More in Global Economy


3 years

Has the Health Crisis Killed Music For Good?

Proclamations of the death of music are not exactly new.

3 years

Odebrecht: “The Largest Foreign Bribery Case in History”

If you don’t know about the Odebrecht case, which the the US Department of Justice called “the largest foreign bribery case in history,” Nicolás Campos, Eduardo Engel, Ronald D. Fischer, and Alexander Galetovic tell the story and offer some insights in “The Ways of Corruption in Infrastructure: Lessons from the Odebrecht Case” in the Spring 2021 issue of the Journal of Economic Perspectives (where I work as Managing Editor).

3 years

Ransomware Attacks: No Longer a Matter of “If,” but “When”

COVID-19 was certainly not the only virus to sweep both the nation and the world in the past year.

3 years

The Potential of Geothermal Energy

There isn’t likely to be one magic bullet that addresses all the issues related to carbon emissions in the atmosphere.

3 years

How Do the Very Wealthy Invest Differently?

It’s hard to get data on the investment patterns of the very wealthy.

3 years

Interview with Daron Acemoglu: Technology and the Shape of Growth

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 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, 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 with 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 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 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.

3 years

As R&D Goes Global, How Should the US Respond?

There was a time, about a half-century ago, when a majority of the world’s research and development happened within the borders of the United State. Researchers, inventors, and firms didn’t move much. What was invented in the US had a tendency to remain, at least for a while, as domestic US economic activity. Those times are now behind us. Bruce R. Guile and Caroline S. Wagner raise this issue in “A New S&T Policy for a New Global Reality” (Issues in Science and Technology, Summer 2021). Their essay is part of the beginning of a promised series of essays on the “The Next 75 Years of Science Policy.” They write: In the past, countries depended on the low mobility of researchers, inventors, and entrepreneurs to link R&D to innovation and innovation to wealth creation. When researchers were less mobile and less engaged in close collaboration with peers in other nations, new knowledge tended to be retained by institutions and the countries that housed them. From a national perspective, this arrangement had the benefit of aligning intellectual property ownership, early applications, and company growth with the location of the R&D activity. …  At the same time that global collaboration has become ubiquitous, the rest of the world has begun doing more research. During the 1960s, US public and private R&D investment accounted for almost 70% of the global total. Today, even though US spending has increased, US R&D is less than 30% of the world’s total. Twenty nations now match or exceed US R&D intensity, with public and private R&D spending in these countries near or in excess of 2% of gross domestic product per year. In absolute dollars, China spends approximately the same amount on R&D as the United States. Furthermore, according to figures from the Organisation for Economic Co-operation and Development, China has nearly 2 million full-time equivalent researchers now, compared with the United States’ 1.5 million. The common pattern is now that research and development happens in many places around the world, often coordinated by large companies, but also by the movement of academic and corporate researchers between places and organizations. Simultaneously, US multinational corporations have established global networks of research laboratories, research university relationships, and talent recruitment efforts that partially decouple them from the science and engineering enterprise in the United States. Virtually every technologically advanced manufactured good is created by a production process (supply chain) that crosses national borders several if not dozens of times and draws on innovations from many countries. Being first with new scientific knowledge or having a pioneering innovative company based in the United States does not guarantee success in domestic industry. Nor does it guarantee that the nation will capture substantial economic value from the new knowledge. In a globalized knowledge network, knowledge spreads so quickly and widely that being in “first place” is a notional distinction at best. New scientific and engineering knowledge and innovation cross US borders in both directions—as part of commercial exchanges and collaborations—every day. Economic value cannot be captured by erecting barriers to the flow of knowledge or trade as the United States needs new knowledge and innovation from outside its borders as much as it needs robust US-based scientific and engineering capability. Thus, the US economy is confronted with two realities. One is that a rising standard of living in the future, which in turn would make it so much easier to address our ongoing economic and social problems than the alternative of not having a rising standard of living, depends on the increases in productivity that grow in substantial part from new technology. But the other reality is that relying just on US-created R&D is going to be a poor strategy, because US-created R&D (like all R&D), is flowing much more easily around the world than ever before. What are the implications of this situation? Guile and Wagner argue that it still very much matters for the US to have cutting-edge domestic R&D capabilities, but in the modern world, this means being an attractive location for researchers from around the world–and adjusting immigration policies accordingly. It also means systematic and expanded ” US government support for tracking and monitoring research activity and output, regardless of where it occurs, and support for dissemination of that information to US-based companies and centers of research.” Finally, it means paying more attention to the ingredients needed for the US economy to capitalize on new R&D, which implies a focus on policies for a workforce with the necessary skills, as well as the design of tax, investment, and trade policies.

Save
Cookies user prefences
We use cookies to ensure you to get the best experience on our website. If you decline the use of cookies, this website may not function as expected.
Accept all
Decline all
Read more
Analytics
Tools used to analyze the data to measure the effectiveness of a website and to understand how it works.
Google Analytics
Accept
Decline