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Diane Coyle
Says More…
This week in Say More, PS talks with Diane Coyle, Professor of Public Policy at the University of Cambridge, and is the author, most recently, of Cogs and Monsters: What Economics Is, and What It Should Be (Princeton University Press, 2021).
Project Syndicate: In 2022, you observed that high inflation was reinforcing the backlash against prevailing economic orthodoxy, including the “terms of social consent for business.” In your forthcoming book, The Measure of Progress: Counting What Really Matters, you note that while a “fully formed new public philosophy” has not yet emerged to replace that orthodoxy, a “fragmented picture is starting to take shape.” Which elements of that picture hold the most promise, which miss the mark, and what is missing altogether?
Diane Coyle: Conflicting visions of how society should be organized are a central feature of the political polarization so many countries are now experiencing. Writing in the 1930s, the Italian Marxist philosopher Antonio Gramsci famously observed that the crisis of politics in his own times – a clash of fascism and liberalism – consisted “precisely in the fact that the old is dying and the new cannot be born; in this interregnum, a great variety of morbid symptoms appear.” The comment seems apt today.
While discontent with the economic status quo is widespread, the ideas and values that should shape what comes next remain a source of sharp disagreement. Whereas some believe that the solution lies in doubling down on individualism, deregulation, and the minimization of taxation, others point out that government action is vital to address climate change and environmental degradation, limit the excessive market power of big corporations, and invest in public goods and services.
While the outcome is yet to be settled, I am confident that the new public philosophy will have to leave behind the view of “government” and “market” as opposites, focusing instead on the way each set of institutions can improve the functioning of the other.
PS: You argued in 2022 that today’s antitrust authorities “are ill-equipped to address the challenges posed by winner-takes-all digital markets.” Since then, officials on both sides of the Atlantic have taken increasingly aggressive action against Big Tech, with the US Justice Department even considering breaking up Google. How do you rate recent (and prospective) antitrust actions in the United States, and how are they likely to affect market competition, innovation, and consumers?
DC: There has been a significant ratcheting up of antitrust enforcement on both sides of the Atlantic. But the extent to which such efforts will be sustained, and their practical effect in opening up digital markets, remains to be seen. After all, the vertical integration of digital ecosystems, together with the complex network of relationships and investments that underpin them, poses a substantial barrier to market entry. As for generative artificial intelligence, it doesn’t have the powerful network effects of other digital markets, but the scale required to train large AI models is enormous – and, it seems, still growing.
That said, competition authorities are strengthening their capacity, both by adding new tools and engaging in the analysis needed to use them. As a result, they are increasingly well equipped to ensure that today’s cutting-edge technologies work well for “the many,” rather than simply increasing market power and creating fortunes for a few.
PS: One way digital technology is undermining consumer well-being, you recently noted, is through a “time tax,” levied whenever “tech companies offload tasks from their employees onto users.” In The Measure of Progress, you advocate incorporating “time use” and “time saving” into economic analysis and policymaking. How would time-use data inform our understanding of production and consumption in the digital economy, and how might the resulting insights be translated into policy?
DC: How we spend our time is central to our well-being. Income matters largely because it affects how we can spend our time. Few people wake up in the morning thinking, “What am I going to spend money on today?” Instead, they ask, “What am I going to do today?” As incomes increase, reaching the point where people do not have to work all day every day to live comfortably, people generally choose to enjoy more leisure time. (The US is something of an exception here.)
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So, from a consumption standpoint, a key benefit of technology would be to save time either on work or on dull or unpleasant personal tasks, such as paying bills, buying groceries, or making appointments. But companies have largely been using digital technologies to do precisely the opposite: saving themselves money, at the expense of their consumers’ time. Cumbersome self-checkout systems and unhelpful customer-service bots are just the start of a trend that must be reversed. Guided by time-use data, companies should develop new AI applications focused on saving people time.
On the supply side, speeding up processes is vital to long-term productivity growth. We saw that when steam engines replaced sails, and in the just-in-time manufacturing and logistics revolution. AI might be able to do the same for administrative processes or mundane coding activities, but only if it is developed with time use in mind.
BY THE WAY . . .
PS: The Measure of Progress examines the limitations of how we quantify human well-being and economic development, and argues that the prevailing approach is “hampering policymakers’ ability to tackle slow growth in productivity and living standards.” Discussions about the need to look beyond GDP have been gaining steam for a while, largely reflecting growing awareness of threats to sustainability. You have quite a bit to say about artificial intelligence in this regard. How might AI facilitate (or complicate) the search for better welfare metrics?
DC: There are two sets of issues here. One is that AI itself is extremely energy-hungry, so any assessment of its contribution to productivity growth must account for the energy it consumes. The other is that, even though the digital revolution has been transforming the economy for well over 20 years, data on that transformation – which should inform policy and business decisions – remain inadequate.
There are many gaps, which the book documents in some detail. Examples include data on whether and how businesses are using various technologies, the extent to which open-source software is replacing paid versions, and how valuable individuals’ data are to firms and government bodies. Data about work and skills – How many people are working via digital platforms? How many are working in AI companies? – are also sorely lacking. All of this information is essential if we are to design effective policies or business models.
AI compounds the urgency of this imperative, because research indicates that AI is capable of greatly enhancing productivity, but only for a small minority of firms. Yet policymakers are developing regulations for AI, and planning public investments in the technology, in a conceptual fog. Fortunately, AI may also be able to help in the construction of new metrics. There is an exciting agenda here for statisticians.
PS: A pillar of the alternative approach to economic measurement that you sketch in your book is an “asset-based framework,” which you describe as a “broadly defined balance sheet for the economy,” with assets – including natural and human capital – “valued at shadow prices reflecting societal values rather than market exchange values.” How would these “prices” be determined, and what advantages does this approach have over the revised System of National Accounts that the United Nations is set to adopt?
DC: Since 1946, the economy has been measured according to the System of National Accounts, which seeks to value economic activity at market prices. Efforts to account for our use of the natural environment have adhered to the same basic principle. But this leaves out crucial externalities that drive a wedge between market and social values: for example, valuing the oceans according to the price of fish and the cost of shipping leaves out the collective costs raised by over-fishing or pollution. The UN’s revision of the System of National Accounts, due next year, will do nothing to close this gap.
Researchers are currently working to devise better methods for systematically estimating societal “shadow” prices. This will help us understand not only the economics of environmental sustainability, but also the economy’s intangible and digital aspects, which also feature a gap between market and social values. For example, data that can be widely accessed might have a larger social value, which exceeds the value to a single user.
PS: You note at the beginning of The Measure of Progress that the research you draw on is “not limited to economics.” Which disciplines or literatures proved particularly fruitful?
DC: Since the fundamental question the book addresses is about human welfare and progress, philosophy has been particularly important: What does it mean for life to get “better”? The progress dimension also makes economic history and the history of technology highly relevant. My focus on the digital economy means I have also worked with computer scientists and engineers, and read relevant parts of those literatures.
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