What’s in a name? Ask American Superconductor Corp, a stock that today is up 57%, with no apparent underlying news to justify such an increase. Well, there does exist somewhat related news — an announcement by a group of Korean scientists that they may have invented a room temperature superconductor — however the discovery as of this moment is still doubtful, and even if true, seems somewhat tangential to the underlying business.
This is just the latest example in a long-litany of companies that have benefited from a name connected to the next big thing, dating back to at least the great depression. In the early 1930s, there was an overall boom in airline stocks. This was good news for a company inaptly named Seaboard Air Line. According to Life Magazine,
the price of Seaboard Air lines, even thought it had recently undergone bankruptcy, advanced along with many plane stocks under a public belief that the company is an airline. Actually, the Seaboard Air Lines Railroad Company, its full name, is a railroad.
The story can be somewhat verified in the data — CRSP data for Seaboard Airlines shows the stock price trippleing from $.31 a share in march 1933 to $2 a share in July 1933, along with a massive increase in share volume. Around this time there is also a boom in volume and price of other airline stocks.
The thing to understand about capital gains and taxes is that most capital gains have not been taxed yet, and may never be taxed, unless the tax code is changed. Consider the case of mutual funds:
From 1997-2012 households had a total of $1.75 trillion in taxable net capital gains on mutual fund assets — meaning that this the amount of income they would be taxed on if they realized the capital gains during this period. But how much of this was actually taxed? It turns out, almost none. The total amount of net gains reported on tax returns was $-100 billion, for an overall negative tax rate.
The explanation is, of course, that capital gains are taxed only on realization. And for most mutual fund holders it never made sense to realize their gains, but instead to only realize their losses, a process known as tax loss harvesting.
This is a particularly extreme example of the general principle that the capital gains tax is a particularly leaky sieve, and it is unclear the extent to which the massive capital gains we’ve seen over the past forty years will ever show up on tax returns.
To look over longer time periods, we can use my estimates of aggregate capital gains, compiled from the Financial Accounts, to capital gains reported on tax returns, estimated from the Treasury. The data is below, and again shows that most capital gains have not yet showed up on tax returns:
From 1954-2020 aggregate taxable capital gains in the Financial Accounts totaled $70 trillion. The total reported on tax returns over the same period was $22 trillion, significantly less than a third of the total.
The low levels of taxes paid on capital gains compared with the massive aggregates means that the effective average tax rates on capital gains are vanishingly small.
Will they ever show up on tax returns?
There are a number of reasons why capital gains haven’t shown up yet on tax returns, and research remains to be done on the relative importance of the different causes.
Many wealth taxpayers can avoid realizing capital gains indefinitely by borrowing against their assets rather than selling them.
In a growing economy (or, similarly, in an economy where capital gains are growing rapidly), if only a small percentage of capital gains are realized each year, then even in a steady state the amount realized relative to the aggregate amount will be low.
There are a variety of other loopholes, legal and otherwise, that have allowed wealthy Americans to avoid paying capital gains taxes.
I’ve been harping on the importance of capital gains for years — mainly emphasizing that (i) this is a massive and growing source of income, especially for the very wealthy, and (ii) this income is largely untaxed, due to a variety of loopholes around capital gains taxation (iii) the gains are not well understood from a macroeconomic standpoint.
The latest data release from the Federal Reserve contains data through the end of 2021, and it’s a doozie. The top-line number is absolutely huge: $16.2 trillion in nominal capital gains (93.8% of net national income), or $6.97 trillion in real capital gains (39.2% of net national income).
Nominal capital gains Real capital gains
Most of the capital gains came from equities (very unequally distributed) and real estate (somewhat unequally distributed).
Capital gains were by far the main contributor in the increase in household net worth over the past year. Nominal net worth increased by $18.9 trillion in 2021. Of the total, $2.3 trillion came from savings / increased capital investment, and $16.7 trillion came from capital gains.
Private savings, as traditionally measured in the National Income and Product Accounts, does not include the value of real capital gains. The figure below adds in real capital gains to savings and compares the two magnitudes.
I have some gripes on twitter about the Quantity Theory of Money. My main complaint is that everybody has their own private version of (i) which quantity theory they are talking about (ii) what definition of money they are using. So I thought I would lay out (a) all of the different versions that circulate (b) which ones I agree/disagree with.
A couple of recent pieces have revitalized the debate over whether money matters. The Washington Post has an interesting story about how Federal Reserve officials no longer care about the money supply:
But the current Fed chair, Jerome H. Powell, has dismissed claims that the Fed’s money-printing is fueling today’s price spiral, emphasizing instead the disruptions associated with reopening the economy. Like his most recent predecessors, dating to Alan Greenspan, Powell says that financial innovations mean there no longer is a link between the amount of money circulating in the economy and rising prices.
Krugman has likewise picked up on the comments from the Fed in his own blog post.
So what is the quantity theory of money. Jason Furman sets out the basic idea. The theory starts out from an accounting identity
M * V = P * T
M(oney) times V(elocity) equals P(rices) times T(ransactions). V(elocity) is supposed to roughly represent how many times a dollar changes hands in a given period. The “money” in this equation is usually taken to be a broad measure of money, such as M2, that includes checking account balances, some time deposits, as well as currency.
Now, transactions are very difficult to measure in the economy. Aside from buying and selling goods, transactions occur for financial assets, between banks, or when workers are paid — they are very difficult to get a handle on. And I don’t think anyone really even tries to make this work.
To make progress, we simplify and make the assumption that transactions are proportional to income (Y), giving the formula
M * V = P * Y
This is still (mostly) an accounting identity — but becomes a theory when we put additional structure on the equation. If we assume that velocity is roughly constant, we have
P = M*V / Y
Quantity Theory 1 (QT1): prices should grow at the roughly the rate of broad money minus the growth rate of output.
This is the classic statement of the quantity theory. For example, Wikipedia defines the quantity theory as “general price level of goods and services is directly proportional to the amount of money in circulation — the money supply.”
This is actually quite an interesting theory because it is a statement about a correlation of potentially endogenous variables. A theory that says there should be a correlation between variables! Okay, great. The beautiful thing about this theory is that we can go ahead and test it. In his twitter post, Furman gives the evidence that this tends to hold pretty well over long periods of time:
Over short periods, however, this doesn’t even come close to holding:
During the current pandemic there is been a dramatic drop in money velocity, again showing that the dramatic increase in M2 has not been matched with a commensurate rise in prices.
So my general view towards QT1 is that it holds in the long run but not in the short run.
Note, however, that this does not say anything about causality. In fact, I don’t that QT1 has any causal content as a theory. It does not state that money causes an increase in prices, or that nominal spending growth causes an increase in the money supply. It simply says that they should be correlated.
The reason why there is no causal content is because measures of broad money are an endogenous variable. The Federal Reserve does not directly control the level of M2 in the economy. In fact, under the Federal Reserve’s Ample Reserves regime, the Fed does not even strictly target the level of reserves in the economy.
Quantity Theory #2: the money multiplier version. An increase in base money translates to a given increase in broad money, and then to prices. This is the version that Art Laffer and others espoused during the Financial Crisis:
This is the worst version of the theory and is clearly contradicted by the data. First off, the “money multiplier” is not even close to constant and has seen wild fluctuations after the financial crisis. In a liquidity trap there should be little effect of an increase in base money on broad money aggregates.
Second, as discussed above, there isn’t much correlation between broad money and inflation at short time horizons. So both legs of this theory don’t work great.
Note that unlike QT1 this is a causal theory. So if it did hold (and it does not) we would at least
Quantity Theory #3: the “vaguely causal” version. An increase in broad money causes an increase in prices, but broad money is assumed to be exogenous.
This is often a version which crops up and is my least favorite because it is incomplete. If you are going make a causal statement of how broad money affects prices, you have to specify what is causing the change in broad money!
What is my takeaway from this tangle of thorns?
My baseline view of the world is similar to that of the Federal Reserve: monetary aggregates are endogenous. The money supply expands when the economy and credit expands, and contracts when the economy contracts. Given the evidence, I think the QT1 holds roughly in the long run but not in the short run, but this says nothing about the causal direction.
Now, I wouldn’t go as far to say that changes in the broad money supply are unimportant. In fact, a sharp contraction in broad money would be a very worrying sign of economic collapse, such as during the Great Depression.
But I would not use the MV = PY equation to understand the current state of the economy.
Business Insider has a very nice run down of the most positive development in economic policymaking in a generation: the willingness of the Fed (spearheaded by Yellen, continued by Powell) and the Biden administration to run the economy hot in the face of a once in a century economic crisis. Enter the backlash — concerns about inflation from Larry Summers, shortages of lumber and computer chips, and a moral panic over a “worker shortage”. To this bruhaha I want to make three simple points:
The risk of a slow recovery vastly outweighs the risks of moderately higher inflation. Total employment is still 10.2 million jobs below the pre-crisis trend. Even at the rate of this very large monthly increase, it could take two years or more for the the labor market to recover from the pandemic. In a nightmare scenario, job growth slows down, and there is a repeat of the post-financial crisis anemic recovery.
2. Running the economy hot, and yes, accepting some inflation, is likely necessary for there to be a sufficient recovery in the labor market. Within the sectors hardest hit by the pandemic, there is a strong relationship between consumer demand and employment.
3. It may be necessary to run the economy hot for significant period in order to coordinate the economy to a high level of output. In certain sectors demand has been extremely high, yet employment has stabilized well below pre-pandemic levels. Take employment in motor vehicle production. Although consumer demand has surged, employment is still down more than 10% from February 2020. Chip shortages have undoubtedly contributed to this laggard behavior, yet the fact remains that the major auto producers have been slow to respond to increased consumer demand. This suggests that (a) firms are understandably reluctant to vastly increase operations in what they may expect to be a temporary surge in demand (b) the economy faces a coordination problem in moving from an equilibrium of low output to an equilibrium of high output. Policy makers should be clear they will not hold back the economy until full employment is reached.
The US economic response to the Covid-19 crisis has been nothing sort of revolutionary. As J.W. Mason pointed out, The massive government payouts of unemployment and stimulus payments ensured that for the first time ever, personal income rose substantially during a recession. Low interest rates and the stimulus payments have likewise boosted both stock and housing markets, again leading to an unprecedented increase in household net worth during a recession. This is in stark contrast to the 2007 recession, where personal income fell by more than 5%. The administration and the Fed need to hold the course, and ensure the mistakes of 2007 are not repeated.
This is the first of several posts discussing my new working paper “Old Keynes and New-Fisher: a Model of Animal Spirit Driven Recessions”.
The idea of “Animal Spirits” — that individuals are animated not by rational economic calculation, but by instincts, proclivities and emotions — is of course an old one, going back to Keynes, but over the last decade its importance has become increasingly obvious. It’s hard to open Twitter or a newspaper without examples — Tesla, Bitcoin, Gamestop come immediately to mind. On a macroeconomic level, the frenzied increase in real estate prices in the run up to the 2008 financial crisis also immediately comes to mind. But even beyond the economic realm the rise of conspiracy theories like Qanon on a large scale show how beliefs of large groups can animate social actions.
In the midst of the Covid pandemic, are businesses and individuals making decisions about hiring, investment, and consumption based upon rational calculation of expected probability of vaccinations, re-openings, and recoveries? Or are they swayed by individual proclivities, rumors, and media narratives? I can’t resist letting Keynes have his say:
Our basis of knowledge for estimating the yield 10 years hence of a railway, a copper mine, a textile factory, the goodwill of a patent medicine, an Atlantic liner, a building in the City of London amounts to little and sometimes to nothing. In fact, those who seriously attempt to make any such estimate are often so much in the minority that their behavior does not govern the market.
Keynes, General Theory
As a macroeconomist, the most important question related to animal spirits is if they can cause recessions with involuntary unemployment. One natural channel for animal spirits to have an effect would be through the financial system. Animal spirits could drive up the price of the stock market or another asset to dizzying heights. A crash naturally ensues, with bank failures / widespread freezing of credit then leading to recessions. This is a focus of one branch of the current literature on animal spirits.
But there is a more direct channel through which expectations can lead to unemployment — the effective demand channel. If consumers and investors develop a bout of pessimism, they will cut their spending. Firms, facing lower demand, will cut hiring, which in turn leads to worse pessimism and further spending cuts. Although the basic idea is simple, formalizing it in a model raises a number of important questions. The first question is whether to model expectations as “rational”.
The idea of Animal Spirits would on the surface appear to be at odds with the technical concept of “Rational Expectations”, or the idea that in an economic model an individual’s subjective expectations of an event should coincide with the mathematical expectations of said event. However, the concepts are not necessarily at odds. Take for example, a sudden outbreak of “animal spirits” which causes people to become optimistic about the price of bitcoin. They then purchase bitcoin en masse, driving up the price. If the equilibrium price happened to coincide with their initially optimistic projections, their animalistic expectations would technically be ‘rational’. Another way of thinking about rational expectations is that people’s beliefs become a self-fulfilling prophecy — the simple act of believing the economy will tank in fact causes the economy to tank, exactly in line with expectations.
Most of the macro literature that writes about ‘animal spirits’ actually works within the rational expectation framework. And a lot of it would seem at first glance to really capture the idea that fluctuations in optimism and pessimism can cause recessions. In Liquidity Traps and Jobless Recoveries by Schmitt-Grohe and Uribe (2017), negative expectations of inflation and employment cause agents to cut spending, leading to a low employment / inflation equilibrium that is self-fulfilling. Even better, the equilibrium features involuntary unemployment. Heathcote and Perri (2017) have a model with recessions caused by a similar channel. In Fiscal Policy in an Expectations-Driven Liquidity Trap, Mertens & Ravn (2014) have a self-fulfilling recession caused by low expectations. Roger Farmer has written papers in which depressions are caused by self-fulfilling expectations about the economy or the stock market.
At a first glance, then, the ideas of Keynes seem to be well represented by these modern macro models. Surprisingly, however, the policy conclusions that fall out of the three strands of this literature on ‘self-fulfilling recessions’ is decidedly anti-Keynesian. In Schmitt-Grohe and Uribe (2017) and Heathcote and Perri (2017), the best way to ensure the economy never reaches a bad equilibrium is to raise interest rates through an interest rate peg. In Mertens & Ravn (2014), the best way to raise output is to cut government spending. And in Farmer’s models, government spending has no impact on output. These surprising effects of government policy I refer to as Neo-Fisherian results.
Both classes of Neo-Fisherian effects are closely driven by the expectations that the policies induce. Interest rate pegs in Schmitt-Grohe and Uribe (2017) eliminate any potential rational expectation equilibrium with inflation below target. When a peg is introduced, agents immediately realize there is no possibility of a bad equilibrium, raising expectations of output and causing the economy to converge to full employment. In Mertens & Ravn (2014), a cut in government spending immediately raises expectations of output, increasing spending and employment.
These results are startling, and seem on the surface unrealistic. How do agents know that the ‘bad equilibrium’ goes away if the central bank pegs interest rates? What causes agents to become optimistic about the future when government increases spending? And why is the traditional Keynesian multiplier reversed in sign?
See full post on the Equitable Growth Blog here, and see the working paper here.
When states reopened after the Covid shutdowns, what happened to consumer spending? Most previous research surprisingly finds little effects of the reopenings on spending. Our work goes against this, and in fact finds substantial effects of reopenings.
Why are our results different? We have more detailed data that can examine the spending categories that are directly affected by the shutdown policies. In addition, we do a much more careful job in distinguishing between different policies that are likely to curtail spending. For example, while we find that policies that forced retail businesses to close had substantial effects on spending,
Does the fact that restrictions cause lower GDP / employment mean we should reopen asap? No! The main point of restrictions is to restrict economic activity that can spread Covid.
As states further reopen it is likely the remaining restrictions on activity (capacity limits, large events) will become binding constraints. It’s important not to chase false idols of GDP and employment, but continue to prioritize public health.
Among the many unprecedented effects of the pandemic, the ‘V’ shaped pattern in consumer spending was something we’ve never seen before in a recession. The new paper looks into one particular cause of the pattern: the business closing and reopening policies that state governments pursued in order to stop the spread of Covid-19.
Early work has generally found that business shutdown policies didn’t have large effects on spending (Chetty et. al. , Goolsbye and Syverson), and that the pattern was caused mainly by fear of Covid. Our paper finds quite different results, that in fact the retail shutdowns substantially affected spending.
An advantage of our paper is the really great data that was provided by Earnest Research, which allowed us to really pinpoint the sectors that the shutdowns and reopenings affected.
We find reopening policies substantially increased spending for categories directly impacted by the laws: a 68.4 p.p. increase in non-essential in-store spending and a 16.7 p.p. increase in full-service indoor dining. For sectors not directly impacted — essential retail, limited-service restaurants, and online — we find a limited impact of reopenings. We estimate that retail reopenings are responsible for 34% of the total trough-to-peak recovery in spending, while restaurant reopenings are responsible for 15% of the recovery.
The current overall chain of command for the national supply chain seems to be:
(1) Federal Emergency Management Agency (FEMA) supply chain task force, lead by Rear Adm. John Polowczyk.
(2) Jared Kushner’s private sector team, which also seems to be working closely with the FEMA team. Kushner is the liason with the FEMA team to the White House, and can relay requests from Trump / others to the team.
The supply chain task force is providing resources in several ways. It is coordinating the airlift of materials from abroad to the US from private supply firms. Some of the materials it is buying for itself, for the FEMA stockpile, but most of it is still in the possession of the medical supply firms. The medical supply firms are then filling orders like normal, with no priority system.
As to how FEMA is distributing supplies, it seems that they are tapping into the private data on where materials are going, and they can see a little bit which areas need more supplies. They are then sending supplies to the places that need them the most. Before sending supplies from the stockpile, i.e. ventilators, Kushner is asking about utilization rates.
On April 2nd, the White House gave an update of the supply situation at the daily press briefing. So far, the US strategic stockpile has sent out: 27.1 million surgical masks, 19.5 N95 masks, 22.4 million gloves, 5.2 million face shields, 7,600 ventilators.
Production in the US is usually 30,000 ventilators per year, but apparently by end of June we will produce 100,000. In April and May, several thousand will be available each month.
When the Vice President first asked me to help on the task force with different tasks, I asked the President what he expected from the task force and how I can best serve him in the task force.
The task of the task force member is to perform the tasks which must be performed to complete the tasks.
What the President asked is that all of the recommendations that we make be based on data. He wanted us to be very rigorous to make sure that we were studying the data, collecting data. A lot of things in this country were happening very quickly and we want to make sure that we were trying to keep updating our models and making sure that we were making informed decisions and informed recommendations to him based on the data that we were able to collect and put together.
The task is clear: the data will be used to make informed decisions using data driven methods based on reliable data.
The President wanted to make sure that we had the best people doing the best jobs and making sure that we had the right people focused on all the things that needed to happen to make sure that we can deliver in these unusual times for the American people.
This was stated unironically.
The President also wanted us to make sure we think outside the box. Make sure we’re finding all the best thinkers in the country.
Once again, unironically.
Just very early this morning, I got a call from the President. He told me he was hearing from friends of his in New York that the New York Public Hospital System was running low on critical supply. He instructed me this morning, I called Dr Katz who runs the system. I asked him which supply was the most supply he was nervous about. He told me it was the N95 masks. I asked what his daily burn was and I basically got that number. Called up Admiral Polowczyk. Made sure that we had the inventory. We went to the President today and earlier today the President called Mayor de Blasio to inform him that we were going to send a month of supply to New York Public Hospital System to make sure that the workers on the front line can rest assured that they have the N95 masks that they need to get through the next month.
After using the word ‘data’ seven times, we now have an example of what data is being used and how. The president heard off hand from a friend in New York that they were having problems in the public hospital system, Trump told Kushner, who called the head of the public hospital system in NYC, and asked him for the one item that the hospital was having supply troubles with. Kushner then arranged for 200,000 masks to be sent the next day.
