Intro

In our highly globalized economy, exogenous shocks and unsettling headlines are everywhere. It makes sense that market forecasters should be biting their nails, but so often their prophecies of doom prove completely false. Philipp Carlsson-Szlezak is a proponent of “rational optimism.” He believes there’s a calmer, more measured way of going about financial and economic analysis that sets us up to be more flexible to the highs and lows of the economic events. Today on Faster, Please! — The Podcast, I talk with Carlsson-Szlezak about why an overreliance on models — and a tendency to assume the worst — can impair our ability to roll with unexpected events and make the best of them.

Carlsson-Szlezak is the global chief economist at Boston Consulting Group, and leads the Center for Macroeconomics at their Henderson Institute. He is the co-author of the new book, Shocks, Crises and False Alarms: How to Assess True Macroeconomic Risk.

In This Episode

* Optimism during a polycrisis (1:39)

* AI employment panic (7:47)

* Risk-assessment strategy (13:08)

* Federal Reserve predictions (19:44)

* Impending shocks? (23:41)

Below is a lightly edited transcript of our conversation

Pethokoukis: Philipp, welcome to the podcast.

Carlsson-Szlezak: Thanks for having me.

Optimism during a polycrisis (1:39)

It seems as if there are multiple challenges facing the world and the global economy simultaneously. People described it as a “polycrisis,” and it could be everything from trying to navigate economies to a soft landing after a bout of inflation; part of it, I guess, is the big rise in debt; we have war in Europe; maybe, eventually, war in Asia over Taiwan; and, of course, climate change; aging population; falling birth rates; and some people seem to view AI as more of a threat than a positive, it's going to take jobs, and perhaps other bad things. If I've given a sensible description of the world, how can one be a “rational optimist” in a world of polycrisis?

Just taking maybe two of your examples: The soft landing that was supposed to be impossible, remember that? We need, what was it, six percent unemployment for how many years to bring inflation down? So clearly that didn't pan out like the pessimists said. Or think about the war in Ukraine that you mentioned, which, of course, is the tragedy, but the fact is that there is no recession in the Eurozone so far. The fact is that industrial production has held up rather well, even in the heartland of industrial production in Germany. So real industrial output is actually remarkably resilient. Overall, there's little doubt that there are many, many risks. There are crises, but, more often than not, we're focusing on the tail ends of the distribution and pretend that those risks are at the very center of the risk distribution.

It sort of reminds me — it's not a perfect analogy — of where we were in the early 1990s. I suppose you can always point to, and maybe this is part of your point, if you want to focus on bad news, the world will give you plenty of bad news to focus on. But I remember at the beginning of the 1990s, very bad recession here in the United States, a lot of concerns about the ability for rich economies to grow quickly. Again, debt was an issue, and though, looking back, it may seem like, wow, people should have been really excited: end of the Cold War, there was a lot of uncertainty what would happen to the former Soviet Union, a lot of talk back then about suitcase nukes, who knew what was going to happen in the world? And of course, all of this kind of concern and uncertainty led right into a big economic boom.

So to kind of get back to what you were saying, it seems to me that, rather than being rational optimists, we’re sort of naturally irrational pessimists.

Yeah, but we shouldn't be. I think your example of the ’90s and what the mid- and late ’90s delivered, I think is not a bad analogy for what I think will play out in the 2020s, the rest of the decade. We're in an “era of tightness,” is what we call it in the book, which is really a structural condition of the labor market. Lots of people think that shortages in the labor market are a byproduct of Covid, but that's not true. The labor market turned tight already in 2017, so, in technical language, that's when unemployment dropped below U*. Covid was an interruption of this tightness. Unemployment went to almost 15 percent, and then it came down just as fast, but we are in this era of tightness, and I think it will persist.

To be clear, even if and when you get another recession, you will return to a tight stance. And there are a lot of silver linings that come with eras of tightness: It translates into better real wage growth, it nudges and forces firms do capital-for-labor substitution, it pushes them towards the technological frontier in their respective areas, and all of that should lead into some boost of productivity growth. I'm not of the kind where we're predicting this big jump in productivity growth, I think that's too hyped, but I do believe that the structural tightness, which is almost like a spark to the fuel of technology, I think that will push gradually and measurably over the years to come.

And the driving forces of that tightness are what?

We have a number of things going. Essentially, you had a mismatch of demand of supply already in the late ’10s, as I described, so there's a supply issue, we don't have enough labor supply. You have certain forces on the demographic side that constrained that. And I think often we hear the story that AI is going to produce so much unemployment that there will be mass unemployment, and I don't believe any of this. I think that will play out very differently. Historically, technology has never given us structural or technological unemployment. On the contrary, technology is the deflationary force in the medium- and long-run. Firms that can deliver cost savings, they can lower prices, they will do so to grab market share. That is a real income boost for consumers. So when their real incomes grow, they redeploy that gained real income to new services, new goods purchase and consumption, and that leads to new employment. And so I think, essentially, you will remain with a story where labor is tight, and that is the defining underpinnings of what's coming.

Today people don't remember that even in the 2010s, I think it was Bill Gates, he wanted a robo-tax, a robot tax. Because why? Because automation was taking over the assembly lines and we're going to have to provide for all these people who are going to lose their jobs! Well, where are we today? Near record-low unemployment. And this is in a line of a long tradition where Nobel Prize winners, and technologists, and politicians, they've all predicted technological unemployment.

AI employment panic (7:47)

I wanted to talk a bit about AI labor, since you brought it up. If you don't think mass-unemployment is a valid concern, could there be other downsides from deployment of generative AI in an economy? Could it be, instead of higher employment, might it just be greater inequality? Maybe, before, we had technology hurting blue-collar workers; certainly there's a lot of white collars worried about it. I was just reading a story in the New York Times — all of these people in Hollywood are just terrified, whether they're doing special effects, or they're sound editors, they're all terrified that their white-collar jobs are going away. So do you see, in the near term, any downside from AI?

In macro there's always this tension between the aggregate, which is what macroeconomics is about, and then the distribution of experiences under the hood of macro, if you will. So there will be winners and losers, and there will be those that are harder hit than others, but I think when you look at the aggregate, you add it all up the net-net, I don't anticipate this being a structural or technological unemployment situation.

To go to the micro level, you can take the other side of that argument, too. This is not a big area of research for me, so I'm straying outside of my field of expertise here, but plenty of people have argued that perhaps AI will give a lift to those least-skilled. Why? Because AI is a companion for them that makes them more productive and allows them to create more value, and therefore to be paid better. So I think the jury is out on that.

I don't anticipate a smooth ride where everyone will be a winner and everything will be just plain sailing. Of course this is disruptive, of course there will be gyrations, but the story about technological unemployment's been told for so long. Today people don't remember that even in the 2010s, I think it was Bill Gates, he wanted a robo-tax, a robot tax. Because why? Because automation was taking over the assembly lines and we're going to have to provide for all these people who are going to lose their jobs!

Well, where are we today? Near record-low unemployment. And this is in a line of a long tradition where Nobel Prize winners, and technologists, and politicians, they've all predicted technological unemployment.

There's a nice story with Wassily Leontief, a Nobel Prize winner in economics. He said in the ’70s that human labor was going to go the way of the horse after the introduction of the automobile. Well, 50 years on, we're here with very tight labor markets. And Kennedy was worried about it, too, and others before him. And so I think we have to point to something that's truly different about AI to tell that story. I think we can potentially find some reasons that are genuinely different about AI, but before we all become hysterical about it, I think we should take a deep breath.

Does it strike you as odd that, in a world of low unemployment and, if you're correct, perhaps a longer-term structural tightness, that it's at this moment that people are very worried about technological unemployment, it's at this very moment that they're very worried about immigration coming in and taking jobs. You would think these would be concerns at periods of very high unemployment: people standing in line around the block to get a job, but that's not where we're at. Yet the public mood seems to not be in sync with that.

I think that's a good observation, I don't have a great explanation for it. The technology that's on display is impressive, it is novel, and what's different, generally, is that it makes a credible promise to impact the service economy. In our book, the way we position the slump of productivity growth has little to do with high debt and all those explanations that are occasionally fashionable. It has a lot to do with the fact that the US economy transitioned from being a physical economy to a service economy. And in the physical economy, the production of goods, you always had pretty respectable productivity growth, including the last few decades. But because of this mix shift into services where you did not have the technology to make progress on the productivity side, this mix shift dragged down aggregate productivity growth. If you have zero or very little productivity growth in services, which is like 65, 70 percent of the economy, and you have very high productivity growth and the part that is 30 or 35 percent of the economy, well the blended average is going to be low. And so, as we now have productivity growth promises from AI and services, I think a lot can change there. Again, not in a step change way, this is not like flipping a switch, it's going to be hard slog, and incremental, and cumulative, but something will happen there.

There's plenty of risk out there. New crises will come and happen, but for every true crisis, there are many false alarms, and that is something that I think we need to internalize more, and that is something that can help us see risk a little more calmly and in a measured way.

Risk-assessment strategy (13:08)

Just to take a quick step back: Describe your process for assessing risk. Where do you begin? What are the factors? Do you have a fundamental baseline model of the way the world works? How does that process start and work for you?

The way we go about risk in the book is to say macroeconomic risk should be viewed both as the downside, which is how we commonly view risk, like a recession, or even a structural downside like a deflationary depression — these are downsides. But for practitioners, risk is also hidden in potential upside if you miss out on it. And both the downside and the upside, they come in two flavors: tactical, short-term stuff, cyclical stuff; and the more structural strategic kind, like shifts that happen over longer periods of time. And so we like to think of macro risk in those four flavors: the short, the long term, and the upside and the downside, if you will

Generally when we look at risk, it's very seductive and tempting to focus on bad outcomes and then start analyzing how bad will it be and how quickly they're going to happen. In most situations, it pays off to take a step back, and take a deep breath, and say, “Well, how is the system constructed? What are the drivers? What is the history of this thing?” and an important question, “What would it take to get that outcome from the edges of the risk distribution? What does it take to get there?” Too often in public discourse, we jump straight to the tails of the risk distribution. We're immediately obsessing with the cliff edge and the fall into economic death, and then we're pretending that that risk outcome, which is part of the distribution, so we can't ignore it, but we're pretending that the edges of the distribution are the very center.

And so what we do in the book for a number of areas of risk in the real economy, the financial economy, and the global economy, we go over and over again into these approaches of asking, “How is this thing constructed? What are the drivers? What do you have to believe for the truly bad outcome?” There's plenty of risk out there. New crises will come and happen, but for every true crisis, there are many false alarms, and that is something that I think we need to internalize more, and that is something that can help us see risk a little more calmly and in a measured way.

How well did markets, investors, economists — how well was their risk-assessment process in 2020, given where we are today? My guess is that the global economy is better in 2024 than people thought in February, or March, or April of 2020 as the pandemic was kicking in. Did we do a good job assessing risk and reward back then?

No. Public discourse did a terrible job at that. The conventional wisdom and received wisdom in March and April, May, even June, July, and even August, the summer, when you had the first signs of recovery, the conventional wisdom was: This is worse than 2008, and this could be as bad as the Great Depression. And we have a great collection of headlines that we keep, there are lots of them in the book as well. It was, in my mind, a prediction failure. Why? Because what happens is that a lot of the commentary, a lot of the thinking, was too model-based, what we call “master-model mentality” in the book.

So how do you project a recovery, typically? You look at the unemployment rate as a proxy for the health of the economy, and if you have a high unemployment rate and a recession, well, it can take a long time to bring that down. After 2008, it took the better part of the 2010s to bring unemployment down, and you had “only” (in quotation marks) unemployment of 10 percent after the Global Financial Crisis. Now with Covid, you almost went to 15 percent. So the models extrapolated outside their empirical range. They said, “Well, if it took almost a decade to bring down this unemployment rate after 10 percent unemployment, then after 15 percent unemployment, well it's going to take even longer.” Hence, the narrative of “worse than 2008,” “as bad as the 1930s,” and blah, blah, blah.

Even at the time, you could ask exactly these questions, and I'm not saying this with hindsight bias. We did a piece on March 28th, 2020 in Harvard Business Review where we did exactly that thought experiment. We said, “What does it take for this to be a structural downgrade for the US economy? What does it take for to be worse than 2008?” You're going to need to see damage on the supply side of the economy. You're going to need to see the downgrading of the labor market of a window of capital investment that isn't happening, and the loss of skills, et cetera. And we asked, “Well, how likely is that?” And, of course, it comes down to stimulus. Of course it does, and it comes down to how innovative, fast, and big are we in backstopping the real economy? And we were. And so even in March — and this is shelter-in-place phase, right? This is not even sort of the full lockdown. Even then, you could ask sober questions about very bad risks. And if you did that, you arrived at answers that weren't predictive in “this will happen” at a point forecast level of accuracy. But there was clearly a path and a narrative in March 2020 that was consistent with what actually happened: the tightest recovery on record, and a US economy that was not pushed off its trend path. So after 2008, the US economy was actually pushed off its prior trend path, never made it back in terms of what the trend was, did make it back in terms of growth rates after 2008, but it never made this levels recovery in that sense. All of that was avoided in 2020, and you didn't have to be a magician to at least entertain the possibility that that was a meaningful part of the outcome distribution.

Federal Reserve predictions (19:44)

Speaking of models, how has the Fed's model performed? Which is another way of saying, how has the Fed performed, and continues to perform?

In the recent inflation surge, et cetera? I think the Fed has taken too much flak for what happened in the inflation spike. The inflation spike, by the way, again, was immediately spun into a structural inflection point, the 1970s narrative, off to the races, wage price spirals, all that nonsense. It was an idiosyncratic mismatch of demand and supply. It was an overshoot in consumption following stimulus, and you had the supply chain crunch, and then you had a number of exogenous shocks that nobody could foresee, and those who like to take credit for having predicted the spike, they didn't foresee the Ukraine war, the shock in oil that followed, and many other of the things that played into the spike. The bigger story, though, is quite simply that, as this mismatch of demand and supply unwound, inflation also came down pretty fast.

Now, back to the Fed: Yes, there were slow in responding. Would it have been better for them to go early, perhaps, yes, let's say yes. But at the same time, the idea that they would fail to step up and act, and reign this in, and stand by and watch this whole thing go to hell. I mean that was never credible. And they did step up, and they did what they had to do, and I think also attempting the soft landing was the right thing. People at the time did say, “We need a draconian recession right now to remove all the risk of a regime break in inflation!” Well, you would've cut short a really tight labor market that has a lot of real wage gain that delivers a lot of good things for particularly the bottom of the labor market. So it was the right call to attempt a soft landing rather than saying, “Look, we're going to cut this cycle short right now to remove any risk of inflation spiraling out of control.” They did the right thing. They're successful at it. The soft landing is a success. We're well into it. And so I give them more credit.

Is that what you think is the biggest mistake people are making, that they're still asking, “What about the soft landing?” And what you're saying is, “We're already into it. You sort of missed it. It happened. You're still looking for it.”

Every so often you still see the headline, “Are we going to get a soft landing?” And I'm like, “Well, let's just take a step back.” What is a soft landing? The task was to cool down the labor market, best seen through the eyes of job openings, to cool that down without pushing up the unemployment rate. These two are mirror images of each other. When firms stop hiring, they usually also start firing, so we had to pull off this trick: You stop hiring, but you don't start firing.

That was the soft landing. That's the definition of a soft landing, nothing else. And that is what happened: Job openings are down more than three-and-a-half million or so — don't nail me on the decimal — and the unemployment rate is up a little, but, as you and I know, the unemployment rate is not up because of firings. The unemployment rate is up for compositional and participation reasons. So if that is not a soft landing, at least, I would call it the second of three stages, if you will, I don't know what it is. And back to the topic of headlines, most of them are just confusing people more than they're helping them. It's always nice to write something clickbaity that people will be scared of and think this is the cliff edge. How about a headline: “Wow, this is a really great soft landing! This is remarkably good!” Why don't we acknowledge that for a moment?

You can always speculate. You can speculate on, for example, exogenous shocks. Covid is an exogenous shock, and there are others: There are solar flares, and there are new pandemics, and there are things that can do immediate damage, and you can spend millions of dollars on models, and they simply won't capture that exogenous risk.

Impending shocks? (23:41)

Yeah, I mean, the name of the book is Shocks, Crises and False Alarms. As I look over the rest of this decade, if you take the most bullish and extravagant predictions about AI, it’s not clear to me what the economy looks like a decade from now. Again, if you take the most bullish kind of [view]: we get the human-level AI and all that. So there's that.

I also am not quite sure what the world looks like if some of these worst-case scenarios with Taiwan, and the US, and China, because that seems to me to be so potentially bad, I don't want to think about it. I don't know what the global economy looks like on the other side.

What are the big risks, or the things which you believe pose the greatest risk of disruption? Disruptions are going to be good and bad. If you're really worried about AI, that must mean AI is very powerful, it can do a lot of good things, too. So what out there do you really worry about that the disruption will be just bad and have far more downside?

You can always speculate. You can speculate on, for example, exogenous shocks. Covid is an exogenous shock, and there are others: There are solar flares, and there are new pandemics, and there are things that can do immediate damage, and you can spend millions of dollars on models, and they simply won't capture that exogenous risk. So that's one story.

Geopolitics, since you mentioned it, a third of the book, roughly, the third part is about those types of risks, and they can be devastating, there's no doubt about it; but would you build a central case around this and make that the base case and expectation of how to view the future? Geopolitics is extremely treacherous when it comes to translating its impact on the economy. It's fascinating to me often how little the complexity is acknowledged and understood, and, in the book, we use an example juxtaposing a start of World War I and World War II.

So when World War I breaks out, the Dow is down 10 percent, they close it for 136 days, and when they reopen it, it's down another 20 percent. Exactly as you would expect, right? It makes a lot of sense, a world war and the market's in the gutter. Yet, when World War II breaks out in ’39, the market jumps 10 percent and stays up. Why? It ends the Great Depression, it puts to use labor, it has capital expenditure and investment, and it singlehandedly ends a decade of malaise.

And there is a silver lining to this, and all of this doesn't sit well with how we want to think and should also think about geopolitics, which is in humanitarian terms, and also values and idealistic views. All of this is true and correct, but if we are to assess the impact on the economy, we are going to have to restrain some of these instincts, and we're going to have to say, what are the transmission channels from geopolitics to the real economy, to the financial economy, and to the institutions that we have in place that govern our economy? What are those transmission channels? And often, the bar is higher than you think.

Just think about it, the Ukraine war. It's left no mark on the US economy at all, virtually. Why? Because the real linkages weren't there; virtually no trade into this part of the world, either Ukraine or Russia. The financial linkages weren't really there; it's not like balance sheets of US banks were impaired by shutting off that part of the world. And on the institutional side, we can discuss sanctions, and we can discuss using the US dollar as a means of punishing Russia, and all that. But essentially, once you think soberly about, well, how is that shock supposed to transmit to the economy? Well, it looks a lot different.

The same could be said about the tragedy in the Middle East. The oil price is lower than before the attack on Israel, right? If you look at futures and forward pricing, or the price of insurance against swings in the oil price, it’s lower today than before the attack on Israel and before the retaliation that Israel enacted. So any of these geopolitical risks and hotspots, they are to be taken seriously. I'm not saying they don't matter, but when we extrapolate from them straight to the economy, it more often goes wrong than it goes right.

And a final thought: I’m not a Taiwan and China watcher, but one thing that's also clear to me, since you mentioned the unthinkable and the worst-case scenario, my next question would be, okay, what shape would that take? Would that be a blockade? Would that be an actual invasion? Would it be something that involves airlifting semiconductors out of the island? Would that be . . . There's a myriad ways of how such a thing could play out. It'd be a big shock, and terrible, but I can't say with confidence what it would do linearly to an economy like the US economy. It would come down to the details of that.

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