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A legislator in New York is introducing a law that would tax companies for the revenue they earn on consumer data, part of a wave of similar legislation.
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States are broaching a new way to incentivize companies to keep data private—a sales tax. The latest effort comes from New York State Senator Andrew Gounardes, who has introduced the Data Economy Labor Compensation and Accountability Act in collaboration with Brooklyn Borough President Eric Adams. The proposal would enact the equivalent of a 2% tax on annual receipts earned off of the data of New York residents.
“Data is here and is being used and commoditized and commercialized in ways that we as laypeople don’t fully understand,” Gounardes says, noting that he subscribes to the idea that data is a new form of labor ** and that people are not being fairly compensated. This law is trying to fix that problem. “We’re trying to compensate people at large,” he says.
If this legislation is passed, it could generate hundreds of millions of dollars in annual revenue for the state. Gounardes says the earnings would be put toward educational and workforce programs, including funding for STEAM (science, technology, engineering, arts, and math) education in public schools. It would also go toward workforce retraining courses and digital literacy programs.
The rule would apply to any company that derives profit from controlling or processing personal data, including Facebook, Google, and Microsoft, among many others. New companies, erected within the last three years, will be temporarily exempt from the rule, and those with less than $5 million in revenue will be able to evade it altogether.advertisement
In the digital economy, user data is typically treated as capital created by corporations observing willing individuals. This neglects users’ role in creating data, reducing incentives for users, distributing the gains from the data economy unequally and stoking fears of automation. Instead treating data (at least partially) as labor could help resolve these issues and restore a functioning market for user contributions, but may run against the near-term interests of dominant data monopsonists who have benefited from data being treated as ‘free’. Countervailing power, in the form of competition, a data labor movement and/or thoughtful regulation could help restore balance.
Keywords: data economy, big data, data as labor, artificial intelligence, machine learning, monopsony power
JEL Classification: C55, D40, J42, L96
Suggested Citation: Arrieta Ibarra, Imanol and Goff, Leonard and Jiménez Hernández, Diego and Lanier, Jaron and Weyl, Eric Glen, Should We Treat Data as Labor? Moving Beyond ‘Free’ (December 27, 2017). American Economic Association Papers & Proceedings, Vol. 1, No. 1, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3093683
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