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Interview with Daron Acemoglu: Technology and the Shape of Growth

Summary:
Michael Chui and Anna Bernasek of the McKinsey Global Institute have a half-hour interview with Daron Acemoglu. “Forward Thinking on technology and political economy with Daron Acemoglu” (July 14, 2021 audio and an edited transcript available). Acemoglu has focused for decades on the idea the idea of “directed technological change”–that is, the idea that the direction of technology is not a random event determined by scientists, but is to some extent a response to the incentives of what areas are being investigated by researchers and the incentives for firms and entrepreneurs in applying new ideas in the economy. In Acemoglu’s view, economists of the past too often treated “technology” as a general all-purpose ingredient that tended to raise productivity of workers. In contrast,

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Michael Chui and Anna Bernasek of the McKinsey Global Institute have a half-hour interview with Daron Acemoglu. “Forward Thinking on technology and political economy with Daron Acemoglu” (July 14, 2021 audio and an edited transcript available). Acemoglu has focused for decades on the idea the idea of “directed technological change”–that is, the idea that the direction of technology is not a random event determined by scientists, but is to some extent a response to the incentives of what areas are being investigated by researchers and the incentives for firms and entrepreneurs in applying new ideas in the economy.

In Acemoglu’s view, economists of the past too often treated “technology” as a general all-purpose ingredient that tended to raise productivity of workers. In contrast, Acemoglu points out that technology often has quite particular effects. He says:

And if you look at the way that economists think about technology, it’s this latent variable that makes you just more productive. But very few technologies actually do that. Electricity didn’t make workers more productive. It made some functions in factories more feasible, and some few items more productive. A hammer doesn’t make you more productive in everything. It makes you just more productive in one single, simple task—hammering a nail. And many technologies don’t even do that. The example of spinning and weaving machinery that I gave, or the factory system, or, more recently, databases, software, robots, numerically controlled machines, they are mostly about replacing workers in certain tasks that they used to perform. …

It may benefit some workers more than others. It may well be that computers augment educated workers more than high school dropouts, so inequality can increase. But at the end you shouldn’t see the high school dropouts lose out. Their real wages shouldn’t decline. And the real wages of workers shouldn’t decline. But, in fact, one of the striking but very robust features of the last 40 years of economic development in the US and the UK has been that many groups, especially low-education or middle-education men, have actually seen their earnings fall, some groups by as much as 25 percent, in real terms, since 1980. Phenomenal. This isn’t the American dream.

In the traditional economics approach, this is a nuisance that we often sweep under the carpet. ,,, [I]it is something that doesn’t really fit into this technology as augmenting framework. But when technology, at least in part, is about automation, replacing, displacing workers from their tasks, then this happens quite often. You can have productivity improvements—capital benefits, firms benefit, but workers, especially some types of workers, all workers overall can lose out in real terms. …

[O]nce you go to this micro level, then the direction of technology, the future of technology looked at through the perspective of what type of technologies we’re going to build on, that becomes much richer and much more interesting. It’s not just whether we’re going to increase the productivity of skilled workers versus unskilled workers, both of which benefit all of them since they are complementary. It’s more like, are we going to completely give up on unskilled workers? Are we going to try to replace them? Are we going to try to replace humans? Are we going to create new tasks for humans? How are you going to use the AI platform? All of these questions about the direction of technology become much more alive, and then also the productivity implications become much more interesting.

Acemoglu points out that from the 1950s through the 1970s, the US economy had a high rate of technological change and productivity growth with a pretty stable distribution of income. But since the 1980s, technological change and productivity growth have been accompanied by a more unequal distribution of income. What changed? Acemoglu argues that the overall effects of technological change will be determined by factors like whether it gives rise to new sectors of the economy with opportunities for displaced workers of the present and future workers, and whether the technologies generate large gains in productivity (think of large-scale mechanization of agriculture and how it raised output per acre) or if it just allows replacing workers with machines. In Acemoglu’s view, too much of the innovation surrounding modern automation, robotics and artificial intelligence is about replacing existing workers, rather than empowering new industries.

The details of an appropriate policy response here are hard to enunciate, and Acemoglu (wisely) shies away from offering granular recommendation. But he does offer these general thoughts:

One is we have to free ourselves from the excessive obsession on automation. It is true in the area of AI. It’s true in other areas, too. [In] our current business community, for a variety of reasons, some of it is cost cutting, some of it is because where the technology leaders in Silicon Valley have sort of set the agenda, some of it is because government policies are just too focused on automating everything.

Instead, we have to come back to a world in which we put as much effort in increasing human productivity, both in the tasks that they already produce, but also creating new tasks in entertainment, in healthcare. There are so many new things that we can do, especially with AI, but some of it with just our existing technologies, some of it with virtual reality or augmented reality. There are many, many things ranging from judgment, social skills, flexibility, creativity, that humans are so much better at than machines.

But we’re not empowering them right now. That’s the first leg. That second leg is that we also have to pull back from using AI as a method of control. And again, that’s about how we use the AI technology. Do we use it to empower individuals? To be better communicators, better masters of their own choices and data? Be able to sort of understand the veracity or the liability of different types of information? Or do we develop these tools in the hands of platforms so that the platforms themselves are doing all of that thinking and all of that direction for the individuals? I think that those two are very different futures as well.

For a more detailed version of Acemoglu’s argument, a useful starting point is his essay with Pascual Restrepo, “Automation and New Tasks: How Technology Displaces and Reinstates Labor,” in the Spring 2019 issue of the Journal of Economic Perspectives (where I work as Managing Editor). Basically, they suggest a framework in which automation can have three possible effects on the tasks that are involved in doing a job: a displacement effect, when automation replaces a task previously done by a worker; a productivity effect in which the higher productivity from automation taking over certain tasks leads to more buying power in the economy, creating jobs in other sectors; and a reinstatement effect, when new technology reshuffles the production process in a way that leads to new tasks that will be done by labor. They then apply this framework to US data.

That JEP symposium includes three other papers:

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