Economics of data (Part 2: Market approach to valuing data)

Economics of data (Part 2: Market approach to valuing data)

Phuah Eng Chye (1 August 2020)

Data has always toiled quietly in the background and its contribution to economic development taken for granted. Hence, it was not recognised as a distinct factor of production and much of the credit of economic value creation was attributed to land, labour, capital and even machinery and technology. But this is changing. The digitalisation of products and services means data now accounts for a rising proportion of trade and output[1]. Data is now acknowledged as a distinct factor of production in its own right.

But the theoretical framework on how data contributes to economic value is lacking. Currently, the most popular are the markets for data and markets for information approaches which rely on markets to provide an estimate for the value of data.

Markets for data

The enthusiasm for the markets for data approach stems from its usefulness as a means of promoting fair distribution of value[2]. Samuel Lengen notes some US senators recently proposed a DASHBOARD Act to force “tech companies to disclose the true value of their data to users. Specifically, companies with more than 100 million users would have to provide each user with an assessment of the financial value of their data, as well as reveal revenue generated by obtaining, collecting, processing, selling, using or sharing user data.” He adds that “sadly, the DASHBOARD Act doesn’t specify how it would estimate the value of user data…calculating the value of user data isn’t that simple. Estimates…vary widely… evaluations of less than a dollar for an average person’s data to a slightly more generous US$100 for a Facebook user. One user sold his data for $2,733 on Kickstarter”. He adds estimating the value of user data has become more complex as big data has broadened the type of data collected to include Facebook interactions, Google searches, tweets, purchases and Alexa living room commands.

Antonio García Martínez notes recent proposals for “an ambitious data dividend plan, whereby companies like Facebook or Google would pay their users a fraction of the revenue derived from the users’ data”[3]. However, he argues such proposals may be misconceived. First, the data is mostly of value to competitors and “the owners of the data would never in a million years sell it”. Second, while a market for user data exists – such as for location data[4] – it is relatively small. Third, “there’s a serious question of how much some of this data is worth…the dividend will likely be paltry…The annual revenue per user for Facebook globally is about $25. In the US and Canada, it’s about $130”. Fourth, the data is generated because of the use of a service. “You’re not contributing to some limited pool of data on whose resulting revenue you can stake a claim; you’re an infinitesimally small part of a data cooperative whose benefits accrue to the very users that generated it”. In addition, it is costly to maintain these services.

Antonio García Martínez argues that partly due to legislative pressure (for tech firms to obtain user consent) and partly due to the failure to compete against the data majors, the “third-party data ecosystem is already imploding”. He predicts “the majors like Google and Facebook will raise the castle walls around their data (and users) and disclaim any knowledge of data brokering, the data-as-oil traders. It’ll be first-party data all around: Publishers, apps, and ecommerce all huddling around their data and user piles…No, data isn’t the new oil. And it never will be, because the biggest data repositories don’t want it to be”.

Alessandro Acquisti, Curtis Taylor, Liad Wagman notes several economists “have proposed the establishment of markets where individuals can trade (rights over) their personal information. With the advent of social media, a number of startups began offering services along those lines. However, it is not clear that such markets for personal data could ever be successful. First, when interacting with services that offer trade and protection for their data, consumers face…the hurdle of estimating the fair value of their personal information. Second, in the absence of regulatory frameworks that enforce protection of traded data, the possibility of secondary usage of personal information (after the subject has traded it to another party) may run counter to the very idea of protecting consumer data. Third, much of consumer data that is of value to advertisers is non-static information that is dynamically generated as part of the interaction of the individual with other online services – such as search engines or online social networks. These services would be unlikely to relinquish control over the personal information that their technologies help generate”.

Yano Makoto suggests “digital data is a new economic resource…Currently the ownership of data and the basic rules on trading data have not yet been clearly established anywhere in the world. Large internet service companies are permitted to collect all data passing through them in data transactions, by which they are establishing the status of monopolistic positions…Before the organization of digital data markets is fixed, it is important to set ownership of digital data and other rules of transactions so as to prevent market participants from acquiring unmanageably strong market power…once proper rules are adopted, people’s awareness on fair practices in the digital data market will develop and that proper compliance procedures will be created through private economic activities”.

He argues the Coase Theorem “implies that once ownership is established for such a resource, an efficient allocation will be established in the resulting market if transaction costs are ignorable…If improper rules are adopted, it is expected to have a long-lasting detrimental effect on resource allocation and terms of trade determination”. In this regard, “it is important to broaden the concepts of transaction costs by adding participation and concentration costs and awareness and compliance building costs to the standard list of transaction costs (encompassing search and information costs, bargaining and decision costs and policing and enforcement costs)”.

Yano Makoto envisages a future where “blockchain technology can be harnessed to effectively assign data ownership and develop a healthy cyber ecosystem… blockchain technology can facilitate data trade for all sorts of data pieces, including not just deposits and withdrawal, but also ownership of securities, tickets, real estates, births, and addresses”. Towards this end, “blockchain technology enables us to define the ownership of each piece of data and makes data searchable by assigning ownership to the specific individuals who generated it. Indeed, by taking advantage of this technology, we can attribute the ownership of digital data to individual data producers”.

To incorporate “a completely new resource, it is also necessary to explore ways to shape new economic and social systems…resembling an ecological ecosystem”. “In order to build market infrastructure to support this system, it is also necessary to involve economic, legal, and policy experts – those who understand market movements and the law, and those capable of designing and implementing business-related rules”.

Yano Makoto cautioned “it is wrong to assume that a healthy cyber ecosystem will evolve spontaneously as innovation advances…great technological advancement leads to a degradation in the quality of the market. And when the market quality is degraded to a certain level, the social structure begins to change, causing various social problems. These include the labour exploitation after the First Industrial Revolution, the creation of industrial monopolies after the early stage of the Second Industrial Revolution, and the securities market failure leading to the Great Depression after the last stage”. “In order to build a healthy cyber ecosystem by preventing the concentration of resources in the hands of large companies, it is important to ensure that resources (i.e. data) are shared, distributed, and used widely throughout all of society. To this end, it is necessary to return data ownership to the individuals who generate the data”.

Overall, there are two camps on the data for markets proposition. Sceptics think these concepts are idealistic and unworkable. Advocates believe it represents a vision of an equitable future and that it is a matter of time that the technology to track data interactions and fairly allocate value from data is available.

Markets for information

Markets for information is a mainstream perspective of the commercial trade in information and personal data. Dirk Bergemann and Alessandro Bonatti notes markets for information have grown in tandem with “the availability of a growing number of data sources”. They cover raw and processed data “in the form of predictions, ratings, recommendations, and through the customizing of other products and services”. “Today we have moved to an environment where every large retailer or online service provider – basically anything you sign up for – is a potential trade in the market for data, where personal information can be directly sourced, packaged, and resold”.

In particular, intermediation growth is driven by large data brokers sourcing information “from individual sites selling their traffic flow, from mining publicly available online and offline data, and in the case of social networks, from users’ own activity” and on-selling “information about a consumer (or a group of consumers) to downstream data buyers, such as advertisers or retailers”.

The direct sale of information includes original lists that “is often simply a customer segment, i.e., a collection of potential consumers with certain characteristics”. They are sold by marketing and lead-generation companies, providers of financial data (e.g. news, analysis, credit ratings) and individual sites. Indirect Sale of Information cover information sold “indirectly in the form of customized goods and services.” This includes original lists sold contextually to access to the consumer (eyeballs) e.g. sponsored-search advertising or to provide informative signals (e.g. investment recommendations).

Dirk Bergemann and Alessandro Bonatti note “generally, the nature of the information collected, and its potential or actual uses determine a consumer’s willingness to share it. As awareness of data-sharing practices increases, users will need to be compensated (through monetary payments or other terms of service) to make it worthwhile to reveal their information”.

Dirk Bergemann and Alessandro Bonatti argues the “data intermediary’s central role affords him considerable market power. In particular, the ability of the data intermediary to provide terms to both sides of a product market plays a critical role in determining what kind of information gets traded, as well as the welfare and allocative properties of information markets. At the same time, the possible and actual uses of information place severe limits on the acquisition of information by a data broker, and on its ability to trade it…The structure of markets for information is bound to impact the availability, granularity, and security of individual-level information. In turn, privacy concerns will shape the types of data transactions that take place…The market for information is also bound to have implications on industry structure and on the internal organization of production”.

It should not be overlooked the oldest market for information are in fact financial markets[5]. Financial markets deal exclusively in information and the value of data is derived from its perceived impact on asset prices. Trading is supported by a robust ecosystem with strict rules for disclosure and standards. Specifically, the data trade is generally wholesale but is offered free to retailers to broaden participation.


The markets for data approach appeal to those seeking to establish a market mechanism to value data for purposes of ensuring fair distribution of its value. The markets for information approach appeal for those that wish to understand the competitive and industry aspects affecting the value of data. These approaches present microeconomic perspectives on the value of data. This need to be complemented by macroeconomic perspectives on the relationships between data and value at the aggregate level.


Alessandro Acquisti, Curtis Taylor, Liad Wagman (8 March 2016) “The economics of privacy”. Journal of Economic Literature; Sloan Foundation Economics Research Paper.

Antonio García Martínez (26 February 2019) “No, data is not the new oil”. Wired.

Charles Goldfinger (2000) “Intangible economy and financial markets”.  Communications & Strategies, No. 40, 4th quarter 2000.;jsessionid=83F42CBBC58994AFC46DCD93DF9D0988?doi=

Dirk Bergemann, Alessandro Bonatti (2018), “Markets for information: An introduction,” Cowles Foundation for Research in Economics Discussion paper.

Phuah Eng Chye (2015) Policy paradigms for the anorexic and financialised economy: Managing the transition to an information society.

Phuah Eng Chye (3 August 2019) “Information and development: Globalisation in transition”.

Phuah Eng Chye (28 March 2020) “The transparency paradigm”.

Phuah Eng Chye (11 April 2020) “Anonymity, opacity and zones”.

Phuah Eng Chye (18 July 2020) “Economics of data (Part 1: What is data?)”.

Samuel Lengen (11 July 2019) “How much is your data worth to tech companies? Lawmakers want to tell you, but it’s not that easy to calculate”. The Conversation.

Yano Makoto (14 April 2020) “Creation of a healthy cyber ecosystem”. Voxeu.

Yano Makoto (2019) “Market quality theory and the Coase theorem in the presence of transaction costs”. Research Institute of Economy, Trade and Industry (RIETI).

[1] “Information and development: Globalisation in transition”.

[2] Economics of data (Part 1: What is data?).

[3] One proposal is based on “the Alaskan Permanent Fund, which doles out annual payments to Alaskans based on the state’s petroleum revenue…the average Google or Facebook user is conceived as standing on a vast substratum of personal data whose extraction they’re entitled to profit from”. See Antonio García Martínez.

[4] Market-research firm Opimas estimates it will reach $250 million by 2020. See Antonio García Martínez.

[5] See Charles Goldfinger.