“If data is the new oil, then why aren’t we taxing it like we tax oil?” That was the essence of the provocative brief that set in motion our recent 6-month research project funded by the Rockefeller Foundation. The results are detailed in the new report: Data Financing for Global Good: A Feasibility Study.

The parallels between data and oil break down quickly once you start considering practicalities such as measuring and valuing data. Data is, after all, a highly heterogeneous good whose value is context-specific — very different from a commodity such as oil that can be measured and valued by the barrel. But even if the value of data can’t simply be metered and taxed, are there other ways in which the data economy could be more directly aligned with social good?

Data-intensive industries already contribute to social good by producing useful services and paying taxes on their profits (though some pay regrettably little). But are there ways in which the data economy could directly finance global causes such as climate change prevention, poverty alleviation and infrastructure? Such mechanisms should not just arbitrarily siphon off money from industry, but also contribute value back to the data economy by correcting market failures and investment gaps. The potential impacts are significant: estimates value the data economy at around seven percent of GDP in rich industrialised countries, or around ten times the value of the United Nations development aid spending goal.

Here’s where “data financing” comes in. It’s a term we coined that’s based on innovative financing, a concept increasingly used in the philanthropical world. Innovative financing refers to initiatives that seek to unlock private capital for the sake of global development and socially beneficial projects, which face substantial funding gaps globally. Since government funding towards addressing global challenges is not growing, the proponents of innovative financing are asking how else these critical causes could be funded. An existing example of innovative financing is the UNITAID air ticket levy used to advance global health.

Data financing, then, is a subset of innovative financing that refers to mechanisms that attempt to redirect a slice of the value created in the global data economy towards broader social objectives. For instance, a Global Internet Subsidy funded by large Internet companies could help to educate and and build infrastructure in the world’s marginalized regions, in the long run also growing the market for Internet companies’ services. But such a model would need well-designed governance mechanisms to avoid the pitfalls of current Internet subsidization initiatives, which risk failing because of well-founded concerns that they further entrench Internet giants’ dominance over emerging digital markets.

Besides the Global Internet Subsidy, other data financing models examined in the report are a Privacy Insurance for personal data processing, a Shared Knowledge Duty payable by businesses profiting from open and public data, and an Attention Levy to disincentivise intrusive marketing. Many of these have been considered before, and they come with significant economic, legal, political, and technical challenges. Our report considers these challenges in turn, assesses the feasibility of potential solutions, and presents rough estimates of potential financial impacts.

Some of the prevailing business models of the data economy — provoking users’ attention, extracting their personal information, and monetizing it through advertising — are more or less taken for granted today. But they are something of a historical accident, an unanticipated corollary to some of the technical and political decisions made early in the Internet’s design. Certainly they are not any inherent feature of data as such. Although our report focuses on the technical, legal, and political practicalities of the idea of data financing, it also invites a careful reader to question some of the accepted truths on how a data-intensive economy could be organized, and what business models might be possible.

Read the report: Lehdonvirta, V., Mittelstadt, B. D., Taylor, G., Lu, Y. Y., Kadikov, A., and Margetts, H. (2016) Data Financing for Global Good: A Feasibility Study. University of Oxford: Oxford Internet Institute.