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As the next generation of robots arrives in the workplace, will they enable workers or replace them? According to MIT’s Daron Acemoglu, one of the most frequently cited economists in the world, this distinction is the difference between technology that raises workers’ wages versus tech that reduces overall employment and stifles wage growth.
Daron Acemoglu is a professor of economics at MIT, a frequent contributor to Foreign Policy Magazine, and co-author of the book Why Nations Fail: The Origins of Power, Prosperity, and Poverty. He joined me to discuss his recent research and what technological innovation means for the future of work. What follows is an abbreviated version of our conversation.
The standard Econ 101 view of technology is that automation will eventually lead to higher average real incomes across the country, with similar overall rates of employment. To what extent do you agree with this scenario?
I think there is a lot of comfort in thinking there is an inexorable link between productivity, wages, and employment, and everything is going to be fine; but, there are so many unknowns. I would interpret my research here, and it really goes perhaps even against my own priors, as concerning but not alarmist.
Alarmism would be: We’re losing jobs so quickly that within the next generation’s lifetime, we have some major problems if we don’t do something about it. That’s not the case. The rate of job loss from automation is relatively small over the last 20 years or so, perhaps less than half a percentage point of the working age population. So, we’re not talking about huge numbers.
I think what the result signifies is important to put in a broader context. The way that Econ 101 thinks about productivity, employment, and wages, is that whatever increases the productivity is going to translate into wages relatively quickly. We used to chastise Keynes for predicting in 1929 before the Great Depression that rapid technological changes would reduce demand for labor and lead to technological unemployment, or much shorter working days or working weeks for people. And that hasn’t come true obviously in the intervening 90 years. But, the conceptual point that new technologies could reduce the demand for labor, rather than increase the demand for labor, is a possibility and it’s not a crazy idea.
Your research makes a difference between “enabling” and “replacing” technology.
The conceptual point that new technologies could reduce the demand for labor, rather than increase the demand for labor, is a possibility and it’s not a crazy idea.
That is exactly the crux of the matter. Enabling technologies are essentially what we focus on in the basic economic analysis. Those are the things that make the workers more productive in tasks and functions they are already performing, and perhaps even also expand those tasks. They tend to increase wages and labor demand because they are making workers more productive.
When you look at the details, it’s more complex because when you make workers more productive, sometimes you need fewer of them in some activities, and they can get reallocated to other activities. But at its root, this type of technology is directly making workers more productive; but this is very different from replacing technologies, which directly displace workers from the tasks they were previously performing.
Can public policy ensure we get more enabling rather than replacing technology?
Policy can play a role, but not an obvious one. Because I think there’s a lot of evidence that shows the exact composition of innovation and therefore the exact types of technologies that firms develop are responsive to policies and to financial profit incentives. The problem is, it is difficult for us to describe or recognize exactly enabling technologies when they are in the incubation period; so you cannot say we’re going to subsidize the enabling technologies. It was much easier for us to recognize green technologies, but even there, our track record of giving subsidies to green technologies is a mixed one. These sorts of fine incentives are very easy to game.
But I think there are other problems in the labor market and the innovation market that we can start thinking about. We implicitly subsidize production with machines relative to production with labor. If you buy a machine, you don’t have to pay payroll taxes, you can debt finance it, and that’s going to get a subsidy from the government. And the capital income that is generated is going to be taxed at a lower rate. If you hire a worker, you have to pay payroll taxes, they are going to be taxed at a much higher rate, you have to put up with lots of other costs coming from regulations, and other things you have to do when you’re employing workers. So that creates a very non-level playing field, so we’re essentially subsidizing firms implicitly for using machines rather than labor. So I think it’s probably a good idea to start thinking about how we can even that playing field a little bit.
Do you have any sympathy for the argument we should slow down the pace of automation to give workers more time to adjust?
First of all, I believe that if we are indeed, as I just have tried to articulate, subsidizing capital, then that’s distortionary, and that’s a bad idea. We should be having a level playing field. I think we need to go much deeper into this and look exactly at various different types of technologies and see if some of these numerically controlled machines or other automation technologies, as well as robots, are being introduced precisely because labor is being artificially made more expensive. And if that’s the case, there is an obvious thing for us to do.
Second, I totally agree with you; the issue is one of adjustment of labor. So if labor is suddenly caught unaware about what’s going on, and is thrown out of work because of automation, the costs of that are very high. Detroit is the case in point. But, we know self-driving cars and self-driving trucks are coming, so it’s not as if it is going to hit anybody as a big surprise. It’s just that US society does not have the institutions to prepare either the workers currently in this occupation, or even worse, the youth that will be graduating from high school or college in the next few years. We’re just not providing them with the human capital and training opportunities and vision to prepare for them so that they can work with the machines rather than try to do what the machines are doing.
The changes that are going to come in the next 20 years shouldn’t surprise anybody, but we are totally unprepared for them.
So slowing down the progress, I think, yes and no. If the problem is we are totally unaware and this is hitting us as a surprise, there might be some adjustment process that might help us. But I think, at the end of the day, these technological changes are also our future; we want to have rapid technological change because that’s where the productivity gains are coming, and that’s where productivity gains are going to come from. So we don’t want to slow down the technological progress, we want to turn the technological progress and our skills so that they can work with each other.
What should college students be studying to prepare for the future job market?
We know that flexibility is a great asset in the current labor market and will become a bigger asset, and a more valuable asset in the decades to come. That we want students who can adapt to different circumstances, who can reconfigure themselves to use and deploy different types of skills, and that’s very important, and that’s not something that our high schools or even our colleges do a good job in. But if you go in greater detail, do we need workers who have numeracy skills? Do we need workers who have better software skills? Do we need workers who are better at teamwork?
I don’t think we know these things. Obviously, there is going to be a small elite group of workers, perhaps 2 or 3% of the US labor force, who are going to work in computer programming, designing these AI, big data machine learning programs, or designing new products, and those workers need to have all the computer science and all the technical expertise they can get. As well as the vision; I mean, it’s not just about knowing how to program. Steve Jobs didn’t become Steve Jobs because he was the best programmer. He had a vision, he had a way of conceptualizing the product that other people could not, so that’s actually a broad skill.
But let’s take somebody who’s going to work in the financial industry. Do they need to know much better programming so that they can seamlessly program the AI machines? Possibly, but probably not. Probably the AI technology is going to be developed enough that people who use it don’t actually need to reprogram it. But they may need to have a broader set of skills so that they can act as the conduit between these programs and the customers. They may need to have much better teamwork and soft skills in the new labor market. We just don’t know that because we have not been studying it.
So in some sense, this is reemphasizing what I was trying to say earlier on. The changes that are going to come in the next 20 years shouldn’t surprise anybody, but we are totally unprepared for them.
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