When we talk about the global economy, we tend to turn to automotive metaphors. A recession brings things to a “screeching halt.” Boom times are said to be “in overdrive,” to have “found a higher gear.” And since the recession, one of the major components of the economy has been stuck in neutral. According to the Conference Board, productivity has barely budged since 2007, was flat in 2014 and 2015, and fell last year. We asked professor Chad Syverson, alumnus Matt Tracey, ’14, and Executive MBA student Crystal Lam to tell us why it’s stuck and what might kick it into gear.
Chad Syverson, J. Baum Harris Professor of Economics, is the author of “Challenges to Mismeasurement Explanations for the US Productivity Slowdown,” published in the spring 2017 issue of the Journal of Economic Perspectives.
Is productivity stuck in neutral? The short answer is yes. It’s been slow for the last decade—truly slow, not mismeasuredly slow or illusorily slow.
The mismeasurement hypothesis says that although productivity has been slow since the mid-2000s, that isn’t real. The hypothesis argues that what’s actually going on is that our ability to measure economic growth has gotten worse. New things that people like and use a lot—Google, Facebook, Snapchat—are all free. We calculate GDP by adding up what people spend money on. Those things don’t show up because they’re free, so it looks like output per worker hour isn’t going up much. In my recent paper I asked, if that’s true, what else would it be true of? The patterns I found were consistent with an actual productivity slowdown rather than with mismeasurement.
The other hypothesis for why it’s stuck says: we had a good run, but that’s done, and we’re never going to have another tech boom. I don’t ascribe to that either.
Kicking productivity into gear is the hard thing. We don’t have a dial that we can turn up and down.
If you go back to a previous period in the early 20th century—the development of what I call portable energy: the electric motor, the internal combustion engine—and compare that to the information-technology era, the overlap is striking. From 1890 to 1915, productivity growth was slow, just as it was between 1970 and 1995. In 1987, Robert Solow said, “You can see the computer age everywhere but in the productivity statistics.” But after an initial buildup, people figured out how to use the new technologies, and there were decade-long productivity booms—1915 to 1925 for portable power and 1995 to 2005 for IT. However, these booms were followed by another slowdown. The relevant lesson for us today is that there was a second productivity boom from portable power technologies starting in the mid-1930s. We haven’t had the second act in IT yet, but history suggests we can’t rule it out. We know from the portable power era that benefits can come in multiple ways.
Kicking productivity into gear is the hard thing. We don’t have a dial that we can turn up and down. We want markets to work well, to move activity from less-efficient firms to more-efficient firms, or from low performance to high performance. If workers can’t move because of occupational licensing, for instance, or if it’s hard to move capital around, or hard for customers to move because of lack of competition, the market doesn’t work as it should.
Zombie firms are real. The productivity spread between the best and worst companies in an industry is growing, and we’re not sure why. These firms are not going out of business at the rate they should. Companies that have no economic right to still be in business are dragging everything down. If you force those companies out of business, you’ll raise the productivity of that sector.
There’s a strong case for the government supporting basic research to jump-start productivity. Companies have an incentive to do applied research, because they’re going to see a product as a result, but basic research has more positive externalities.
I am not optimistic about much happening in the policy area, but I am optimistic that there’s potential for productivity growth in the future. There are pretty exciting possibilities with some existing technologies, such as artificial intelligence. There’s all this anxiety about whether robots will take our jobs, and there are some real, plausible scenarios. We will have to find things for all the people displaced by new technology to do—but we did it in the past, and we’ll do it in the future. I’m willing to take the stand that the second act—the second blossoming of the IT boom—could yet happen.
Matt Tracey, ’14, vice president, US Financial Institutions Group at Newport Beach, California-based PIMCO, is the coauthor of “Productivity: A Surprise Upside Risk to the Global Economy,” with Joachim Fels.
Productivity is stuck in neutral, but we don’t really know why. Productivity is cyclical. We go through years-long slumps and periods of positive productivity shocks.
The reasons you hear for the current slump—aging workforce, underinvestment, monetary policy, secular stagnation (either demand-side or supply-side)—are all macroeconomic. But that’s too big picture. In the end, productivity is a microeconomic phenomenon: advances come from individual companies doing things differently.
When we look underneath the surface, we find leading companies in manufacturing and services that are innovating. Productivity growth is strong among the “frontier firms,” the 100 most productive in each industry, per the Organisation for Economic Co-operation and Development. Everyone else is lagging behind. Overall, we see a global economy with “underappreciated potential,” as noted in our paper.
When you think of the long run, innovation—an initial revolutionary development—takes many, many years, often decades, to fully realize its commercial potential. And the path typically isn’t linear. Edison invented the electric light bulb in 1879, but we had to wait decades for many first-generation electrical appliances. Today, the underlying technologies that drive all those things we enjoy for leisure, such as smartphones, Apple watches, and social networking, are being put to work in industry. But it’s usually the largest, most-sophisticated firms that are the early adopters. Faster diffusion of leading practices and technologies, from corporate leaders to laggards, could accelerate creative destruction. Creative destruction will work; it’s just a question of how long it will take.
Productivity is notoriously difficult to forecast, but we see five developments, one macro and four micro, that could kick things into a higher gear.
Productivity is notoriously difficult to forecast, but we see five developments ... that could kick things into a higher gear.
One macroeconomic possibility is that as central banks rein in unconventional monetary policy, we end up with less capital misallocation. This is (almost) the zombie-firm scenario: that monetary policies such as quantitative easing have enabled inefficient firms to stay alive longer, because they haven’t had to face the imperative to innovate or exit.
The first of the microeconomic possibilities is a rising synergy in the use of leading technologies. As examples, artificial intelligence, big data, and web-enabled physical devices are being put to use together in companies’ operations.
Second, declining costs: many productivity-enhancing technologies haven’t been adopted because they’re too expensive or complex. That’s changing. Consider that the amortized cost of a general-purpose robot is falling below the global average human wage. And you no longer need a PhD to run it.
Third, small firms are becoming adopters. Many small- and medium-sized enterprises (SMEs) operate far from the productivity frontier. Statistically, they’re stagnant. They employ a huge percentage of workers around the world. As productivity-enhancing technologies become affordable and simpler to use, expect more and more SMEs to employ them. The impact will be faster diffusion, as SMEs “catch up” to the frontier.
Finally, green shoots in service industries: there’s been a dramatic shift in the composition of the economy from manufacturing to services. Services have lagged in adopting new technology, even basic digitization. There’s low-hanging fruit such as healthcare and retail. Both pack a punch in terms of size in the economy. E-commerce, for instance, hurts productivity in the short run but will boost it in the long run.
Crystal balls are dangerous things, but those are optimistic signs—bottom-up catalysts that could trigger a global productivity rebound in the coming years. That would be welcome; our living standards depend on it.
Executive MBA student Crystal Lam is managing director of Vinawood Ltd., a 700-employee manufacturing company based in Ho Chi Minh City, Vietnam, and the country’s leading producer of wood blind and shutter components.
I do not believe that productivity is stuck in neutral. It is beneficial to address what the term “productivity” could mean.
Vinawood perceives productivity as achieving the optimal result per resource (raw materials, man-hours, and machine hours), which could be measured in terms of yield rate and output. Thus, productivity can be achieved through initiatives such as waste reduction and increased throughput, as opposed to replacing man-hours with machine hours.
Technology increases productivity for many industries, but not at the same rate in all sectors. For example, equipment that improves crop management for farmland may develop more rapidly than that for fabric weaving. It is not only technology that leads to efficiency. Increasing productivity is a continuous effort of improvements from multiple perspectives: space management, processes, material, talent development, technology, environment, culture, and so on. Finding the right balance among all of them will lead to optimal results. At Vinawood, we have a dedicated operations excellence team that is tasked with the primary focus of incorporating these variables with lean-manufacturing principles to achieve higher productivity.
Productivity does not necessarily mean replacing manual labor with machines.
Vinawood’s processes are as automated as possible for a wood manufacturing facility. Since we work with wood, a natural product with characteristics such as knots and sugar deposits in unpredictable locations, an operator is needed to decide the location of the optimal cut. Therefore, our operation is largely automated with respect to material movement and milling, but decisions are still determined by our operators. When evaluating the optimal level of automation, one needs to be mindful of the material that is used and the skill sets of the manpower involved. Technology will always support the process, but will not necessarily drive it. As my mentor once said, “To make it work, we need to integrate hardware, software, and humanware.” We value our operators and work toward building their skill sets as we expand our business activities.
Contrary to what some may believe, it is still possible for optimal results to be achieved by employing thousands in the context of emerging and frontier markets such as Vietnam. Productivity does not necessarily mean replacing manual labor with machines. Higher productivity translates to obtaining more value through their combination. The key to sustaining long-term profits is understanding the future costs of each resource, and identifying how to maintain optimal results as the input prices change.
—By Rebecca Rolfes
An alumnus, a professor, and a recent graduate all weigh in on how the school’s unique approach to learning has influenced the way they solve problems.
What Does the Chicago Approach Mean to You?
Meet the newest member of the NVC family, serving graduates from across the University of Chicago and from all over the world.
Polsky Center Launches Accelerator for Alumni-Led Startups
Booth’s London campus brought together experts to discuss how to measure social sector gains.
Measuring Success to Maximize Impact
