Governance of the digital economy was a key topic on the global agenda at Davos 2019, from panel discussions to Shinzo Abe’s announcement of a G20 track for data governance. It seems as though we are at the start of a new era in globalization, in which global trade in data is ever rising, while trade in physical goods is levelling off. New thinking about economic development is needed, otherwise we will see old dynamics play out again, with digital globalization favouring the few countries who build up early dominance at the right points in the value chain.
Does dominance matter?
Let’s start with the big picture: technology benefits those who use it most productively. This sounds trivial, but dominance in technology matters because it can be easily entrenched, perhaps more so in the digital economy than before.
A striking trend over the last two centuries is that developing countries - those that most need to boost productivity - are benefitting the least from new technology. Two hundred years ago, at the cusp of the first industrial revolution, the spinning jenny for weaving cloth or the Bessemer process for making steel were just as economically impactful in Asia and Africa as they were in Britain. But recent research shows that new technology, such as the internet, is used much less intensively in developing countries. The productivity gains from tech are disproportionately benefitting nations that are already ahead of the curve.
This could introduce a pernicious dynamic. If the returns to technology are greater to those countries already ahead, then the emerging digital economy may simply exacerbate geopolitical and economic inequalities. Rather than a continued convergence, rich countries may start to pull further away from developing and least developed countries.
The firms that benefit in the digital economy are those that can both amass large volumes of data and analyze them to create a competitive advantage. Because of the value of these companies, it’s easy to think of data as a highly valuable commodity. But singular data points are virtually worthless on their own. Even if I had a pile of data - a year of my browsing history, say - I wouldn’t know how to turn a profit from it. But Google does. Digital companies take low-value “raw” data and turn it into high-value competitive advantage: a better product or a more efficient process. Whether the customer is buying online advertising or a buying a ride to the airport, data can help make it better.
Dominant firms are those who have the most data that can be deployed behind the most profitable business models. In practice, Alphabet and Facebook are currently the global titans. Alphabet operates multiple services with billions of active users, including Android, Google Search and Google Maps, as does Facebook (which also owns Instagram). Indeed, a recent sample of 1,000 Kenyans showed that half of users’ mobile data usage flowed through apps owned by these two companies. Other firms are leveraging the power of data to carve out a niche (such as insurance, transport, finance or jobseeking), but deep expertise in a single domain is unlikely to allow these firms to dominate in other business categories.
Chinese firms are worth mentioning: WeChat (owned by Tencent), Alibaba and Baidu already operate on similar scales to Facebook and Google, but they aren’t chasing the billions of newly connected people across Africa and Asia with the same gusto as the US giants. Their global dominance will depend on the extent to which they set their sights beyond Chinese borders, and the extent to which their trove of data on Chinese citizens (their driving routes, shopping habits and search histories) can fuel business models for other markets.
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If a large part of new value in the digital age is coming from firms in a handful of countries, then these countries will become rich off the profits. This isn’t necessarily a zero-sum game: digital services will likely boost prosperity and quality of life in developing countries. But this rosy picture, in terms of mutual gains for all, could mask a worsening picture for between-country inequality, as wealthy nations pull further ahead of developing countries. It’s never good to be trapped at the bottom of a value chain; it’s even worse when you are producing the commodity (raw data) for free.
In his new book AI Superpowers, Chinese venture capitalist Kai-Fu Lee lists four inputs that power the data revolution: “abundant data, hungry entrepreneurs, AI scientists and an AI-friendly policy environment”. We should also add another input: the hardware and tools required to process data. Here the silicon chip engineers and factories of California and China can skim a slice off the top of every digital profit.
Of all these inputs, raw data is the most abundant and goes for the cheapest price. The other inputs enjoy the economic profits, and it is a lack of these that should worry countries. A lot of the current debate approaches data from the supply side, asking about ownership and privacy. These are no doubt important questions. But countries need to think deeply about the demand side: are they growing local industries that will make use of data? If not, they will find themselves forever exporting raw data and importing expensive digital services.
How valuable is data?
People say data is like oil. But it isn’t, really. For one thing, data isn’t “fungible”: you can’t swap one piece of information for something else. Knowing my Amazon purchase history won’t help a self-driving car identify a stop sign. This is true even when data is the exact same type: my browsing history may not be as valuable as yours.
This non-fungible nature shows up in my estimations of Facebook’s average monthly revenue-per-user (extending 2017 analysis from Caribou Digital), which shows that the average Canadian user generates 100 times more revenue than the average Ethiopian user. Not everyone’s data carries equal value - at least not in an ad-based business model. This makes intuitive sense: the value of an advertisement depends on the purchasing power of the viewer.
But advertising is a small part of the world’s output. Even more difficult to grapple with will be the next wave of data-driven production: not consumer websites, but industrial robots or automated radiology. For years now, we have had useful economic theories to describe global value chains in manufacturing. It is crucial that we develop similar theories and methods to describe the flow and processing of inputs in the digital economy.
Data dominance isn’t a simple question about who is hoovering up the most data. It is also about who dominates the supply of hardware, talent and systems that process the world’s infinite supply of raw information.