be powerful countervailing forces against Google’s market dominance.

DuckDuckGo is a search engine that prioritizes user privacy. It does not store user data and is free to use as a browser extension or mobile app. DuckDuckGo currently processes over 60 million search queries a day. In comparison, Google processes over 3.5 billion search queries a day. As of June 2020, DuckDuckGo had a share of approximately 1.5% in the market for search engines. This summary describes how DuckDuckGo works, its business model, competitors, and implications for innovation and competition policy.

Big tech companies like Google, Amazon, and Facebook have amassed an unprecedented collection of individual data. The authors demonstrate why data ownership is insufficient to give consumers control over their data and how individual data is actually social data due to data externalities. The paper explores the effects of collecting individual data, terms of trade between consumers and digital platforms, the social dimension of data, and how data intermediaries change the level of aggregation and precision of information they provide. The authors find that the revenue-maximizing data policy is to collect consumer data but not forward it to producers.

The goal of antitrust policy is to protect and promote a vigorous competitive process. Effective rivalry spurs firms to introduce new and innovative products, as they seek to capture profitable sales from their competitors and to protect their existing sales from future challengers. In this fundamental way, competition promotes innovation. We apply this basic insight to the antitrust treatment of horizontal mergers and of exclusionary conduct by dominant firms. A merger between rivals internalizes business-stealing effects arising from their parallel innovation efforts and thus tends to depress innovation incentives. Merger-specific synergies, such as the internalization of involuntary spillovers or an increase in the productivity of R&D, may offset the adverse effect of a merger on innovation. We describe the possible effects of a merger on innovation by developing a taxonomy of cases, with reference to recent US and EU examples. A dominant firm may engage in exclusionary conduct to eliminate the threat from disruptive firms. This suppresses innovation by foreclosing disruptive rivals and by reducing the pressure to innovative on the incumbent. We apply this broad principle to possible exclusionary strategies by dominant firms.

Firms are increasingly turning to artificial intelligence to analyze marketplace trends and determine pricing strategies. This paper demonstrates how AI pricing algorithms may actually learn, with no human instruction, to set anti-competitive prices under standard economic conditions. The authors’ findings highlight the need for antitrust reform that recognizes the potential for algorithms to autonomously collude.