Governance by markets

Data as an input to market power (Sergey)

Draft 

In the present digital economy, data has emerged as a valuable economic commodity that entities leverage to gain a competitive edge over their rivals. Today, all enterprises in the digital economy have data-driven business models; that is, these entities thrive by collecting, processing, and analysing data on a large scale. Due to such a data-centric business model, it is crucial to consider the commercial use of data when assessing competitive concerns in digital markets. Economists point to economies of scale in information and the dominance of large, data-intensive firms as evidence that the unequal accumulation of data is responsible for a decline in competition. As a result, the data-rich firm exerts more market power.  

Data can be considered as a metric to assess the market power of a digital platform. Market power is a firm’s ability to act independently of its competitors. Data is an input into production, so firms that control data can have market power and impede the entry of would-be competitors. 

Data affect the competitive dynamics of markets in four main ways. First, they serve as an important input for products and services. Second, they may constitute a dimension of quality from the perspective of consumers (both in terms of the amount of information available to consumers, and the amount of personal data collected from consumers). Third, data may themselves constitute a product that leads to the creation of new markets. Finally, they may be used to facilitate consumer decision making.

Data access can constitute a significant barrier to entry when this type of analytics is necessary to participate in a market, particularly when alternatives are limited. For example, Google offers free search engine services to acquire data and uses this data for targeted online advertisements to generate revenue.

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Switching costs and lock-in (Kate)

Goods Matrix

Excludability and rivalrousness (Paul) 

Excludability and Rivalrousness

The terms excludability and rivalrousness both refer to consumption characteristics that can be assigned to- or identified in goods. 

Excludability: Goods to which economic actors can technically or legally restrict access are considered to have ‘high excludability’. Conversely, goods to which access cannot be restricted are of ‘low excludability’, or, in absolute terms, ‘non-excludable’.

Rivalrousness: Rivalrousness on the other hand refers to the finitude of the good in question. The greater the depletion of the stock, quantity, or amount is on account of one party’s consumption, the more rivalrous a good is. 

Tabulating the characteristics against one another allows for inference and definition of the type of good an object, service, or resource may constitute (see fig. 1). Excludability and Rivalrousness of goods can therethrough serve as a predictor for certain types of market failures: Goods which are non-excludable or of low excludability, but nonetheless costly to provide, are vulnerable to so-called free rider problem. Goods which are non-excludable, but highly rivalrous (common goods), may lead to the tragedy of the commons, that is over-exploitation.

AI Training and the (non-)excludability of information.

The unusual nature of excludability and rivalrousness of data further compromises the applicability of the goods classification built thereon. In traditional economics, a non-excludable and non-rivalrous good would be seen as a public good. This does apply insofar as data is vulnerable to corporate exploitation without commensurate indemnification. Data usage may however actually be anti-rival in the sense that increasing aggregate usage increases the value of the data (better labeling, easier access, previously undrawn conclusions, etc.), rather than diminish the ‘stock’ of data. 

Free-Rider Problems & Public Data

As any party can benefit from publicly available data at no cost or low cost, the incentive for private market actors to fund its creation is comparatively weak. Consequently, public bodies (and less frequently open-source foundations and non-profits) often assume the responsibility for the provision of this type of data. A classical example is weather data and its creation by national weather agencies.

Interoperability (Sisi)

1. What is interoperability?[1]

   Interoperability is a “standards-based” approach that enables different IT systems to communicate with minimal end-user interaction to exchange data and “share functionality”. Each system also allows the other system to “read, update, modify, and analyze” data with minimal human interaction. [2]

   Interoperability works by having different systems share data by employing the same standards for data formats (e.g., Extensible Markup Language or JavaScript Object Notation), exchange, and vocabulary to title data elements. These standards are agreed upon “in advance” by organizations, industry types, and user groups. Then, software developers build the system based on the model agreed upon.

1. Concrete example from a digital technology or platform

   Interoperable systems are vital in healthcare, commerce, and public safety. A notable example includes the integration of Electronic Health Records (HER). This system allows different healthcare providers to securely access and share personal patient information regardless of the specific EHR system used.[3] An example of such a system is the GNU Health Hospital Information System, which is a model employed by small private offices and large national public health systems.[4]

   For example, a health care professional may access lab results and patient history data from various platforms to enable them to make a diagnosis.

1. One governance implication: what does this concept explain about how platforms exercise power?

   Interoperability is a powerful tool because it helps provide a “seamless user experience” and optimizes data sharing between separate systems. It helps companies and organizations access data from multiple sources and improve efficiency, such as accelerating workflows and reducing the possibility of errors by pooling data from its sources.

   Interoperability standards are mandated by law on the technical side to ensure “critical systems” are compatible, especially in public safety and health. [5]

   In Europe, interoperability is governed by the European Health Data Space regulation (EHDS); the “first common EU data space dedicated to a specific sector as part of the European strategy for data.”

   This Regulation aims to benefit EU citizens such as patients, researchers, healthcare professionals, and industry players by establishing a common framework for the exchange of electronic health data within the EU. In addition, it enables certain data to be “reused” for public purpose and innovative scientific research. [6]

1. Where does the concept break down or fail to explain something

   While the EDHS harmonizes a legal and technical framework for sharing EU data, its shortcoming is its gradual implementation. The EDHS entered into force in 2025; however, most of its obligations will not apply until 2029. This leaves four years for health care companies to use interoperability in a deregulated manner. [7]

   Furthermore, there are actual barriers to achieving full interoperability in healthcare, including data silos (which isolate data collections and hinder data exchange), lack of standardization (where one system records blood pressure as free text, another system stores it as a structured numeric value), and deficient infrastructure, which requires significant technological upgrading.

   The burden remains on health companies to decide which standards to adopt and how much to invest in stable technological infrastructure to strengthen their data governance and interoperability. [8]