I love data, I love maps, and I love data visualizations.
While we tend not to remember entire data sets, we often remember some patterns related to rank. Speaking for myself anyway, I usually remember a handful of values that are pertinent to me. If I have a list of data by state, then I might take special note of the relative ranking of Florida (where I live), the populous states, Kentucky (where my parents’ families live), and Virginia (where my wife’s family lives). I might also take special note of the top rank and the bottom rank. See the below table of liquor taxes by State. You can easily find any state that you care about because the states are listed alphabetically.
A ranking is useful. It helps the reader to organize the data in their mind. But rankings are ordinal. It’s cool that Florida has a lower liquor tax than Virginia and Kentucky, but I really care about the actual tax rates. Is the difference big or small? Like, should I be buying my liquor in one of the other states in the southeast instead of Florida? Without knowing the tax rates, I can’t make the economic calculation of whether the extra stop in Georgia is worth the time and hassle. So, the most useful small data sets will have both the ranking and the raw data. Maybe we’re more interested in the rankings, such as in the below table.
But, tables take time to consume. A reader might immediately take note of the bottom and top values. And given that the data is not in alphabetical order, they might be able to quickly pick out the state that they’re accustomed to seeing in print. But otherwise, it will be difficult to scan the list for particular values of interest.
During the week of thanksgiving in 2020, our thirteen-year-old microwave bit the dust. NBD, I thought. Microwaves are cheap, and I’m willing to spend a little more in order to get one that I think will be of better quality (GE, *cough*-*cough*). So, I filtered through the models on multiple websites and found the right size, brand, and wattage. No matter the retailer, at checkout I learned that regardless of price, I’d be waiting a good two months before my new, entirely standard, and unexceptional microwave oven would arrive. I’d have to wait until the end of January of 2021.
The aggregate supply & aggregate demand model (AS-AD) is nice because it’s flexible and clear. Often professors will teach it in levels. That is, they teach it with the level of output on one axis, and the price level on the other axis. This presentation is convenient for the equation of exchange, which can be arranged to reflect that aggregate demand (AD) is a hyperbola in (Y, P) space. Graphed below is the AD curve in 2019Q4 and in 2020Q2 using real GDP, NGDP, and the GDP price deflator.
The textbook that I use for Principles of Macroeconomics, instead places inflation (π) on the vertical axis while keeping the level of output on the horizontal axis. The authors motivate the downward slope by asserting that there is a policy reaction function for the Federal Reserve. When people observe high rates of inflation, state the authors, they know that the Fed will increase interest rates and reduce output. Personally, I find this reasoning to be inadequate because it makes a fundamental feature of the AS-AD model – downward sloping demand – contingent on policy context.
At the same time, I do think that it can be useful to put inflation on the vertical axis. Afterall, individuals are forward looking. We expect positive inflation because that’s what has happened previously, and we tend to be correct. So, I tell my students that “for our purposes”, placing inflation on the vertical axis is fine. I tell them that, when they take intermediate macro, they’ll want to express both axes as rates of change. I usually say this, and then go about my business of teaching principles.
But, what does it look like when we do graph in percent-change space?
Sometimes, large entities have enough money to throw at a problem that by sheer magnitude they produce something great (albeit at too high a cost). The iPhone app from the FRED is not that thing. But the Excel add-in is something that every macroeconomics professor should consider adding to their toolkit.
Personally, I include links to FRED content in the lecture notes that I provide to students. But FRED makes it easy to do so much more. They now have an add-in that makes accessing data *much* faster. With it, professors can demonstrate in excel their transformations that students can easily replicate. The advantage is that students can learn to access and transform their own data rather than relying on links that I provide them.
The tool is easy enough to find – FRED wants you to use it. After that, the installation is largely automatic.
Installed in excel you will see the below new ribbon option. It’s very user friendly.
In game theory, coordination games reflects the benefits of everyone settling on the same rules. Settling on the same rules can avoid a conflict and destructive competition. For example, some rules may be arbitrary, such as on which side of the road we’ll all drive. It doesn’t much matter whether a country’s vehicles drive along the right or left side of the street. As long as everyone is in the same lane, we overwhelmingly benefit from our coordination. The matrix below describes the game.
The above game reflects that whether we agree to drive on the left or on the right is trivial and that the important detail is that we agree on what the rule is. Rules like this are arbitrary. No amount of cost benefit analysis changes the answer. Other coordination rules are seemingly arbitrary, but do have different welfare implications. For example, according to English common law, a farmer was entitled to prohibit a herdsman’s flock from trampling his crops even if the farmland had no fence. Herdsmen were responsible for corralling their flocks or paying damages if they grazed on the farm. With lots of nearby farms, total welfare was higher with a rule of cultivation rights rather than grazing rights.
But the property rights could have been assigned to the herdsman instead. The law could have said that the sheep were free to graze with impunity and that the onus was on the farmer to build fences in order to keep the sheep at bay. In a world where there are a lot of farmers who are very nearby to one another, a small flock of sheep can do a lot of damage. And so, the cost-benefit analysis prescribes that herdsmen bear the cost of restricting the flock rather than the farmer. The matrix that describes this circumstance is below.
The above matrix reflects that agreeing on any rule is better than no rule at all. And, the rule that is selected has societal welfare implications. Choosing the ‘wrong’ rule means that we could get stuck in a rut of lower payoffs because coordinating a change in the rules is hard.
Another way in which the specific rule can be important is by whether it instantiates or works contrary to pre-existing incentives. Before compulsory schooling laws were passed, US states already had very high school attendance rates. Most parents sent their kids to school because it was a good investment. The ages at which children should be required to attend is largely, though not entirely, arbitrary. And wouldn’t you know it, most states applied their compulsory schooling legislation to the age groups for which the vast majority of children were already attending school. Enforcing a law against the natural incentives of human capital investment would have been more costly. The particular ages of compulsory schooling had different welfare implications due to the differing costs of enforcement.
Inflation is on everyone’s mind. Everybody freaks out. You cannot do anything about it. As such, lets talk about something mildly related: how price indexes (those that we use to talk about inflation) deal with quality changes.
One big problem when we try to measure the cost of living is that the price information we collect does not reflect the same thing we consume. I know that sentence seems weird. After all, 1$ for a pound of bread is 1$ for a pound a bread. And if prices go up 10%, then the price per pound of bread is 1.10$!
If you think that, you’re wrong. Think about the following example from my native province of Quebec. In the 1990s, Quebec deregulated opening hours for grocery stores. The result was … higher prices at large superstores. Why? Before the reform, stores had shorter hours especially on sundays. This meant that stores were competing with each other on a smaller quality dimension which meant more price-based competition. With deregulation, some consumers were willing to pay slightly higher prices to shop at ungodly hours. What were these consumers consuming? Were they consuming only the breadloafs they bought or were they consuming those loafs and the flexible schedule of the grocery stores? The answer is the latter! Ergo, the change from 1$ per pound to 1.10$ per pound does not meanthat the price of bread alone increased — it may have even fallen all else being equal!
So how do you adjust for that? There are many papers on how to do hedonic adjustments (hedonic is the fancy words we use to say “quality-adjusted”) and they are all a pain to read unless you are very familiar with real analysis, set theory and advanced calculus (and even there, its still a pain). Fortunately, I recently found a neat little application from an old econometrics graduate text from the 1960s (see image below) that allows me to teach this to my students (and now, you too!) in an easy-to-get format.
The book has a neat chapter by one of the most famous econometricians of the 20th century, Zvi Griliches, titled “Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change”. In the chapter, Griliches points out that from 1954 to 1960, car prices went up some 20% — well above the overall price index. From 1937 to 1950, prices for cars went up in line with inflation. Taken together, these two facts suggest that the real price of cars stayed constant from 1937 to 1950 and increased to 1960. But that suggestion is wrong Griliches points out because of our aforementioned quality issues. Up until 1960, there were considerable improvement in vehicle quality: better gears, better brakes, more horsepower, safer settings, automatic transmission, hardtops, switching to V-8 engines rather than 6 cylinders engines etc.
How do you account for these quality changes? Griliches simply went about consulting guide books for autobuyers. He collected price data for the cars as well the details regarding quality. And he used this very simple specification where the log of the nominal price is set as a dependent variable.
The vector Xis all the quality dimensions he could find (horsepower, shipping weight, length, V-8 engine, hardtop, automatic transmission, power steering, power brakes, compact car). All of these dimensions were statistically significant determinants of the price of cars (with the exception of V-8 engines which was not significant). Then, Griliches assumed that all quality dimensions were “unchanged” from 1954 to 1960 in order to see how prices would have evolved without any changes in quality. The result is the figure below. The blue line depicts the actual prices he collected where you can see the 20% increase to 1960 (which is a 30%+ increase to 1959). The orange line depicts the price holding quality constant. That orange line is unambiguous: quality-constant car prices didn’t change much during the 1950s. Adjusting for inflation during the period suggests a drop in 10% in the real price of a quality-constant car.
Isn’t that a fascinating way to understand what we are actually measuring when we collect prices to talk about inflation? I find this to be an utterly fascinating example (and a useful teaching tool). Okay, I am done, you can go back to freaking out about inflation and how bad the Fed, Bank of Canada, ECB are.
I’m a big fan of Milton Friedman. I’m also a big fan of easy-to-remember phrases that impart great wisdom. It honestly made me wince the first time I said the following:
“Inflation is *not* everywhere and always a monetary phenomenon“.
The reasoning is as plain as day. Consider the quantity equation:
For the uninitiated, M is the money supply, V (velocity) is the average number of times dollars transacts during a period, P is the price level, and finally Y is real output during a period. This equation is often called the “equation of exchange” or “the quantity equation”. Strictly speaking, it is an identity. It is a truism that cannot be violated. All economists agree that the equation is true, though they may disagree on its usefulness.
Inflation is simply the percent change in price. We can rearrange the quantity equation, solving for price, in order to see the relationship between the price level and its determinants.
What does this mean? It means that more money results in more inflation, all else held constant. It means that higher velocity results in more inflation, all else held constant. It means that less output results in more inflation, all else held constant.
Why would Milton Friedman say that inflation is always caused by changes in the money supply if it is clear that there are two other causes of the price level? When Milton Friedman said his famous quote, output growth was relatively steady. Velocity growth was relatively steady. For his context, Milton Friedman was right. The majority of price and inflation volatility was found in changes in M. See below.
Strictly speaking however, Milton Friedman knew better and he knew that the statement was not strictly correct. Friedman was a public intellectual and he was a great simplifier. He taught many people many true things. At the time, people were blaming inflation on a great variety of things: taxes, fish catches, and unions, to name a few. Arguably, Friedman got them closer to the truth.
What does this mean? It means that higher NGDP results in more inflation, all else held constant. It means that less output results in more inflation, all else held constant.
But economists dismissing M in lieu of AD are committing the same oversimplification. Y can also change! Maybe economists figure that our recent history is full of relatively stable Y growth and that we ought not pay attention to it. And indeed, unsurprisingly, RGDP growth has been less than NGDP growth.
But what is driving the current bought of inflation?
Pardon the crude image. The pink lines are eye-balled trend lines on natural logged data for AD, Y, and P. Prices are up. Is it because of exceptionally high NGDP? Nope. Total spending is back on pre-2020 trend. Does Y happen to be down? Yep, it sure is.
Right now, assuming the previous trend was anywhere close to potential output, inflation is not being driven by excess aggregate demand. It’s being driven by inadequate real output. The news tells the story. There have been supply-chain bottle-necks, difficulty employing, lockdowns, and fear of covid. Right now we have an output problem and higher prices are a symptom. We do not have an aggregate spending problem.
PS – In fact, it is my belief that the Fed successfully avoided a debt-deflation aggregate demand tumble that would have been catastrophic. Inflation is expected when supplies of goods decline.
An ex-co-worker was once complaining to me that the prices of things that he liked kept going up.
He was an economics major. Of course he knew that wages also increase. He wasn’t simply cantankerous about inflation. He knew all about improving productivity, income, and price level changes. He was being more specific. The *particular* items that *he* liked were getting more expensive. He was complaining about what, to everyone else, were relative price changes.
Unrelatedly, I was floating around the bls.gov website and examining their Producer Price Index (PPI) FAQs (I learned a bunch). The content is extensive. CPI is broken up into some subcategories. But PPI, being used by multiple industries and trade groups for real-life costs and benefits, is excitingly granular.
You want to know what happened to the price of red, white, rose, and carbonated wines each in particular? They’ve got you covered. It really is amazing.
Back to my co-worker. I tried to explain that relative price changes reflected underlying economic value and scarcities. We wasn’t having any of it. He just didn’t want his prices to go up. We economists are known for being kind of dispassionate. We see relative prices change and we shrug. Man-on-the-street sees a relative price change and, boy, does he care about it – if it’s the purchasing price that *he* faces.
See the below graph. What kind of consumer are you? Since the start of the pandemic, canned, bottled, and kegged beer have all changed in price. Or maybe you’re a teetotaler and you’ve noticed the increasing price of bottled water. For interpretability, let’s consider what had cost $10 at the start of the year 2020. Bottled water has gone up to $10.50 and bottled beer has gone up to almost $10.30. You may not blink at a 3% price increase – unless it’s for 6 bottles of your favorite craft beer.
The price of canned beer, on the other hand, hardly increased at all. And in the last couple of months, the price *fell*. I sure hope that my co-worker is a canned-beer kind of guy. Otherwise, someone is sure to hear a lot of belly-aching.
On Sunday the world lost a great teacher, economist, and all-around fantastic person in Steve Horwitz. If you don’t know about Steve, I recommend reading the tributes from Pete Boettke and Art Carden.
Pete and Art speak to Steve’s overall legacy and greatness. But I will tell you about a very specific piece of advice that Steve gave me about teaching undergrads.
Steve called it “the graduate student disease.” By this he meant the tendency of newly minted PhD economists to teach undergraduate courses as if they were mini versions of graduate courses. Steve insisted this was the wrong approach.
A little background for those not completely familiar with the academic world: schools are usually considered either teaching or research schools. At first this seems confusing: both Clemson (where Makowksy is) and the University of Central Arkansas (where I am) require that faculty engage in both research and teaching. The difference is subtle, but the big hint is that Clemson is considered an “R1” school (the highest research designation) and has a PhD program with many graduate students. At a school like Clemson, research is valued more than teaching. At UCA, teaching is valued more than research. (Much more could be said about the differences, perhaps in a future post.)
We both engage in both teaching and research (as well as service!), but the emphasis is different. For me at UCA, the expectations of which journals I will publish in and how frequently I will publish are lower than at a school like Clemson. At Clemson, some of your publications should be in the Top 5 (or at least Top 10) journals from time-to-time. At UCA, if you published in one of the top journals, the assumption would be that you are probably leaving soon to go to an R1 school
I’m glad both types of schools exist, and my point here is not to disparage either type of school. But the difference is important for thinking about the academic publishing process.
For someone at an R1 school, publications in top journals are positional goods. Makowsky doesn’t say this exactly, but that’s my takeaway from his post. There are only so many spots available in these journals, and they have value because there is only a fixed number available. And since there has been, over the years, a lot more economists doing a lot more research not all of the great papers will end up being published in one of the top journals.
Upshot: there are a lot of great papers being published in Top 50 or even Top 100 journals! Let me pick on myself. As I said, I recently successfully survived the tenure process. My publication record was good enough. You can inspect my publications over at Google Scholar. I’m proud of these publications. I think some of them are really great. But I’m fairly confident that I would never earn tenure at Clemson with these publications. Instead, you need a publication record like Makowsky.
What’s interesting here is that Mike and I occasionally publish in some of the same journals. Public Choice and Constitutional Political Economy jump out to me. These are, in my view, very fine journals. Lots of interesting research is published in these journals. I’m especially proud of this paper in Public Choice. But if someone published only in these two journals and journals like them, they wouldn’t get tenure at an R1 university.