Data as an economic good, data as a commons, and data governance
Open accessAbstract
This paper provides a systematic and critical review of the economics literature on data as an economic good and draws lessons for data governance. We conclude that focusing on data as an economic good in governance efforts is hardwired to only result in more data production and cannot deliver other societal goals contrary to what is often claimed in the literature and policy. Data governance is often a red herring which distracts from other digital problems. The governance of digital society cannot rely exclusively on data-centric economic models. We review the literatures and the underlying empirical and political claims concerning data commons. While commons thinking is useful to frame digital problems in terms of ecologies, it has important limitations. We propose a political-ecological approach to governing the digital society, defined by ecological thinking about governance problems and the awareness of the political nature of framing the problems and mapping their ecological makeup.
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Applying commons theory to AI resources is a live research move rather than a speculative one.
“These works establish that applying commons theory to AI resources is a live research move rather than a speculative one”
From Commons-Governed Artificial Intelligence: A Taxonomy of Collective Governance (2026) · cites this paper as Purtova and van Maanen (2024)
In “When the data commons analyses can be used” (page unavailable), the authors review existing applications of commons analysis to data, so this is an active research approach rather than a merely speculative proposal. The passage does not specifically establish its application to AI resources beyond data. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe supply of high-quality openly licensed data is shrinking under enclosure pressure as rights-holders restrict reuse for AI training.
“the supply of high-quality openly licensed data is in fact shrinking under enclosure pressure as rights-holders restrict reuse for AI training (Purtova and van Maanen, 2024).”
From Commons-Governed Artificial Intelligence: A Taxonomy of Collective Governance (2026) · cites this paper as Purtova and van Maanen, 2024
In “When the data commons analyses can be used” (page unavailable), the paper says Big Tech’s capture makes less data available or findable to others. It does not establish that the supply of high-quality, openly licensed data is shrinking or that rights-holders are restricting reuse specifically for AI training. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPurtova and van Maanen (2024) survey the data-commons literature and assess governance models against the common-pool-resource tradition, and Linåker and Runeson (2022) apply Ostrom's design principles directly to open-data ecosystems.
“Purtova and van Maanen (2024) survey the data-commons literature and assess governance models against the common-pool-resource tradition, and Linåker and Runeson (2022) apply Ostrom's design principles directly to open-data ecosystems.”
From Commons-Governed Artificial Intelligence: A Taxonomy of Collective Governance (2026) · cites this paper as Purtova and van Maanen (2024)
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Cited by 37
- Authors
- Purtova, N., & van Maanen, G.
- Affiliations
- Utrecht University (NL)
- Utrecht University School of Law, Utrecht University, Utrecht, Netherlands
- Corresponding author
- Purtova, Nadya
- Venue
- Law, Innovation and Technology
- Publisher
- Informa UK Limited
- Published
- 2023-11
- Volume
- 16
- Issue
- 1
- Pages
- 1-42
- ISSN
- 1757-9961 (ISSN-L)
- 1757-997X
- PMC
- PMC11614035
- OpenAlex
- W4388125693
- Cited by
- 37
- References
- 0
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 4 · 2023: 1
- FWCI
- 68.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Law Innovation and Technology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research portal (Tilburg University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- European Research Council
- Horizon 2020
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Keywords
- data commons, data governance, digital society, political ecology, data production, digital policy
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