The video can be watched here. Here’s my interview with Doug Campbell, winner of the SSC forecasting competition!
Isadore:
Hi Doug. It’s nice to meet you. I wanted to put you on my show originally. When I say show, I mean very loosely. I like to interview public intellectuals. Once in a while I find the subject area to be interesting and I like to create the sort of content that I would like to consume. So as we were just talking about, you moved to, or you lived in Moscow and were working at the New Economic School. Tell me a little bit more about that.
Doug:
Yeah, so basically when I finished my PhD, I just applied to schools all over the world. I happened to get this interview in Moscow. I had actually no interest in going into Russia and I never, it’s not something that I dreamed about. But during the interviews, just before the interview, finally I looked up who I was doing the interview with, and one guy had a PhD from Harvard, another one’s from MIT, and I saw they had very good publications. And so I assumed, okay, this school, they must have a lot of money. During the interview, they said that the teaching load is a one one, which means you just teach basically two semester classes a year. So that’s pretty light and that the pay is on par with a top 100 school in the us. And so then I immediately became interested just because the cost of living is much less in Moscow than in the us.
I’d also had some experiences when I was younger. I spent over a year studying in Japan when I was younger, and I kind of had liked the expat experience in a country that’s just so different from the us and so going to Russia was a similar kind of experience, and so I really enjoyed it. I mean, Moscow, it’s a hugely underrated city. I mean now it was very good until it wasn’t. And so once since the war started, I first was a digital nomad for about six to nine months, and then I ended up, now I’m in Washington DC area.
Isadore:
So that’s a great starting off point. What I was curious about is first of all, what is the digital nomad life and does that influence your intellectual influences at all?
Doug:
So I’ve always liked to travel, and one of the things I really liked about the job in Moscow is that they encourage me to travel to conferences from Moscow in the era before all the sanctions, it was also easy to fly to different countries in Europe. And so I really enjoyed my first few years flying all these different countries and giving seminars or going to conferences. And so I just generally had traveled a lot. Also, the one thing I didn’t like about Russia is the Russian winters are so harsh. So I would typically fly south during the winter for a month or two months maybe I’d spend a month in Thailand or Vietnam or South America. And so I’d always kind of just enjoyed traveling. And so it wasn’t that much of a difference for me to go and become a digital nomad. And basically I was living out of a carry on suitcase essentially.
I would typically go to one location, you can get a discount when you book an Airbnb for a month. So I’d book a month in a city and go and just kind of experience the local culture. I was also doing a lot of work at the time, so working a lot, but then also just enjoying the local scene. I think the big thing that’s cool about it is you can go to a lot of places where the cost of living is very low and you can live very well, and as long as you have fast wifi, you can earn income compared to now living in Washington dc So my wife is waiting on her green card, and so we kind of got stuck in the US for a while and pay a lot just for housing, whereas we could probably pay a third as much for a month long Airbnb and Bali if we could. So it doesn’t feel super worth it if I’m now working mostly online. Also, there’s some tough aspects. It’s kind of nice to have route somewhere to have a set group of friends. When you travel it, it’s easy to meet people and it’s cool to meet new people and have new experiences, but if all of your friends are changing each month, it can also, that’s not always nice.
Isadore:
Well, I guess that begs an interesting question, which is if it is a significantly cheaper to live abroad and work an American style job remotely, why don’t more people do it?
Doug:
I think a lot of people just are kind of biased towards what they know. I mean, back in the day, I think only 15% of Americans had a passport, even though we have one of the strongest passports in the world. And so I just feel like there’s a huge bias or fear part of it’s language. Most Americans only speak English and maybe just a lack of experience with traveling, but I think it is definitely a mystery and it’s some kind of home bias. And I think it’s also true that wherever you are, you tend to just want to stay there rather than pick up and move somewhere else. I mean, even within the US they say that people are pretty biased towards just staying in their home city, and even if they get higher wages in a town in the next state, a lot of people don’t do it. So the implied moving costs of making new connections, leading new people, also just moving is a hassle itself. That’s not fun.
Isadore:
I actually just moved too. I was working in technology in Madison, Wisconsin area, and I recently just packed up and moved back to Greenwich. I thought I’d find better opportunities there. Right,
So I guess that just to stay on the academia subject a little bit longer, before we go into the forecasting topics, how do you think academic culture in Russia differ from academic culture in the United States? Are departments similar? Do they have the same sort of mores?
Doug:
Yeah, interesting question. So the institution where I am is extremely westernized, and so it’s kind of modeled after a US PhD program. And we were always essentially a standalone economics department. And then of course we added finance and some other programs that are closely related to economics. But I think our institution was somewhat unique in that we never had to deal with vast amounts of bureaucracy. I might have if I worked for almost any other educational institution in Russia. Also, I think our salaries were just much higher than almost any other university. So even I’ve heard that tenured professors at Moscow State University, which is one of the most prestigious institutions in Russia, can make maybe just 50,000 rubles a month or something, which is $500 a month. And so it’s not really enough to survive in Moscow. I dunno how they do it. Maybe they supplement their income in various ways.
There is a lot fair amount of corruption in universities in Russia, so maybe professors will take bribes from students. At my institution, I never saw this and I would be very skeptical that had ever happened at my institution in particular, but I’ve heard stories about people who went to say a university in Crimea or somewhere else where people had issues, one where someone had to pay a bribe to get a PhD at their institution, for example. And so I think corruption is out there. Another thing that’s kind interesting is just the Russian way of coming to decisions in an organization and a Russian organization. The head, the CEO makes all the decisions, and this is a little bit true of my institution, even though I think it’s probably also somewhat different than most, which is that the rector of the new economic school, the head of our school, has a very difficult job because he has to make all these little decisions that in a western institution might be made by someone much lower down the organizational chart.
And this is also, by the way, it’s true of the Russian military that I think Putin is kind of making decisions on the battlefield that in the US maybe just a colon would be making about which troops are going to attack where. And so also it’s like basic decisions in Russia tend to get made very high up the food chain. And this is partly just a function of culture and just the long run political economy of Russia and the Soviet Union where Stalin’s word was God, no one wants to cross Stalin. And so no one wants to make a decision if they can give it to Stalin, but then it ends up the head guy has to make a huge amount of decisions. It’s just not feasible to think through all these things.
Isadore:
Yeah, it sounds like an IO problem.
Doug:
Yeah, exactly. Exactly.
Isadore:
That’s super interesting. I was originally going to move into something else, but I want to talk a little more about this. So what sort of methods does data get to the high up decision makers? It sounds like middle management is sort of not very much a thing in Russia.
Doug:
Oh, you mean in Russia? Do you mean politically?
Isadore:
Yeah, politically or also in institutions in general for what you were talking about before.
Doug:
I think information flow to Putin is a very big issue, and I think he doesn’t get a lot of the real information of what’s going on. And I think this is a big impediment towards ending the war because he’s always been thinking Ukraine is on the verge of collapsing or that Ukraine would be easy to conquer. And so I think his information perception is it’s a key issue in the war, people said that Stalin was actually a good leader in some ways, at least a war manager, because he was so paranoid and distrustful of people that you couldn’t bullshit him. He was always letting people relying to him. And so he was always going to try to get the truth out of you, but Putin, you can bullshit. He likes to hear certain things and you can stroke his ego. And so he’s not as grounded in reality in the situation.
And it’s kind of been clear from a lot of decisions that Russia’s made. I mean, it’s been reported that this is the case. What extent is it true or not? So for example, they invaded with a pretty small force. I think they really did expect to take Kiev or at least come close or conquer half of, or the whole of Ukraine, they thought it was a realistic outcome, turned out not to be after the war started. They really didn’t mobilize the economy until the last year. And even recently, I don’t think they’ve really been spending the amount of money that you would expect if this war was really as important as you thought. In the first year of the war, I think they spent three or 4% of GDP on the war, which is the US spent like 45% or something during World War II and almost 20% during the Korean War.
And Russia was spending 3%. And they were also lacking a lot of material that they could have just spent heavily on building and buying drones and things like this. And they didn’t do it, in part because I think Putin didn’t realize what the situation was. They also waited a long time to mobilize initially, even though it was kind of well known that Russia had a manpower shortage in Ukraine. And then it wasn’t until after Kyiv, the Kiv offensive happened that Putin grudgingly decided to mobilize their troops. And so there’s been a bunch of decisions like this where it’s clear that whoever’s leading Russia doesn’t really understand what’s going on the battlefield. So unfortunately, the West and Ukraine haven’t punished them yet for it as badly as could have been, but yep.
Isadore:
Okay. So moving on to another topic. Very interesting, by the way. Tell me a little bit about forecasting. I know that you run your own prediction market. Can you tell me a little bit about what a prediction market is and how maybe your experience might’ve helped you to beat the market?
Doug:
Yeah, sure. Yeah. So a prediction market. So I do run a prediction market called Inside Prediction, and essentially it’s a market where you can predict what’s going to happen with event future. So our first big market was on will Russia invade Ukraine, and you could bet these binary options contracts, which would resolve either one or zero, one if it happens, zero if it doesn’t, and you could buy shares at different prices between zero and $1. And so I’ve been interested in prediction markets for a long time. I’ve always thought it was very fascinating the way that by aggregating people’s information in this way, you can reach a more likely probability of an outcome happening. And I think it’s the benefit of prediction markets and just kind of the wisdom of the crowd logic. I think it is very powerful, and it’s also part of the reason I think in Russia you have Putin making decisions and maybe he talks to a small amount of advisors, but it’s only specific with specific characteristics he talks to.
He doesn’t talk to everyone else. And you also saw this with President Xi in China, I think got in this situation a little bit too, with the zero covid policy. If you just have one guy picking something, he can be very wrong about something. Where the one thing you can say about Western democracies is that, and this is definitely true when I worked in DC, is I felt like a lot of people were just, they were always looking for building consensus. And this democratic Republican party, they’re kind of consensus driven enterprises, which is kind of a little bit like averaging predictions in a prediction market across different participants where each person who participates maybe has a little bit different information. And once you average that all together, you get a more precise prediction about an outcome. So that’s one of my fascinations. Another part of this is that, so I’m an economist, and I’ve always felt like the public’s idea of what an economist does is they predict what’s going to happen with the economy.
Academic economists don’t do this, or at least very few of us do this. But I’ve always felt like to be useful, you should be able to make predictions especially about the future. And so I always thought it was something that the profession, academic economists should really take more seriously. And so a lot of people, economists, they don’t make predictions in part because if you make a prediction and it turns out you’re wrong, then people can find out maybe you don’t really know as much as you think you do. But on the other hand, if you make a lot of predictions and maybe you can improve at it over time and get better and you can learn more. And so I think it’s important part of learning how to predict is just to make predictions, make them publicly and try to do the best you can and learn from each iteration.
Isadore:
So with that in mind, it sounds like you’ve been doing this for a while, and that probably explains to some extent your highest score in Scott Alexander’s forecasting competition. Yep. What frameworks or methodologies have you found have been helpful to predicting things?
Doug:
So one thing, yeah, I mean, there’s a bunch of things I could say here. One is that for some of these topics that were in Scott Alexander’s prediction market contest, he had a lot of predictions about the war and just reading about the war on Twitter or about almost any other topic, there’s going to be some people who they act like they’re experts about a topic, but maybe they’re very passionate in their analysis. They really have, maybe they know a lot about a topic, but they got married to one view or another view. And so about Ukraine especially, I think there was a lot of commentators on Twitter or elsewhere who had a very strong and passionate views in one direction, either pro Ukraine or pro-Russia. So you always want to avoid those people with the analysis. You probably want to read what they say, but you don’t want to be brainwashed into it.
And so especially when you have a whole community of people either on maybe on some chat room that you’re involved with, where everyone has the same view about something and everyone wants to bring in more information that confirms everyone else’s view about a topic, you want to avoid that. So if you’re only reading right wing media or left-wing media or Ukraine media or anti Ukraine media, then you’re going to really limit yourself. And so you want to really read everything and have an open mind. I mean, this is kind of obvious stuff, but I think this is a big part of it. Another thing that’s important, if I’m betting in the market or I’m predicting in the market, I’m very curious in what other markets predict about a topic. And so just one basic thing I did in Scott Alexander’s market is I would definitely, I’m sure I looked at what the prices were for related markets on inside prediction, on poly market, on meticulous manifold, all these different prediction markets. And I probably even read also what kind of comments people wrote. And sometimes people write comments on these markets that are meant to pump the market in one direction. So you can’t always believe what’s getting written, but I think it’s still interesting to read and learn what people are saying about a market. So did
Isadore:
Did you mostly aggregate what the markets did and then adjust them slightly?
Doug:
Yeah, so another thing, I guess a lot of the topics that year, if I remember correctly were there was a bunch of markets about Ukraine, and I was kind of obsessed with the Ukraine war for a while. I mean, I’ve calmed down in the last six months or so, but I was reading everything about it. And I think that’s one thing too, where you definitely want to almost be a little bit obsessed with the markets that you’re predicting in, or if you really need to predict something, you should be obsessed with it. And then there’s also a lot of markets on economics topics, which were kind of in my wheelhouse. And I was also really thinking, because there’s this big debate at the time about will the US be into a recession? Will inflation come down? Will we have a soft landing or not? And I kind of thought it was likely that we were going to have a soft landing. And I think those are some of the predictions from that year. But I feel like my background as an economist and reading the debate probably just helped naturally with that one.
Isadore:
So would it be fair to say that you are like, all these topics are correlated, so if I get one, I’m probably going to get most of them surrounding Ukraine, and then you had technical expertise in another large area, which played a large role?
Doug:
For sure. And so what I would do typically, maybe I would start with, I don’t know, take the recession odds from meticulous and then kind of adjust them in which direction. Another thing that can be powerful is a lot of these platforms, they might have a political bias in one direction or the other. And so something like manifold or meticulous, I think it’s fair to assume they’re probably going to have a Ukraine bias. And so if it’s a Russia related war question, and the platform itself has this bias, anti-Russian bias, just adjusting everything by 10% is a way to beat the market average, for example. So this certainly happens.
Isadore:
So it sounds like your intuition is largely, things are influenced about 10% by the foundational or institutional bias?
Doug:
For sure. For sure. It’s something to pay attention to. And sometimes you can assess this by what people are posting. I mean, another thing is just all the tips and super forecasting I think are useful. So things like what’s the probability event to happen in the future? Well, how many times has it happened in the past? How often does it happen in the past? It’s just to give you some kind of a base rate to produce in the future and then adjust that based on the other information.
Isadore:
Yeah. Moving into one of your papers, I know you argued that the effect of entering a currency union is unclear when you more rigorously control for results. I’m sort of curious, how did you start writing this paper? Did you come in with certain intuitions that you felt were important and considered? Walk me through your process.
Doug:
Yeah, so basically I was a first year grad PhD student at uc Davis, and this paper came up, it was on the syllabus for a second year course I was taking during the first year. And this finding came up, and it was a big finding in international macro at the time that when you enter into a currency union, it doubles the amount of trade, had some hugely large impact on trade. And I was immediately like, this is bullshit. I don’t believe this all, it must be reverse causality. It must be that countries that already trade with each other trade more. That turned out not to be quite true. It was more subtle than that because these guys were looking at country payers which had entered or exited a currency union and then looked at how much they traded before or after the currency union switch status.
So I coded up the data and it took me about 30 minutes to find partly what was driving this. And so it turned out that there weren’t a lot in this sample. And this guy’s dataset, and this was a prestigious Berkeley professor, it turned out a lot of the data points that he looked at were actually former UK colonies where they ended a currency union kind of at the end of colonization or a little bit after. But the UK had free trade throughout the empire. And then over time, when these countries were decolonized, they still traded quite a lot with the uk, but over time, they started to treat more and more with other countries and relatively less with the UK and only some of the UK’s former colonies. It had tons of former colonies had ever shared a currency union with the uk. So the first thing I did was just kind of control for a trend between kind of the slow decay and former UK colonial trade ties versus countries that actually had a currency union and separated and just putting in a simple time control kind of killed the impact.
And then I looked at other examples, and another example I found was India and Pakistan shared a currency union up until they fought a war with each other. And then for a while, they didn’t trade at all after the war because they just hated and they still hate each other essentially. And so these guys had ascribed this breakout in the currency union to actually this kind of brutal war between the two countries. And so there ended up, there’s a lot of examples like this. There’s another example about Madagascar and I think, or one of these islands near Madagascar, and it turned out that in Madagascar, which is an island at some point, well, it turns out that even though they’re a big island, they don’t like people from small islands. And there was an ethnic episode where they started killing all the people from smaller islands.
And this episode of killing a bunch of people from W WA two and Madagascar led to the breakup of this currency union. And of course, these countries had a hostile relation after that. And so these guys were ascribing this breakup in the currency union to what happened after. And of course, I wrote these guys at some point about this, but they didn’t change their tune. They even wrote another paper later on. It didn’t cite my work, but it thanked to me, which is kind of very dirty. And it didn’t take into account any of this stuff I’m saying. So it still had Indian Pakistan in the dataset. I thought it was just like, it kind of gives you an idea of the experience I had and academia where I felt like this original published research, it was very well cited, showed up in a lot of textbooks.
(
Some of the most prestigious trade economists, mark Melis at Harvard had also written a paper where he said the currency union effect was large, and he published it in the QJE, the most prestigious journal and economics, and he was tenured at Harvard, very prestigious institution, but he still came to, the guy had the totally wrong idea about this and was kind drawn in. I kind of felt like it wasn’t the most just situation. I think I published my paper in a minor journal, even though it was correct, it didn’t change the trajectory of the literature. People continued to publish a bunch of papers that said currency unions had a big effect. And then years after that, I published a second paper on the same topic with updated data, finding that, no, it’s still not super robust. And so yeah, another frustration I guess is that the most academics are very bitter, as you can tell.
When I published this years later, I sent it to a journal where essentially they said, oh, this topic is not interesting anymore. And while I sent it to this journal, they published another paper which said, currency unions have a big impact on trade. So this is a problem with academia in that a positive finding is always interesting, but a null result is not interesting. Someone did come up with a journal like the Journal of Null Results, and all of academia kind of has this spiral drawer problem, which is that if you find X doesn’t really have an impact on Y, nobody really wants to read it. But if you a surprising result that, I don’t know, property rights has a big negative impact on marijuana consumption, it’s big surprising that’s going to be publishable because it’s kind of surprising, it’s sounds interesting, but results like that are also more likely to be wrong.
Isadore:
Yeah, I could see that. Kind of getting into that, I know that the topic you’re describing sounds like it’s a little bit creating the conditions for p hacking.
I’m curious, do you see systematic ways that P hacking takes place that can be monitored or given additional scrutiny? And if so, what’s stopping this from happening?
Doug:
Yeah, so for sure, I think so. So me with a few co-authors, we have this made a replication project actually. We have a couple of major replication projects. And so one of co-authors, one the projects, essentially, we are now testing robustness on every paper published in some of the top journals and economics these days. And so through this, we’ve often found bad coding errors, which might have invalidated results. We’ve found papers where just kind of a slight change in the author’s specification ends up with totally different results. For example, I think this will be a positive force for change just to, I think if enough people get caught with coating errors and very flimsy results, I think people will maybe think twice before they might check their code one more time before they send it to the journal. They know that somebody’s going to be checking this, some grad student’s going to be assigned to replicate this paper and check the code, and if they’ve screwed something up, they might get a retraction.
When that’s a real threat, I think people might start to react a little bit because up until now, historically in the economics profession, it was considered kind of unpolite to criticize somebody else’s research. And so I think you can realize the problem with this. It’s very unscientific, but it’s just natural human nature. So Jeff Bezos made this argument, I think it applies to academia, that humans are not really made to be truth tellers. And so if you go back to hundred gatherers or people living in small groups, being a truth teller was not the best way to survive and thrive. Instead, if you go along to get along, you can probably do pretty well. But if you’re going to be the town truth teller, this might get you in trouble if it interferes with the power structures. If you’re telling inconvenient truths that people with higher status don’t want to hear, that could get you in trouble.
(32:54)
And that’s very true in academia as well. So it was always thought that, oh, if you’re a junior economist, don’t write comment papers. Don’t rock the boat. And so Bezos would, when he was at Amazon, one thing he would do when he had a meeting is he would usually have the most junior person speak first, because he just realized if he, Jeff Bezos, the CEO of the company, first step is, I want to start Amazon Prime. And okay, what do the rest of you think about it? Everyone’s going to say, that’s the greatest idea of all time. Everyone realizes they can’t be a come cross opposed to what Jeff Bezos says. But academia is set up in a way where the most senior position, they speak first and they speak last. If they publish a paper on a topic, it becomes, you’re not allowed to say, oh, that’s not true. And if you do, you can become persona non grata pretty quickly. But I think we realized that this is totally antithetical to being a scientist and to science. And so, yeah, go ahead.
Isadore:
I was going to say, it sounds kind of like a collective action problem, and that the people who would do these corrections would be the people who would pay the cost for doing it with the broader industry, benefiting
Doug:
Without a doubt. Without a doubt. And so another way this comes into play is that, so I’m actually up for tenure right now. I’m kind of a little bit on my way out of academia, and also it’s just become complicated for me to be in Russia. So whatever happens, I’m not super concerned. But during my tenured track process, I had published a comment paper on something, and I got kind of a negative evaluation in one of my pre-tenure evaluations about having published a very true, well-written comment paper. And so the other thing is that there’s kind of a view that I have a publication in the top five journal, but it’s a comment. And so for some people’s eyes, oh, it doesn’t count. If you’ve invalidated a seminal paper that’s not as good as publishing your own new study. Even if your new paper to get published in the top five journal, it probably has to be a little bit hacked.
Right? So another thing that’s interesting that we found in this, one of these studies I’m working on now is that we replicated a full year of the American Economic Review, and every paper has a result that the authors argue is very robust. So each paper has a result where they have a result. Maybe they ran 40 robustness checks on that result, and everyone had a P value less than 0.05. And this is true for all 17 papers that everyone, so we’re about to run out of time.
Doug: (after we got the video up again)
I’ll say that again. So basically one big difference between the two is that prediction markets are kind of fundamentally about seeking truth and providing relevant information. I mean, most of the time. So not always, some of the markets are just kind of fun, but if you’re participating in a market and you are not adding to the informational output of the market, you’re going to lose a lot of money. Whereas in academia, you can write a lot of bullshit and as long as you have friends in the profession or that the bullshit you write makes the right people happy, as long as you can maintain the right alliances and you can make friends, you can do very well without really ever contributing anything of value in academia. So I feel like for this reason also, it’s just academic economics and my personal view is just not an enterprise that’s associated with seeking truth.
So that said, I do think there’s a lot of individual academics, which very interesting, very useful things. So Brian Kaplan, a professor at George Mason once wrote a book, the Case Against Education. And so basically with one book, it had a lot of the problems and shortcomings in the educational system. But on the other hand, he’s an academic at a university and he is writing a very interesting book. And so there are some positive things about academia too, and I do think there’s a lot of good things happening, but overall there’s also a lot left to be desired. There’s also a theory that 90% of anything is kind of bullshit. And so this is probably true about academic research and a lot of disciplines, but overall, a lot of things, great things have come out of academia. You think about the covid vaccines, the basic research for that was done in university labs, for example. And this is true of a lot of basic research and a lot of disciplines. There’s been a lot of great academic economists who’ve written a lot of interesting things, interesting books, which I felt like I learned a lot from in grad school. So there’s some great things too.
Isadore:
That’s super interesting. I guess something I’m kind of curious about, do you think that basic research sort of crowds out other forms of research? Or do you think that they’re sort of complimentary?
Doug:
Yeah, I would definitely, that’s a good question. I would definitely see them as kind of complimentary. It’s hard for me to judge research on very specific narrow topics in other fields, but I think it’s certainly been the case that maybe you have some basic research and then to apply that to a business can be a big leap or to take some of these new ideas from academia or new medical research and then make that into a successful startup business. It’s just a totally different skill. And also the research even about economics topic that gets done in private space or in government just tends to be very different from what’s going on in academia, but certainly complimentary. And it’s important to have both.
Isadore:
Do you think that the think tank equilibrium where it’s people who are a little bit more rightward or a little less progressive are pushed into an adjacent sector is sustainable?
Doug:
Interesting question. I think, yeah, so I’m a big believer in persistence, and so this is kind of the way things are now. And so I think most things, and this is also true with predicting most things, is like the status quo today. That’s probably what the world’s going to look like tomorrow for a lot of questions. And so I guess my instinct on this would be that probably not much is going to change. Where academia is mostly pretty liberal. Economics is a bit different from a lot of other disciplines. I think it is much less liberal, but still there’s a lot of biases within academic econ and some fields are very liberal, some are very conservative. I guess it’s a fair question. Will academia survive being as liberal as it is given compared to the rest of the populace? And I think there’s probably some pressures there, but a lot of people are kind of writing the obituary for academia, but I don’t think it’s going to happen.
My wife’s sister, she’s a few years out of undergrad and she was thinking about going and doing a nighttime MBA and to do a nighttime MBA, which is a kind of a bullshit degree. You are not going to learn that much in the MBA program. You’re going to get a degree slip a diploma at the end, which says, Hey, you passed these courses, which are not going to be super difficult and probably you’re going to learn some things but not going to be super useful, but it’s this degree. And you can say, I have an MBA from this fancy university, and they were charging, they’re looking to charge like 120,000 over two years for a part-time MBA program. And that’s a huge amount of money. Apparently. There’s people, they wouldn’t be offering that much if there weren’t a lot of people that were willing to pay. So just because of the way that academia is associated with prestige and with proof of competency and pedigree, I don’t think academia is going away anytime soon. I feel like if universities like Harvard were allowed to just charge whatever they wanted, I bet Harvard could charge 5 million a year and they would be able to fill up their student body. It’s a good business to be in.
Isadore:
I agree. Well, Doug, it has been wonderful chatting with you. I want to make sure I’m respecting your time, and I had a couple other questions, but I wanted to thank you for taking the time to chat with me today. I’ll try to splice this and have this out soon, and let’s keep in touch. I really appreciated chatting with you.
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