 Good morning, everyone. The topic this morning that I have is the most exciting topic of all of Mises University, Austrian Alternatives to Conventional Economic Statistics. Yeah, that's right. It's a great way to get started in the morning. It's talking about stats. Right off the bat, I'd like to just acknowledge the stereotype that we all have and we've heard of, which is that Austrians are the students who started their econ programs and realized that they just were bad at math. They didn't like math. And so they just decided to stick with the school of thought where you don't have to do much math. I've definitely seen and been on the receiving end of that stereotype in my past, but it's important to note that Austrians do have a healthy appreciation for numbers and data and statistics. Probably a great example of that is the presentation that we saw yesterday afternoon with Jean Epstein talking about data. So if we just think about this stuff in a correct way, then we actually come up with better numbers that tell a more coherent story, a better story that corresponds with reality. Also just add that there are plenty of Austrians that are good at math or like math. I like math, so take that. Rothbard was a mathematician. He was well-trained in math. And even in the books that you see in the bookstore here, there's plenty of references to data and statistics, especially in economic history. So we're not afraid of math. We just have a healthy appreciation for math. Probably the two best examples of this is in what I would consider the crown jewels of Austrian economics, which are the economic calculation critique of socialism and also Austrian business cycle theory. Both of those hinge on good numbers. Are they hinge on accurate numbers? The economic calculation critique of socialism is based on the fact that entrepreneurs don't have the right numbers, or they don't have any numbers at all actually. So the entrepreneurs need profit and loss accounting. They need to be able to add up revenues, or at least anticipated revenues, and the cost of production to be able to have some sort of anticipation of profitability for the line of production that they're considering. But if they don't have that, then their production decisions are chaotic at best. The whole economy tumbles down into darkness and despair. So you need good numbers. And the same applies to Austrian business cycle theory. When the central bank increases the money supply, or the money supply increases in other ways that comes through credit markets, the interest rate goes below the social rate of time preference. And so the interest rate no longer reflects time preference. And so we have all of the chaos and the structure of production and the boom-bust cycle results. Once again, another conclusion in Austrian economics that hinges on numbers, hinges on numbers being correct, and we need good numbers. And like I said, for Austrians to do good economic history, we need to be able to look at numbers, look at data from the past, and make sense of it. First, let me go through some essential reading here on this topic. There's a wonderful article by Ray Rothbard. It's in the Economic Controversies Compilation that Gene Epstein wrote the introduction for. He mentioned that yesterday. It's called a Torta Reconstruction of Utility and Welfare Economics. And in this article, Rothbard, he criticizes the neoclassical view of utility and welfare. And he shows what's an appropriate way to think about welfare, what's an appropriate way to think about utility. And it all comes to the conclusion that the only way that we could say that some sort of situation is welfare improving, or there's an increase in welfare, is that there's unanimous consent by all parties. So in voluntary exchange, both people consent to the outcome. And so we could say that that's a welfare enhancing process, as opposed to what you see in neoclassical, where they're all using math. They're looking at utility functions. And if there's an increase in total utility as a result of exchanges or other actions and events, then we would say that that's welfare enhancing. But Rothbard says that you can't treat utility that way on fundamental grounds and comes to a nice result. So he sort of resurrects welfare economics. There's a paper by Roderick Long in which he contrasts positivism with the logical deductive method of praxeology in Austrian economics. And the key difference that he points out is that in positivism, in positive economics, or just positivism, they make precise abstractions. They make really specific assumptions about what human motivations are, what influences human action. If you think about a utility function, if an economist comes up with a mathematical equation that describes how much utility somebody gets out of consuming a certain bundle or other considerations about their environment, they're making really specific assumptions about outcomes of human behavior, what humans will do based on all of the particulars. But we don't have that in Austrian economics. So we still have the law of diminishing margin utility. We still have demand curves. We can sketch all of this out, but we don't make these very precise assumptions, these very precise abstractions. And the result is the theory that we get by using the logical deductive method is theory that can be applied universally, everywhere. All people, all cultures, all times, all places, if they're a human and they're acting, then we can apply praxeology to make sense of their actions, make sense of what we see. But if you have these really precise abstractions, then you've got to find really specific situations. And you probably won't find the exact situation where somebody's behavior is determined in the way that you have this equation set up. Anyway, another great article. I have a little snippet from Human Action for you to check out here where Mises talks through the different ways that economists of his time were starting to use math and using math to a great degree. And he points out some of the fundamental problems there. Another article by Hans Hermann Hoppe talks about the regressions and using econometrics. And he talks about how whenever we use an equation to try to describe or relate data that we've collected, it means that we're making this assumption of constancy. We're making this assumption that there is this relationship that we've discovered in the data in the past. And if we think that's meaningful to any extent at all, it means that we're making assumption that there's some sort of constant relation in human action. And of course, he points out that that's false. We can't do that. However, importantly, I will try to get to this in the talk, he points out that there is a safe way to interpret econometric results. There is an innocuous interpretation of econometric results that allows us to do economic history, for example, even if we're not taking to the full extent that we see mainstream economists interpreting regression results. In Statistics, Achilles' Heel of Government by Rothbard, he talks about some of the nefarious things that governments do with data and how they twist it. Of course, we're all familiar with this in our day and age of things being redefined. So the things that Rothbard was talking about are alive and well today. A lot of the stuff that Gene Epstein was talking about last night would fit into the same category of the nasty things that governments do with data to twist it to tell a particular story. And finally, I've got an example of good economic statistics being constructed by Austrians. So in this paper by Murray Rothbard and in other related papers that he wrote with Joseph Solerno, they constructed a better measure of the money supply than what you can get anywhere else. You can go to the Fred website and see all of the different measures of the money supply, MZM, M1, M2, and M3. And in a series of papers by Rothbard and Solerno, including this one, they showed that all of those measures are wrong. Isn't that nice? You've got all these measures, but none of them are quite right. And the reason why is because they were measuring, making their measurements based on something besides the definition of money, which is a final means of payment, the widely accepted medium of exchange. Instead, they were using liquidity as a sliding scale to make their different measures. We'll get into this later on, but I just wanted to give you these essential reading at the start. All of this discussion really hinges on the distinction between theory and history. And you don't really see this distinction in the other schools of thought, but Austrians are really good about knowing the boundaries of their science. Like, we really understand what are the limits of what we can say? What is epistemologically valid? You don't see this discussion in other schools of thought, unfortunately. It seems like they just sort of skate by all of the methodological concerns and just get straight to the math without really thinking about what they're doing. Austrians are not that way. We start with the epistemology, like how do we know what we know and then what does that mean? What can we say about human action and so on and so forth? In economic theory, we use logical deduction. We apply logical deduction to some very safe, very universal axioms like humans act. We define action and we come up with claims like the law of diminishing margin utility or the law of demand or even way on down the road, we can talk about the effects of inflation, like what happens when we increase the money supply. So all of this is a part of a huge edifice, a huge body of claims that are derived logically and the result of deriving those claims in that way is that we get universal claims. So since we're not making precise abstractions, we're not making very specific assumptions about what motivates humans, it means that we get these universal claims. Like I said, the particulars of choice, they're not relevant. So the law of diminishing margin utility applies to somebody who's looking at an expanded set of apples but also an expanded set of oranges. Like there's additional units of whatever good. It doesn't matter what the good is, then it means that the margin utility decreases because the stock of the good is increasing. So we don't have to make particular statements about what the good is or what the motivations, the specific motivations of the actors are. So they might want to eat the apple because they're hungry, they might want to eat the apple for all sorts of reasons. I don't really know what are the reasons you would want to eat apple besides you're just hungry. There's probably other examples I could have used there. But the point is we don't try to guess what people's specific motivations are. And they're not relevant either, it doesn't matter. The claims of economics are what they are no matter what the motivations are. Very importantly, if we're using logical deduction and the logic is sound, the premises are true and we come to the conclusion in a safe way, it means that the results are not falsifiable by observation. And this is a man, the mainstream is, they really hate it when we say that. It's like, we have the truth and there's nothing that we can see that would ever falsify, like these truths that we know. It sounds like we're being cocky, it sounds like we're super, super confident. And it's true, we are confident in those results but it's not because we just have this self-inflated ego, it's because we see the way that the truths, the claims are derived. A very common analogy is with the Pythagorean theorem. So if we went out and measured the links of the sides of the right triangle and we saw that the results contradict what we would expect by looking at the Pythagorean theorem which relates the sides of the right triangle, if we got this one measurement that seemed to go against it, we would not toss out the Pythagorean theorem because the Pythagorean theorem is put together in a logical deductive way based on the definition of what a right triangle is and what distances are, how addition works, how squaring a term works, all those sorts of things. So it's just true by definition, right? It doesn't, if you go out and you measure a right triangle and you get something that's off, it means that your ruler is wrong, possibly. Or maybe you're not measuring a right triangle, maybe you're measuring something else or maybe it's something that looks like a right triangle but it's on a curved surface. Some sort of weird example like that where the assumptions aren't there in the real world. The same applies to the claims that we get in economics. It's the same thing if we go out in the real world and we see oh, it looks like that person enjoyed the second apple way more than the first apple. Well, I guess the law of diminishing margin utility is just bunk, you know, it's like we just gotta toss it up. It's like no, it's like we understand the sorts of things that are true for the, or that have to be held constant for these economic laws to hold in the real world. If we see change, if we see something that's different, it simply means that there's something else going on that we haven't taken into account in our observation. Similar to the right triangle example. There are no constant relationships in human action which means that there's no constant mechanical, numerical, quantitative relationships in economic theory. It's, we can never ever say that consumption of apples will increase by 3% every year, something like that. And the reason why is because there's no constants in the way we behave. There's no constants in the choices that we make. So we don't have constant relationships in economic theory. The goal of economic theory is also different than economic history and also the goals of the way Austrians do economics and the way the mainstream does economics is very different as we'll see. The goal as you've seen this week is we want to explain the real world. We wanna analyze cause and effect. So humans have motivations, they act in a certain way and we wanna be able to come up with some true claims about why they behave, why they act, why they make the choices that they do in a way that allows us to make sense of the real world. That's the idea. The purpose is not prediction per se, although good economics should allow us to at least make better predictions than if we didn't have economics. Some good examples of this edifice of economic theory that I'm talking about being constructed is in human action by Musis and man economy and state. Probably man economy and state by Rothbard is even a better example because it's very step by step. It's probably the best source that we have of seeing this edifice of economic theory being put together. Now all of this contrasts with economic history. In economic history we take what we know from economic theory and we apply it to the past. And this doesn't have to be 100 years ago, this could be five minutes ago, could be one minute ago. So if we're ever trying to make sense of what we observe and if we're observing something, it means it's necessarily in the past, it means that we're doing history and not theory. So it's the application of theory to the past. When we're doing history, we can identify some particulars and some of this might be some speculation, there might be people who have two different perspectives on the same event happening and so they'll see different things and so there's a little bit more speculation about it. Motivations can be guessed, we're all human, so we understand what it means to be incentivized to do something. For example, if we see somebody going to school to learn skills so that they can get a promotion at their job, we can guess all those motivations. All we see is the person signing up for school and then they either get or they don't get the promotion that they were seeking, right? So we only see that and all of the extra stuff is just us trying to guess what their motivations are and those could be really safe assumptions or it could be a lot of guesswork involved just depending and like I said, it's all open to interpretation. We don't get the epistemologically rock solid claims when we're doing history, all we get is if we're going beyond just a basic description of what is observed, it means that we're doing some guesswork, we're doing some speculation. Some great examples of this, of economic history is America's Great Depression by Rothbard. I love that book. You should definitely read that. Every Austrian student should read that book because it starts off with theory then it applies that theory to the past in a great way and also another good example is Robert Higgs crisis in Leviathan where he's doing economic history. Great, great book. And here you'll notice in both America's Great Depression and in crisis in Leviathan they look at numbers. They use statistics, right? And it's totally safe. They're not using statistics to try to falsify economic theory, they're using statistics to make sense of the past in conjunction with a good knowledge of economic theory to make claims about why the government grows the way that it grows or why there was a big bust in 1929 and so on and so forth. To give you a picture visually of the difference between Austrian economics and what the mainstream looks like, these are random draws. I'm not lying, these are true random draws from both Mannequin and State and then just a top mainstream journal, the AER. On the left there's a great example of Rothbard using this deductive method to come up with a fresh claim. In this case it's a footnote on page 116 where he's talking about what if the goods are discrete and we can't get an exact equilibrium. Like what if the market can't clear exactly because of the way the preferences of the suppliers and the demanders are set up and the goods are discrete so we can't get that exact intersection. And he just talks to them all, this would have to be the case. They would have to just get as close as they can. Very simple deduction there. So we don't get an exact equilibrium but they get close to my bounce back and forth over time. But compare just visually what that looks like to the mainstream where it's another language. It's these mathematical equations with a bunch of symbols and hopefully the equations mean something to the authors. Probably doesn't mean much to anybody else. Everybody else just sort of nods their head when they're going through these papers at their academic conferences allow me a quick story. I was at a paper presentation at Auburn where I studied and it was a macroeconomist who was presenting this paper. And one of the microeconomists who specialized in labor, they're all siloed, there's health economists and consumer economists and labor economists. This guy specialized in labor economists. He leaned over to me and he says, I have no idea what's going on. So like there's this like two mainstream economists, the macroeconomists and the micro guy and they're just like, they're talking straight past each other. And so the feeling that you get by looking at the stuff on the right is similar to what even mainstream economists feel when they're seeing these papers presented. But I just wanted you to see visually like what does the mainstream look like? Rothbard has a great preface to a book by a Mises called Theory and History, one of the more underrated books by Mises, I would say it's a great book. Here, Rothbard is talking about the subject of economics as humans and what that means for how we do economics. How do we go about doing social science with what we know about how humans are different from say inanimate objects. Therefore, atoms and stones can be investigated, their course is charted and their paths plotted and predicted, at least in principle, to the minutest quantitative detail. People cannot, every day people learn, adopt new values and goals and change their minds. People cannot be slotted and predicted as can objects without minds or without the capacity to learn and choose. So an example that I like to use in classes is dropping a tennis ball. We can hold the tennis ball and drop it and we see, we could do this a million times. It'd take a long time to do that but you could drop the tennis ball a million times and you could time it. You could make observations and you would come up with this conclusion that the tennis ball falls and you might come up with the law of gravity by doing this over and over and over again. So you have this explanation for the behavior of the tennis ball by dropping it a million times. It would be wholly inappropriate, incorrect, nonsensical to say the tennis ball likes the ground. Or the tennis ball likes to bounce or the preference of the tennis ball is to fall towards the ground. It's a nonsense statement. And the reason why is because we know the tennis ball does not have a human mind. However, when we're talking about the end of this lecture when everybody stands up and gives me a standing ovation to because the talk was so great then we would use that sort of language. Like we would refer to your motivations and values and preferences and these sorts of things. So the way that we should treat humans is not the way that we should treat inanimate objects. It's a very simple conclusion. However, if you look at the way mainstream economics is done is they're treating humans like objects. They're treating humans like these bodies that have mechanical constant relationships with their environments, right? So great point by Rothbard. Here's another one. The use of statistics and quantitative data may try to mask this fact and he's talking about the fact that it's impossible to test economic theory. But their seeming precision is only grounded on historical events that are not homogeneous in any sense. Each historical event is a complex, unique resultant of many causal factors. Since it is unique it cannot be used for a positivistic test. And since it is unique it cannot be combined with other events in the form of statistical correlations and achieve any meaningful result. So we might try to do the same thing that we did with the tennis ball. Like dropping the tennis ball and just repeating observations to come up with a conclusion about how tennis balls behave when they're dropped. We might try to do that same sort of method with humans and just watch people go into a restaurant, right? We see people go into the restaurant over and over again. We might even see some of the same people come in over subsequent weeks, right? So like the same person goes into the same restaurant. And we would never ever be able to come up with a constant relationship, right? We would never be able to say that people always do this or people are even likely to do this. We might come up with probabilities but the point is that those probabilities could change with additional observations. We might see people start buying different dishes, right? Or they sit at different tables. And the reason why is because humans, they're not tennis balls. Humans are, we make choices, even if we're in very similar situations we can behave, we can act, choose in totally different ways. So humans are different so we can't use that method to study human action, that's the point. So why are they obsessed with data? So I'm gonna try to get into the statistics part here. So why is it that the mainstream, they love their statistics? And so here I just have very quick overview of the way mainstream microeconomics or neoclassical economics is set up. Here consumers, they have utility function and with that utility function which is based on consuming different bundles of goods you can map out indifference curves where they would have the same level of utility by consuming the different quantities of the good. So that's what those curved lines are. Those are the indifference curves. And you see right off the bat just by drawing the curve either the utility function or the indifference curve you'll notice that they've already made a bad assumption. And that bad assumption is that the goods that are consumed by people are continuous and not discreet. We don't consume continuous goods. There are no exactly perfectly continuous goods anywhere just because of the fact that you could go down to the atom or the molecule, right? So there are no continuous goods. Like you never go up to an ice cream shop and they'll say you can buy different numbers of scoops and it's not the possibility for you to say I would like 1.2136925 and you're there for 16 hours with the going out to this very long fraction telling them how many scoops that you want, right? So we don't make decisions on those sorts of margins. We make decisions on discreet margins. Yet if you're drawing this nice smooth curve like this you're making the assumption that the goods that are consumed by consumers are continuous and not discreet. They combine these indifference curves with the budget constraint and what that means is you can do some calculus. You can find the tangent line where you get the highest indifference curve, the indifference curve that's associated with the most amount of utility and you consume the bundle where there's that tangency point with the budget set, the budget constraint and the indifference curve. Some of you are having nightmares right now. Others of you are like this is so boring because I see it all the time in school. Hopefully I'm bridging the gap somewhere. But you'll notice that they're making another assumption. Well, depending on the economists it's either an assumption or just something to make it more tractable. And that is that we use math to come up with our decisions. Like how many of you walked down the grocery store aisle and you're trying to figure out how many jars of peanut butter or whatever to put in your cart to take to their cash register. And like the first thing that you do to make that decision is you pull out your calculator and you take a derivative of your utility function to find the tangency point of your budget set and your difference. It's like no, nobody does this. So that at the very least they violated Occam's razor. They've made this simple qualitative event of deciding to put peanut butter in your cart and they've made it much more complex than it needs to be. So it's a violation of Occam's razor there. But yeah, that's the reason I show this to say they're thinking about human behavior in a mathematical sense, which means that they're gonna want data so that they can test their theories, so that they can make predictions, so they can do all this sort of stuff. So if humans are behaving in the sort of mathematical way or we can at least think about their behavior in a mathematical way, then they're gonna desire all of those statistics. Just a quick contrast to causal realist or Austrian economics. How do we think about consumer choice? Individuals act to bring about a preferred state. Very controversial, right? Preference can only be demonstrated in action. Action is the use of means for a purpose, the attainment of an end. And when we're acting, we're trying to substitute, we're trying to get a more satisfactory state of affairs and forego a less satisfactory state of affairs. So we describe human action in qualitative terms. We're not making precise of abstractions. We allow humans to consider and consume discrete goods as opposed to continuous goods. We don't assume that people are doing math to come up with their optimal consumption bundle and so on and so forth. So it just seems like, if you, suppose you're just a random person off the street who came in here, it seems like this is a closer approximation, we'll say, of what's going on in our minds when we're making decisions, right? It just seems more intuitive. All right, so let's make, I can do this quickly since I've already talked about some of the differences along the way. Importantly, the two approaches have very different goals. So in the mainstream, they're trying to model behavior so that they can make good predictions. They want to make quantitative predictions as well. However, in the causal realist tradition where our goal is to explain and understand what's going on in the real world. Consumer behavior is explained in very different ways. In the mainstream, they use or act as if they use math to make decisions. If you ask them, they wouldn't say that consumers actually use math to make their decisions. They would say that they act as if, but it's still fun to make fun of them for doing all of the math that they do. And in fact, here's one example of that. So here's a couple that they're trying to decide that they should have kids. And so they say, let's consult the utility function. So they do a bunch of math in their heads and they plug all of the data into their family utility function and then out just timelessly pops a couple kids. It's like, so this is how they consider humans and their behavior. The elements are conceived in different ways. We have continuous goods that are consumed to achieve a certain level of utility that's actually, the process is actually using or thinking of utility in a cardinal way. Now, there are some ways that you can justify this by saying that, well, the utility functions could be mapped to any set of preferences that are ordinal, you hear this a lot. But the point is that the way that they're constructing at least the utility function is that you consume a certain quantity of goods and that gives you a certain quantity of utility, much different than the way Austrians conceive of it. And they make a lot of assumptions about the scope of the consumer's knowledge as well. So you have to be able to isolate one consumption bundle in the neoclassical view. You have to rule out all other possible bundles. But in Austrian economics, we understand that, no, for you to make a decision, you just have to make a comparison to two possible states of affairs, the one in which you consume blank and the one in which you don't consume blank, for example. Okay, moving on to mainstream macro. And once again, I'm just trying to lay out the case for or show you why the mainstreamers love their data. And once we go from micro to back row, there's no big surprise, hopefully, they're also very mathematical, right? The macro models are, if not more, more high-minded, very sophisticated math in macro than there is in micro. And I have a few examples up here. There's the Keynesian models with aggregate supply and our demand. I'll talk a little bit more about the Keynesian models when we go through the GDP in just a second. But the point is that these are all math-driven. With the solo growth models, you're plugging in numbers to get a growth rate for the economy based on depreciation rates and so on. And then there's the dynamic, stochastic general equilibrium models where you're applying a certain numerical shock to the economy to see what the result is, numerical result is. All math, everywhere. Okay, so finally, let's get to some data. The way GDP is thought of is it's a, if you put a sensor up to the pipeline, the flow of money and the circular flow, then you're measuring total spending in the economy. You're measuring total income. And so that's, the reason why they like GDP is because it's a measure of the circular flow in the economy. So let's look at GDP. You can open up any principles, text book and see this definition. GDP is the market value of all final goods and services produced within a country over a given time period. And they use prices as a common denominator, but we'll see that even though they use this common denominator with market prices, there's still some heterogeneity within GDP that's problematic. They rule out intermediate goods. And the reason why is because they don't want a double counting problem. We'll see that there's an issue with that as well. They only look at domestic production, that's fine, depending on the question that you're trying to answer. And it's over a specific time period, so it's a flow variable. And it's all based on this Keynesian equation of the y is equal to c plus y plus g. If you have an open economy, then you include net exports as well. These first issues with GDP, you'll actually find in the text books. You'll find in the principles, they'll give you the definition and they'll say, so here's what GDP is, and here's all the problems with it. So you would think that they would do a little bit of introspection, or maybe we should fix some of these problems, or at least acknowledge it. If we're acknowledging it at the principles level, then why aren't we acknowledging it at the higher level? But the issues that they point out is that you're ignoring household production. So since you're adding up all spending, if you're producing something, but it's not exchanged in the market, then there's no spending, like mowing your own lawn, or cleaning your own room, those sorts of things. So even though it's a part of national product, technically speaking, it's not included in GDP because there's no spending associated with it. In a similar way, black market transactions are excluded because we don't have a record of that spending. By definition, it's a black market transaction. Another issue is that GDP per capita is not a good measure of overall well-being. So they try to correlate GDP per capita with different measures of happiness and life satisfaction, and usually they're using survey data to make that comparison. They notice that there's not much of a correlation there. There's lots of issues with doing that. So either the survey data is wrong or GDP is wrong, or at least we can't use GDP per capita to measure well-being and satisfaction. Austrians, we get to just say, well, maybe they're both wrong. Survey data's probably bad as well. Another issue with GDP that they would point out is that leisure is valuable. So actually stopping production, going home and relaxing on the weekend, or retiring at the end of your career is valuable. Like we like that, but it would decrease GDP. We're getting something that we want, but it decreases GDP. All right, so these next three, these are more Austrian. So we'll spend a little bit more time on these. Even though they have the market price as a common denominator, the stuff inside GDP is very heterogeneous. First of all, consumption is different than investment. And government spending, as you'll see in 0.7 here, is extremely different, and actually should probably be excluded. I mean, depending on the sort of question that you're trying to answer. But it's not just a heterogeneous inside. It's also heterogeneous throughout the population, meaning that GDP doesn't tell you anything about the distribution of wealth or the distribution of income throughout the economy. It doesn't tell you anything about growth potential. So GDP could be massive, but if it's because we're just going through a big consumptive binge, like in the artificial, yeah, the boom of a boom bus cycle, then GDP is increasing and you might, if you're a macroeconomist, you might think, oh, we're on this nice trajectory of growth, right? But if you're an Austrian economist, you know, well, actually, you have to maintain your structure of production. Your structure of production has to be consistent with time preferences and the subsistence fund or the amount of consumption goods that would last you through the period of production. If you don't have that, then you're actually, you don't have much growth potential, even though your GDP is increasing. Also the size of the government, once again, we'll wait for number seven. Number six here, consumption is exaggerated in GDP. So GDP is not total spending in the economy. It's total spending minus spending on intermediate goods, right? So it's only tallying up the spending on final goods and services, which means that if you're trying, if you're a politician, perhaps, and you're trying to make policy based on what are the important things in the economy, and you're looking at the components of GDP, you would come to this bad conclusion that consumption is really important for the economy, right? However, you're missing out on a lot of investment spending. So consumption spending is exaggerated as a proportion of GDP. If you include all of the other spending on the intermediate products through the structure of production, then we put consumption in its place. It's a much smaller portion of our western industrialized, very large economies that we have. So you get a bad picture of the importance of consumption when you look at GDP. Well, there's a fix for this. We'll talk about that later. And finally, most importantly, I would say, government is different, right? So consumption spending is based on people's own net worth calculations, and they have their own preferences, their own budgets, right? They're making decisions on what to buy. Investment spending is based on similar preferences and anticipations about the future. It's based on profit calculation, profit anticipation. The same thing applies to net exports. So exports and imports are people trading goods across borders based on their own preferences and profit calculations. But government is not subject to those sorts of things. The level of government spending is based on political wins. It's based on politicians in there and what helps them get elected. The stuff that government spends money on is not necessarily related to what consumers value. I know it's a very controversial statement here at Mises University, but there's really not a connection there. Since their revenues is based on forcible taxation or through their printing press, right? They get their money this way, and then they spend money on stuff, but there's no connection, right? It's not like you pay more taxes because you like what the government is producing for you, right? There's no connection like there is in the private market where producers, they make stuff for a profit and they only make the sale that they convince the consumer that what they're giving up, the payment for the product is less important to them than the product that they're receiving as we've seen this week. So government expenditures are just categorically different than the other things. They should probably be excluded in certain situations like what Robert Hicks does in this article about the myth of wartime prosperity. If you look at GDP through World War II, it looks like we had a gigantic economic boom. And the reason why is because the government was spending a ton of money on waging war, right? So a huge increase in G, right? So the government spending component of GDP was just massive, right? Because we were waging war, spending lots of money on different things. However, if you take the government spending component out, you get private GDP. So here's a great Austrian alternative to conventional economic statistics. Private GDP takes government spending out of GDP and you get a better picture of what your average Joe felt experienced during wartime, which was basically a depression. Look at the dip there in private GDP. It looks almost like the Great Depression that was a few years before. We did not have wartime prosperity. We had wartime depression. We had wartime shortages and they had to ration all sorts of consumer goods during the war as well. So this was not, if you pretend you don't see the black bars, right? If you're just looking at GDP, you're like, oh, wow, war is great for the economy. As we know that this is, because of the broken window fallacy, right? We know that this is, that's just wrong. And if you look at the data in a correct way, you see, oh, yeah, there's the cost of the war right there. We had to take a cut into what regular people, what the citizens were consuming. Another important result that we get from this exercise is you'll notice that if you just look at GDP, it looks like we had this massive depression, massive recession at the end of the war, right? And all the Keynesian economists, as we know, were saying that, they were saying that if we end the war then we're just gonna go back into the Great Depression. But if you'll notice the private GDP shot up. So what's actually felt and experienced by the citizens of the United States was totally different than what GDP reflected in. It's because government spending is categorically different. David Howden goes through a similar exercise. He's looking at the impact of recessions on two different groups, private workers and public workers. People who work in the private market economy and people who work for the government. And if you divide government spending by public workers then there's really not much of a bad effect of the recession. So the recession mainly affects private workers. Another great conclusion here. Since government is not subject to the profit and loss test of the market, recessions don't really matter to the government. In fact, you'll notice that governments do quite well during recessions, right? So another great, using Austrian insights and coming up with better data we can come to some enlightening conclusions. So you remember I talked about how consumption is exaggerated in GDP. One way that we can get around that is by just including all the intermediate spending. And there's good reason to do that, namely because it exists. So here's the Austrian conception of production. We've got the structure of production. There is spending on all the consumption goods at the bottom, but there's also a lot of spending on the intermediate goods, all the capital goods in the early and middle stages of production. So if you want to measure a total spending in the economy, you should include all of those. And if you do that, then you get what's called gross output or gross domestic expenditures. And both Murray Rothbard and Mark Scousen were, they were advocates for this. And I'm not sure if it was because of their calls for, I guess Mark Scousen's calls for it. In 2014, the BEA actually started releasing statistics on gross domestic expenditures. But you'll notice the blue line is gross output. The red line is GDP. And one nice result here is that there's more variation in gross output during the recession. You see the recession there, that's when gross output goes down, right? So there's more variation in gross output. And why is that? See, people were paying attention to my lecture yesterday on Austrian business cycle theory. It's a huge collapse in demand for capital goods. All those specific capital goods, they decrease in value during a recession, during the busts. So you see that very clearly in gross output because you have all the spending in those intermediate stages. But the red line at the bottom, GDP, there was a small decrease, right? At least relatively small, but it's not as clear there. So something like looking at or measuring recessions, which is, people are talking about it a lot these days, something like gross output would probably be a better measure. Now let's talk about the price level. Austrians hate the price level. We don't hate it. We just don't think that it exists. We're a price level, I guess. We're an anti-price level, or a price level. But to just set up the problem, mainstream economists, they would like to be able to talk about price inflation. They'd like to be able to talk about prices in general throughout the economy, but there's a very serious unit's problem when you're trying to come up with something like an average price. And the unit's problem looks like this. So you have, suppose you're the bundle that's important to the average consumer. So our consumer average price would look like this, where you have a $10 burrito at $22.15 in haircut and then the $393,000 Lamborghini. If you add all those prices up and divide by three, you get a number here, 131,242.383. But if you ask the question, what are the units of this figure, you get nonsense, right? It's just, it's not useful in any application at all. So to get around it, they construct an index. They compare the bundle, the price of the bundle to itself. So it's a unitless solution. So we have this unit's problem, so let's just annihilate the units by comparing this series to itself. So they come up with a bundle of goods that's important to the average consumer using survey data. The Bureau of Labor Statistics does this. And they compare the price of that bundle, the total price of, like if you imagine putting it all into one basket and taking it up to the checkout to pay for it. And you see how the price of that basket changes over time, then you're measuring the price index or price inflation in that way. So that's how they do it. All right, so there's some issues with this. Even with indexing, there's still an apples and oranges problem, mainly because they change the bundle from year to year. All right, so the basis of comparison is changing. They're not comparing something to itself. They're comparing the price of the basket of goods that was important to the average consumer in 2021 with the price of the basket of goods that was important to the average consumer in 1972. Totally different baskets. So they're still a fundamental units problem here. Even though they've gotten rid of the units, there's still apples and oranges problem here. Importantly, if you're looking at CPI, it hides relative prices and you don't see canteon effects. Canteon effects refer to the unevenness of increasing the money supply always comes to a specific point in the economy. So somebody spends it first and then somebody has the higher income and then they spend it second and then somebody takes that and they spend it. So prices increase like rippling out from the source. It's not like we had this C level rise when there's an increase in the money supply. CPI hides all of that. You don't see any of that, but it's extremely important, right? Canteon effects is actually extremely, I didn't even mention this yesterday. It's extremely important in telling the Austrian business cycle story because Austrian business cycle theory is actually just a special application of canteon effects. New money pours forth on credit markets first and then the money is used to do all sorts of things like expanding consumption and expanding and lengthening the structure of production, right? All these sorts of things. Those are all canteon effects, unevenness and the flow of new money into the economy. You don't see that with CPI. Finally, most importantly, like I mentioned, there's no such thing as the price level. So we would like for the statistics that we're coming up with, if we're trying to apply economic theory to history, we would like for the numbers that we're coming up with to relate to what our theory actually has. But unfortunately in economic theory, there's no such thing as the price level as one number. The price level is best considered as an array. It's all of the prices, right? All of the prices that consumers face. Great quotes from Mises that housewives, those who do the spending, they go to the grocery store and buy the goods for their household, they are in a better position to measure price inflation than the government officials with their price indexes, right? And simply because that's what the price level is. It's the prices that you face. A good quote from Rothbard here on that specific point. Let's look at money supply measures. The way that the money supply measures are put together is based on liquidity. So they have money zero maturity, MZM. They have M1, which has some extra stuff. So M2 that has some more stuff that's not quite as liquid as the stuff in M1, in M3, in M4. I don't even know how far out they go, but they just keep adding stuff to these different money supply measures based on how liquid it is or how easy is it for you to sell those different financial assets. And of course, Salerno and Rothbard point out that this is bogus, that's not the money supply. If you're doing it based on liquidity, then you're not measuring money, you're measuring something else. They say what matters for the money supply is what is immediately spendable. What is a final means of payment right now? So that means you have to exclude all credit transactions, you have to exclude travelers checks, you have to exclude time deposits where there's a delay in you being able to access your money. So there's all sorts of things that you would have to exclude. I recommend that you check it out. The article that I mentioned at the beginning is a good exercise in showing well, yeah, we can include this because it does fit the definition. No, we can't include this because it doesn't fit the definition. So much better measures that we get by applying good economic theory to statistics. Thank you.