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Influential economist Daron Acemoglu: Forward Thinking on technology and political economy

McKinsey Global Institute | Michael Chui and Anna Bernasek  | Jul 14, 2021

Daron Acemoglu - Influential economist Daron Acemoglu:  Forward Thinking on technology and political economy

The influential economist connects the dots between artificial intelligence, productivity, wages, and inequality, and how to counterbalance the impacts of automation.

In this episode of the McKinsey Global Institute’s Forward Thinking podcast, hosts Michael Chui and Anna Bernasek speak with Daron Acemoglu. Daron is a professor of economics at the Massachusetts Institute of Technology (MIT), a coauthor (with James A. Robinson) of Why Nations Fail: The Origins of Power, Prosperity, and Poverty, and the author of many influential academic papers. His research covers a range of topics, including political economy, economic development, economic growth, technological change, inequality, labor economics, and economics of networks.

Michael Chui: Can you state simply what was wrong about the way that economists thought about technology?

Daron Acemoglu: There are three interrelated things about it. One is actually its descriptive realism. And a good, rich model has to have close connection with the data. 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.

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But the second is that 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.  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.

And then third, 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.

Michael Chui:  It comes in different forms, but in 20 years, 60 percent of the jobs that will exist will be ones you’ve never heard of. And we’ve tried to uncover that, and it seems that that rate of change in occupations, and even tasks and activities, is quite a bit slower than that. What is your research saying?

Daron Acemoglu: That’s exactly what I’m talking about. We haven’t done enough of that. We don’t have the same detailed data, but at a more ocular, statistics level, at high level in the 1920s and ’30s, it’s much faster. We do have many new tasks. It’s not like we don’t have any, but they are not enough, exactly. That is the imbalance that I’m talking about.

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Michael Chui: Are we just not inventive enough to come up with new tasks? Or do we need universal basic income, because we’ve come up to such a pace?

Daron Acemoglu: That brings me to my second explanation. It’s not that we have this imbalance because we have suddenly found much better ways of doing automation, doing machines, doing algorithms. Perhaps it is that we are inefficiently biasing our direction of technology. You can think that perhaps the possibilities offered to us haven’t changed, but because of who’s making the decisions, incentives, institutions, aspirations, we have gone more and more in an inefficient direction of doing too much automation and not enough of the other things.

Let me give you one example. I don’t think it’s the most important example, but let me give you one example to explain what I mean. Our tax system, if you look at today, if a company buys a machine to displace workers, it will pay about a 5 percent tax on the spending on that machine. If instead you hire the workers, you’ll pay something like 25 percent to 30 percent tax.

The US tax system provides an enormous subsidy to firms to buy the machines and replace the workers. Did this subsidy exist before? Yes. But much, much smaller. In the 1990s, the difference was 25 percent to 15 percent to 20 percent. So we are increasingly subsidizing firms to automate. If you have many incentives like this—and we can talk about what the other ones are, which are softer perhaps, but are no less important in my opinion—then you’re going to go more in the direction of automation. You’re going to leave lots of low-hanging fruit. And those low-hanging fruit might have been very important for that productivity improvement.

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