Governance by markets

Data as an input to market power (Sergey) 

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.

Switching costs and lock-in (Kate)

[File:Untitled Diagram.drawio (1).png|300px|thumb|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.