Competing data intermediaries

Ichihashi, Shota

doi:10.1111/1756-2171.12382

Cited by 39 publications

(13 citation statements)

References 39 publications

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“…Relative to earlier studies on the social value, sales, and property rights of information (e.g., Hirshleifer 1971, Admati and Pfleiderer 1990, Murphy 1996, recent studies focus on connecting digital information with privacy issues (e.g., Akçura andSrinivasan 2005, Casadesus-Masanell andHervas-Drane 2015) or the nonrivalry of data and competition (e.g., Ichihashi 2020a, Easley et al 2019. We differ by being the first to connect data usage to knowledge accumulation and endogenous growth with privacy considerations.…”

Section: Introductionmentioning

confidence: 95%

Knowledge Accumulation, Privacy, and Growth in a Data Economy

Cong,

Xie,

Zhang

2021

Preprint

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We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement.Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data-an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers' data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.

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Section: Introductionmentioning

confidence: 95%

Knowledge Accumulation, Privacy, and Growth in a Data Economy

Cong,

Xie,

Zhang

2021

Preprint

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“…Anticipating this, platforms compete less aggressively for data to reduce the amount of overlap between their datasets and increase their market power with intermediaries. This strategy allows platforms to earn positive profits at the expense of consumers (Ichihashi, 2021).…”

Section: Attention Platformsmentioning

confidence: 99%

The microeconomics of data – a survey

Pino

2022

J. Ind. Bus. Econ.

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In recent years, academia, institutions, and policymakers have been focusing their attention on the impact of data in digital markets. The economic literature that explicitly models data and their collection as strategic variables is growing, but most studies focus on distinct settings with specific data uses. This survey aims to organise this literature to extract general insights that hold across different models and assumptions. To do so, I identify three classes of models according to the way they model data collection. I find that each class is characterised by a specific impact of data on the market outcomes, regardless of the specific data use. First, when firms obtain data without strategic interactions, their use has a pro-competitive effect on the market. However, firms fail to fully internalise the data externalities, leading to data overuse and, in turn, privacy concerns. Second, when firms collect data from their interaction with consumers, data can facilitate market tipping, especially if firms are asymmetric in their starting positions. Third, when firms acquire data from data intermediaries, data are strategically sold to temper competition in the downstream market, allowing intermediaries to extract most of the surplus at the expense of firms and consumers. These general insights can facilitate future research and help policymakers to have a more general understanding of the competitive effects of data, depending on the situation at hand.

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“…Therefore, in the pricing subgame, once a report of consumer i is sold, the other intermediary becomes indifferent about whether to sell i's information, which breaks the unique equilibrium price in that subgame. This causes multiplicity of equilibria, as in Ichihashi (2019).…”

Section: Competing Intermediariesmentioning

confidence: 99%

“…For example, Ball (2020) analyzes the design of a quality score that aggregates multiple dimensions of an agent's performance, and Bonatti and Cisternas (2019) examine the optimal aggregation of consumer purchase histories. Ichihashi (2019) studies competing data intermediaries that acquire perfect information from consumers. His model predicts multiple equilibria, in some of which competition harms consumers to an equal extent as monopoly-see also Westenbroek, Dong, Ratliff, and Sastry (2019) on this point.…”

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confidence: 99%

The Economics of Social Data

Bergemann¹,

Bonatti

2019

SSRN Journal

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A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices.The social dimension of the individual data-whereby an individual's data are predictive of the behavior of others-generates a data externality that reduces the intermediary's cost of acquiring information. We derive the intermediary's optimal data policy and show that it preserves the privacy of the consumers' identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows large.Individual Data and Data Intermediaries The rise of large Internet platforms-such as Facebook, Google, and Amazon in the US, and of similar large entities in China, such as JD, Tencent and Alibaba-has led to an unprecedented collection and commercial use of individual data. The ever-increasing user bases of these platforms generate massive amounts of data about individual consumers: their preferences, their locations, their friends, their political views, and almost all other facets of their lives. In turn, many of the services provided by large Internet platforms rely critically on these data. The availability of individual-level data allows these companies to offer refined search results, personalized product recommendations, informative ratings, timely traffic data, and targeted advertisements. 1 The recent disclosures on the use and misuse of social data by Internet platforms have prompted regulators to limit the hitherto largely unsupervised use of individual data by these companies. As a result, nearly all proposed and enacted regulation to date is centered on ensuring that consumers retain control over their data. The idea is that, because consumer data must be acquired, aggregated, packaged, and sold, the allocation of control rights impacts the ultimate use of the data. 2 In particular, by assigning ownership and control to individual consumers, regulators hope to enable an efficient use of information or at least to grant individual consumers the appropriate compensation for the data they reveal. 3 Despite the appeal of the Coase theorem implicitly invoked in the regulation, it is far from evident that the resulting allocation of information is efficient due to multiple potential market failures. In this paper, we explore the implications of externalities across consumers.A central feature of the data collected from individuals is its social aspect. Namely, the data captured from an individual user are not only informative about that specific individual but also about appropriately defined nearby individuals. Thus, these individual data are really social data. The social nature of the data generates a data externality, the sign and magnitude of which depend on the ultimate use of the information so gained. In the context of geolocation data, for instance, an individual user conveys information about the traffic conditions for nearby driver...

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Competing data intermediaries

Cited by 39 publications

References 39 publications

Knowledge Accumulation, Privacy, and Growth in a Data Economy

Knowledge Accumulation, Privacy, and Growth in a Data Economy

The microeconomics of data – a survey

The Economics of Social Data

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