Covid-19 Didn’t Break the Supply Chains. You Did.

This is my last post in a series that uses the AS-AD model to describe US consumption during and after the Covid-19 recession. I wrote about US consumption’s broad categories, services, and non-durables. This last one addresses durable consumption.

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.

¡Que Ridiculo!

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AS-AD: From Levels to Percent

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?

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Dressed for Recess(ion)

In my previous post, I decomposed consumer expenditures to figure out which service sectors experienced the largest supply-side disruptions due to Covid-19. I illustrated that transportation & recreation services were the only consumer service to experience substantial and persistent supply shocks. Health, food, and accommodation services also experienced supply shocks, but quickly rebounded. Housing, utility, and financial services experienced no supply disruptions whatsoever.

What about non-durables?

Total consumption spending is the largest category of spending in our economy and is composed of services, durable goods, and non-durables. Services are the largest portion and durable goods compose the smallest portion. So, while there were plenty of stories during the Covid-19 pandemic about months-long delivery times for durables, they did not constitute the typical experience for most consumption.

Even though it’s not the largest category, many people think of non-durables when they think of consumption. Below is the break-down of non-durable spending in 2019. The largest singular category of non-durable spending was for food and beverages, followed by pharmaceuticals & medical products, clothing & shoes, and gasoline and other energy goods. Clearly, the larger the proportion that each of these items composes of an individual household budget, the more significant the welfare implications of price changes.

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It’s Still Hard to Find Good Help These Days

Consumption is the largest component of GDP. In 2019, it composed 67.5% of all spending in the US. During the Covid-19 recession, real consumption fell about 18% and took just over a year to recover. But consumption of services, composing 69% of consumption spending, hadn’t recovered almost two years after the 2020 pre-recession peak.  For those keeping up with the math, service consumption composed 46.5% of the economic spending in 2019.

We can decompose service consumption even further. The table below illustrates the breakdown of service consumption expenditures in 2019.

I argued in my previous post that the Covid-19 pandemic was primarily a demand shock insofar as consumption was concerned, though potential output for services may have fallen somewhat. When something is 67.5% of the economy, ‘somewhat’ can be a big deal. So, below I breakdown services into its components to identify which experienced supply or demand shocks. Macroeconomists often get accused of over-reliance on aggregates and I’ll be a monkey’s uncle if I succumb to the trope (I might, in fact be a monkey’s uncle).

Before I start again with the graphs, what should we expect? Let’s consider that the recession was a pandemic recession. We should expect that services which could be provided remotely to experience an initial negative demand shock and to have recovered quickly. We should expect close-proximity services to experience a negative demand and supply shock due to the symmetrical risk of contagion. Finally, we should expect that services with elastic demand to experience the largest demand shocks (If you want additional details for what the above service categories describe, then you can find out more here, pg. 18).

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Covid Evidence: Supply Vs Demand Shock

By the time most students exit undergrad, they get acquainted with the Aggregate Supply – Aggregate Demand model. I think that this model is so important that my Principles of Macro class spends twice the amount of time on it as on any other topic. The model is nice because it uses the familiar tools of Supply & Demand and throws a macro twist on them. Below is a graph of the short-run AS-AD model.

Quick primer: The AD curve increases to the right and decreases to the left. The Federal Reserve and Federal government can both affect AD by increasing or decreasing total spending in the economy. Economists differ on the circumstances in which one authority is more relevant than another.

The AS curve reflects inflation expectations, short-run productivity (intercept), and nominal rigidity (slope). If inflation expectations rise, then the AS curve shifts up vertically. If there is transitory decline in productivity, then it shifts up vertically and left horizontally.

Nominal rigidity refers to the total spending elasticity of the quantity produced. In laymen’s terms, nominal rigidity describes how production changes when there is a short-run increase in total spending. The figure above displays 3 possible SR-AS’s. AS0 reflects that firms will simply produce more when there is greater spending and they will not raise their prices. AS2 reflects that producers mostly raise prices and increase output only somewhat. AS1 is an intermediate case. One of the things that determines nominal rigidity is how accurate the inflation expectations are. The more accurate the inflation expectations, the more vertical the SR-AS curve appears.*

The AS-AD model has many of the typical S&D features. The initial equilibrium is the intersection between the original AS and AD curves. There is a price and quantity implication when one of the curves move. An increase in AD results in some combination of higher prices and greater output – depending on nominal rigidities. An increase in the SR-AS curve results in some combination of lower prices and higher output – depending on the slope of aggregate demand.

Of course, the real world is complicated – sometimes multiple shocks occur and multiple curves move simultaneously. If that is the case, then we can simply say which curve ‘moved more’. We should also expect that the long-run productive capacity of the economy increased over the past two years, say due to technological improvements, such that the new equilibrium output is several percentage points to the right. We can’t observe the AD and AS curves directly, but we can observe their results.

The big questions are:

  1. What happened during and after the 2020 recession?
  2. Was there more than one shock?
  3. When did any shocks occur?

Below is a graph of real consumption and consumption prices as a percent of the business cycle peak in February prior to the recession (See this post that I did last week exploring the real side only). What can we tell from this figure?

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Half of Deliberately Exposed Unvaccinated Volunteers in UK Study Did Not Get COVID; Why?

A British study by Ben Killingley and 31 co-authors recently appeared in pre-print form, where 36 (heroic) healthy young adult volunteers were deliberately exposed to the Covid virus by nasal drops. These volunteers then went into quarantine for 14 days, and logged their symptoms and were subjected to various tests for a total of 28 days.


Of the 36 subjects, only 18 (53%) became infected with the virus, as determined by PCR testing (the gold standard for Covid tests) and by direct counting of viral loads in mucus cells by FFA.

The study found that viral shedding (as estimated by mucus viral loads) begins within two days of exposure and rapidly reaches high levels, then declines. Viable virus is still detectible up to 12 days post-inoculation. This result supports the practice of people quarantining for at least 10 days after they first exhibit symptoms of infection. There were significant higher viral loads in the nose than in the throat,  which supports the practice of wearing masks that cover the nose as well as the mouth.


The cheap, fast, LFA rapid antigen test method (used in home tests) performed fairly well. Because it is less sensitive, it did not it did not yield positive results for infected individuals until an average of four days after infection, or about two days after viral shedding may have begun. But from four days onward, the LFA method was sensitive and reasonably accurate which supports the ongoing use of these quick, cheap tests.

These direct inclusions from the paper are helpful, but not earthshaking. The elephant in the room, which the paper did not seem to directly address, is why nearly half of the people who were exposed did NOT become infected. This raises all kinds of issues about what mechanisms the human body may have to naturally fight off COVID or similar viral infections. Gaining insight on this could lead to breakthroughs in preventing or mitigating this pernicious virus.

An article by Eileen O’Reilly at Axios probes these questions. There is nothing conclusive out there, but four ideas that are under investigation are:

1. Cross-immunity from the four endemic human coronaviruses is one hypothesis. Those other coronaviruses cause many of the colds people catch and could prime B-cell and T-cell response to this new coronavirus in some people.

2. Multiple genetic variations may make someone’s immune system more or less susceptible to the virus.  Some 20 different genes have been identified which affect the likelihood of severe infection, and a genetic predisposition to not getting infected is seen in other diseases where people have one or multiple factors that interfere with the virus binding to cells or being transported within.

3. Mucosal immunity may play an underrecognized role in mounting a defense.

This suggests nasal vaccines might have a chance at stopping a virus before it invades the whole body.

4. Where the virus settled on the human body, how large the particle was, the amount and length of exposure, how good the ventilation was and other environmental circumstances may also play a role.

These considerations support continuing with the usual recommendations of social distancing, wearing facemasks, and ventilating buildings, especially when caseloads are peaking. Also, the doses administered to the volunteers in the study were considered quite small by clinical standards. It was surprising that such a low dose was effective as it was in causing full-blown infections; and the particular strain used in the experiment was not necessarily one of the more recent highly virulent variants. After reading these results,  it is more understandable to me why so many reasonably careful friends and family members of mine (nearly all vaccinated, fortunately) have come down with (presumably) omicron COVID in the past two months. Just a little dab will do ya.

Did we repeat the Christmas Covid Wave?

The year is 2021

Around January of 2021, hospital staff and other select personnel received the first vaccines meant for the public. As a classroom teacher, I was designated important enough in the state of Alabama to get a Pfizer vaccine as early as February 2021.

Imagine what could have happened next

Americans grew antsy in May of 2021, because less than half of the population had been able to get a vaccine. It was frustrating to see the vaccine winners carrying on with life without fear of the virus while supply constraints made it impossible for everyone to join them at once.

An unintended consequence of the gradual vaccine rollout was that Americans who were initially concerned about vaccine safety had months to observe their family members and neighbors who got in line first. By July of 2021, most Americans personally knew of someone who died from Covid, and almost no one had witnessed a bad vaccine outcome.

By the end of the summer of 2021, over 90% of the American public was fully vaccinated. The economy roared back to life and working parents did not have to worry about school closures anymore.  

Americans felt proud to have invented and implemented the world’s best Covid vaccine. Considering that Trump has started the research and Biden had overseen the distribution, it was one thing that red and blue Americans could unite over.

The internet as a concept was vindicated because anyone who wanted to understand vaccines could do their own research. Scientific knowledge is no longer the domain of a select elite. Anyone can see the Covid death rates for vaccinated versus unvaccinated people. Amateurs can create data visualizations to share. Information on mRNA technology is free to all.

Speech remained free with regard to vaccine dialogue, but those who tried to discourage Americans from getting Covid vaccines were shouted down in all forums or accused of being foreign trolls.

The first Covid wave in April of 2020 was terrible and the second big event around Christmas of 2020 resulted in thousands of deaths per day lasting for months. No one wanted to repeat that.

Of course, that is not what happened.

Now I have the answer to the question I asked two months ago when I wrote https://economistwritingeveryday.com/2021/12/18/will-we-repeat-the-christmas-covid-wave/

The number of Americans who died from Covid in January 2022 is available from the CDC website.

Number of Covid deaths in January 2022, CDC 59367
Number of Covid deaths in January 2021, CDC 97866

We came fairly close (60%) to repeating the tragedy after the Christmas of 2020. The exponential rise and fall of a new Covid variant and the ensuing pattern of deaths is something we have been through several times. We knew this would happen.

Would every one of those deaths have been prevented by higher vaccine take-up? No. But the death rates among vaccinated people are much lower. Charles Gaba, a data analyst, estimates that about 143,000 Americans have died since the summer of 2021 who would have lived if we had a higher vaccine uptake rate.

Ezra Klein also engaged in some wishful thinking this week, so I’m not the only one.

My best explanation for this is that people want to feel like they are in control of their own lives. Due to a variety of factors, a large number of adults have a different concept of being in control than I do.* Something that shaped my personal attitude toward the vaccine was reading about the research and development process in real time, which I largely did by keeping up with Marginal Revolution.

Unrelatedly, Jeffrey Clemens has given our blog a label this week that I’m happy with: “speculative but engaging”

* According to Andrew Sullivan, “There’s something about masking … and vaccines themselves, that some men seem to find feminizing.”

Controversial Study Finds Lockdowns Don’t Reduce COVID Mortality; Some Less Controversial Takeaways

A recent working paper, A LITERATURE REVIEW AND META-ANALYSIS OF THE EFFECTS OF LOCKDOWNS ON COVID-19 MORTALITY,  released by Steve Hanke (professor of Applied Economics at Johns Hopkins) and other applied economists (Jonas Herby of Denmark and Lars Jonung of Sweden) has been understandably controversial. I will survey some of its methods and conclusions, and (very briefly) some of the reactions to it.

I will not take a position on how valid its conclusions are, for the simple reason that I am totally unqualified to make such a judgement. What I would like to contribute are a couple of takeaways that are worth considering for the next pandemic or even the remainder of this one.

Methodology of the Paper

Where and how you start largely determines where you will end up. The authors included studies which were (as much as possible) straight apples-to-apples ex post empirical observations (e.g., between otherwise similar countries or U.S. states, at similar times), while avoiding ex ante studies which relied primarily on models of what-would-have-happened-without-lockdowns:

They write (I omit some details, marked with ellipses):

We distinguish between two methods used to establish a relationship (or lack thereof) between mortality rates and lockdown policies. The first uses registered cross-sectional mortality data. These are ex post studies. The second method uses simulated data on mortality and infection rates. These are ex ante studies.

We include all studies using a counterfactual difference-in-difference approach from the former group but disregard all ex ante studies, as the results from these studies are determined by model assumptions and calibrations.

Our limitation to studies using a “counterfactual difference-in-difference approach” means that we exclude all studies where the counterfactual is based on forecasting (such as a SIR-model) rather than derived from a difference-in-difference approach. This excludes studies like…We also exclude all studies based on interrupted time series designs that simply compare the situation before and after lockdown, as the effect of lockdowns in these studies might contain time-dependent shifts, such as seasonality. This excludes studies like….

The authors in particular address a study by Seth Flaxman, which had claimed great effectiveness for lockdowns. They note Flaxman’s modeling approach likely overstated the effects of lockdowns, as noted by other critics of Flaxman:

Given our criteria, we exclude the much-cited paper by Flaxman et al. (2020), which claimed that lockdowns saved three million lives in Europe. Flaxman et al. assume that the pandemic would follow an epidemiological curve unless countries locked down. However, this assumption means that the only interpretation possible for the empirical results is that lockdowns are the only thing that matters, even if other factors like season, behavior etc. caused the observed change in the reproduction rate, Rt. Flaxman et al. are aware of this and state that “our parametric form of Rt assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behavior.” Flaxman et al. illustrate how problematic it is to force data to fit a certain model if you want to infer the effect of lockdowns on COVID-19 mortality.

Conclusions and Controversy

In the interests of time/space, I will give just a few snapshots here. A key conclusion is:

Overall, our meta-analysis fails to confirm that lockdowns have had a large, significant effect on mortality rates. Studies examining the relationship between lockdown strictness (based on the OxCGRT stringency index) find that the average lockdown in Europe and the United States only reduced COVID-19 mortality by 0.2% compared to a COVID-19 policy based solely on recommendations. Shelter-in-place orders (SIPOs) were also ineffective. They only reduced COVID-19 mortality by 2.9%.

The authors are well aware that this is highly controversial, so they cite other studies that have reached similar conclusions. They offer further defenses against a number of other objections, which again I will not elucidate here.

As might be expected, U.S. mainstream media outlets (which have long accused red-state governors of reckless endangerment for not locking down as hard as blue states) have either ignored this paper, or tried to discredit it. An article in the Sacramento Bee, for instance, devoted nearly a whole paragraph to statements by Seth Flaxman (yes, that Seth Flaxman, see above) attacking the paper, while not reaching out to the paper’s authors for a response. And as might be expected, right-leaning media outlets are citing the study as vindicating the freedom-loving red states’ policies over against the heavy-handed Establishment.

Some Maybe Useful Takeaways

Pushing past this predictable partisan unpleasantness, I’ll share a couple of items from the paper that seem worth pondering. One was a strong statement of the harms done by lockdowns, with a plea for considering these in future policy-making. This sort of balancing of wide-ranging consequences is normally considered enlightened economics; in general, we as a society do not say, “The only thing that matters is saving/prolonging every life, no matter the other costs” :

The use of lockdowns is a unique feature of the COVID-19 pandemic. Lockdowns have not been used to such a large extent during any of the pandemics of the past century. However, lockdowns during the initial phase of the COVID-19 pandemic have had devastating effects. They have contributed to reducing economic activity, raising unemployment, reducing schooling, causing political unrest, contributing to domestic violence, and undermining liberal democracy. These costs to society must be compared to the benefits of lockdowns.

The other general issue that was touched on at several points in the paper was the importance of voluntary (as opposed to mandated) social distancing. Nothing in this paper disputed that social distancing, especially in pandemic peak periods, will slow the spread of a disease. The issue here is the effectiveness of state-imposed measures versus voluntary actions. These voluntary actions could be (on the positive side) conscious adoption of distancing and masking with or without legal requirement, or (on the other side) flouting of the laws or careless interpersonal contacts which were unsafe even if they were not illegal. These more risky actions may simply reflect local cultural attitudes (which are hard to change), or they may reflect less urgent government messaging (which is something that can be addressed by policy). A couple of relevant paragraphs are:

What explains the differences between countries, if not differences in lockdown policies? Differences in population age and health, quality of the health sector, and the like are obvious factors. But several studies point at less obvious factors, such as culture, communication, and coincidences. For example, Frey et al. (2020) show that for the same policy stringency, countries with more obedient and collectivist cultural traits experienced larger declines in geographic mobility relative to their more individualistic counterpart. Data from Germany Laliotis and Minos (2020) shows that the spread of COVID-19 and the resulting deaths in predominantly Catholic regions with stronger social and family ties were much higher compared to nonCatholic ones…

Government communication may also have played a large role. Compared to its Scandinavian neighbors, the communication from Swedish health authorities was far more subdued and embraced the idea of public health vs. economic trade-offs. This may explain why Helsingen etal. (2020), found, based on questionnaire data collected from mid-March to mid-April, 2020, that even though the daily COVID-19 mortality rate was more than four times higher in Sweden than in Norway, Swedes were less likely than Norwegians to not meet with friends (55% vs. 87%), avoid public transportation (72% vs. 82%), and stay home during spare time (71% vs. 87%). That is, despite a more severe pandemic, Swedes were less affected in their daily activities (legal in both countries) than Norwegians.

And:

We believe that Allen (2021) is right, when he concludes, “The ineffectiveness [of lockdowns] stemmed from individual changes in behavior: either non-compliance or behavior that mimicked lockdowns.” In economic terms, you can say that the demand for costly disease prevention efforts like social distancing and increased focus on hygiene is high when infection rates are high. Contrary, when infection rates are low, the demand is low and it may even be morally and economically rational not to comply with mandates like SIPOs, which are difficult to enforce. Herby (2021) reviews studies which distinguish between mandatory and voluntary behavioral changes. He finds that – on average – voluntary behavioral changes are 10 times as important as mandatory behavioral changes in combating COVID-19. If people voluntarily adjust their behavior to the risk of the pandemic, closing down non-essential businesses may simply reallocate consumer visits away from “nonessential” to “essential” businesses, as shown by Goolsbee and Syverson (2021), with limited impact on the total number of contacts.

Looking at the vastly different death tolls per capita between, say, Australia (with a more rigorous lockdown and quarantining policy) and the U.S. or U.K, I find it difficult to believe that policy mandates have as little effect as found in this study. That point aside, I think the study is helpful in reminding us that it is what people actually do that matters. Foot-dragging compliance with imposed regulations is a different thing than fully-bought-in compliance, which speaks to motivation and values.

Regarding messaging by governments and other organizations, I suspect that there is not a one-size-fits-all motivational message here. It could be worth reflecting on what sort of message would resonate with a particular population subgroup. (This is just basic Marketing 101: Identify your various segments and tailor the messages to them). Berating some subgroup for their poor choices to date may make the berators feel warmly superior, but that does not move things forward.

I’ll close with some anecdotal observations regarding behaviors, independent of mandates. I have personally continued to generally avoid gatherings where large numbers of people are talking or singing, and wear an effective mask*  when in such a meeting, regardless of what the current rules are.

Also, I have shuttled back and forth between northern Virginia (very blue) and Alabama (very red) in the past two years. Whether or not formal lockdowns or mask mandates were in force, I saw much more mask-wearing in northern Virginia, compared to Alabama. I suspect this reflected overall attitudes and behaviors regarding social distancing. Not saying one is right and one is wrong, but the total COVID deaths per 100,000 in Virginia (196) to date are roughly half of deaths in Alabama (356).

*See Suggestions for Comfortable and Effective Face Masks, e.g., Korean KF94’s on effective, comfortable face masks

Covid-19 & The Federal Reserve

I remember people talking about Covid-19 in January of 2020. There had been several epidemic scare-claims from major news outlets in the decade prior and those all turned out to be nothing. So, I was not excited about this one. By the end of the month, I saw people making substantiated claims and I started to suspect that my low-information heuristic might not perform well.

People are different. We have different degrees of excitability, different risk tolerances, and different biases. At the start of the pandemic, these differences were on full display between political figures and their parties, and among the state and municipal governments. There were a lot of divergent beliefs about the world. Depending on your news outlet of choice, you probably think that some politicians and bureaucrats acted with either malice or incompetence.

I think that the Federal Reserve did a fine job, however. What follows is an abridged timeline, graph by graph, of how and when the Fed managed monetary policy during the Covid-19 pandemic.

February, 2020: Financial Markets recognize a big problem

The S&P begins its rapid decent on February 20th and would ultimately lose a third of its value by March 23rd.  Financial markets are often easily scared, however. The primary tool that the Fed has is adjusting the number of reserves and the available money supply by purchasing various assets. The Fed didn’t begin buying extra assets of any kind until mid-March. There is a clear response by the 18th, though they may have started making a change by the 11th.  One might argue that they cut the federal funds rate as early as the 4th, but given that there was no change in their balance sheet, this was probably demand driven.

https://fred.stlouisfed.org/graph/?g=JYVL
https://fred.stlouisfed.org/graph/?g=JYVy

March, 2020: The Fed Accommodates quickly and substantially.

In the month following March 9th, the Fed increased M2 by 8.3%. By the week of March 21st, consumer sentiment and mobility was down and economic policy uncertainty began to rise substantially – people freaked out. Although the consumer sentiment weekly indicator was back within the range of normal by the end of April, EPU remained elevated through May of 2020. Additionally, although lending was only slightly down, bank reserves increased 71% from February to April. Much of that was due to Fed asset purchases. But there was also a healthy chunk that was due to consumer spending tanking by 20% over the same period.

https://fred.stlouisfed.org/graph/?g=JYXj
https://fred.stlouisfed.org/graph/?g=JYYz

In the 18 months prior to 2020, M2 had grown at rate of about 0.5% per month. For the almost 18 months following the sudden 8.3% increase, the new growth rate of M2 almost doubled to about 1% per month. The Fed accommodated quite quickly in March.

April, 2020: People are awash with money

Falling consumption caused bank deposit balances to rise by 5.6% between March 11th and April 8th. The first round of stimulus checks were deposited during the weekend of April 11th. That contributed to bank deposits rising by another 6.7% by May 13th.

By the end of March, three weeks after it began increasing M2, the Fed remembered that it really didn’t want another housing crisis. It didn’t want another round of fire sales, bank failures, disintermediation, collapsed lending, and debt deflation. It went from owning $0 in mortgage-backed securities (MBS) on March 25th to owning nearly $1.5 billion worth by the week of April 1st. Nobody’s talking about it, but the Fed kept buying MBS at a constant growth rate through 2021.

May, 2020 – December, 2021: The Fed Prevents Last-Time’s Crisis

Jerome Powell presided over the shortest US recession ever on record. The Fed helped to successfully avoid a housing collapse, disintermediation, and debt deflation – by 2008 standards. The monthly supply of housing collapsed, but it had bottomed out by the end of the summer. By August of 2021, the supply of housing had entirely recovered. The average price of new house sales never fell. Prices in April of 2020 were typical of the year prior, then rose thereafter. A broader measure of success was that total loans did not fall sharply and are nearly back to their pre-pandemic volumes. After 2008, it took six years to again reach the prior peak. A broader measure still, total spending in the US economy is back to the level predicted by the pre-pandemic trend.

The Fed can’t control long-run output. As I’ve written previously, insofar as aggregate demand management is concerned, we are perfectly on track. The problem in the US economy now is real output. The Fed avoided debt deflation, but it can’t control the real responses in production, supply chains, and labor markets that were disrupted by Covid-19 and the associated policy responses.

What was the cost of the Fed’s apparent success? Some have argued that the Fed has lost some of its political insulation and that it unnecessarily and imprudently over-reached into non-monetary areas. Maybe future Fed responses will depend on who is in office or will depend on which group of favored interests need help. Personally, I’m not so worried about political exposure. But I am quite worried about the Fed’s interventions in particular markets, such as MBS, and how/whether they will divest responsibly.

Of course, another cost of the Fed’s policies has been higher inflation. During the 17 months prior to the pandemic, inflation was 0.125% per month. During the pandemic recession, consumer prices dipped and inflation was moderate through November.  But, in the 16 months since April of 2020, consumer prices have grown at a rate of 0.393% per month – more than three times the previous rate. Some of that is catch-up after the brief fall in prices.

Although people are genuinely worried about inflation, they were also worried about if after the 2008 recession and it never came. This time, inflation is actually elevated. But people were complaining about inflation before it was ever perceptible. The compound annual rate of inflation rose to 7% in March of 2021. But it had been almost zero as recent as November, 2020. That March 2021 number is misleading. The actual change in prices from February to March was 0.567%. Something that was priced at $10 in February was then priced at $10.06 in March. Hardly noticeable, were it not for headlines and news feeds.

If Tyler is talking about a new variant…

For some Americans, this Thanksgiving was the first holiday that felt normal in a long time. Being re-united, without Covid restrictions, is something to celebrate.

On the other hand, a new coronavirus variant was just discovered in South Africa. It’s scary enough that travel bans might be imposed. We have all (just about) learned to live with the original strain from Wuhan, but scientists want time to figure out how dangerous and infectious this new strain is. Maybe at this point people are tired of being lectured about risks. No matter how much or little a person sacrificed for Covid-19, they might feel like that storyline has become too boring to deserve any more of our attention. We cannot stop looking out for new variants that might force us to put cherished traditions on hold again. Coronaviruses kill. My advice is to keep following news from Tyler Cowen, Alex Tabarrok, and Emily Oster.

Oster has been consistently reasonable about family and health risks. She argued to open schools and essentially said that you can see grandparents if the risk is small enough (even though the risks are never zero). As I said before, another trustworthy source of information throughout the pandemic has been Tyler and Alex, who put up almost all of their material in real time at Marginal Revolution.

I’ll share something a friend wrote to me today:

Although [his wife’s name]’s chemo treatment continues to show good long-term signs, this morning we discovered that [she] tested positive for COVID. That’s bad news, the good news is that [she] is already getting the antibody treatment and some extra fluids at the hospital as I write this.

“The antibody treatment” did not exist when the first Covid-19 waves swept through New York with such devastating consequences.

If the newest strain turns out to be a serious development, then in many ways we are better prepared to deal with it than we were before. We probably will blow through the red tape on at-home rapid tests faster the next time around (I’m such an optimist!). We already have contact tracing apps that protect privacy. Vaccine scheduling software is already in place. Everyone has masks at home.

The biggest difficulty I foresee is not coming up with scientific solutions but agreeing as a society about which tools to use. Some people might (will) not even believe the new strain is real.

EWED was started right at the moment when Marginal Revolution commentary on Covid seemed the most crucial. So, sometimes I will do little more here than keep up the echo. Do tweets, phone calls, letters, blogs, or talk about Covid around the Thanksgiving table. Don’t give up.

It’s now clear, whether or not the news out of South Africa turns out to be serious, that we are living with a new problem that will last a long time. It’s a marathon, not a sprint.

If you ever read much of the New Testament, you’ll see a theme in the letters of Paul to cities he has visited. The brand-new churches were doing well, while he was with them in person. Then time goes by and the community or doctrine starts to fray.

Paul wrote these words to the church in Galatia, more than a year after he had visited them:

Let us not become weary in doing good, for at the proper time we will reap a harvest if we do not give up. 

Galatians 6:9
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