Author: Zachary Bartsch

Future Consumption Has Never Been Cheaper

Zachary Bartsch1 Comment

Economics as a discipline really likes to boil things down to their essentials. There are plenty of examples. How many goods can one consume? Just two, bread and not bread. How can you spend your time? You can labor or leisure. How do you spend your money? Consume or save. It’s this last one that I want to emphasize here.

First, all income ultimately ends up being spent on consumption. Saving today is just the decision to consume in the future. And if not by you, then by your heirs. One determinant of inter-temporal consumption decisions is the real rate of return. That is, how many apples can you eat in the future by forgoing an apple eaten today? The bigger that number is, the more attractive the decision to save.

Further, since most saving is not in the form of cash and is instead invested in productive assets, we can also characterize the intertemporal consumption problem as the current budget allocation decision to consume or invest. The more attractive capital becomes, the more one is willing to invest rather than consume. The relative attractiveness between consumption and investment informs the consumption decision.

How attractive is investment? I’ll illustrate in two graphs. First, if the price of investment goods falls relative to consumption goods, then individuals will invest more. The graph below charts the price ratio of investment goods to consumption goods. Relative to consumption, the price of investment has fallen since 1980. Saving for the future has never been cheaper!

https://fred.stlouisfed.org/graph/?g=1p0C7

Of course, as in a price taker story, I am assuming that individuals don’t affect this price ratio. Truly, prices are endogenous to consumption/investment decisions. For all we know, it may be that the prices of investment goods are falling because demand for investment goods has fallen. But that doesn’t appear to be the case.

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Corporate Landlords Make Rent… Lower?

Zachary Bartsch6 Comments

Let’s keep it brief. Stick with me.

You know how perfect diversification means that one bears no idiosyncratic risk? That means that one is willing to pay more for some given return, driving up the price of assets included in such a diversified portfolio. That means that, without an informational advantage, index funds should place upward pressure on the price of assets that compose them. Anyone who invests in individual stocks, again without an informational advantage, would be priced out of the market because they bear idiosyncratic risk and would need to enjoy a risk premium that lowers the maximum price that they are willing to pay.

What about real estate?

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Taxes & Unemployment – Know Your Bias?

Zachary Bartsch2 Comments

Say that there is a labor market and that there is no income tax. If an income tax is introduced, then what should we expect to happen? Specifically, what will happen to employment, the size of the labor force, and the number of people unemployed? Will each rise? Fall? Remain unchanged? Change ambiguously? Take a moment and jot down a note to test yourself.

As it turns out, what your answer is depends on what your model of the labor market is. Graphically, they are all quantities of labor. The size of the labor force is the quantity of labor supplied contingent on some wage that workers receive. It’s the number of people who are willing to work. Employment is the quantity of laborers demanded by firms contingent on to wage that they pay. Finally, the quantity of people unemployed is the difference between the size of the labor force and the quantity of workers employed (Assuming that the labor force is greater than or equal to employment).

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Hotel Taxes and Quality: Why Georgia Sucks (Value)

Zachary Bartsch4 Comments

Every year my family travels from SW Florida to the mid Atlantic area. Without stops it takes 16-17 hours. With small children, it’s definitely a two day trip. We find that they handle it better if we leave super early, take a longer leg on the first day, then stop at a hotel midway and get the kids in the pool to help burn off some energy. We also rent a suite whenever possible.

We’ve made this trip many times. I use the Bonvoy app which is for Marriott hotels. We even have a particular hotel that we prefer: The Fairfield Inn in Santee, SC. It’s clean, spacious, the employees are welcoming and kind, the breakfast includes cooked items that aren’t bad, it’s within walking distance of a grocery store, and the price isn’t bad at all. Fairfield Inns are generally a great price per quality…. But not in Georgia.

I’ve stopped at several Fairfield Inns in GA: near Atlanta, near Savannah, and we’ve been disappointed. Every. Single. Time. All the margins on which the Fairfield in Santee is great are the same margins on which Georgia ones are poor. I’m sure that there is not just one reason. Maybe there is a bad regional manager or bad assistant to the regional manager. That’s not my primary hypothesis though.

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Almost Observable Human Capital

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I’ve written about IPUMS before. It’s great. Among individual details are their occupations and industry of their occupation. That’s convenient because we can observe how technology spread across America by observing employment in those industries. We can also identify whether demographic subgroups differed or not by occupation. There’s plenty of ways to slice the data: sex, race, age, nativity, etc.

But what do we know about historical occupations and what they entailed? At first blush, we just have our intuition. But it turns out that we have more. There is a super boring 1949 report published by the Department of Labor called the “Dictionary of Occupational Titles”. The title says it all. But, the DOL published another report in 1956 that’s conceptually more interesting called “Estimates of Worker Trait Requirements for 4,000 Jobs as Defined in the Dictionary of Occupational Titles: An Alphabetical Index”.  The report lists thousands of occupations and identifies typical worker aptitudes, worker temperaments, worker interests, worker physical capacities, and working conditions. Below is a sample of the how the table is organized:

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Not Crazy: Insurance Premiums

Zachary Bartsch4 Comments

Higher homeowner’s insurance premiums have been in the news. But are we just hearing about the extreme cases? This post is inspired by the FRED Blog post about property and casualty (P&C) insurance premium producer price indices. I dive a little deeper.

The insurance premium data is composed of seven components:

  1. Private passenger auto insurance
  2. Homeowner’s insurance
  3. Commercial auto insurance
  4. Non-auto liability insurance
  5. Commercial multiple peril insurance
  6. Worker’s compensation insurance
  7. Other property and casualty insurance

Non-auto liability insurance is further split up into A) medical malpractice insurance and B) other non-auto liability insurance.*

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Social Cost Irregularities

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If you want an economist to support a government intervention, then there are two major sets of logic that they generally find attractive.

The first concerns rate of return and attracts narrower support. If the government can invest in a project in a way that the private sector couldn’t/wouldn’t and the payoff is bigger than the investment by enough, then the project should be built. 

The second set of logic is more accepted more broadly. If there is an externality, and the administration costs are small relative to the change in the externality, then the project should be pursued in order to increase total welfare.

I’m going to criticize and refine the second argument.  I was inspired by a student who wrote about education creating positive externalities for “all”. They kept using the word “all”. And I notated each time “not *all*”. While we might refer to something called ‘social’ cost and value, the existence of externalities does not imply that everyone is affected by the them identically. That’s a representative agent fallacy. The externalized costs and benefits are often irregularly distributed among 3rd parties. This is important because government intervention can impose its own externalities depending on how the administrative costs funded.

I’ll elaborate with two examples that illustrate when an irregular distribution of externalities is a problem and when it isn’t a problem.

Electric Plant Pollution

The first example illustrates how resolving an irregular distribution of externalities can be resolved without issue. Consider a coal-powered electric plant that serves a metropolitan area and creates pollution. That pollution drifts east and passively harms residents in the form of asthma exacerbation and long-term ill health. The residents to the west are unaffected by the pollution, thanks to favorable weather patterns. Obviously, one would rather live on the west side, all else constant (importantly, all else it not always constant and there is a case to be made that there is no externality here).

To resolve the externality, the government imposes a tax per particle on the power plant at a low administrative cost. That’s nice and efficient – we won’t waste our time with means-oriented regulations. In turn, the cost of electricity increases for all metropolitan residents, both those in the east and in the west. Why is this appropriate? Prior to the intervention, the electricity users in the west were enjoying electricity at a low price, failing to pay for the harm done by their consumption. For that matter, the residents to the east are also paying the higher rates, but now they enjoy better health.

In the end, the externality is resolved by imposing a cost on all consumers of the good – which happens to be everyone. This circumstance is not pareto efficient, but it is Kaldor-Hicks efficient. Everyone now considers the costs that they were previously able to impose on others and ignore.

That’s the best case scenario.

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Fossil Fuel Frenzy: The Driving Force Behind US Extractive Growth

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What with all the talk about semi-conductor production and rare-earth mineral extraction, I think that it’s worth examining what the USA produces in terms of what we get out of the ground. This includes mining, quarrying, oil and natural gas extraction, and some support activities (I’ll jump more into the weeds in the future). I’ll broadly call them the ‘extractive’ sectors. How important are these industries? In 2021 extractive production was worth $520 billion. That was roughly 2% of all GDP. Below is the break down by type of extraction.

Examining the graph of total extraction output below tells a story. The US increased production of extracted material substantially between the Great Depression and 1970.  That’s near the time that the clean water and clean air acts were passed. But the change in the output growth rate is so stark, that I suspect that those were not the only causes of change (reasonable people can differ). For the next 40 years, there was a malaise in output. This was the period during which it was popular to talk about our natural resource insecurity. As in, if we were to be engaged in a large war, then would we be able to access the necessary materials for wartime production?  

https://fred.stlouisfed.org/graph/?g=1kWNU

But for the past 15 years we’ve experienced a boom with extracted output rising by 50%, an average growth rate of 2.7% per year. That’s practically break-neck speeds for an old industry at a time when the phrase ‘great stagnation’ was being thrown about more generally. By 2023, we were near all-time-high output levels (pre-pandemic was higher by a smidge).

For people concerned about resource security, the recent boom is good news. For people who associate digging with environmental degradation, greater extraction is viewed with less enthusiasm. Those emotions are especially high when it comes to fossil fuel production. Below is a graph that identifies the three major components of extraction indexed to the 2021 constant prices. By indexing to the relative outputs of a particular year, the below graph is a close-ish proxy to real output that is comparable in levels.

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The Time it Took for Price to Rise

Zachary Bartsch1 Comment

Last month, Jeremy wrote about how long it takes for prices to double. He identified a few intervals of time that are sensible. But I want to pick up the ball and move it further down the field. Not only can we identify how long it took for prices to double in particular eras, we can also do it for *every month*. Below, is a graph that shows us how many years had passed since prices were half as high (PCE Chained Prices).

Expectedly, the minimum time to double consumer prices was in the early 80s, taking just under 9 years for price to double. The prior decade included the highest inflation rates in the past 70 years.  Since that time, the number of years needed in order for prices to double steadily rose as the average inflation rate fell. That is, until after the pandemic stimuli which caused the time to plateau. But to be clear, that must mean that prices aren’t doubling any fast that they used to, despite what we’ve heard on the news.

Except… prices are in fact rising faster by 21st century standards. Indeed, measuring the time that it took prices to double covers up a lot of variation. After all, The PCEPI was 15.19 in 1959 and is 122.3 now. That’s only enough difference for three doublings. But as we lower the threshold for price changes, we can see more of the price level patterns. Below-left is the time that was necessary for prices to increase by 50% and below-right is the time that was necessary for prices to rise by 25%.

In these graphs we can see more of the action that happened post-Covid. The time needed for prices to rise by 50% has fallen by about five years since 2020. That’s a 20% shorter time necessary for a 50% increase in prices. The time needed for a 25% increase in prices is even more drastic. As of 2020, people were accustomed to experiencing upwards of 14 years before overall prices rose by 25%. That number fell below 8 years by 2024.

And finally, the most unnerving graph of all is below: the time that was needed for prices to rise by 10%.

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The Self-Correcting Property

Zachary Bartsch1 Comment

Say that the Federal Reserve Prints a boatload of money. We can use the AS-AD model (aggregate supply & aggregate demand) to evaluate the effect on prices and output.

Printing money results in more total spending in the economy. How much of that initial greater total spending is composed of higher prices versus higher output depends on business marginal costs and whether firms know or expected the greater demand to be due to a broad inflationary event (rather than just greater demand for their particular products).

If there is broad inflation, then the price level that is observed in the economy, including inputs, will deviate from what firms expected. Naturally, firms update their expectations. In so doing, they increase the price that they would require in order to produce every quantity of output. The vertically rising SRAS reflects both of these. The rising itself reflects the higher required prices, and the intersection with the LRAS reflects the expected price level. Notice that updating the expectations places upward pressure on prices, resulting in still higher than anticipated prices. This occurs repeatedly and each time that expectations are updated, the difference between the actual and the expected inflation gets smaller. 

This is what macroeconomists call the “self-correcting property’. The economy will adjust to an AD shock ‘automatically’. Of course, automatic isn’t quite the right word. It’s automatic from the perspective of a policy maker. But the self-correction is the result of an economy’s worth of people bidding for scarce goods and changing their price expectations. It’s automatic in the sense that people don’t need to be told to make the effort. The same results won’t occur if buyers and sellers do nothing, which sounds less automatic.

Since the fundamental productivity of the economy hasn’t changed, we eventually return to the original level of output. If monetary policy doesn’t change in the meantime, then prices will simply rise until the long-run price change composes 100% of the change in total spending. Indeed, given the AS-AD model above, half of the price difference between the current price and the long run price is eliminated each period. Similarly, half of the output gap is eliminated each period. This is why monetary and fiscal stimulus that just focuses on total spending only has short-run output and employment effects. The self-correcting property asserts itself and prices rise in the long run.

*In the figures above, I’ve illustrated an initial sharp price change, though sticky prices and very surprising inflationary stimulus can cause a delay in the initial price adjustment.

**Of course, all of this can be expressed in percent change rather than levels.