The Robert B. Menschel Economics Symposium generates critical thinking about the consequences of herd mentality behavior in global economics. The 2018 symposium will examine the insights that big data has provided into economic—and political—behavior.
This symposium, presented by the Maurice R. Greenberg Center for Geoeconomic Studies, is made possible through the generous support of Robert B. Menschel.
ORSZAG: OK. Good afternoon, everyone. My name is Peter Orszag. And I’d like to welcome you to this symposium, which is presented by the Maurice R. Greenberg Center for Geoeconomic Studies and made possible through the generous support of Bob Menschel. I should note that today’s symposium is on the record and is being livestreamed. So just please keep that in mind.
I am delighted that to kick off the Bob Menschel Economics Symposium we have Hal Varian with us. He is the chief economist at Google and has had many academic positions before that. I still remember when I was in graduate school the “in Varian” saying, which is if you had a question the answer was “in Varian,” and that meant it was in his textbook. (Laughter.) And he’s gone on to do many terrific things since then.
So the way this will work is Hal is going to kick off with some opening remarks. I then have a series of questions for him. And then we’ll open it up to all of you. So looking forward to a lively discussion.
VARIAN: OK. Well, I thought what I’d start with is talking a bit about this role of automation. Actually, I have a whole involved presentation that I started out calling Automation and Procreation. (Laughter.) But I finally shortened it to Bots and Tots—(laughter)—because when we look—as economists, when we look at a market, particularly the labor market, there’s two forces, things that shift demand around. And certainly automation, computerization, robots, and so on, shift the demand for human labor. But we sometimes forget about the supply issue; that is, the demographic determinants of the supply of labor and what’s going on on that side of the market.
So first I’ll say a word or two about bots, and then I’ll turn to the tot side. So on the bots, Jim Bessen, who’s an economist at BU, looked at the 1950 Census, looked at occupations listed in the 1950 Census. There were, I think, 270 of them. And he traced what happened as the Census occupational classification evolved. There was only one occupation for that 1950 Census that was displaced by automation, and that is elevator operators. OK, I know there are some still here in New York—(laughter)—but not in Silicon Valley. I don’t think we have tall enough buildings.
Anyway, so the elevator operators were replaced. But if you think about it, the tasks that elevator operators did were not really replaced. They just moved to different people in the organization. So now when you walk into a building, there are a couple of people sitting there at desks. Maybe there’s a receptionist or security people. The elevator operator used to do safety inspections. They used to answer questions that people had. They gave people guidance. They delivered packages. They told people about sales and promotions in department stores and so on.
Those things are still done. Those tasks are still around. They’re just done by a different set of occupational titles. So when you start looking at jobs from the task point of view, you get a very different picture of what that demand displacement looks like. For example, if you think about the assembly line, we spent 100 years optimizing the assembly line, right? You’re trying to have a situation where there’s one person in one position who does the same task over and over again, and it’s not too surprising, since you’ve optimized to that extent and made the work very routine on a station-by-station basis, you could replace that with computers, with robots.
And now, if you look, 50 percent of all robots are in automobile manufacturing plants, OK. So it’s very focused towards that environment—a routine environment that’s replicable and kind of stationery in some sense.
So if you look at environments that don’t have those characteristics, then the possibilities of automation become much more problematic. So let’s take a job like gardener. We don’t think of gardener as being a really high-skilled, super high-skilled job. But it’s a very varied job. There’s lots of different things that gardeners do. They trim the trees. They cut the grass. They plant flowers. They do this, they do that; all sorts of activities. And automating any one of those tasks you could do with several million dollars and several years of research. But automating all of them would be extremely difficult.
And the same thing is true of a task like a maid in a hotel. All the rooms are different. They all have different layouts. They’re very heterogeneous. It would be very hard to build a robot which did all of those tasks that a maid does. Now, assisting the maid by providing better ways to clean things and better ways to pick things up and better layouts and so forth, that can certainly be done. That kind of augmentation of labor is certainly possible. But I think there are some jobs which are quite difficult to automate all of the tasks associated with that job. So I think we’ll see automation in those categories move more slowly.
Now, one example that people like to bring up is, well, what about autonomous vehicles? We’ve got all of this investment going on in driverless cars, and it’s very successful. In fact, I will tell you, we would have driverless cars now if it weren’t for those pesky humans. They’re the problem, because it’s those other cars that are driven by humans and the pedestrians that are going every such direction. They add the heterogeneity and the noise to the environment that make it very difficult to get around. Going down an empty freeway, or even the freeway that has a number of cars on it, is much easier to do than to navigate around New York City, as an example—maybe one of the worst-case examples, except for Boston, which is—(laughter)—so, which is even worse. So I think you will see cars. And there are going to autonomous vehicles. They’re going to be available first in the most homogeneous environments and then expand to a wider gap. And that has been really billions of dollars of investment over decades to get to the point we’re at now in terms of making these a reality.
OK, that’s my short sermon on bots. Let me turn to tots. There’s only one social science that can predict a decade or two in the future, and that’s demography. So we don’t really know where technology will be in 10 years or 20 years, but we have a good idea of how many 25- to 55-year-old people there’ll be in 10 years or 25 years. And what happens there, when you look at it, the two big shocks to the 20th century—namely the Baby Boomers and women entering the labor force, those are both no longer—(laughs)—they’re not going to be sources of growth in the labor force anymore because Baby Boomers are retiring. Remember, the Baby Boom decade was 1946 to 1964. So those people are retiring. And they’re continuing to be retiring.
And yet, they still expect to consume. So the labor force is what’s producing goods and services for the nonworkers to consume. And what’s happening on that side is pretty grim. It’s the labor force is now growing at half the rate of the population growth. And if you look at the next decade, the 2020s, you’re seeing the lowest growth in the labor force ever, OK? And the U.S. is in good shape compared to other developed countries. Look at Japan, Korea, China, Germany, Italy, Spain. They all have a very serious demographic crisis. And what that means is there will be fewer workers to support a larger group of nonworking people. And if we don’t increase productivity—which really means automation—then we’re going to be in big trouble in those developed countries.
All right? So the automation, in my view, is coming along just in time—just in time to address this coming period of labor shortages. And all our intuitions were developed around a world where there is a plenty of labor, in part because of the Baby Boomers and the entry of women into the labor force, which happened in the ’60s and ’70s. But the future is going to look quite different. And so the intuitions we have now about firms can always buy the labor they need, I think that’s going to change rather dramatically in the 2020s and into the 2030s, and things don’t actually get back aligned together until about 2060. So the next several decades are going to be periods with tight labor markets and a strong desire for having more workers who can produce the goods that the entire population can enjoy. So that’s my quick spiel on bots and tots.
ORSZAG: All right. Well, lots to talk about. So, actually, let’s just—before we turn to other topics just stay on this for a second. So let’s take the example of the maids in, you know, a different configuration of hotel rooms. We already have robots that can figure out a room. The military has drones that can enter a house—actually, unlike military squads that have to enter from one door so they’re not shooting each other from both sides—scope out all the parameters of the house and navigate. So why are you skeptical that the full array of maid services will not be automated at some point over the next, you know, couple decades?
VARIAN: So how much did it cost the Army to develop that capability?
ORSZAG: A lot, yeah, yeah.
VARIAN: A lot. A lot. And so—
ORSZAG: But it exists, is my point.
VARIAN: No, so I think you’re right. Eventually we will likely to get to more automation in these very heterogeneous environments. But that’s not something that’s going to happen overnight and displace millions of people immediately. It’s something where, I think, you’re going to see time to adjust. So I don’t want to be too pessimistic on this. We’ve had a lot of tremendous accomplishments on artificial intelligence and machine learning just in the last five or six years. And there will be further breakthroughs, most likely. But it’s not something that’s going to be—in my view, it’s not something that’s going to be as disruptive as you would guess from reading the newspaper headlines.
ORSZAG: So let’s take that, and then turn to—I mean, you touched briefly on productivity. And one of the great puzzles is we seem to have advances in ML, AI, and lots of other dimensions, but productivity growth has plummeted, including the most recent reading from the end of last year. So there’s this ongoing debate about whether we’re just mis-measuring and, even on a GDP concept, things are actually better than the measured statistics and so that might be part of the explanation. But where to you come down on—are we mis-measuring both GDP and other concepts of economic well-being, and has that mis-measurement gotten worse by enough to explain the measured productivity deceleration?
VARIAN: So I would say there are two aspects to that question. One is, take the traditional concept of GDP and productivity measurements. Are we doing it correctly, and has it changed, has it gotten worse lately? And then the second question, is GDP the right thing to look at to begin with?
So let me—let me say a word or two about the first part. It has gotten much more difficult to measure GDP because of the rise of services and intangibles of all sorts, so services—that’s—80 percent of the labor force is in services. It’s quite hard to measure quality improvement in those areas, it’s quite hard to measure them directly.
GDP came out of World War II. It was easy to measure tanks rolling off the assembly line, or automobiles, or physical goods being produced. It’s much harder to measure those intangibles. And a lot of the economy is in intangibles now, so for example, think of things like design and software, both of which are intangibles. So when Apple draws up the plans for the iPhone and builds the software operating system for the iPhone, sends it to China, iPhone is assembled in China, comes back into the U.S. So the import of that physical product is relatively easy to measure. What’s hard to measure are the email attachments, and the files, and the software updates, and the design plans, which go the other direction because those don’t go through a market—you know, they’re going through an internal process of the firm.
And if you look at something like Google doing its Pixel phone, we developed this open source operating system android. It’s got a price of zero, by definition, because anybody can download and use its operating system for their mobile phone, and that, too, is very, very hard to measure—the value of that service. But if you think about a mobile phone, it costs $150 for the parts and labor in a low-end iPhone—iPhone SE—but at least half of that has got to be the value of the software and design. So mobile phones—that’s a $400 billion industry. If half of it is due to the software and design—and I think that’s a very conservative estimate—then there’s a big chunk of GDP that we may not be measuring properly, OK?
There’s a lot of debate about this, and it’s something—it isn’t the same problem we faced a few decades ago when you had the design, the engineering, the assembly of a product all done in the same country. Now all of those things can be done in different countries.
So it’s a challenge—it’s a challenge to the whole idea of GDP measurement. People are working on it. I think we’re going to improve in this dimension, but it’s—but it’s part of the story. I don’t want to claim it’s the whole story, by means, but it’s a part of the story about the productivity growth.
And then this question is, is that what we want to measure? Well, GDP is gross domestic product. Now that means it only relates to things that are produced domestically. But welfare, from an economist’s point of view, is about what is consumed domestically, and those are not necessarily the same things. Not only do we have the imports as a difference, but it’s this whole question of the pricing of what’s produced because, to be in GDP, you have to be sold in a market somewhere, generally.
So let me give you my favorite example: photos. Back in 2000 there were 80 billion photos taken in the world, and I can give you that very precise number because there were only three companies that made film, so you could kind of count up the film. And the film and developing costs about 50 cents a photo, roughly speaking. So now there are 1.6 trillion photos taken in the world—in 2015, according to my estimates, 20 times as many—and the price has gone from 50 cents apiece to zero essentially, OK? So any normal human would say, wow, what an incredible increase in productivity, but not an economist. (Laughter.) No, we wouldn’t say that—because that’s about what’s consumed. All of those photos, for the most part, are given away or shared or you go over them in your—on your phone and you say, oh gee, it’s so nice to see Jimmy’s birthday party or whatever it was. Another example like that is GPS machines. So GPS was originally a very expensive technology. Only truckers and people in logistics could use it—thousands of dollars per unit.
Price came down, down, down, and as that price came down, real GDP went up, OK, but until it hit zero. Then it’s not in GDP anymore. So you’ve got this tremendous improvement in the product as the price is going down, down, down. Hit zero. It’s not in GDP and, by definition, there’s no quality improvement anymore for those zero-price goods or, for that matter, for imports, right.
So look at the smart phone, the defining device of our time. What’s happened there is it’s replaced the camera, the GPS system, the music player, the alarm clock, the flashlight. I mean, we could go on and on and on in all of these things that are bundled into that one product, and all those reductions in the sales of cameras, GPS machines, alarm clocks, music players and so on, those probably had a negative effect on GDP because they were things that were not produced.
And the phone itself, unfortunately, does not have any quality adjustment in the—in the figures—in the figures produced by the BLS. They would love to do that. They want to—they want to put quality adjustment in for these kinds of products but they’re very constrained in budget and they’re very constrained in the capability in doing this in any rapid way.
So, again, things will get better as we improve our measurements in these areas. But right now, there’s some really important stuff left out of the consumption side of measurement.
ORSZAG: OK. So two quick questions on this topic. So the first one is, just to pin you down, let’s say productivity growth has fallen by 1 percent—a hundred basis points. How much of that is actually an illusion because mismeasurement has gotten worse?
VARIAN: Twenty-five percent.
ORSZAG: OK. Boom.
VARIAN: That's the number I use. A fourth. A fourth.
ORSZAG: There we go. Good, OK.
VARIAN: But—and so let me—let me—
ORSZAG: I didn’t expect that precise an answer. That’s great.
VARIAN: No, because I’ve been—(laughter)—I’ve been asked the question so many times—
ORSZAG: Very good. OK.
VARIAN: —I better come up with an answer. (Laughs.) But let me—let me say one other thing. What about the other 75 percent, because that’s still a mystery, I would say.
VARIAN: And part of it is this issue of leaders and laggers. Firms that are adopting these technologies more quickly are becoming more productive. Firms that are sticking with traditional technology—and so the gap between the best performance in terms of productivity and the worst performance I believe that’s widened.
Now, there’s some evidence for that. I won’t say it’s conclusive but it looks like that may be part of the story as well.
ORSZAG: Yeah. That was going to be another topic, which is that widening dispersion at the firm level. But let me just also ask you, so one of the things you mentioned was constrained funding at the statistical agencies and so difficulty in kind of doing a quality adjustment on a phone.
It opens up the question—the obvious question of, well, we have this exploding data in the private sector. Alan Krueger and others had, you know, been interested in trying to see whether the BEA and BLS and other official agencies could incorporate more private sector data. We have the Billion Prices Project. What is your perspective on how much we’re going to see melding of the official statistics and these, you know, additional sources of information?
VARIAN: So I think that will happen. But, again, it will likely happen fairly slowly. So just as one example, let’s take something about mobile phones—if you wanted to do quality adjustment how would you do it? You’d have to start with a database of lots of mobile phones and what features they had and what their prices were and how that changed over the years, and that’s a big task, except when you stop and think about it, well, Amazon, Google—we’ve already done that. We’ve compiled those lists. When you go online and use Google shopping or you use Amazon or you use Walmart or any of those other places, the private sector has compiled the data in the way that would be useful to the BLS to be able to draw upon. So that’s kind of a natural case where I think you would see this happen.
Now, if you went to more exotic sources of information, like the Google queries we’ll hear about this afternoon from Seth, or look at tweets or any of these other things, those need further study. They may be useful in understanding what’s going on in the economy, but I’m not ready to import them into official statistics yet.
ORSZAG: All right, I’m going to ask two more quick questions and then we’re going to open it up. So please be thinking of your questions.
The first one is we just last weekend had the natural catastrophe of the Philadelphia Eagles beating the New England Patriots—(laughter)—at the Super Bowl. And you have provided, along with Seth and some other co-authors, some insight into whether those $5 million ads that run on TV are actually worth it from the perspective of the sponsor, so in particular movies.
Can you explain what you did, what you found, and more broadly the nature of kind of concluding things about causality in a big-data world?
VARIAN: Yeah. So a couple of years ago, at the National Academy of Sciences, we had a meeting on causality meets big data, because big data, like lots of other statistical analysis, often uses what we call observational data, where you’re just looking at what people did, and you can’t necessarily infer causal relationships from observational data.
But there are some tricks that you can use, some methods that you can use, to approach those questions. And I would say economists have been at the forefront in utilizing these techniques. So Seth Stephens-Davidowitz, who’s here in the front row, is going to talk about this a little more later this afternoon. But I will say a word or two about our Super Bowl study, because I think it’s kind of cute.
So there are two things, two facts about the Super Bowl that everybody knows. One is that the ads in the Super Bowl are often sold out substantially ahead of time, sometimes as early as October, November. The Super Bowl doesn’t happen till February. So that’s point one.
Point two is the home cities of the teams that are competing in the Super Bowl see elevated viewership, on the order of 10 to 15 percent more. And this obviously is true of other kinds of athletic events as well.
So if you put these two together, what you’re seeing is in October, November, advertisers are laying out the money, betting on the—you know, putting their—buying their ads or locking their ad purchases in place. And then, a few months later, two random cities—right, random from the viewpoint of the ad purchase back there in the fall—end up getting 10 to 15 percent more viewerships.
So you can look at ads that play during those Super Bowls and you will see, gee, those ads are seen by 10 to 15 percent more people in these two cities than they would have been otherwise. And so that’s almost as good as a controlled experiment, like picking two treatment groups and using everybody else as a control. So you could do that kind of interpretation of this as an experiment with a control group, and you can actually infer a causal connection between the—how much those ads affected purchase behavior.
And what we looked at was movies, where there were, what, seven, eight movies advertised in the Super Bowl. And, sure enough, in those cities that were the home cities of the teams that were played—playing—there was an elevated audience for those movies. The opening weekend was larger than any other city. So it was a kind of cute example.
And I think it extends to all sorts of other athletic events. You look at the World Series and the playoffs in basketball and hockey. In all those cases, that final game for the championship is going to be much more viewed by the home cities of the teams that are playing. They see more ads. And so we can get an idea of how well those ads are working.
ORSZAG: By the way, just so you know, the opening weekend revenue was something like $7 million more for a $3 million average ad. So it was a pretty big, high return.
VARIAN: Although what’s happening now is the price of the ads is—(laughs)—
ORSZAG: Of course. Well, that’s what you expect.
VARIAN: They all read our paper and said, hey, whatever (you’re paying ?). (Laughter.)
ORSZAG: That’s what you’d expect.
All right, final question before we open up to members, just to deal with one of the elephants in the room. So there’s been a growing drumbeat, both from the left and in Europe, about the nature of competition in networks and platform technologies. You’ve been—your firm has been kind of in the middle of all that.
How should we think about the application of antitrust to a platform technology, A? And B, there have also been questions about whether the free-content, paid-advertising model is the right model or whether we should have some different economic model for these platform technologies.
VARIAN: OK. So the Europeans have this term they use called GAFA or GAFAM, which is Google, Apple, Facebook—what’s the A? Oh, Amazon, I think I’ve got them all, and Microsoft sometimes, OK? A term that they use commonly. And they argue—they claim that these platforms have undue monopoly power. But I think when you look at those platforms in an objective way, you will see a very remarkable fact. Namely, they are competing against each other intensely. So Apple, Microsoft, Google all have an operating system. Apple and Google make mobile phones. So does Microsoft, I guess. And in fact, Amazon has tiptoed a little into that market as well. Look at productivity software, like Google Docs or Microsoft Word, competing intensely there.
So each of those companies has a core competency or historical legacy. In Google’s case it’s search, in Apple’s case it’s devices, Microsoft operating systems, Facebook social networks, and Amazon retail. But they are constantly competing with the other firms across completely different industries. Who would have guessed a few years ago that Amazon would create cloud computing, OK, that had nothing directly to do with their core business, and that would create a hugely competitive industry with Microsoft, Google, and Amazon all struggling to improve the quality and to lower the price of their offerings? Who would have guessed that a few years ago?
So I look at this industry. I say, gosh, it’s that competition that’s, in fact, creating the innovation and the low prices and the expansion of these firms because they are one of the most competitive industries around. Far from the least competitive. So that’s my answer to the competition point of view—the competition issue.
And the other part was?
ORSZAG: Paid advertising versus other models.
VARIAN: Oh, yeah. So the interesting thing there is you look at TV. We had this evolution of ad-supported TV. And then along comes services like Netflix, and YouTube Red, and Amazon and so on that started offering subscription-based TV on a much broader scale. Of course, there was HBO, and Starz, and all these other networks as well. Well, those are now some of the most popular channels out there. The creative energy that’s going into producing this non-advertising-based, subscription-based TV is huge. I think I saw Netflix is going to have 80 new productions next year.
And so the whole industry has shifted away from the ad-supported model toward the subscription-based model. And there’s some suggestions, there’s some thoughts, that the textual word is going the same way. The newspapers, The Wall Street Journal and The New York Times, all the papers are pushing very hard on subscription-based models, probably with some advertising as well—kind of like the printed version of the newspapers. But subscriptions seem to be a very active area of interest these days.
ORSZAG: With ad-free or reduced—
VARIAN: With ad-free or reduced—
ORSZAG: So the future is that we could pay more Google and get rid of the ads? Is that what you’re saying? (Laughter.)
VARIAN: Well, if you—if you use—first of all, Google ads are really very informative. Hopefully you understand. (Laughter.)
ORSZAG: I understand that. (Laughter.)
VARIAN: If you go to YouTube, you can watch YouTube with the ads, or you can pay a subscription fee of ($)9.99 a month and get YouTube Red, which is ad-free. So consumers have choice. They can pick what they want. And that’s a good thing, from the viewpoint of the economy.
ORSZAG: OK. With that, we’re going to open it up to your questions. Typical schtick of please identify yourself and please ask a question.
VARIAN: And there are runners with microphones.
ORSZAG: Yeah. And don’t forget this is—(audio break)—
Q: (In progress following audio break)—graphic analysis that leads you to the conclusion that we’re going to have a labor shortage because, as you know now, the participation rate in the labor market is about 62.7 percent, down where it was in the ’70s. And the older cohort is actually doing better than the younger cohort. So I’m wondering how to bridge to your demographic analysis. Thank you.
VARIAN: Right. So I didn’t actually say surplus. I said a tight labor market, because the wage will adjust, and characteristics of the job will adjust to clear the market. I’m an economist, so I’ve got to say that. But I do think you will see a tighter labor market than we’ve been used to in the last—in the last 50 years. It’s also true that people’s retirement decisions are changing. They’re retiring later. And when they do retire, they often don’t go to zero labor hours because of part-time work. And that’s a good thing from the viewpoint of the economy. When you look across developed countries, like the length of a workweek, the shortest workweek among developed economies is in the Netherlands. It’s 29 hours a week—29 hours. That’s a whole day less than we work, which is about 37.5, I think. And—
ORSZAG: What do you mean by we? (Laughter.)
VARIAN: Not you and me—
ORSZAG: OK. I’m just—
VARIAN: —nor anybody in this audience. But I mean we as a country. So what happens, there’s nothing sacred about a five-day week. And there’s a lot of demand for flexible work. And the reason the Netherlands has such a short week is, in fact, because they have much more flexible part-time work, partly because of subsidized day care, partly because of the tax treatment of those earnings by nontraditional workers, and so on.
So I think we’ll see the U.S. move in that direction. That tightness of the labor market is going to encourage people to work more, particularly after they retire and are still in good health. And, by the way, the one sort of other message I should say about that demographics is not only are there going to be more retirees and relatively fewer people in the labor market, but, of course, as the retirees age, they become more expensive. So it’s even more important to be able to produce goods that people can utilize in the health and medical industry, for example.
Q: But what about in the context of the displacements that are anticipated? For example, as you said, in autonomous driving there’s 2 million jobs. How do you think about the demographics and the future of employment in the context of what we know is going to be—
ORSZAG: Interaction between the tots and the bots.
VARIAN: So, of course, the demand curve is shifting and the supply curve is shifting. So which shifts the most is the answer. We’re going to see a reduction in labor, that’s for sure, because both curves have shifted to the left. But the question is what happens to the wage. And already you’re seeing this push for higher wages because of the shortage of hiring people at the low end of the labor market. So in Europe and in many places in the U.S., you go into McDonald’s and there’s a kiosk where you order—not through a person, but you check off what you want at the kiosk and you go over and pick it up.
So you can see those things being automated to a large degree in the future. If you want to see what the future looks like in an extreme case, go to Japan, where there are vending machines that are providing so many things because of the shortage of labor or the tightness of the labor markets there.
So as I said, I can’t predict what technology will be like in 10 years, how many autonomous vehicles will be on the road. But I can predict how many 25- to 55-year-old people will be around in that period, and it’s going to dampen and maybe overcome this reduction in the demand for labor from the automation side. So it’s not so much I’m saying absolutely one’s going to displace the other. I’m saying there are countervailing forces at work.
ORSZAG: OK, let’s go over here.
Q: Nise Agwha (ph) of Pace University.
In your futuristic predictions in terms of how the world will evolve in the face of automation, I was wondering how you would incorporate the following basic facts of economics that I was taught. Economics is the science of scarcity. So, at the end of the day, time is scarce. Energy is scarce. Attention span is scarce. So how do you weave that reality into your predictions?
VARIAN: Well, I would say what happens is what’s scarce at one period of time—let’s say getting authoritative answers to questions—(laughter)—could be widely available at a later date. So some of these things that are scarce now are going to be more bountiful in the future. But I think the things you alluded to—for example, there’s only 24 hours in a day and the scarcity of attention—those are going to continue to be scarcities even into the future.
So, again, I don’t want to come up—I don’t want to be pinned down, let us say, to a definitive, specific version of the future, whether there’ll be lots of automation or whether there’ll be a crisis from scarcity of labor. But I do want to say that these forces are important to consider together. And if you read the typical article in the news, you’ll see it’s all about the bot side of things and very little mention of the tots side of things. And, by the way, it has very important global implications as well, because look at the countries that are getting younger—which is basically India, as a prime example, and many countries in Africa, like Nigeria as a specific case. So with China getting older, is India going to be the new China? Is Africa going to be the new China? How are global resources divided among these forces that are driven—these countries and regions are driven very heavily by demographic change. Well, we’ll wait and see.
ORSZAG: Up here.
Q: John Biggs, former CEO of TIAA-CREF.
John Maynard Keynes wrote a famous letter to his grandchildren back in the ’30s that the average workweek by the time they had matured—and I think we are the age of his grandchildren. I’m a little senior for that. But the average workweek would be a day and a half, because you could produce everything you needed in a day and a half. Now I—
ORSZAG: The Netherlands has gotten there.
VARIAN: Yeah, exactly. (Laughter.) That’s what I was going to say.
Q: I have always argued that he was wrong on that because people didn’t retire back then. And the retirement need increased enormously the time of consumption. And so he’s wrong. But I don’t see how I can explain this to my grandchildren, what kind of—I think a workweek of a day and a half is absurd. But conceivably, when you look at the total economy, we’re going to have that kind of relationship. And where are the jobs going to be?
VARIAN: Well, everybody loves three-day weekends. So to me, it’s not at all implausible that our grandchildren would see four-day workweeks, for example. After all, it’s there in the Netherlands, right? And the question of the division of your life between work and leisure is interesting. Well, you take it when you’re young or you take it when you’re old, the point you made about retirement, because we have seen, of course, people retiring earlier and living longer. And so you have seen a shift towards more leisure. Back in—back in the 1700s—17(00)-1800s, the workweek was 70 hours a week—70 hours per week. And now, as I said earlier, it’s about half that. And if you look at chopping another day off of it, it seems to me to be perfectly feasible.
And there’s also this blurring distinction between what’s work and what’s leisure, because we know at Google that a lot of surfing the web goes on during work hours. (Laughter.)
ORSZAG: Nowhere else, just at Google.
VARIAN: No, no, no, not at—no, no, not at Google. I’m talking about Google users. We look at the—(inaudible). (Laughter.) So we know from looking at the query makes that there’s a lot of, you know, shopping, planning, purchases, all sorts of things that goes on at work, and vice versa there’s a lot of work that goes on at home. In part, because of those mobile phones and computers and the internet that makes everybody accessible over the weekends and other times. So these too are countervailing forces. It may be that work will be spread out in a different way than we’re seeing it with this conventional 40-hour work week.
ORSZAG: Could I just ask—
VARIAN: I’m not confident that will happen.
ORSZAG: We’ll come over here in a second, but just—a lot of the comments have been about the average workweek and, you know, aggregate. But there’s also this important both socioeconomic and educational dimension to it. So one of the questions that always arises is to date, basically, technology, I think, has largely been complementary to high-skilled workers, and so has been one of the forces driving wage inequality upward. It’s plausible to imagine that that might reverse, that, you know, legal skills and bankers, lord forbid, and others might be displaced by automation. Just give us a little bit more on the kind of gradient, instead of just the average, by education as you see this unfolding, including, you know, the race between technology and demographics.
VARIAN: Yeah. So there’s this labor displacement versus labor—labor displacement verses labor augmentation. And I think that we’re already seeing it happen. So let me give you a few examples. It used to be, to be a cashier you had to know how to make change. No longer necessary. In fact, cashier is the number-two job in the U.S. with respect to occupation and number of people employed. It used to be to be a taxi driver, you had to know how to drive around town. No longer necessary. (Laughter.) Of course!
It used to be that to work in a kennel, you had to recognize dog breeds. Well, now if you use Google Lens, you can photograph the dog, and it will say, oh, that’s a collie, or that’s a German Shepherd, or whatever—you can identify those dog breeds.
It used to be that to be a gardener, you had to know different plants and what kind of characteristics those plants—well, that’s gone away, too.
Now those are low-level jobs—all the examples I gave you—and they’ve been augmented dramatically by information technology, OK? So that will continue, I think, and of course our jobs have been augmented by information technology—consulting online resources, and producing documents, and all of those things—but I see it really as much more on the augmentation side than the replacement side because, when you look at the lists of tasks that a gardener does, some of them require this kind of cognitive assistance, but some of them don’t. And the nice thing is that, of course, if you work in the kennel, or on the garden, or any of these other things, you will pick up these terms anyway, so it’s not just a place—a way of totally displacing people; it’s just really educating people and training people to be able to do their job more effectively.
In fact, every day there are 500 million views on YouTube of how-to videos, OK. That’s how to solve a quadratic equation, or how to compute the area of a trapezoid, or all those cognitive stuff, but there’s all these other things: how to bake a soufflé, or how to play the piano, or how to weld, or how to fix a screen door, or how to remove a stripped bolt. I will bet almost everyone in this audience has used some instructional video on YouTube, OK? And what’s interesting about it is we never think of that as being part of the educational system, but it’s a fantastic way to deliver information on an as-needed, as-necessary basis—both the high-level cognitive stuff and the manual stuff.
And so we never had a technology before that could educate such a broad group of people any time on an as-needed basis for free. So it really is a remarkable time to have that capability, and I think that’s going to have an impact on the labor market.
ORSZAG: All right, let’s go over here, and then we’ll come up here.
Q: Stephen Blank.
I think your very sunny statement needs to be pressed a little bit harder.
Q: If we’re talking about aggregate jobs, what we see a lot of now is increasing jobs—an increasing number of people who work very hard, very long hours, and make a good deal of money. We see a lot of people who don’t work at all—not because they don’t—aren’t willing to get off their asses and do something, to coin a phrase, but rather because—for health reasons, for access to education, home, whatever—they cannot access these matters.
We’ve now learned how generational this becomes. Once people get caught in this, it’s very hard for their kids to get out.
It seems to me that the scenario you’ve suggested is very optimistic and very unlikely—that we will see more people, as we move forward, who work much harder, and—
VARIAN: Unlikely we’ll see more people who work harder—
Q: No, no. Well, your scenario is unlikely.
Q: What we will see is people who work—a large number of people who work harder, and an increasing large number of people who don’t work at all, who don’t have access to these matters, and a widening division between the two.
Is that a reason—is that less likely in your view?
ORSZAG: And maybe I can just add on to it for a second. I mean, one of the kind of histories or sort of permanent effects could come from—I mean, the classic examples of going on disability benefit and you never come off, the new phenomenon of opioid epidemic where you basically are—have a really hard time reentering the workforce. So how does that interact with the rest of your analysis?
VARIAN: Yeah, well, analysis—this may be too abstract a word—
ORSZAG: OK. Vision. Vision.
VARIAN: —my speculation—
VARIAN: —my vision, my—(inaudible).
ORSZAG: Yes, OK.
VARIAN: Yeah, so that’s a—that’s a possibility. I’m not going to deny that. Things could get better; things could get worse.
My argument really is that a lot of these factors that you described are due to having this loose labor market, this fact that people are readily available, and you could always go out and hire people that you need. That’s going to be less so in the future. It’s going to be a tighter labor market, to an extent. So it’s a tighter labor market, and you’ve got this way to deliver training on an as-needed basis, I think some of those effects are going to be reversed.
Now you could be right, I could be right, you know. I’m not saying I have a magic ball, but I do think that we’re going to see a different set of dynamics in the future than we’ve seen in the last 50 years. And I hope we can solve the problems that you described.
ORSZAG: Let’s go up here.
Q: Thank you. Nili Gilbert from Matarin Capital.
There are those who have argued that the impacts of technology and automation will keep prices very low for a long time—that they will continue to put a downward pression on inflation, therefore, interest rates and other yields, maybe even for generations.
But when I listen to what you’re saying about demographic trends, particularly though the lens of wage growth, it makes me think that there could also be countervailing inflationary trends, and in that sense, automation, technology and human labor are not perfect substitutes. So how do you see the equilibrium between those two forces evolving as they relate through the lens of inflation and other forces that may make them imperfect substitutes?
VARIAN: Yeah. Well, come back to my first statement about demand and supply. If you shift the demand curve to the left and you shift the supply curve to the left, then we can be pretty sure the amount of hours worked goes down. But we don’t know what happens to the wage. The wage could go up or it could go down. Depends on the magnitude of those shifts.
Well, that’s just a metaphor. That’s something from Econ 1 and we know the real-world labor markets are much more complicated than that. But it does give us one place to start looking. And I will say that this will change over time. As you have, let’s say, fast food becomes more automated—because you can go from the kiosk to voice recognition or punching things in to controlling the appliances, et cetera—we may see that having a big impact on that sector of the—of the market and it may be a rather different impact when we look, let’s say, at the impact of autonomous vehicles or, particularly, autonomous trucking.
Right now, if you look at the near-term future, well, there’s a shortage of 50,000 truck drivers in the U.S. You’ve got—the trucking industry just put in orders for—I forget the number—a very large number of new trucks because the demand for logistics and delivery and moving things around has increased along with recovery of the—of the economy.
So my guess would be look over the next decade. I don’t think we’re going to see huge displacement in those areas but in the longer-term future, of course, it’s certainly going to be there. So, you know, the line in Silicon Valley is we always overestimate the amount of change that can occur in a year and we underestimate what can occur in a decade. So I think that’s a very good principle to keep in mind. Things aren’t going to dramatically change overnight. But a decade from now, things will look different, particularly with respect to the labor/automation mix.
ORSZAG: I think we have—let’s go way in the back there, sir. Yeah.
Q: Hi. Jeffrey Young, DeepMacro.
I just wanted to pick up on a thread that’s been going through a lot of the questioning about what we used to call forecasting. Now, I think we call it predictive analytics. This may be a little bit technical—
VARIAN: (Laughs.) Right.
Q: —but it really relates to, you know, data and which Google and others have a lot of. Do you feel that the methods, the standards, the techniques that—you know, that a lot of predictive analytics is based on are at the same level of quality—better, worse—than more traditional methods? And I ask that just because it, obviously, is a buzz word. There’s a lot of hype around it. What do you really think about does it work?
Q: And are people cutting corners when they make the claims?
VARIAN: So that’s an excellent question because, obviously, there is hype around it. But is it excessive? I mean, there’s—some hype is deserved and—
ORSZAG: It’s not bragging if it’s true. (Laughter.)
VARIAN: Exactly. So—I’ll have to remember that line.
ORSZAG: It wasn’t mine.
VARIAN: Yeah. (Laughter.) That’s OK.
VARIAN: All right. So a really good place to look is a company called Kaggle. That’s at K-A-G-G-L-E dot com. And I was actually an angel investor in Kaggle and it was acquired by Google last year. No connection in those two things. But what they do is they sponsor machine learning contests. So you’ll say here’s a data set of people who were discharged from hospital and were readmitted within the next six months, and here’s a whole bunch of characteristics of those people and the procedures, and so on; build the best predictive model you can build of who will be readmitted, and then we can intervene and not discharge people before they’re really ready to be discharged. And that’s a million-dollar prize. It was a million dollars to whoever could come up with the best predictive model for that.
And the—now there’s another one from Zillow. Zillow, who does real estate valuation as part of what they do, they’re offering a million-dollar prize to anybody that can improve their prediction algorithm for housing prices as a function of characteristics of the—of the house and the market.
And Google sponsored one. YouTube videos—we took 4.5 million YouTube videos that were labeled according to what people were doing, OK? Were these people dancing? Were they fighting? Were they exercising? Were they walking down the sidewalks of New York, you know? How could you tell? Well, they labeled all those things with what people were doing, and the person that come up—the group that came up with the best predictions there were—got, I think in their case, was several hundred thousand dollars.
So these are real data, problems people really care about and are willing to pay for, and they’re very carefully evaluated. There’s a (holdout ?) set, a trading set, a validation set, and so on. So the contests are conducted under very careful conditions. And, yes, you can really get improved performance out of these new algorithms and new compute capabilities. But remember, it’s a contest. So—(laughs)—the individuals’ training, experience, knowledge, intuition still plays a very big role in using these technologies, and I think that’s going to continue to be the case.
ORSZAG: All right. We will sneak in one last question. Right here.
Q: Thank you. Thanks. Juan Ocampo with Trajectory Asset Management.
Hal, thank you for joining us. Your insights are great.
Question about not the level of the wages in the future, as you look forward, but the dispersion, the fungibility of labor. And one thing that we’ve observed is that education has made a big difference in terms of kind of segmenting the labor force. That, and the degree to which the other factors of production, if you want to use that word, you know, are relatively rigid. Some of the things that you have mentioned might correct that, if I can use that word. For example, people who didn’t get the same kind of—they didn’t go to MIT—might be able to learn on YouTube a number of skills that make them more productive, so forth and so on. How do you see the balance between the different forces affecting that dispersion, if you will, and the fungibility of labor that underlies a better dispersion, a tighter one?
VARIAN: Well, those are very deep questions, I think, and I’m not sure I’m capable of coming up with an overall answer. But I’ll make a few points relevant to what you—what you said.
If you look at the Khan Academy—people are nodding their heads; they know what the Khan Academy is—it’s a fantastic resource, especially for people who don’t necessarily have mom and dad at home who can answer their Algebra questions, which could be many, many people—(laughter)—by the way.
And, secondly, if you look at the—if you look at it, mathematics, that’s one of the real stumbling blocks. Algebra is really a stumbling block for kids in high school because some get it right away and some don’t. And if you are out sick for a week, or you miss something, or you don’t understand some point, it can derail you, derail your whole education and chances of going to college. And now, again, we have great services like the Khan Academy that help people survive those problems. So that’s a really, really big plus. They may not have local expertise. They may miss something. They may need a patient tutor to go over it. Well, that’s there now. So that helps to some degree. It’s not a total answer by any means, but it is potentially helpful.
And this gentleman over here who mentioned the problem of if you don’t have educated parents at home, it may be very hard for children to acquire the educational skills that they need to succeed in school. And so you get this development that’s very unhealthy from the viewpoint of the people, obviously, but also from the viewpoint of the economy, because you want people to be able to contribute productively to economic growth.
So I don’t have a good answer to your question of whether we will be able to solve those problems, but there are some helpful technologies that may be useful in addressing those issues. That’s all I can say on that.
ORSZAG: All right. Well, with that, I hope everyone will join me in thanking Hal. Thank you very much. (Applause.)
There is a second session of this symposium that will delve into many of these topics in much more detail that will commence at 2:15, in 15 minutes. In the meanwhile, there’s coffee and refreshments. So please join us back here at 2:15. Thank you again.