Transcript: Philipp Carlsson-Szlezak, BCG
The transcript from this week’s, MiB: Philipp Carlsson-Szlezak, Global Chief Economist for BCG, is below.
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00:00:02 [Speaker Changed] Bloomberg Audio Studios, podcasts, radio News. This is Masters in business with Barry Riol on Bloomberg Radio.
00:00:17 [Speaker Changed] This week on the podcast, I have an extra special guest, really fascinating conversation with Philip Carlson Lesak. He’s got a really interesting background, chief Economist at Sanford Bernstein. Worked at the OECD, began at McKinsey, ended up as global chief economist for the Boston Consultant Group Group, and really approaches economic analysis from a very different perspective critical of the industry’s over-reliance on models which have proven themselves to be not great predictors of what happens next, especially when the future in any way differs from the past. And so when we have things like the.com implosion, or especially internal to the market, the financial crisis of oh 8, 0 9, and even COVID models just don’t give you a, a good assessment. And he describes how he reached this conclusion in his book, shocks, crises, and False Alarms, how to assess true macroeconomic risk. He calls out a lot of people who get things wrong, especially the doomsayers who not only have been forecasting recessions incorrectly for, I don’t know, the better part of 15 years, most especially since CO. But their models just simply don’t allow them to understanding a dynamic changing global, interconnected economy. I, I thought the book was fascinating, and I thought our conversation was fascinating, and I know you will also, with no further ado, my discussion with the Boston Consulting Groups, Philip Carlson Lesak.
00:02:06 [Speaker Changed] Thank you for having me.
00:02:07 [Speaker Changed] So, so let’s start with a little bit, I wanna talk about the book, but before we get to that, let’s talk a little bit about your background, which is kind of fascinating for an American, you get a bachelor’s at Oxford, a PhD at the London School of Economics. Was becoming an economist, always the career plan.
00:02:26 [Speaker Changed] Well, let me correct you right there. I’m not American.
00:02:29 [Speaker Changed] You’re not, where are you originally from? I,
00:02:31 [Speaker Changed] I was born in Switzerland. I grew up there, but in a, in a number of other countries as well. So
00:02:35 [Speaker Changed] You have sort of an American accent. How long have you been here? I spent a lot of time here,
00:02:39 [Speaker Changed] Yeah. Early on as well in my youth. And so growing up in, in different places, I always compared and contrasted what I saw. So I developed an interest in, in economics. So when it came to going to college, studying economics was a very natural choice.
00:02:55 [Speaker Changed] Huh. Where, where did you grow up in Switzerland,
00:02:57 [Speaker Changed] Zurich. I was born
00:02:58 [Speaker Changed] There. Okay. I, I recently visited both Geneva and Lake Geneva up, and it’s just spectacular. What a beautiful part of the world. It is. It really, really impressive. So first job out of school, McKinsey, is that right? That’s right. And and what was that experience like? Well,
00:03:17 [Speaker Changed] So I, I studied economics at LSC, actually not at Oxford. I did my PhD at Oxford, so the other way around, and that was at the turn of the century. Let me take a step back. It was the turn of the century. And I emphasized that because that was peak economics. So, you know, the, the hubris and the arrogance of the economics profession was at its peak. And, you know, we’re still seven, eight years out from the global financial crisis, which was a big humbling moment for the profession. So everything was very model driven theory, Quin, econometrics and all that. So, you know, I, I didn’t feel comfortable even then as an undergraduate, then as a graduate student, I branched out, I started reading a lot more, you know, going to political theory, finance history, much broader, building a mosaic of knowledge and, and also methods and, and approaches, frameworks. And so at the end of, of my graduate studies with a PhD, that’s when I landed in, in, in consulting at McKinsey. And the work was very different. So very nitty gritty, right? You go deep into corporations, other organizations, you do very, very granular work. So coming with this big picture view of the world and analyzing and going into this super nano, micro part of, of business was, was a big change.
00:04:30 [Speaker Changed] Let, let’s stay with the concept of peak economist. Yeah. I think it was Paul Krugman who did the saltwater versus freshwater comparison, which was essentially the economists along the coast seemed to have a very different model and very different approach to doing macro versus people more inland, at least in the us. Does that sort of dichotomy resonate with you? How do you, how do you think about that? Well,
00:04:58 [Speaker Changed] I, I generally view all of of mainstream economics as, as two model based master model mentality in the book, sort of this belief that economics is a bit like a natural science and we can pass it off as a natural science. That belief is still still very much alive. And so physics envy, which has long been identified as the problem of the discipline, still reigns supreme in my view. And the book is really partly a repudiation of that. So my co-author and I, we take master model mentality to task in the book. And we think economics deserves a much more eclectic approach, drawing on many more disciplines than, than just sort of standards
00:05:44 [Speaker Changed] Economics. What are your thoughts on the impact of behavioral economics that really took apart the homo economists that was front and center of classical economics and showed, Hey, people aren’t rational profit maximizing actors. They’re emotional and flawed and human
00:06:04 [Speaker Changed] Right. I, I think that is very, very interesting. It’s very valuable that we have that strand of research and economics, but it’s more in the micro side. It’s not really macro predominantly. And so I I firmly live in a global macro space where I think we still have very commoditized economics. You know, it’s, it’s all about a set of forecasts. People are still wedded to their models. It’s very much point forecast driven. And I think what we need is much more narrative based, judgment based, more eclectic approaches to reading the landscape. And that’s what the book is really, really about.
00:06:40 [Speaker Changed] So we’re, we’re gonna talk more about how poorly economists have done as forecasters over the past few decades. And you, you have numerous, numerous examples, but let’s stay with your early career. You’re, you’re going deep at McKinsey into the granularity of corporate behavior, then you very much a, a sea change alliance. Bernstein or Sanford Bernstein, you become chief economist. How different is it applying those wares on Wall Street in an investment environment versus the corporate world in a more, you know, execution basis?
00:07:17 [Speaker Changed] You know, the, the switch to the sell side was, was really good for me. There was something I, I’d been missing in my skillset. I’d done a lot of deep thinking, writing, researching. I’d done the, the more microeconomics I, I learned more about the corporate world, but I hadn’t been exposed to the finance angle of it as much. I hadn’t talked to the buy side at all really before. And being at Sanford Bernstein, a firm with a storied history and, and equity research really, and swimming in this pool of, of really great equity analysts, just taught me a lot of things, not least how to frame research angles, how to be quick with research notes, how to get the thoughts out. And then the constant exposure to investors on the buy side really, really helped me sharpen my research skills. So that was almost like a, a, a missing piece in my recipe. It really unlocked something for me, and I learned a lot there and, and I had a really good time doing that work publishing, you know, many, many research reports over those years and often going very, very deep, often going very historical in the approach. So Bernstein is a firm that, that very much appreciates lateral thinking, differentiated approaches out there kind of ideas. And so I, I ran wild for a while just doing, doing work that I don’t think I would’ve done anywhere else. So
00:08:41 [Speaker Changed] You started a consultant, you briefly, at A NGO, at the Organization of Economic Cooperation and OECD, I don’t even know, development is, I guess the last date. Yep. You’re on the sell side. So you, you see the universe of career options as an economist. What brought you back to the Boston Consulting Group?
00:09:03 [Speaker Changed] So I had a history with BCG already, and, and I was well connected there and at some point I was approached if I’d like to come back and do the same kind of work I was doing on the sell side. But at, at B-C-G-B-C-G is a really great platform because not only is it deeply ingrained in the, in the corporate world, so you know, the access to boardrooms is, is very wide. You, you get to meet a lot of interesting executives and, and the prevalence they’re grappling with, but you also still have access into the institutional investor world who are also clients. So you really get both sides of the landscape and they, they’re really different, right? On the buy side, it’s mostly a, a, a look at firms outside in, they’re outside of what’s happening in the boardrooms. They’re trying to decode it from the outside. Being a consultant, working and talking with ’em, you’re much closer to what’s actually happening in their deliberations, the problems they’re facing, the questions they’re trying to answer. So to me, that platform is very attractive because it’s, it’s very versatile. It’s, it’s never gets boring. And I’ve, I’ve had a good run the last five years doing my work on that BCG platform.
00:10:14 [Speaker Changed] So I have no expertise in the consulting world, but I kind of hear people lump all the consultants together, McKinsey, B, C, G, all these different firms. I get the sense from speaking to various people that that’s kind of inaccurate that BCG is not McKinsey, they’re very different organizations. What’s your experience been?
00:10:34 [Speaker Changed] Yeah, I mean they, they, they have different cultures for sure. They certainly vie for the same business, the three that you mentioned. So, so you, you constantly bump into those other two competitors. If you’re at any one of those three firms, I would think
00:10:48 [Speaker Changed] The third being
00:10:50 [Speaker Changed] Bain, I think you may Okay, yep. Be McKinsey, BCG, Bain, those three, there are others, but those are the core strategy consultants, if you will. And, you know, I would think the type of work that is done is obviously very similar to vying for the same business, but culturally it’s different. And, you know, they’re, they’re slightly different sizes. These three firms. B, CG today is about 12 billion in, in revenues annually. And we have about, I think 60, 70 offices and, and, no, sorry, well, well over a hundred offices in 60 countries, I think is the right metric here. Right. And you know, it’s, it’s, it’s a space that is, is very, very competitive, but that, that keeps everyone on their toes.
00:11:34 [Speaker Changed] I, I would imagine. So let’s, let’s talk a advising companies and advising executives. You talk about explaining economic uncertainty and as we’ll get into in the book why there is this risk aversion and these fears of crises that never seem to come around. How do you approach advising executives on navigating all this? It seems like there’s always this fear of a disaster and lately it hasn’t really showed up.
00:12:11 [Speaker Changed] Yeah, so a lot of what I do in conversations with executives is to uns skew, if you will, some of the perceptions they pick up in the press, in public discourse, which is reliably dialed down to the, to the sort of do mongering side of things, right? That is really true. It’s not just lately, since you mentioned it, sort of the inevitable recession that never, that never came, we’re really at the end of a string of such false alarms. You know, when covid hit, it was very common to predict a depression. Not just a recession, but a depression was very conventional wisdom in 2020 that this would take many years to recover. Then when interest rates rose, it was, it was fashionable to predict an emerging market, a cascade of, of defaults then, then of course when inflation spiked, it was cast as a
00:13:03 [Speaker Changed] Hyperinflation,
00:13:04 [Speaker Changed] Hyperinflation, structural inflation regime, break the 1970s, all that stuff that, that clearly even then I think was, was very clearly not, not what was playing out. And then the inevitable recession is really just the most recent in a string of false alarms. So often what I do is, is to meet people where they are. They, they pick up doomsday narratives because they’re very prevalent in public discourse. And we often go back to basics and ask, well, how does the system work? And importantly, what would it take for these big bad outcomes to, to happen? It’s not that they can’t happen, they’re part of a risk distribution, but very often we take these risks and public discourse that are the edges of the risk distribution, tail
00:13:46 [Speaker Changed] Risks,
00:13:46 [Speaker Changed] Tail risks, and we pretend that they’re in the middle of the distribution. Right? If you go through financial news, if you go to financial TV kind of conversations, you, you often get the impression that these risks, which are genuine risks are real. They’re part of the distribution, but you get the impression that they’re really the center of everything we should be watching. And so often,
00:14:07 [Speaker Changed] Yeah. So this leads to an obvious question. Whenever I have an author in, I often ask what inspired them to write their book? It’s pretty clear what inspired you. It seems like it, it got to the point where, hey, everybody is freaking out about things that are either not happening or just so low probability events that they’re not contextualizing it well, what actually was the aha moment that said, I gotta put all this down in a book and instead of repeating myself over and over here, read this and it’ll it’ll explain why you’re fearing all the wrong things.
00:14:46 [Speaker Changed] Yeah. It was the, it was the accumulation of, of situations where my co-author Paul Schwartz and I felt we had a pretty good access to this topic. We, we kind of got that one right, not because we were using models and sophisticated analysis, but we, we looked at it from a narrative driven perspective. We asked the right questions about what does it take to get to that really bad structural situation. And so we wanted to wrap that into a coherent story of how we think about economics. Not because we can get it right every single time. Even if you use a more eclectic approach to economics, you will get things wrong, but I think you hit rate can improve. And that was the motivation to, to write that all down in the book and, and yeah, that, that’s how this came about.
00:15:35 [Speaker Changed] So first, let, let’s just start out generally, you, you described the book as calling out pervasive dooming in public discourse about the economy and demonstrating how to navigate real financial and global risks more productively. Explain. So
00:15:55 [Speaker Changed] Over the last few years, call it, since the, since the Covid pandemic, we’ve had a string of, of false alarms, as I would call ’em, right out the gate in 2020, we were told this will be a greater depression, maybe as bad as the 1930s. Worse in 2008, that wasn’t the case at all. Then we had an inflation spike that was spun into an inflation regime break forever, inflation hyperinflation that didn’t pan out. Then we had rising interest rates and that was spun into a doomsday story of emerging markets cascade of, of defaults. And then we had the, the story of an inevitable recession that we’re still waiting for, right? So we have across the board a lot of negativity across the board. We have a lot of doom saying public discourse is pervasive in that regard. The story always skew to the downside. And what the book does it, it provides a framework to think about this differently, more productively. And it does so across real economy risks, think recession, but also sort of longer term growth. It does so in the financial economy, think about stimulus and the effectiveness of stimulus, interest rates, inflation bubbles, that type of stuff. And it does so across the, the global space, the institutions that govern trade, et cetera.
00:17:11 [Speaker Changed] So you combine data analysis with both narrative storytelling and judgment over traditional macroeconomic models. Explain what led you to this way to contextualize what’s going on in the real world economy.
00:17:29 [Speaker Changed] So I, I, my path for economics was fairly eclectic. I started out studying economics in a traditional theoretical macroeconomic econometric sense. And then I went into studying much broader adjacent fields that, that are relevant to economics, finance, history, political theory, political economy, et cetera. Then I had different experiences in my career just just putting together different views of how to approach these problems. And over time and working on the sell side, as we discussed, I put all these together. And so it is just the insight that the models will not deliver. You cannot accurately forecast the economy. Economists shouldn’t feel so ashamed about that. It’s not like natural scientists are always doing better, think about epidemiologists. They also struggle to accurately forecast covid deaths, for example. So, you know, the, the whole physics envy and the whole inferiority complex that often besets the, the economics profession is misplaced in, in my view, we should embrace the uncertainty that prevents us from making precise point forecast. And we should live with that uncertainty, embrace the eclectic nature of what we’re trying to solve. It isn’t just about economics and policy, it’s about myriad other things that play into this. And when we do that and do it rationally, I think often we we’re gonna land in, in, in better, better predictions.
00:18:55 [Speaker Changed] You know, it’s funny about the physics envy. Richard Feynman once said, imagine how much harder physics would be if electrons had feelings. Feelings, yeah. Right. So it, it’s, it’s not a pure natural world. You have human behavior getting in the way. And, and you know, one of the quotes from the book, doom Cells, hasn’t that always been the case? That it appeals not only to our fear of existential threats from an evolution perspective, but just generally speaking, good news is sort of sneaks by and bad news gets our attention.
00:19:35 [Speaker Changed] Yeah, it’s the, the clicks and, and the eyeballs that we’re, that we’re trying to attract in the, in the news business model. And that, that gives you the slant to the downside. I think it’s, it’s particularly pronounced these days,
00:19:49 [Speaker Changed] Social media and the rest,
00:19:51 [Speaker Changed] That’s part of it. But it’s also the case that when you think about the last 40 years or so, there was a window that we call good macro in the book. So a lot of macroeconomic variables, a lot of macroeconomic context was benign and was a tailwind, you know, for executives, but certainly for investors. So in the real economy cycles grew longer, volatility came down, like recessions were, were less frequent. The financial economy inflation structurally decline, pulling down interest rates with it in the, in the global realm, you had, you know, institutional growth and, and where we’re aligning value chains and, and all that really was a tailwind to executives and investors. And more recently, not just COVID, you can, you can go back to 2008. It’s sort of a growing crescendo of, of new noise and new disturbances. I think that good macro window is, is challenged, right?
00:20:44 We had a lot of generations, we had a lot of shocks, all the whiplash there. And so for executives, when it used to be possible to ignore the macro world or take it for granted, it’s now moved into the boardroom. N now you need to have a view on what these things mean for your business and you kind of need to do that almost ongoingly. Mm. So that has changed and because there’s more gyrations, there’s more whiplash, I think that has dialed up all the angst and it has dialed up the doom saying, and the string of false alarms that I went through earlier in my mind is, is is pretty dense. It’s, it’s, you know, every year we had a new doomsday narrative and, and every single year it, it just didn’t pan out that way.
00:21:23 [Speaker Changed] You know, there, there was a, I’m trying to remember which economists wrote this up at, at one point in history, your whole world was your local region and what happened globally or what happens across the ocean was not relevant. Now it doesn’t matter what corner of the earth you’re hiding in, the global macro world is knocking on your door regardless. How significant is that to both, to both coming up with a better macroeconomic framework and all of these false crises and fears that seem to be never ending? Yeah,
00:22:02 [Speaker Changed] I think the greater in interconnectedness and the, the real time aspect of economics and the pass through of, of influences and, and in, in, in often just hours transmitted often through financial markets, that just adds to that. It’s, it never, it never stops. It never takes a break. You know, you, you go to sleep with with sort of the latest data, you wake up with the latest data, right? I mean it’s sort of constant in that regard and I think that certainly feeds into that sense of heightened risk and, and crisis.
00:22:33 [Speaker Changed] So let’s talk about some shocks. Over the past quarter century. We had, and this is really just less global than US focused, but obviously international ramifications. We had the dotcom implosion in 2000. We had the September 11th attacks in oh one. Not long after that. We had the great financial crisis. We had COID in between, we had a couple of market events. The flash crash V again, I don’t know if you really consider those true economic shocks, but certainly.com nine 11 GFC and Covid were huge. Is this, have we been through more than the usual number of shocks or does it just seem that way recently?
00:23:19 [Speaker Changed] Well, we’ve always had shocks. I think 2008 stands out among the ones you mentioned because that’s where the US economy actually came close to the precipice of this could be a structural depression. Without the intervention, without the stimulus that was deployed at the time, this could have gone a lot worse. Covid in some sense was a replay of that risk, but, but action was more swift and more decisive. So it seems like we’ll learn something there
00:23:44 [Speaker Changed] And much more fiscal as opposed to the financial crisis, which was primarily a monetary response. And we ended up with two very different years that followed address that if you would.
00:23:57 [Speaker Changed] Yeah. So I think in 2008 you’ll remember tarp tarp was, was a, what now looks like a poultry sum of 700 billion. And it got voted down in Congress. Right, right. So
00:24:08 [Speaker Changed] I remember that week in October. Yeah. And the market seized so aggressively in the stock market sold off that it was voted down on a Monday by Friday it passed overwhelmingly.
00:24:19 [Speaker Changed] Exactly. And I think this is one of the big themes that we emphasize in the book Stimulus comes down to the willingness of politicians to act and the ability to act ability is more about financial markets, will bond markets, finance, this, this kind of action, which they do in times of crisis. But the willingness has to be there to act. And in times of crisis, the willingness to act usually arises. Partisanship is, is put aside. Politicians come together. They, they, they act to, you know, when the house is on fire, you, you, you will step up and and do something about it. And I think in 2020 that was in display and there was a learning curve from the more timid approach in 2008 and then in perhaps it was overdone in 2020 and the, and the following years. But certainly the, the risk was perceived perhaps we’re doing too little, so let’s rather go large and backstop the system.
00:25:11 [Speaker Changed] My favorite story from the 2020 Cares Act was a week before the country was shut down, Congress couldn’t agree on renaming a library in DC ’cause it was just along partisan lines. Everything got tabled then the world shut down. And the largest fiscal stimulus since World War ii, at least as a percentage of GDP flew through the House and Senate and was signed by CARES Act one was President Trump Cares Act. TRU two was President Trump Cares. Act three was President Biden. Did we learn something from the financial crisis about the lack of fiscal stimulus and maybe the pendulum swung too far the other way? What, what’s your takeaway from that?
00:25:58 [Speaker Changed] No, for sure. Look, I I think two crises were very different. You had in 2008 damage balance sheets, not just in the banking system, but households, their balance sheets had to be repaired. Households had to dig themselves outta that hole, had to rebuild our wealth. And that that would’ve called for more intervention than, than what we got in 2008. In 2020, I think policy makers, politicians, they had internalized that learning. So they went extra large on the fiscal side. And that hole that covid created was basically filled with, with fiscal stimulus as you know, it’s widely believed and accepted that this was extremely big, too much perhaps. And so we had an overshoot in, in certain consumption areas, particularly in the, in the good space, there was an overshoot and, and consumption. It, it, it pushed up demand it, it, it together with supply crunches, it pushed up inflation in an idiosyncratic and more tactical, cyclical way. Not structural, but, but tactical way. And so I think yes, policy makers did learn something and they were risk averse, so they went extra large.
00:27:08 [Speaker Changed] So you said the financial crisis clearly a shock. The other things not as much as a shock. And we’ve had plenty of false alarms. How do you define what a true shock or crises is and what do you put in the category of false alarms or things that are genuine, but just don’t rise to the level A as described? Yeah.
00:27:31 [Speaker Changed] There, there are two things to consider. One is sort of the news cycle level. We have a constant doom saying about suppose things that could lead to recession or otherwise downgrade the economy. You know, just the last few years we, we went numerous, you know, for example, consumers were supposed to run outta cash and consumers were not gonna keep up their spending. We had lots of false alarms about the labor market even last summer, right? We had last summer in August, there was a somewhat of a panic because supposedly the labor market was gonna be very soft and, and very weak. So we have these new cycle false alarms stories that, that often are rooted in a data point that is noteworthy, that is interesting, that does signify risk. But we extrapolating from the data point to conclusions that don’t hold up. That is one category of false alarms.
00:28:21 The other category is where you have real crises, but the question is, are they gonna have structural impact? Are they gonna have a long-term impact on the economy? Are they gonna downgrade the economy’s capacity? So 2008 does qualify, 2008 left an indelible mark on the US economy, but 2020 didn’t in terms of performance and output. We’ve regained the output to trend output that we were on the path we’re traveling on pre covid. We’ve, we’ve come back to that trend, output path. It has not left the kind of permanent mark on economic performance that you saw after 2008. Huh. So in that sense, we need to differentiate between what is a likely shock that that will pass and that we can fix versus what is something that changes the structural composition, structural setup of the economy, durably. Those are two very different types of, of, of situations
00:29:14 [Speaker Changed] That, that sounds like a usable framework for distinguishing between real crises. And do I call it media alarmism or, you know, I don’t, everybody’s blaming the media these days, especially with this administration, but there has been a fairly relentless negativity, especially in social media. What’s the best framework for, you know, separating the wheat from the chaff?
00:29:41 [Speaker Changed] Well, typically when we see kneejerk reactions and doomsday stories, they’re, they’re taking a data point and then they’re extrapolating usually on the basis of a model. So, I mean, think about the inevitable recession. Even Larry Summers, people like that, they came out and said, look, to bring down wage growth to bring down inflation, you need, I don’t know, five years of unemployment at this and that level. Why? Because
00:30:06 [Speaker Changed] Right, he threw out 10%,
00:30:08 [Speaker Changed] Well, 10% for one year, right? Or 5% for five years, right? So he had different configurations, but they were all based on basically the Phillips curve. This was all a Phillips curve take on the economy, which is,
00:30:18 [Speaker Changed] Which was a great model 50 years ago, wasn’t it?
00:30:21 [Speaker Changed] Yeah. It, it described the UK and certain other countries empirically quite well. It wasn’t ever really a model and a theory. It was more of a description of empirical facts. But certainly it was useful for a window. It’s still useful as, as, as a, as a instrument to think about dynamics, right? But it was basically used as, as the truth. You know, there’s an input and there’s an output. And my model gives me the truth if I give it certain inputs. And then, well, what happens? We’re extrapolating data points often outside the range of empirical facts. The models are only trained on historical facts. You know, you can’t make up data points to train your model. So when a crisis hits likely, you get data points that were not empirically known in the past. So what does the model do? It extrapolates outside, it’s it’s historical empirical range.
00:31:12 And then you get these kind of point forecast that just don’t, don’t work. I mean, case in point in 2008, unemployment goes up to around 10%, right? And it takes almost the whole 2010s a full decade almost to bring down this very high unemployment rate. So in covid, when unemployment shoots up to 14%, what does the model do? It says, well, if it takes, you know, a decade to bring down 10% unemployment, it will take even longer to bring down 14% of unemployment. Right? And that is exactly this kind of, of, of limitation of the model based approach. Empirically, you never had 14% unemployment, right? So if the model extrapolates from past data points, it’s gonna go off the tracks. And that’s exactly what happened in that instance.
00:31:54 [Speaker Changed] So, so the underlying flaw built into most models is that the future will look like the past. And as we’ve learned, that often is not the case.
00:32:05 [Speaker Changed] It’s always idiosyncratic. Look, the, the US economy, since the second World war has only seen a dozen recessions. Now each of those recessions is totally idiosyncratic. And even, even if they had a lot of commonalities, 12 is not a sample size that a natural scientist would consider large enough to, to build sort of an empirical model around, right? Each of these crises or each of these recessions was idiosyncratic. And the idiosyncrasy demands much more than a simple model or even a sophisticated model. It demands the eclectic view across many, many drivers. And that comes down to judgment. There isn’t, there isn’t an output in an Excel sheet or a Python model or anything. In the end, it comes down to human judgment. And, and I think that that is something we lose sight of way too often.
00:32:51 [Speaker Changed] You very much strike me as a fan of Professor George Box. All models are wrong, but some are useful. Tell us a little bit about how models can be useful.
00:33:02 [Speaker Changed] Well, there are always a good starting point. Even the Phillips curve has, has a lot of validity to think about what might be happening. There are always this sketch of, of reality. But the moment we’re translating that from, you know, a sketch and a map into something that is hardwired in a quant quantified model, and the moment we then expect that the output will resemble anything like the truth, we’re, we’re sort of denying the reality of this. It, it just doesn’t work that way. Look, I’m not the first person to make that point. In fact, you know, Hayek, Kanes fund MEUs, they’ve long basically trashed economics for saying like, you’re too gullible and you’re too naive, right? About the constant nature of these variables. They, they’ve long pointed out that you don’t have this, this what the national sciences provide, which is stability in all these relations of variables.
00:33:58 You don’t have that in economics. And there’s a, there’s a, an anecdote that we pick up in the book. When Hayek receives the Nobel Prize in 1974, he actually uses his acceptance speech, or I think it was a dinner speech he gave right after being awarded the, the prize. He uses that speech to say, look, you shouldn’t do this prize in economics. You should, you should have never, you should have never done the Nobel Prize in economics. But if you must have this prize, at least ask the recipients to swear an oath of humility. Because unlike physicists and, and in chemistry and other natural sciences, economists have a big microphone, right? Policy makers listen to them, politicians listen, public listens to them, but they don’t have that certainty of analysis. They don’t have that stability in their model. So they’re gonna go off the tracks all the time. So at least ask them to be humble about what they’re doing. And I think that that is a good reminder of the long history of recognizing the limits of model-based approaches through the eyes of some of the leading, leading thinkers in this, in the space.
00:34:59 [Speaker Changed] So let’s talk a little bit about a lot of the false alarms and, and folk crises. So many economists got 2022, wrong, 20 23, 20 24, they were expecting a recession, it never showed up. Why is that?
00:35:19 [Speaker Changed] It starts with the master model mentality that we call out in the book where we place too much trust in models. So the Phillips curve was essentially used by many forecasters and
00:35:31 [Speaker Changed] Commentators define, define the Phillips curve for the lay reader who may not be familiar. Yeah.
00:35:34 [Speaker Changed] Phillips curve is, is as an, as an old theory going back middle of the last century describing the relationship between wage growth and, and unemployment. So the idea is that you trade off the two variables and that led commentators like Larry Summers to say, to bring inflation under control, you would need either many years of high unemployment or a sharp recession, 10% unemployment for a year to reset the inflation picture. In other words, in layperson’s terms, a soft landing isn impossible. Right? And this is what fit into the inevitable recession. That was the dominant received wisdom the last few years. Now, you know, these things are good starting points. They have validity historically and a lot of empirical data, but in the end it’s idiosyncratic, it’s very idiosyncratic constellation of drivers and risks. And so it was in the last few years. So let’s, let’s look at that for a moment.
00:36:25 One of these master models was also interest rate sensitivity, right? We, we think interest rates go up and that eases into disposable incomes for households, right? But in reality, mortgages in the us, unlike in Canada, mortgages are long term, didn’t actually take a big bite outta disposable income. Mostly fixed rate, exactly. Very long term fixed rate low. And most of them were done at low rates because we had low rates for a long time. Contrast that with the flexible contracts and mortgages in, in Canada where they lost a lot of disposable income. That wasn’t the case here. Same thing about interest rate sensitivity in the corporate sector. You know, the textbook tells you interest rates go up and investment will fall. But does it, you know, when you do the empirical analysis for whatever window, you’ll see a very flimsy correlation between interest rates and CapEx firms invest when they have a narrative to do so when they see a return on the investment, and if they believe the investment is beneficial to them, they’ll do it.
00:37:21 Whether the interest rate is two, three or 4%, and just look at what happened in the last few years. You had a lot of narrative and belief in worth worthwhile investments, data centers, software. So with or without higher interest rates, firms are going to do that. Particularly also because a lot of our investment has shifted away from, you know, fixed structures, physical investment to intellectual property, software type of investment, which has a much higher rate of depreciation. So a bridge or or road will be good for 30, 40 years, but software is maybe three or four years. So you constantly have to invest just to stand still, just to keep the stock of investment in this space, to keep it steady. You constantly have to run faster just to, to maintain that. And so there is, there was a lot of idiosyncratic drivers that led, that led to, to very different outcomes from what was predicted from a model based Phillips car type approach to, to reading that, that context.
00:38:20 [Speaker Changed] So a lot of highly regarded economists like Larry Summers kind of reminded me of the Paul Graham quote, all experts are experts in the way the world used to be. And we’re, we’re seeing a lot of that in that. So not only did people get the recession calls wrong for the past couple of years, what have we had two months of recessions in the past 15 years are, are we in a post-recession economy? Now,
00:38:49 [Speaker Changed] You can still get recessions, but I think we’ve, we’ve become better at fighting them. So this is the topic of stimulus. There, there are three different types of, there are two different types of stimulus that we describe in the book, across three chapters. And we differentiate between what we call tactical stimulus, which is just to smooth the cycle, accelerate growth in between recessions, maybe de-risk the cycle when necessary versus existential stimulus, which is when, when policymakers politicians step in, when the economy’s truly at risk of a structural break, those two types of stimulus, they’re, they’re evolving differently. I think the tactical kind is more challenged going forward. It was very easy when inflation was below target. It was very easy when interest rates were very, very low, there was little cost to the fed put you could do that. There wasn’t sort of an inflation risk as associated with it. That’s different now. And I think they will remain different now that we’re, we’re skewed to the upside. And in terms of inflation, we’re interest rates are, are likely to be higher for much longer. But the existential type of stimulus, the ability to step up when it’s needed, I think that is still very strong. And if you have another shock or a crisis or a recession, I think we’ll be able to deploy stimulus effectively still. So
00:40:03 [Speaker Changed] We said earlier, all recessions are not homogeneous, they’re all idiosyncratic and unique. But one of the things you mentioned in the book that kind of intrigued me, we shouldn’t conflate recession intensity and recovery. Explain what that means.
00:40:19 [Speaker Changed] Yeah. When covid hit, we had extreme data prints unemployment is, is, is sort of the exhibit A of the story. Unemployment went to 10% in 2008, but it went to 14% in 2020, right? So the intensity, the, the sudden collapse of activity was much more pronounced in covid than it was in 2008. GDP
00:40:42 [Speaker Changed] Also much worse during the first few months of covid. Then g
00:40:46 [Speaker Changed] All variables. And we have a chart early in the book that shows the fifth to 90th percentile of, of historical experience of these variables. And covid is like far outside that historical range. So you get data prints that you’re not used to, that the models don’t know. The models were trained on, on data points that were simply not experienced until they happened in Covid. Now all of that fed into extreme intensity was equated with this will be a very long and difficult recovery, why the 10% unemployment rate led to many years of, of recovery in the 2010s, right? So now if the unemployment rate is even higher, it’s gonna take even longer to work it down to a level that is, that is, you know, a good economy again. But that wasn’t, that wasn’t the case. 2020 wasn’t about a balance sheet recession, it wasn’t about banks repairing their balance sheets. It wasn’t about households repairing the balance sheet. We, we took care of that with stimulus and therefore the ability to recover was much faster, much stronger. There were other idiosyncratic factors. Essentially what was underestimated was the, the ability to adapt of society. You know, societies found, found ways to, to work around the virus. The, the pathway to a vaccine was faster. So there were a lot of things that were underestimated.
00:41:59 [Speaker Changed] You know, it kind of reminds me of the Y 2K fear that when there’s a little bit of a fear of panic, the expected crisis may not show up because we’re taking steps to avoid it. We don’t know what was Y two KA false alarm or did the fear lead us to make sufficient changes to avoid problems? I, I honestly can’t answer that question. I, I’m wondering how you look at crises in terms of do some of the fear mongering and some of the, you know, media absolute extremism lead to government action that prevents the worst case scenario from happening.
00:42:42 [Speaker Changed] It’s possible that it shapes the perception of, of policy makers and politicians, but I think the realities on the ground, you know, the variables that are visible and measurable, the unemployment rate, GDP growth, you know, imports, exports, all of that was under pressure. I think that is more telling for those who, who take decisions than what public discourse does. Is public discourse, particularly fearful in a lot of angst, pervades how we think about the economy? Does that spur action? Maybe that, that’s part of it. So we, we don’t know, as you rightly say, what is, what is what would’ve been in a counterfactual world. But essentially when the economy is genuinely in trouble, I think the, the willingness to act on the stimulus side is very strong.
00:43:29 [Speaker Changed] So, so let’s talk about some of those metrics. You, you have a image in the book scanning the recession barcode. So tell us about that and the history of us recessions, which seem to have been more frequent and more intense. You go back a century, they were depressions, not even recessions. Tell us about how this has changed over the past, I don’t know, couple of hundred years.
00:43:54 [Speaker Changed] Yeah, so if you do a very long run chart for recessions in the US economy and you shade each recession as a bar, what you get is a barcode of image that looks a bit like a barcode, but it thins out as you move to the right. So you had recessions very frequently a hundred years ago and, and further back the economy was constantly in recession, essentially half the time it was in recession. Banking
00:44:21 [Speaker Changed] Panics all the time. Yeah.
00:44:22 [Speaker Changed] But also real economy, you know, the economy was very agrarian, A bad harvest could drag down performance of the economy. So, so there were a lot of shocks, but yes, yes, there were also banking crises and, and things like that. And what we identify in the book is a recession risk framework. We say, look, all recessions come in one of three flavors. They’re either real economy recessions, which is when investment and consumption drop abruptly and pull GDP growth down. So that’s the real economy type of recession. The second is a policy error. When policy makers get it wrong, they raise interest rates too fast or too high, which only you ever know exposed whether it was the right thing to do. So it’s a very tricky thing to do. And the third type of recession is, is the most pernicious kind. It’s a financial recession when something blows up in the financial system like, like 2008.
00:45:09 And what we’re showing in, in, in this chapter of the book. Over the long run, the composition of these two drivers has changed over the last 40 years. The real economy recessions, they really took a backseat because the economy calmed down. The volatility come down, services play a bigger role in the economy today. So the less volatile than, than physical production, but also policy makers just got better at, at managing the cycle. So, you know, policy errors kind of also lost a lot of share, if you will, in, in the overall prevalence of, of recessions. But when you think about what has given us the biggest headaches, it was 2008 a financial recession. and.com in a way is also a financial type of recession. So the share and the risk from financial blowups is, is significant if you look at it in recent history. And that doesn’t mean that the next recession will be that type, but its share of the risk spectrum is, is relatively high.
00:46:07 [Speaker Changed] So what should we be listening to when we hear economists discussing various risks? What are the red flags that hey, maybe this is a little too doom and gloomy for our own portfolio’s best interests?
00:46:23 [Speaker Changed] Yeah, I think the, the litmus test for me is often what would it take for a certain outcome, a for a certain doomsday outcome to actually come to pass. Not just will it happen and what would be the damage, but walk me through the conditions that actually lead us to the precipice and then make us fall off that macroeconomic cliff. Right? We, we need to, we need to talk about drivers causes, we need to talk about their probabilities and their constellations. So, you know, it’s, it’s not good enough to say, you know, the model says the recession will happen. Walk us through exactly what is the confluence of headwinds that together make that credible. Right? It’s, it’s, it’s, it’s more than the point forecast.
00:47:05 [Speaker Changed] Huh? Real, really kind of intriguing. I also notice that I’m not an economist, but when I listen to economists talk about the possibility of a black swan or the possibility of this event, it, it’s almost as if there won’t be any intervening actions either by the market or the policy makers. Tell us a little bit about that. What was George Soros phrase? Reflexivity reflexivity. That, that when certain events happen, there are gonna be natural reactions that just prevent this extrapolation to infinity or, or to zero as the case may be. Yeah,
00:47:46 [Speaker Changed] I mean this is back to the topic of stimulus for first and foremost, 2008 came as a big surprise because the models in, in, in the early part of the two thousands, they didn’t even really look at the financial sector as a risk driver. They kind of assumed the financial system away. And then when the, when the problem brewed and, and the financial system itself, the models were kind of blind to that. And then the reaction couldn’t be, couldn’t be gauged if you didn’t have view of that. And the reaction really depended on, on stimulus. And stimulus is about politics. It is about policy. It’s not about economics. First and foremost, it’s about political economy. It’s about people coming together and, and fighting crises. And so I, I think that remains the case that the idiosyncrasy happens before the crisis. The drivers are idiosyncratic. But the moment a crisis starts, a shock hits, what happens as a reaction is also idiosyncratic. It is political, it is, it is about society, it’s about choices. It’s not stuff that you can model in a rigid natural science way.
00:48:50 [Speaker Changed] So, so let’s talk about something that clearly wasn’t in the models. Forget 20 years ago. They weren’t in the models five years ago or even three years ago. And that’s the impact of artificial intelligence on our economy, on the labor pool and on productivity. How do you look at a giant structural change like ai? How do you put this into context as to what it might mean across all these different areas within tra both traditional economic modeling and, and the real world?
00:49:25 [Speaker Changed] You know, we, we’ve had productivity growth the last few decades. Even though often the narrative is productivity growth is really, really low. We’ve had productivity growth just not in services, but in the physical economy there’s been pretty decent productivity growth even the last 20 years where we didn’t have productivity growth with services because it didn’t have the technology to move that part of the economy along. Now why is that? Essentially productivity growth goes up when technology displaces labor. That is really the definition of productivity growth. You need to produce the same with less labor inputs or more with the same labor inputs. But either way, technology, whether we like it or not, is about the displacement of labor. And we weren’t able to do that in the service economy. Now with ai, I think you have a better chance of doing this, at least the promise is very strong that this will work.
00:50:18 But I think we’re getting ahead of ourselves and I’m not saying that now we’ve published on this over the last few years, even even as, as Covid hit and even before ai, when the zoom economy was sort of this dominant narrative. It’s a hard slog to do this. It happens over years and it’s little by little. It’s not a flip of the switch. It happens very incrementally. And I don’t think AI will turbocharge GDP growth. It is a lift to growth over the medium term, but there are many little obstacles. There are many little things that need to fall into place for people to really adopt the technology. And for this to little by little give us a tailwind. So it’s not an abrupt step change, it’s, it’s something that is credible, something we need to work through. And then it will, will show impact over a 10 year frame, 15 year frame.
00:51:05 [Speaker Changed] So let me push back a little bit on one thing you said. And I seem to have this ongoing debate with economists who work in a larger corporate framework. We’re here in Bloomberg, giant company, big operation. My day job is a much smaller company under a hundred employees. And I have noticed just over the course of the past decade how our productivity has skyrocketed and it’s a services business. Finance is a services business and it just feels like the things that used to take so long to do 15 and 20 years ago are now automated. And it’s not that we’re hiring fewer people and it’s not that we’re working shorter hours, but the same size team can just accomplish so much more than they were capable of per like I recall the days of quarterly reporting and having to literally run a model, create a printout for every client, print it out, stick it into the right, and like it, it was like a week long process, right?
00:52:17 That all hands on deck every quarter and now it’s updated 24 7, tick by tick, it’s automated. No one cares about quarterly reports ’cause you could get it. And the joke is you have 24 7 access to your daily, weekly, monthly, year to date, five year, 10 year performance reports. Just try not to check it second by second, right? But the, the way, and that’s just one example, being able to communicate with clients to record and embed an interactive video with charts and everything else, right? That was like a massive undertaking and now it’s like child’s play, even though you’re, you’re doing the same thing, you’re just doing it faster, better, cheaper, easier. Are, are we somehow underestimating the productivity gains or are these just specific to, you know, that Yeah. One area.
00:53:19 [Speaker Changed] Yeah. So I, I have some pushback on that. I think the bar for productivity growth is, is a little higher and it’s very specific. It’s, it’s less inputs per output. So do things get more comfortable? Are they moving faster? Are they qualitatively perhaps better? Yes. But are we using less inputs to generate the same value or are we using the same level of inputs to generate more value? That is what we need to achieve. To speak of productivity growth, and let me give you an example that we use in the book. You know, I took an Uber from my apartment to, to come here into the studio today. And Uber is often upheld as, as the epitome of progress in tech. And it is fascinating. It’s a great app. I love to use it. It’s, it’s nice. But look, if you want to improve the productivity growth in taxi transportation, we have to talk about inputs and outputs, right? And the inputs are on the capital side, a car, and you’re not getting rid of that car. And on the labor side, it’s, it’s the driver. And the Uber car still has that driver,
00:54:17 [Speaker Changed] Not Waymo in parts of, of the west coast.
00:54:21 [Speaker Changed] Yes. And this is why I said it takes time incrementally that will happen and that will unfold. But do you think you’re gonna have driverless taxis in New York in 2028 or 2030? I don’t, it’s
00:54:31 [Speaker Changed] Like, well we have it in 2050, probably in 2040. I can’t tell you what exact year it’ll happen, but Right, it’s coming.
00:54:39 [Speaker Changed] I agree with you. And that’s the,
00:54:40 [Speaker Changed] The sooner we embed those RFID devices in vehicles and on street corners, like doing it visually in lidar is very 20th century,
00:54:51 [Speaker Changed] Right? Yeah. And that’s why I said it takes time over time. This will, this will be substantial lift to, to economic output. But it doesn’t happen overnight. It’s, it’s actually, it takes time, right? And there’s an additional important point about productivity growth that is, can also be shown in this taxi example when technology is truly productivity enhancing. You see that in falling prices, technology is deflationary, right? As technology does away with input costs, firms will compete with lower prices to gain market share. So across history, wherever you look as technology is becoming a credible force in production, prices will fall. Now look at Uber. Uber prices in New York tend to be higher than a yellow cab. Why? Because despite this expensive technology, you’re not able to produce this ride more cheaply. You’re not, in fact, you kind of have to monetize the technological expense. The app is expensive, all is expensive. So generally you’re paying a, a premium for the smoothness of the app and all that. Over time that may change. But watch prices, you wanna see productivity growth, whether it’s happening or not, you gotta look at prices. And that’s one of the arguments we’re making in the book.
00:56:03 [Speaker Changed] So, so let’s phonically adjust. We’ll stay with Uber, let’s phonically adjust that in New York City, if you want to taxi during rush hour, hey, sorry, you’re outta luck. Because the monopoly that, that was imbued by the taxing Limousine Commission and a handful of big medallion chain owners decided in their infinite wisdom that we don’t need to move people around rush hour. We’re gonna change shifts then. Which by the way, is my pet theory for how Uber penetrated. And so a, you could get a Uber during rush hour that you can’t during cab rides. You could get an Uber when it’s raining. Good luck hailing a cab in, in New York City rain. And you have the ability to schedule an Uber, you have the ability to get a higher quality car. You could get an electric car if you choose a larger car. Like I, I’m not a huge fan of traditional hedonic adjustment because it was a way of kind of tamping down on the cost of living adjustments always felt sort of disingenuous.
00:57:08 But I don’t think you could get anybody to say that Uber is not only better and I’m not a big Uber fan, but as a user, Uber is certainly better than a cab. And in many ways orders of magnitude better, more choices. More options and just a higher quality experience. Plus, you know, just the idea of having, Hey, is this a a work thing or I’m gonna use that card on the app. Well, no, this is personal, I’ll use that card. Right? So, so maybe taxis aren’t the best example, but when, let, let’s talk about economists. I, I want, again, I wanna stay with this ’cause I love the topic. Think about the quantity of research you push, you push out the ability to integrate charts and data and like I am been in this business long enough that I can rem First of all, when I started the guys in the technical group, they were doing charts with pencil and graph paper.
00:58:11 I’m not exaggerating, maybe that’s just a function of my age. But think about how, and the, the cheat was, you get a different feel when you’re doing it point by point than when you’re just generating it. Whether that’s true or not, at least that was the, when, when computers came along, people continued to do that. But think about the access you have to the just endless array of data, the ability to, to do that. I, I, I haven’t even mentioned your fortune column. Think about how much time and effort goes into putting out a column and you go back 25 years and it was just a horrific grinds. Like, at this point, everybody seems to use some version of Grammarly or some other editing software. The ability to put out, and I’m not talking about asking chat GPT to generate a garbage article for you. You writing something, cleaning it up, betting a lot of data and images, it just feels like, you know, to quote Hemingway, you know, gradually, and then all at once, it just feels like it’s so much easier to put out a much higher quality product with either the same or less effort than 25 years ago. Maybe I’m just hyperfocused on the junk I do, but what’s incrementally your experience been
00:59:36 [Speaker Changed] Like Incre Absolutely. Incrementally, there’s progress. But again, the, the, the bar we need to meet is, is value. Are we generating more value with the same inputs, or are we generating the same value with less inputs? That’s the definition of productivity growth. So if you can make all these charts faster and you save one economist on the team, well that’s productivity growth. Or you keep the economist and you double your, your number of reports and you also manage to monetize them and earn revenue for it. Well, that’s productivity growth. If the charts get prettier faster, fancier with the same number of economists in the same number of revenues, well, from an economic sense perspective, that’s not productivity growth. So it’s gotta be a change in the relationship of inputs to outputs if we are comfortably talking about productivity growth. And back to the Uber example, you’re right, you can get different cars to ride in. You can get the car, the Uber car when it’s raining, but you’re paying for that, right? So it’s not produced more productively. Right. You’re paying a surcharge, you’re paying the, the, the, the, the search pricing. I think they call it an Uber, right? So, you know, yeah, you can get it when it rains, but you’ll, you’ll pay twice as much. So it wasn’t, it wasn’t done more productively. Right.
01:00:47 [Speaker Changed] Huh. Really interesting. The gap between the increased quantity and quality of output, if we’re not monetizing it, or as a consumer, if you’re not seeing price de declines, then it doesn’t really count as productivity
01:01:02 [Speaker Changed] Gains. No, it’s gotta be a change in the ratio of inputs to outputs on either side. Either we keep all the staff and we earn more revenue with it. That’s productivity growth. Or we keep the revenue constant and we do it with less inputs. That’s more productivity growth. But you know, I, again, I’m not saying there isn’t productivity. There is, and, and there will be more and AI will have impact. It just needs to show up in value. Gotcha. In that, in that relationship between inputs and outputs, I,
01:01:28 [Speaker Changed] I see it qualitatively, but I completely get what, what you’re saying quantitatively are, are you still doing the Fortune column on a regular
01:01:37 [Speaker Changed] Yeah, we publish in Fortune relatively regularly. Whenever we see a, a cyclical or a thematic topic that we feel is pressing, we we publish with, with Fortune. Yep.
01:01:48 [Speaker Changed] Huh. Really, really interesting. All right. I only have you for a limited amount of time. I know you’re catching a flight today. Let me jump to our favorite questions that we ask all of our guests. Starting with what are you streaming these days? What’s keeping you entertained either Netflix or podcasts or whatever?
01:02:06 [Speaker Changed] Yeah, I’m, I’m not very big on, on shows or Hollywood. I mean, to give an idea, I think I’m on the second season of Slow Horses. I think, I think there are four seasons of it. I’m kind of slowly making my way through the second, second one. It’s very entertaining. I, I love Gary Ottman,
01:02:21 [Speaker Changed] So interesting. He’s great in
01:02:22 [Speaker Changed] It. Yeah. It was sort of the taking down the genre of, of spy movies in a, in a very entertaining way. So I’m, I’m doing that, but also I tend to watch late in the day when I’m tired. So it’s, it’s entirely possible I fall asleep and I take like two, three evenings to get through on episode. Yeah. So I, I’m, I’m not, I’m not all that big on, on that, on that front.
01:02:40 [Speaker Changed] Tell us about your mentors who helped to shape your career.
01:02:44 [Speaker Changed] So many people, right, because a lot of it is teamwork and, and you don’t, you don’t progress without mentors and, and role models. I would say in the, in my current role, I would probably call that two people, rich, lesser our long time CEO. And our chairman, he, he had the vision for a macro product, as did Martin Reeves, who, who runs our research institute, the Henderson Institute. And they’re really the two people who brought me into this role and coached me. So they stand out outside of BCG Kathleen Stefansson. She had many, many different roles on Wall Street and economist role. She’s, she’s been a, a great help navigating my career the last many years. And further back and academia, thesis advisors and many others there, there, it’s, it’s always teamwork in a way. So you have many, many role models and mentors.
01:03:34 [Speaker Changed] Let’s talk about books. What are some of your favorites? What are you reading right now?
01:03:39 [Speaker Changed] Right now, I am almost done with making sense of chaos by De Farmer came out last year. De Farmer is a very interesting character. He’s a complexity scientist at the Santa Fe Institute, and I think at Oxford University as well. And his book is interesting to me. I bumped into him at one or two conferences. But it is interesting to me, particularly because he kind of argues the opposite of, of what we argue in our book. So he thinks he agrees that economics is, is poor if you just take standard models and theory. But he believes he can crack the complexity of it. So he thinks with, with, with complexity signs and better data and better models, you’ll essentially be able to make those forecasts. I read it because it’s always important to see what others are arguing. I don’t read stuff that, that reconfirms what I think.
01:04:26 I wanna see what other people are saying about the same topic from different angles. So that book’s been, been very useful and, and also well written. That’s what I’m currently reading. I think of other books that I’ve read over the years. I mean, there’s so many, many great ones. Of course, I think one that early on made an impression on me was seeing Like a state by James Scott, huh? It’s at least 25 years old. I read it as a grad student. And what he does, he, he looks at the ability of governments to do top down policy to improve the lives of, of large amounts of people. And he shows all the pitfalls in a sort of hayekian way. It’s tough to have the local knowledge, it’s tough to do the top down improvements. Things have to grow bottom up. And that book kind of stood out for being very, very eclectic. Very multidisciplinary, and still, I think an excellent book to, to how to think laterally and not in a sort of strict model based way.
01:05:23 [Speaker Changed] Huh, really interesting. Our final two questions. What sort of advice would you give a recent college grad interested in a career in economics, investment finance, anything along those lines?
01:05:36 [Speaker Changed] Yeah, I, you know, I think a career as an economist is challenging in some ways. There, there’s so many economists out there often when, when I hire, you see the, the flood of cvs and often very good cvs. And there’s, I think there’s been an overproduction of economists. So I think doing something adjacent to economics, you know, work in finance, work on the buy side, work on the sell side, unless you tru, unless your heart truly beats for economics. I, I think, you know, you can use economic skills and, and many adjacent disciplines and, and careers, I think are, are plentiful and, and those adjacent disciplines, if economics graduates really feel strongly about economics, it’s fascinating, but your heart has to be in it. And there aren’t all that many seats as economists, right? So, so when has to build that over the long term.
01:06:30 [Speaker Changed] And our final question, what do you know about the world of economics today? You wish you knew 25, 30 years ago when you were first getting started?
01:06:39 [Speaker Changed] Yeah, well, I mean, that’s really what I wrote down in the book. You know, the book is, is the 2025 year journey through the maze of, of the economics, profession and discipline, the themes we touched on the master model mentality, the pitfalls of, of trading economics, like a, like a physical science, the doom mongering, which we have to simply ignore most of the time. And then the eclectic approach to economics. I call it economic eclecticism, drawing on, on, on a broader range of disciplines. Those are the things that I, that I learned through that path the last 20 years. I wrote them up in the book, you know, it would’ve been, would’ve been interesting for me to read that 20 years ago, but I, I wrote it now. And so I’m happy with that. Huh.
01:07:23 [Speaker Changed] Really, really intriguing. Philip, thank you for being so generous with your time. We have been speaking with Philip Carlson Leszek, he’s global chief economist for the Boston Consulting Group. His new book, shocks, crises and False Alarms, how to Assess True Macroeconomic Risk. Co-authored with Paul Schwartz is an absolutely fascinating read. If you enjoy this conversation, well check out any of the past 500 we’ve done over the previous 10 years. You can find those at iTunes, Spotify, YouTube, wherever you find your favorite podcasts. And be sure to check out my new book, how Not to Invest The Bad Ideas, numbers, and Behavior That Destroys Wealth Coming out March 18th, 2025. I would be remiss if I did not thank the Crack team that helps us put these conversations together each week. My audio engineer is Andrew Gavin. My producer is Anna Luke Sage Bauman is the head of podcasts at Bloomberg. Sean Russo is my researcher. I’m Barry Riol. You’ve been listening to Masters in Business. I’m Bloomberg Radio.
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