INFO 202 Chap.3 The Political Economy of IT and Data
The Political Economy of IT and Data
Frameworks with values built in
The third week introduces the analytical frameworks of political economy and applies them to information technology. The syllabus singles out the typology of goods, and it pairs the frameworks with a warning: each rests on assumptions and carries values. A strong answer in this part of the course therefore does two things at once.
It uses a framework to classify and explain, and it says what the framework takes for granted, such as who should own something or whose welfare counts.
Rivalry, excludability and the four types of goods
The typology sorts goods by two properties. A rival good is used up or diminished by one person's use; a non-rival good is not.
An excludable good can be withheld from people who have not paid or been admitted; a non-excludable good cannot. Crossing the two gives private goods, club goods, common-pool resources and public goods.
Information is a revealing case because copying it costs almost nothing, so the rivalry question has a clear answer, and all the argument moves to the excludability question.
Oil, sunlight and infrastructure
The Economist's 2020 article on data uses exactly this logic.
Data are non-rival but can be made excludable with encryption, so depending on how access is set they resemble a private good, a public good or something between the two. Each position has a matching metaphor. Oil implies refining and trading and fits an American view that the extractor owns the data.
Sunlight implies openness and maximum use, and the article treats China as an extreme case of data as public goods under state control. Infrastructure implies shared roads run by users through trusts and co-operatives, a view it links to European regulators.
None of the three, it concludes, is likely to dominate.
Why each metaphor breaks
The oil picture struggles because most data never change hands, corporate data sets are not interchangeable, and externalities mean one person's data can be predicted from other people's, so markets fail. The sunlight picture meets privacy law for personal data and commercial self-interest for corporate data.
The infrastructure picture needs money and political will, and many projects remain small and publicly funded.
The article describes technologies that make middle positions workable, including differential privacy, homomorphic encryption that allows computation on data that stay encrypted, and blockchains that track who accessed what, alongside civic projects such as Barcelona's requirement that suppliers contribute their service data to a municipal commons.
Liu's 2021 article, the second reading, frames data as a question of politics with China in a global comparison, which reinforces the week's point that choosing a goods type for data is a political decision.
What this chapter covers
- 01
Political economy frameworks and the values built into them
- 02
Rival versus non-rival goods
- 03
Excludable versus non-excludable goods
- 04
Private, club, common-pool and public goods
- 05
Why data are non-rival but can be made excludable
- 06
Oil, sunlight and infrastructure as three data economies
- 07
United States, China and Europe in the article's comparison
- 08
Data stewardship: trusts, co-operatives and data commons
- 09
Data politics in a global comparison
Worked example · free
Classify a data policy choice with the typology of goods
- 2Explain why the data are non-rival and why the choice is really about excludability.
- 3Classify the three options as private, public and club goods with the matching metaphor.
- 4Give one strength and one weakness for each option, using privacy, market failure or governance.
Key terms
- Typology of Goods
- A framework that classifies goods by whether they are rival and whether they are excludable, producing four types: private goods, club goods, common-pool resources and public goods.
- Rival Good
- A good whose use by one person reduces what is available to others, such as a barrel of oil; data are the standard counterexample because copies cost almost nothing.
- Non-Rival Good
- A good that many people can use at the same time without diminishing it, such as a data set that can be copied without limit.
- Excludable Good
- A good whose holder can prevent people who have not paid or been admitted from using it, for example through encryption, contracts, fences or law.
- Private Good
- A good that is both rival and excludable, bought and owned by individuals; the oil metaphor treats data this way, favoring property rights and markets.
- Public Good
- A good that is both non-rival and non-excludable, so everyone can use it at once and no one can be kept out; the sunlight metaphor treats data this way.
- Club Good
- A good that is non-rival but excludable, shared among members who are admitted under agreed rules, like data managed in a trust or co-operative.
- Data Stewardship
- Governance that organizes access to data on behalf of the people who produce and use them, carried out by institutions such as data trusts, co-operatives and personal data stores.
The Political Economy of IT and Data FAQ
Why are data called non-rival but excludable?
Non-rival means that one person's use does not reduce what is left for others, and data qualify because they can be copied endlessly at almost no cost. Excludable means that access can be restricted, which encryption, licenses and law make possible. The combination matters because it means the economic character of data depends mainly on how access is set, which is a choice made by firms and governments.
Is data really the new oil?
Only in part. Like oil, raw data need processing before they are useful, and some markets for them exist, notably online advertising and data brokerage. Unlike oil, data are non-rival, most never change hands, corporate data sets are not interchangeable, and the same data can be worth very different amounts to different holders, so they rarely trade like a commodity.
Exam move
Sketch the two-by-two typology of goods from memory and place oil, sunlight, a subscription library and a public fishery in the right cells. Then draw an arrow up the non-rival column and write, beside each position, the metaphor for data, the region the article links to it, and one reason it fails. Finish by writing one sentence naming the value each metaphor assumes, since the syllabus asks you to surface those assumptions.
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