 Talk this afternoon is on Austrian alternatives to conventional or mainstream economic statistics. The first thing that I want to talk about is this stereotype that Austrian economists have, that they're the economists who are just bad at math. And so they've just sort of selected themselves into the version of economics where you don't need as much math to be able to do what you do. How many of you have heard or encountered this stereotype? Okay, so this is not like a therapy session for that sort of problem that we all have. In fact, I want to mention that I enjoy math. I like math a lot. I like numbers. I was pretty good in the graduate classes and also even when I was younger, I liked my math classes and did well on them. So it's not the case that I'm an Austrian economist because I'm afraid of numbers and afraid of math or even bad at math. We actually see this in Austrian economic theory. Two of the greatest conclusions in my opinion of Austrian economics are the conclusion by Mises that calculation is, economic calculation is impossible under socialism. And if you think about it, that's an argument about numbers. It's an argument saying that entrepreneurs do not have, they don't have numbers. They don't have the numbers that they need to be able to rationally plan production, to be able to decide, should I purchase this factor of production or this one? Should I employ this person or this one? Should I produce this good in this color, in this quantity, or the opposite? So all of the decisions that entrepreneurs make are based on the ability to at least do arithmetic with economic data, be able to anticipate profits, anticipate revenues, and compare it to the cost of production that they would have to pay today. So numbers are important in economic theory, in Austrian economic theory. The other case is Austrian business equity theory, which we talked about yesterday, when the central bank or the fractional reserve banking system increases the money supply through credit markets, it lowers the interest rate below the social rate of time preference. So there's a difference in, there's a change in the numbers that's not based on a real change in people's preferences or people's time preferences. So it's another, so we see at least the importance of good real numbers in economic actors' ability to make good decisions in the market economy. So we're not afraid of numbers. We use them quite frequently when we do economics. Before I get into the meat of the lecture here, though, I usually say this to the end, but I really should push it up to the beginning so that I can talk about some of the important works in the Austrian literature. First, we have Murray Rothbard's tour to reconstruction of utility and welfare economics. This is an excellent article by Rothbard that he, he criticizes the neoclassical approach to consumer decision making and he shows that the best way to consider or think about consumers making choices is in the qualitative causal realist sort of sense. So they have subjective evaluations and he uses the critical concept of demonstrated preference and when he's talking about welfare economics, he uses the unanimity principle to come up with these conclusions. It's an excellent work. I'm of the opinion that if, I don't know what the conversion rate would be, but if all of the mainstream economists read this work, we would get a lot of brand new Austrian economists if they just read this and tried to reconcile their own ideas with what Rothbard says in tour to reconstruction. Next is Roderick Long's realism in abstraction in economics. Here he talks about the positivist way of doing economics and the difference between making precise and non-precise abstractions. So the way, if you use mathematical models to do economics, it means that you have to specify what a utility level is. You have to specify what a price might be or make a bunch of, you have to make a bunch of very precise, specific assumptions about what people are thinking, how they're acting, what choices they're making as opposed to the way we do economics in the Austrian school, a lot of those things are left unspecified and it doesn't influence our theory. So the fact is that we know that we have diminishing marginal utility no matter what the good is, no matter what the means are, if we have an additional unit of it, we know that it's going to go towards a less important end. So it doesn't matter what the good, it doesn't matter what the actor's motivation is, so they could have nice motivations, they could have evil motivations, they could be thinking about something else entirely. We have economic theory that is universal and it doesn't rely on precise abstractions. So another excellent article I recommend that you read. I have some pages here from Human Action by Mises. Here he talks about some of the fundamental issues of using math in economics and how just on a fundamental level, we can't use equations to talk about human action. And Hans Hermann Hoppe is research based on the causal scientific principles possible in the social sciences. Here he talks about the issues with also using equations, but in this article he's looking at econometrics, he's looking at regression equations that have constants and variables and the fact that you're estimating constant parameters when you're doing linear regression, it doesn't have to be linear actually, it means that you're making an assumption about some sort of constant mechanical relationship in human action that can't exist. So there are no constants in human action, which means that if you're applying economic data to linear regression in the case that he goes through, it means that you're just coming up with some sort of correlation that happened in the past in history. We're not coming up with something that would apply to all humans in all cases for all time, which is what economic theory deals with. In statistics, Achilles' Heel of Government, Murray Rothbard talks about some of the nefarious things that governments do with statistics and why they might collect statistics and how much money and resources are wasted collecting statistics. And finally, we have Austrian definitions of the supply of money by Murray Rothbard. And we'll talk about this in more detail, but this is a good example of using good economic theory to come up with a better statistic, to come up with a good version of what the mainstreamers are coming up with. We'll see that in more detail later. But first I want to talk about the distinction between theory and history. So this is a Misesian distinction. He's got a book called Theory and History. I think it's one of his underrated works. The economic theory is something that pertains to all choices ever made by anybody. It's universal. It applies to all people. It doesn't matter. The particulars don't matter. Your skin color doesn't matter. Your sex doesn't matter. As long as you're making choices about the uncertain future, as long as you have intention and goals, you're thinking about means and the attainment of ends, then it means that the economic theory applies to you. So it's not something that is particular to certain people in certain time periods in certain cultures. It's universal in that sense. As we mentioned before, the particulars of choices are not relevant. So we can talk about diminishing marginal utility over any sort of good, over iPhones, apples, cars. It doesn't matter. Haircuts. So diminishing marginal utility applies to anything that particulars don't matter, including the specific motivations that people have. Importantly, economic theory is not falsifiable by experience or observation. So since we use logical deduction based on the universal categories of the mind to come up with conclusions in economic theory, it means that it's not possible for us to go out and see something that would make us go back and say, oh, that economic theory is incorrect. That falsified the economic theory. And the reason why is because we use logical, as long as we have correct premises and our thought process is correct, the logic is correct when we're coming up with economic theory, it means that it's impossible for us to see something that would contradict that or that would negate or falsify what we've already developed using logical deduction. A very popular analogy here is it's impossible for us to go out and measure right triangles. And if we come up with a set of measurements that seem to falsify the Pythagorean theorem, we wouldn't toss out the Pythagorean theorem. We know that the Pythagorean theorem, which is a mathematical equation that relates the sides of a right triangle, if we came up with some measurements that go against that, we wouldn't throw out the Pythagorean theorem. We would take another look at our ruler. We would take another look at the triangle that we're measuring. Maybe it's not a perfectly right triangle. Maybe there's something in the way that we're observing it that's incorrect, but we can't toss out something that we know is 100% true based on the way it was developed, either through logic and math or in the case of economic theory, through more qualitative, with verbal language, logical deduction. No constant relationships in human action. So yesterday you ate a certain set of things, like the meals that you consumed were such and such. Today they're going to be different. Tomorrow they're going to be different. There's no pattern in human action that we can say, as a matter of fact, it must be the case that people are going to do something in particular on a given day at a given time. So since we can't do that sort of thing, it means that we can't use math. We can't use equations to try to predict what humans are going to do in the future. Or we can, but it's the outcome of those predictions we have to always take with a grain of salt. It's not necessarily true. It's not pure like the laws of economics and economic theory that we developed through the praxeological method. And the goal of economic theory is to explain cause and effect and explain the real world. So that's really our goal. When we come up with the law of diminishing marginal utility, we're not trying to predict what people are going to do. What we're doing is we're explaining what must be the case if somebody is facing a larger or smaller stock of a homogeneous set of means or an equally serviceable set of means. So it's something that must be the case no matter what. Importantly, all of economic theory deals in counterfactuals. So that if there is some sort of change, it means that what happens is, or the proper comparison is what happens and what would have happened if that initial change didn't happen, not necessarily before and after. And what that means is we're comparing the cause and we're looking at the effect as opposed to just looking at what somebody did yesterday versus what somebody did today. Some good examples of the construction of the edifice of economic theory you see in human action by Mises and man economy and state in Rothbard. Let's compare this to what we do in economic history. In economic history, we're applying theory to the past. So we have some ideas about, or we have some certain ideas about how economic theory works. We know how actors have to not necessarily behave in a particular sense, but we know that they're subject to the law of diminishing marginal utility. We understand that entrepreneurs need the ability to engage in economic calculations so that they can plan production in a way that satisfies consumers. So we have all of these conclusions from economic theory. Once we take those conclusions and we try to make sense of the past, then we're doing history or doing economic history. And when we do this, we can identify particulars because we can look at it. We can just see there's this person consumed an apple. So we see that there's an apple. So we can just observe it. We can identify their particulars. We can guess their motivations. Since you might have eaten an apple in the past, it means that you can sort of guess that the reason this person is eating an apple is because they were hungry for an apple. Since we have that sort of understanding of what it means to be human and to have done similar actions in the past, we can guess what the motivations are. However, it's always open to interpretation. It doesn't have the rock-solid certainty that we have when we're coming up with economic theory. Anybody can come up with another idea as to why the person is eating an apple. Or maybe they might say that it's not actually an apple. It's something else. And so we had to debate over what this person did in the past. There's still no constant relationships. However, we can use statistics. We can look at correlations in the past. So we can see how the price changed from day one to day two or year one to year two. And then we can come up with some ideas as to why the price changed. And if you have the benefit of good economic theory, then you at least know where to look when you're trying to figure out why the price changed the way it did in the past. Some good examples of doing economic history are America's Great Depression by Rothbard and Crisis in Leviathan by Higgs. So you see them use data. You see them use statistics. Sometimes even coming up with their own compilation of statistics to explain what they have seen in the past and apply economic theory in a fruitful way. Here's an example of the difference in theory in Austrian economics in the mainstream. Now what I did is this was a truly random draw. I'm not lying. I just flipped to a random page in man economy and state. I like the footnotes. So I looked at the footnotes and I found this wonderful example of Rothbard talking about what would happen if the particulars of a market were such that they couldn't come at some halfway price that would perfectly clear the market. So like you have two discrete goods, which means that the exchange ratio, it might be the case where you have to like bounce between the two. So he's talking, if you read it, he's saying, if this is the case, then this must be the case. And so market participants would do this sort of thing. It's logically deductive. It's clean. It's intuitive. And so that's the way that we see Rothbard coming up with economic theory in man economy and state. However, I went to a random article from the mainstream. I went to the national bureau of economic research working paper series and just found the first one that my cursor landed on and went to a random page and found this gobbledygook. It's not gobbledygook. I'm sure they understood, well, hopefully they understood what they were writing when they were writing this stuff. But I just wanted you to just visually see the difference in what economic theory looks like for the mainstream. Lots of equations. They have precise abstractions. So they've made very specific assumptions about what the agents in their models are doing and thinking about and how different variables combine to produce a certain outcome. And so they've got all sorts of things going on over here. So the outcome of human behavior is just based on a combination of constants and variables. If you take a look at Rothbard's preface to Mises's theory and history, he's got some great quotes here. First, he says, 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 what Rothbard is noting for us here is that there's a categorical difference between stuff that doesn't have a mind and stuff that does have a mind, us humans. There's a categorical difference between matter that doesn't have goals and ends and those things don't make choices and the people that do make choices. And since there's this categorical difference, what he would say is that we should study those things differently. An example I'd like to give is if we could open these windows and drop a tennis ball out, we could drop the tennis ball and see that it would fall down to the ground and we could do this a bunch of times and we could come up with some sort of general idea of how the ball behaves when we let go of it. It falls down to the ground. And just do this over and over again and we could say that that's a law of gravity. We say that these bodies are attracted to each other but we wouldn't say that the tennis ball just wants to go down. The tennis ball chose to fall down to the ground and bounce a few times because tennis balls like to bounce. So this doesn't make any sense. However, if we were talking about what's going to happen at the end of this lecture when everybody gives me a standing ovation, we would say that the people just, they love the talk so much that they decided to clap their hands together because that means something to the other people in the room and so on and so forth. So the way that we describe, the way humans behave and the way inanimate objects behave is totally separate which means that our sciences should be different as well. The use of, another quote here that I think is excellent, the use of statistics and quantitative data may try to mask this fact, 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 has 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 here humans are inherently unpredictable because of just the fact of human action. It means that even somebody who is in a similar situation, a very similar situation, might choose to do something different than what they did the last time they were in that very similar situation. So there are no constants in human behavior. We can't use these sorts of quantitative methods to predict what humans are going to do. Okay, so let's take a closer look at why is the mainstream so focused on data? Why are they so focused on statistics? What is it about the way that they do economics that leads them to conjure up all of these different data points and put them into regressions and to try to come up with some mathematical relationship between variables. And we'll see that the way that they do microeconomics and the way that they do macroeconomics is fundamentally and through and through it's mathematical. It's all based on math. It's all based on mathematical models that are supposed to depict and describe the way consumers behave or the way the representative household behaves or the way producers behave. So in mainstream microeconomics, individual agents select bundles of goods to consume to maximize their utility. Since we're going to draw curves in this X and Y space, it means that the good, right off the bat, we see that one assumption that you have to make here is that the goods are continuous, that you can have 0.00001 unit of a good and 0.0000001 of a good as well. So as opposed to discrete goods that we all know and have experienced consuming and using in our various activities. So we use and consume and use in production discrete goods, not continuous goods, yet at the very outset we see that you have to make this very implausible assumption about how we do things because you're drawing a curve in X and Y space. So the goods are continuous and utility is given by a mathematical function. One popular version of this is the Cobb-Douglas utility function. So X to the alpha times Y to the beta in this case gives you a certain level of utility. Your choices are constrained by a budget. No argument there. Our choices are constrained by a budget but what that means is that you can do some calculus. You can do a constrained optimization problem to see what is the optimal bundle that this consumer will consume and it's going to be at the tangent point here. So here's their budget constraint, this blue line. Here are their different indifference curves and you'll notice that at point A right here there's the point of tangency between their budget set and the best possible indifference curve that they could consume on. So lots and lots, you'll notice that there are lots and lots of assumptions built in already just in the basic setup of how mainstream micro, neoclassical microeconomics is done. We'll talk about more of the issues here in just a moment but just to immediately compare to the way we conceive of consumers making choices in causal realist economics is that individuals act to bring about a preferred state. Preference can only be demonstrated in action. Action is the use of means for a purpose, the attainment of an end and action less important ends are foregone to attain more important ends and so we can list these things in a qualitative sort of way. We understand that consumers are making subjective judgments about the different options that they have. Lots and lots of differences. I'll just hit a couple of these in the interest of time. So the two approaches have very different goals. The goal in the mainstream modeling is to make good predictions. In the causal realist tradition, we're trying to explain and understand real world phenomenon. We'll do one more here. Consumer behavior is explained in very different ways. Consumers use or act as if they use math to make decisions. So we've all been to the grocery store. Nobody has ever walked down the aisle and done calculus in your head to see what bundle of goods you should put in your shopping cart, right? But that's actually a straw man. So the neoclassical economists, they don't say that you actually use math to figure out what bundle of goods to consume. What they say is that you act as if you do, but it's still fun to make jokes at their expense anyway like this. So here's a couple. They're trying to decide if they should have kids. The future mother says, let's consult the family utility function. There's the family utility function. They do a bunch of math in their heads and it's very complicated. They have to get the calculators out and then just instantaneously outpop a couple kids. So like I said, this is a straw man. So the mainstream economists don't actually think that people are doing the math, but what they do say is they act as if they do math. In mainstream macro, we can do this very quickly because we had a great summary of some of the growth models from Professor Rittenauer. We have more math. We have aggregate supply functions, aggregate demand functions. We've got the solo growth model and dodgenist growth. These are all based on production functions. They're based on plugging numbers into equations and coming up with some sort of outcome. The dynamic stochastic general equilibrium models are the same way. We sort of do miracle shock to a system and see how that economy responds to the shock that we apply. Fundamentally, at least the Keynesian models are based on the circular flow model. So the reason I put this here is because this is really the foundation for gross domestic product figures. So now we can talk about some of the macroeconomic statistics. Here we have households and individuals on the right-hand side, firms on the left-hand side, and we interact in two markets. There's the market for goods and services and the market for factors of production. Peter Klein, he's already extremely mad back there. Sometimes you'll see that they put entrepreneurship down there as a factor of production. Somehow you can buy and sell this thing. So the reason I show this is because they take a little cross-section of this. If you think about putting a sensor up to the flow of money pipeline here and see how fast is money moving through the economy, so they conceive of national income, national product. So that would be the economic theory version of the measurement of gross domestic product. So their gross domestic product is the market value of all final goods and services produced within a country over a given time period. You can see that definition in any principal's textbook. They try to solve the unit's problem by saying that we use market prices. So since everything in the economy has a market price, we can add up all of the spending on everything and it means that we don't have an apples and oranges problem. We'll see the Austrian response to this later. But what's important to note is that they are counting final goods, which means that they exclude intermediate goods. They're only looking at domestic production, so everything produced within a country. And it's a flow variable, which means that it's all production that happens over a certain time period. And then there's this famous equation that Keynes used a lot. The Y is equal to C plus I plus G plus net exports, exports minus imports. And we have GDP over time here. Mainstream economists are actually familiar with a lot of the flaws with GDP, and so these first four you would see in any mainstream textbook. So it seems like they know that there's some problems here. A few of them are household production is ignored. So if you mow your own grass, clean your own house, you know, watch your own kids, do all these things that there are services provided for on the market somewhere. If you do it yourself, it means that there's no spending done since you're doing it for yourself, which means that it doesn't get counted in GDP even though it is a part of national product. It is a part of all production within an economy. It just doesn't get included in GDP. Black market transactions can't be counted because they're below the radar. GDP per capita is not a very good measure of, or it's not a very good measure of overall well-being, but a lot of people like to use that data. So if you take total spending and divide it by the population, people like to use that as which country has the happiest citizens or something like that. So people use the data in that way, but it's not a measure of happiness or well-being in that sense. It's just spending on final goods and services. Another point is that leisure is valuable. So Professor Engelhardt talked about this in the last lecture. We value not producing sometimes. Like on the weekend, we like to relax. We spend all week working. We spend the weekend binge-watching Netflix or something. So we're not producing. We're not increasing the amount of stuff in the economy, but we're getting what we want. We're being voluntarily idle in that sense. We decrease GDP, but we're getting something that we value, which means GDP would decrease if we increased how much leisure we enjoy, even though we're getting what we want. Okay. So like I said, those things appear in most mainstream textbooks. However, these last three here are more Austrian criticisms. The stuff inside GDP is heterogeneous. Even though they're using the market price of all final goods and services, it's heterogeneous and unequally distributed throughout the population. It tells you the total dollar amount, but it can't tell you anything about income distribution, wealth distribution, growth potential, which as we discussed in the business cycle lecture, would be probably a good measure of that, would be how much we've saved in the past. What is our stockpile of saved resources? How much stuff have we put away? What's our stockpile of consumable goods that we can use to last us through a longer period of production that would yield economic growth? So you wouldn't see that in GDP. You don't see things like the size of the government. We'll talk about that more in point seven. Another issue with GDP is that consumption is exaggerated and international trade is understated. Consumption is exaggerated because we've excluded all the spending on intermediate goods in the structure of production. So if we're just looking at final goods and services, final capital goods and consumer goods, it means that we're missing out on all of the spending that happens in the stages of production that's not a final capital good or a final consumer good. So all of that spending gets excluded. And Robert Murphy has a great example that highlights the fact that international trade is understated that involves two islands, an island of meat-eaters that only grows vegetables and an island of vegetable-eaters that only herds cattle or something like that. And so they trade with each other. They don't consume anything that they produce themselves. 100% of their economies is international trade, but if you calculate their GDP by C plus I plus G plus net exports, all you see is the sea. All you see is the consumption that they do. The net exports, the exports minus imports, it cancels it out. So if it's like $1 trillion worth of the vegetables for the $1 trillion worth of meat, that goes to zero and all you see is the consumption. So the importance of international trade for an economy is understated. Similarly, consumption is overstated or exaggerated. Finally, probably most important, I would say, is that government expenditures are included in GDP, but they're categorically different than the other components. Without market prices in the profit and loss test, there's no way to measure what the government produces. All government expenditures are also made possible by taking from the private economy. So in the private economy, businesses live and die by the profit and loss test. They live and die by whether or not consumers value what the firm is producing. So if the business incurs losses, they go away. The business is, or the entrepreneurs are seeking profit, and so there's that motivation that they have to provide what consumers value. There's no such thing for the government if you think about it, which I'm sure you all have here at Mises University. So it doesn't matter how much consumers value, if we can call them that, it doesn't matter how much citizens value what governments are producing for their citizens. If governments are collecting tax revenue or using inflation to acquire revenue, it's not connected to what they're providing to citizens, which means that we can't say, as a matter of fact, that what governments are providing is socially beneficial or something that the citizens like, unlike what happens in the market economy where private producers are producing for a profit. To that end, Robert Higgs has subtracted G from GDP. He's subtracted government spending from gross domestic product. So government expenditures are removed from GDP to make private gross domestic product, and he does this in an excellent article, Wartime Prosperity, a Reassessment of the U.S. Economy in the 1940s, and what he shows is that if you look at GDP during World War II, it looks like we had this massive boom. It looks like the economy was doing great. This is like the greatest economic boom in all of U.S. economic history. But if you look at private GDP, so if you take out all the spending on war goods, what private citizens, what your regular Joe had, regular Jane had, there was a huge decrease in what was left over for them. Huge decrease in private GDP. So the big increase in GDP was just because the government was mobilizing the entire economy to make things that would help them wage war, not necessarily what people want, not necessarily what people need to eat and build homes and drive cars and go to movies and all that sort of thing. So this exercise of just understanding the fact that government expenditures are categorically different has led this Austrian economist to come up with a better measure compared to just regular old GDP. Reveals what real people actually experienced in the war economy. David Howden does something similar, but here he's looking at per capita statistics, and he notices that a gross private product divided by private worker reveals that there's a bigger fluctuation in recessions and financial crises compared to what government spending divided by the public worker would reveal. So here if you do the per capita differentiation between private workers and public workers, you also see some interesting insights. Now you'll remember one of the issues with GDP is that it left out the spending on intermediate goods, and so I just want to show you just a quick reminder. What's missing is all of the spending on the intermediate goods in those middle stages, the early and middle stages. So there's a lot of spending in the economy that's not on final goods and services, but it's still spending. So it almost seems sort of arbitrary, like why just final goods? I know what they would say is because they want to avoid a double counting problem, so they don't want to count the flour and the bread and the sliced bread and the sandwich. It seems like they're double counting the same thing that's being sold, but spending is spending. So if you wanted to measure of total spending on everything, final goods and intermediate goods, then you would use a measure called gross output or gross domestic expenditures, which was advocated by Murray Rothbard and Mark Scousen as well. So GDP only counts final goods and services. Gross output includes the spending on intermediate goods. One thing that this does is it puts consumption spending in its place. When you include all of the spending on intermediate goods, the proportion of consumption spending gets a lot smaller. And you'll also notice that when you look at recessions, when you look at the business cycle, it's more pronounced. And the reason why, as we discussed in the business cycle talk, is that there's a lot of fluctuation in the prices of capital goods in the business cycle. So there's a big decrease in prices of those goods. You don't see that if you're looking at GDP over time, but if you're looking at gross output over time, you get a better measure. I'm going to skip some of the specifics of the price level. They try to come up with a measure of the price level by making an index, long story short, it still doesn't work. They use an index method, but it turns out that there's no such thing as the price level in economic theory. They've got a lot of different ways that they try to fix this by adjusting the data that they put in. They come up with a bundle of goods based on survey data that's important to the average household, and then they track the price of that over time using an indexing method. Skip to the good part, the problems with it, the criticism. So even with indexing, there's still an apples and oranges problem with the CPI, especially when looking at changes in CPI from year to year. And the reason why is because what's important to the average household changes over time. So even if we just grant them, okay, fine, you can use survey data to come up with what's important to the average household, that changes, which means that the CPI for 1978 is based on the prices of a bundle of goods that is different than the bundle of goods that's important to the average household in 2021, for example. So the thing is changing. So if you're looking at changes in CPI over time, you're literally actually comparing apples and oranges because you've got a certain amount of apples in one of them and a certain amount of oranges in another one. So another issue with CPI data is that it hides relative prices, which are important to consumers and producers, and it also hides canteon effects. So you don't see the ripple effect of money entering the economy and then certain prices are bid up first because they're first receivers of the new money, and then the people who receive that money, they can go on and spend it. So prices rise in an uneven sort of way. There's not a proportional increase in all prices. But if you just look at CPI in one year and CPI in the next year, you sort of get the impression that all prices increased by 2% or something. But that's never the case. Some prices rose, other prices didn't, and the process by which that happened had real effects. So like the increase in the money supply is non-neutral. There are non-neutral effects on what happens in the real economy when the money comes into the economy through a specific point. And finally, most importantly, there's no such thing as the price level as one number in economic theory. We've got a quote from Rothbard there that just lays that down. I want to talk about one of my favorite Austrian alternatives to conventional economic statistics, which is the true money supply. That's the best name. If you need any advice on coming up with a better version of what there is, just say it's the better version of this or the true version of this. So kudos to Salerno and Rothbard for saying our measure is the true money supply measure. So in the mainstream, they've got M1, M2, M3. There's a bunch of different ones. But the way that they've set them up is based on liquidity. So they said there's MZM, M1, M2, and as we add numbers, as we go further out, the stuff that we're putting into this measure, the money supply is less liquid than the previous ones. Rothbard and Salerno, they say this is hogwash. There's no reason to do that. There's the money supply. There's not a bunch of different kinds of money supply with more liquidity and less liquidity. There's stuff that's immediately spendable. There's stuff that economic actors subjectively view as redeemable at par in demand, and there's stuff that isn't. So they include all of the things that qualify as the widely accepted medium of exchange in the true money supply and exclude the other things. And so you get a better measure of the money supply that way. And if you're interested, Ryan McMakin at Mises.org, he regularly updates the true money supply if you keep track of that. So it's fun to watch. But you'll notice that the correlation of changes in the true money supply and the business cycle is nice and tight. It has a good correlation. So it's another example of good theory yielding better statistics than what's offered by the mainstream. Here's an example of some of Ryan's updates to the TMS. Finally, we have econometrics. And the reason I have just a slide about this is because it's very important in mainstream economics. Like I said, their goal is prediction. And the main way that they do prediction is by using econometrics. So they get a bunch of historical data. They come up with some sort of equation that is supposed to relate the data to each other. They put it in a statistical program that would give them parameters that are the constants, the leftover, the unknown variables in that equation. So you can get the slope of the line and best fit through a data set, for example. And it's based on minimizing the sum of the squared errors. So we don't need to go into all the details here. The point is that they take a bunch of historical data. They get a line and best fit or a curve of best fit in nonlinear regression to try to come up with a good, not only a good relationship, so we can look at the slope and we could say that this variable is related to this variable in this sort of way. So if this variable goes up by one, then this variable is going to go down by 0.3 or something. So they can come up with a mechanical relationship in that way. But also, they're interested in the unobserved, the space between the data points and the space beyond the data points so that they can come up with a prediction of what might happen if x is this, then what would y be? So we have all this data, what would y be in that case? And they use their mathematical models from micro and macro to come up with what these equations are when they're estimating them. So they say the estimated slopes give the effect of one variable on the dependent variable. However, with all of these high powered regressions and all the math that they use, what we've noticed is that they're actually not very good at making predictions, surprisingly. So they've got these nice mathematical models. They've got very complicated looking regressions in their papers and everything. But if you look at even the top economists at the Fed, the best economists in the top economic journals, you'll notice that they're actually not very... They don't have a very good track record and I've got a few examples of that. So here's Fed forecasts of inflation, inflation-adjusted GDP growth, and the unemployment rate. And you'll notice that they're way off of what the actual figure was. The actual figure is the black line around the colored sections there. Over here, we've got the Fed forecast for inflation-adjusted GDP growth at the specific points. And you notice they were very optimistic about their ability to get the economy to have GDP growth. What actually happened is the gray bars down here. So they had all these nice anticipations. Now, one kind interpretation of this is that you can say, one part of their job as members of Central Bank or being officials there is they need to project optimism. We can get growth by just saying, everything's gonna be great in the short-term future. And so that might lead to better GDP or better outcomes if they just have that sort of announcement effect. I don't have that sort of nice view. I just think that they have bad models and you can't use math to make good predictions like something as complex as the U.S. economy. Here's another great one. So all of the projections are the red dots and they've connected them out into a few quarters, or yeah, a few quarters, but then the actual Treasury yield was way down there at 2%. So these are the top, what do they say, not a single respondent in January's Wall Street Journal Survey of economists predicted the yield on the 10-year Treasury note would fall below 2.5% this year. So it's just way beyond what anybody was predicting. There's some interesting projections here. So here's one guy down here at the bottom and then by the end of 2019, he expected it to be all the way up here. We just connect the dots. Some interesting characters there. Economists know that they're in trouble. They know that their models are not working very well. There's some quotes from top economists and people who are linked a lot in social media and they know that they did not do a good job anticipating the most recent financial crisis. They're not doing a very good job of explaining business cycles. I've got a few quotes here that show that. One of my favorites is Paul Romers here, presenting a model at an academic conference is like doing a card trick. Everybody knows that there will be some sleight of hand. There's no intent to deceive because no one takes it seriously. One of the presidents of the Federal Reserve district banks says that macroeconomics has little to offer by way of answers to these questions like why does an economy have business cycles? Why do asset prices move around so much? I would suggest that they attend Mises University perhaps. There's another one by Robert Solo. I don't think that the currently popular DSGE models passed the smell test. So they understand that there's something that's a big problem here. However, if we look at predictions from Austrians and I tried to go back as far as I could. We had Sean Corrigan and Mark Thornton predicting and making good particular specific predictions about what would happen during the housing bubble while it was getting started. So this is back to 2002 and 2004 that they were making these predictions. So I've run out of time here but the final conclusion here is that good theory leads to better statistics and better predictions but we don't necessarily hang our hat on making predictions. Thank you.