Taxation in the Data Economy: The ‘Invisible’ Competition
The individual is both the creator and user of her own data. Companies collect personal data, use it for various purposes like improving products and anticipating consumer behavior, and return those benefits to consumers.
Big Tech’s multi-faceted uses of data generate new economic value, prompting questions on how consumers can be fairly compensated. Some experts raised the idea of a tax on enterprises that collect mass amounts of private information. One facet of taxation often left unaddressed is data commodification beyond borders. As Future Agenda notes,
Principles that tie tax to physical presence are no longer appropriate for a world in which California-based tech companies can sell services in Spain through a Dublin-registered subsidiary and so pay little or no tax.
Data collection by large corporations reveals a range of purposes, ranging from altruism to economic exploitation, and at times resulting in the violation of privacy. The growing value of data sharpens competition: the mere existence of such massive quantities of data increases demand for it and heightens dependence on it. The “invisible” and intangible competition to create and control this digital resource is further strengthened.
The relationship between data and taxes, as was once the case with the “income tax,” is cultivated by the pragmatism of a new source of public revenue. “Data transmission” and the value of data have suggested to experts a possible Data Tax. Proponents claim it will mitigate some of the externalities the data-driven economy brings, but applying public choice theory raises doubts.
The Data Tax
In a global data economy, traditional rules surrounding the taxing of intangible assets are increasingly difficult to apply. The less we know about “valuable resources,” the murkier the path for tax reform becomes. Geopolitical changes and the rise of emerging economies also challenge international tax reforms.
Given these difficulties in application, what is the foundation for the underlying “Data tax?” First and foremost, the relationship between the tax and “data economy” requires choosing between targeting behavioral changes, or raising revenue. The tax principles of the “digital economy” caused OECD countries to falter in taxing companies who acquire income in other member countries without establishing a physical presence. Taxing bodies are likewise unable to change the lagging legislative framework and measurements.
In response to California Gov. Gavin Newsom’s call for a data dividend in 2019, New York State assemblyman Ron Kim claimed implementing a tax on data would mean “actually validating the extractive and abusive practice by tech companies” because “eventually, [tech companies] would be more than happy to pay a fee or tax to keep it going.” Government entities have no incentive to stop the “abusive” practice of data collection, especially those like the Justice Department, which frequently subpoenas our personal information via Big Tech: call logs, IP addresses, billing information, and much more.
Various notable data tax theories have been circulating in the past two years. Omri Marian, for instance, advocates for a tax based on the volume of raw data, regardless of its use, and states that the user of the data would have to pay the tax. He further elaborates that “only heavy users would pay directly,” making the tax “easier to administer and more progressive.” Avi-Yonah proposes a similar tax, but one that would only apply to “downloads by for-profit firms.” Lucas-Mas and Junquera-Varel propose a Data Excise Tax based on the “volume of collected data, measured in gigabytes.” This tax, in contrast with Marian’s which encompasses amount of data uploaded, merely taxes the volume of data collected or downloaded.
The concept of a data tax is not actually a far-fetched idea, as Gleckman notes, and is administratively not so different from the familiar carbon tax “imposed on the total volume of greenhouse gasses emitted by a firm.” Both the carbon and data taxes have Pigouvian tax elements; in essence, the data tax internalizes the externalities imposed by data, for instance misuse, revelation of personal information, and information asymmetry. Marian notes that since the data tax “is going to be effectively targeted only at taxpayers who use data as an integral part of their business model… the data tax can have the intended functionality of a Pigouvian tax.” But Pigouvian taxes must be equal to the costs generated by the negative externality. In this context, it is difficult to place a value on data, so the effectiveness would be hard to measure. Furthermore, it is hard to tax an intangible asset that consumers themselves don’t even value. Companies can collect our data for free with little protest, and as Zaretsky points out, “if you aren’t paying for the product, you are the product.”
Even more concerning, the cost of corporate taxes invariably falls on consumers. If the tax hurts a company’s profit, those in charge will invest less in innovation, laborers, production, or all three. It remains to be seen whether data experts and policymakers will come up with a tax that encapsulates “fairness, certainty, convenience, and efficiency” as Adam Smith argued for, but each idea is worth hearing in today’s data-governed market.