Which economic channels are responsible for Google's high market share in web search?

We evaluate the economic forces that contribute to Google’s large market share in web search. We develop a model of search engine demand in which consumer choices are influenced by switching costs, quality beliefs, and inattention, and estimate it using a field experiment with US desktop internet users. We find that (i) requiring Google users to make an active choice among search engines increases Bing’s market share by only 1.1 percentage points, implying that switching costs play a limited role; (ii) Google users who accept our payment to try Bing for two weeks update positively about its relative quality, with 33 percent preferring to continue using it; and (iii) after changing the default from Google to Bing, many users do not switch back, consistent with persistent inattention. In our model, correcting beliefs and removing choice frictions would increase Bing’s market share by 15 percentage points and increase consumer surplus by $6 per consumer-year. Policies that expose users to alternative search engines lower Google’s market share more than those requiring active choice. We then use Microsoft search logs to assess the impact of additional data on search result relevance. The results suggest that sharing Google’s click-and-query data with Microsoft may have a limited effect on market shares.

Noisy field-experimental estimates suggest AI-based coding assistants raise developer productivity by 26%.

This study evaluates the impact of generative AI on software developer productivity by analyzing data from three randomized controlled trials conducted at Microsoft, Accenture, and an anonymous Fortune 100 electronics manufacturing company. These field experiments, which were run by the companies as part of their ordinary course of business, provided a randomly selected subset of developers with access to GitHub Copilot, an AI-based coding assistant that suggests intelligent code completions. Though each separate experiment is noisy, combined across all three experiments and 4,867 software developers, our analysis reveals a 26.08% increase (SE: 10.3%) in the number of completed tasks among developers using the AI tool. Notably, less experienced developers showed higher adoption rates and greater productivity gains.

Ukrainean e-procurement auction design allows detection of colluders via re-bidding behaviour.

We develop a method for detecting cartels in multistage auctions. Our approach allows a firm to be collusive when facing members of its cartel yet competitive when facing others. Intuitively, as initial bids are shaded, close initial bids not only imply similar costs but also provide an incentive to undercut. We detect firm pairs that ignore this incentive when facing each other. Our algorithm predicts Ukraine’s Antimonopoly Committee sanctions, yet uncovers additional collusion: 2,371 collusive firms participate in 19% of auctions, increasing costs by 2.12%. Cartels typically comprise just two members, and members often share the same ZIP code.

Does algorithmic pricing allow e-commerce sellers to raise prices above the competitive level?

As the economy digitizes, menu costs fall, and firms can more easily monitor prices. These trends have led to the rise of automated pricing (and re-pricing) tools. We employ a novel e-commerce dataset to examine the effect of algorithmic pricing in the wild. Evidence from an event study suggests that firms that start employing repricing tools drop their prices by 16.7%, with market prices falling by 9.5%. However, algorithmic pricing companies have developed ‘resetting’ strategies (which regularly raise prices in the hope that competitors will follow) in order to avoid stark Bertrand-Nash competition. We find that these strategies are effective at coaxing competitors to raise their prices: when a resetting strategy is adopted, both competitor prices and market prices eventually increase by 8%. While the resulting patterns of cycling prices are reminiscent of Maskin-Tirole’s Edgeworth cycles, a model of equilibrium in delegated strategies fits the data better. This model suggests that the average price over the cycle will be the monopoly price. Moreover, if the available repricing technologies remain fixed, cycling and prices could rise significantly. However, cycling is still relative rare in the data.

Do recommendation algorithms give digital platforms an ability to self-preference and shape consumer search?

We evaluate the problem of firms that operate platforms matching buyers and sellers, while also selling goods on these same platforms. By being able to guide consumer search through algorithmic recommendations, these firms can influence market outcomes, a finding that has worried regulators. To analyze this phenomenon, we combine rich novel data about sales and recommendations on Amazon Marketplace with a structural model of intermediation power. In contrast to prior literature, we explicitly model seller entry. This feature enables us to assess the most plausible theory of harm from self-preferencing, i.e. that it is a barrier to entry. We find that recommendations are highly price elastic but favor Amazon. A substantial fraction of customers only consider recommended offers, and recommendations hence noticeably raise the price elasticity of demand. By preferring Amazon's offer, the recommendation algorithm raises consumer welfare by approximately $4.5 billion (since consumers also prefer these offers). However, consumers are made worse off if self-preferencing makes the company raise prices by more than 7.8%. By contrast, we find no evidence of consumer harm from self-preferencing through the entry channel. Nevertheless, entry matters. The algorithm raises consumer welfare in the short and medium run by increasing the purchase rate and intensifying price competition. However, these gains are mostly offset by reduced entry in the long run.

Voter's uncertainness about each other's preferences can be exploited to cheaply buy votes.

We analyze a vote-buying setup where a committee votes on a proposal important to the vote buyer. We characterize the cheapest combination of bribes that guarantees the proposal's passing in different voting environments. We find that the vote buyer publicly offers small bribes to a large supermajority of members for both simultaneous and sequential votes. Each member accepts because he anticipates that the proposal will pass regardless of his vote. We discuss the committee design that maximizes capture cost: combining demanding majority requirements with diversity among members makes the committee more expensive. In small committees, sequential voting increases cost, but the opposite is true for large committees. On the other hand, additional members and transparent voting rules lower the cost. (PDF of Old Version with Additional Examples.)