Will we repeat the Christmas Covid wave?

EDIT at 7pm, same day as posting: You know you have good friends when someone quietly emails you and tells you that the news about Omicron just got much worse and you should probably edit your post. I’ve been trying to rationalized why this January will be better than last January. Of course if it were not for Omicron, I would expect very little from holiday gatherings among mostly-vaccinated Americans. However, having known Omicron was looming, I probably shouldn’t have even tried to speculate. Get your booster and be prepared to hunker down in January if the 2-3 week data indicates that infections are turning extra-lethal. </edit>

In keeping with the “dismal science” brand, let’s dwell on the horrible death toll of the January 2021 Covid wave in the US that followed the Christmas holiday. Here comes Christmas (and other winter holidays) again, a major public health event.

https://www.cnbc.com/2021/01/27/us-reports-record-number-of-covid-deaths-in-january.html

This graph I borrowed from CNBC shows how fast deaths spiked up after the winter holidays of 2020. See also https://data.cdc.gov/.

According to Google search auto-complete, the public is more interested in whether there will be another Christmas Prince movie than whether there will be another Christmas Covid death wave.

I think it’s unlikely that we will see a repeat of exactly what happened last year. I’ve been looking online for predictions and mostly I have found articles warning that Omicron will cause a some kind of wave. No one wants to commit to predicting how many people will die, because anyone who tries is sure to be wrong. The consensus is that breakthrough infections are likely but that vaccines protect against extreme illness.

Nearly a million Americans have died from Covid already (Jeremy argues for a million). Some of those deaths, in retrospect, can almost certainly be tied to family travel during the holidays in 2020. The January Covid wave has only happened once, so it’s impossible to predict what will happen this time. Unfortunately we may get an interaction from increased holiday travel plus a novel highly infectious variant.

The Omicron variant is spreading fast, but no one knows if it will be worse than we we are currently dealing with from Delta. It seems like triple-vaxxed people are not at high risk, from preliminary data. That is reassuring to me personally. Thank you South Africa for being fast and sharing data with the world. For communities with low vaccination rates, it seems certain that more deaths will result from fast-traveling Omicron. Yet, from my reading this week, it is hard to know if it’s really much worse than what they are currently experiencing from Delta.

I’m keeping a Twitter thread going of what other people are saying. Caleb Watney points out that we have two things going for us. Widely available vaccines keep people safer from infection and reduces the chance of needing medical treatment. Secondly, we have gotten better at treating the disease. Together, that should mean less deaths in January 2022, as long as people seek treatment quickly and hospital capacity does not become a limiting factor. Omicron could multiply cases so quickly that we can’t apply all our best treatments to everyone. That is the biggest reason to worry.

Even though people will be less cautious about winter holiday travel this year than they were last year, the country has been open for many months now, including the recent Thanksgiving holiday. The vulnerable population this time should be smaller, in terms of the people likely to die from Omicron.

To say that we won’t blindly exactly repeat the biggest mortality event of my lifetime is not “optimism”. It seems like this January will not be as bad as last January for the reason Watney states: better medical tech on hand, most importantly vaccines for prevention.

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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.

800,000 Deaths? Or 1 Million Deaths?

According to the Johns Hopkins COVID tracker, the US has now surpassed 800,000 COVID deaths during the pandemic. The CDC COVID tracker is almost to 800,000 too. But is this number right? Confusion about COVID deaths and total deaths has been rampant throughout the pandemic, especially when comparing across countries.

One method that many have suggested is excess deaths, which is generally defined as the number of deaths in a country above-and-beyond what we would expect given pre-pandemic mortality levels. It’s a very rough attempt at creating a counterfactual of what mortality would have looked like without the pandemic. Of course, you can never know for sure what the counterfactual would look like. Would overdoses in the US have increased anyway? Hard to say, though they had been on the rise for years even before the pandemic.

So don’t treat excess deaths as a true counterfactual, but just a very rough estimate. I wrote about excess deaths in the US way back in January 2021 (feels like a lifetime ago!), and at the time for 2020 it looked like the US had about 3 million total deaths (in the first 48 weeks of 2020), which was about 357,000 deaths more than expected (again, based on historical levels of the past few years), or about 13.6% above normal.

But once we had complete data for 2020, deaths were even higher: about 19% above expected, or somewhere around 500,000 excess deaths. This compares with the official COVID death count of about 385,000 in 2020 for the US.

What happens if we update those numbers with the most recent available mortality data for 2021? Keep in mind that data reporting is always delayed, so I’ll just use data through October 2021. The following chart shows both confirmed COVID deaths and total excess mortality, cumulative since the beginning of 2020.

As we can see in the chart, there are a lot more excess deaths than confirmed COVID deaths. There were already over 1 million excess deaths through the end of October 2021 in the US, cumulative since January 2020. This compares with about 766,000 confirmed COVID deaths. That’s a big gap!

We could spend a lot of time trying to understand this gap of 250,000 deaths. Is this under-reporting of COVID deaths? Is it deaths caused by government restrictions? Is it caused by the overwhelming of the health system?

I won’t be able to answer any of those questions today. Instead, let’s ask a different question: is the potential US undercount of COVID deaths unusual?

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COVID and The Young

The CDC just approved vaccines from Americans aged 5-11. That’s great news! But today, I want to talk about another age group: mine.

A few months ago I wrote a post summarizing data for COVID-19 deaths among people in their 30s and 40s. While we have primarily thought of COVID as a disease impacting the elderly (and indeed in the aggregate, it is), there have been major health consequences for those under 65 too. Including major health consequences for the age group 30-49 (which I believe is the age range of all our bloggers here at EWED).

I wanted to update that data because a few new things have come to light. First, I highly recommend reading a recent paper by my friend Julian Reif and co-authors. They estimate the number of Years of Life Lost and Quality-Adjusted Years of Life Lost for different age groups from COVID-19. Their data runs through mid-March 2021, so before vaccines probably had much of a chance to impact the aggregate death numbers (though vaccines were being rolled out at the time).

Here’s their main result: while most of the deaths from COVID were among those aged 65 and older (80% through March 2021), most of the life lost in terms of years was for Americans under 65 (54% of QALYs). And even for very young adults, the risk in terms of years of life lost was not minimal. A comparison from the paper: “Adults aged 85 years or older faced 70 times more excess risk for death than those aged 25 to 34 years but only 3.9 times more individualized loss of QALYs per capita.” Compared to the 35-44 age group, the relevant factor is 2.8 times more individualized loss for the 85+ group.

It’s a great paper, but it only goes through March. What has happened since March 2021? While 80% of the COVID deaths up through March 2021 were among the elderly (65 and older), since April 2021 only 60% of the COVID deaths have been among the elderly. Part of this is because deaths are down among the elderly, but it’s also because deaths are up for the non-elderly. The table is my attempt to show this effect, looking at the period from March-September in both 2020 and 2021 (data is current as of October 27, so the September 2021 data is still not complete, but instructive).

For the oldest Americans, COVID deaths fell by 50%. That’s great! But for younger Americans, COVID deaths roughly doubled. Not good!

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Racial Gaps and Data Gaps

Are there racial gaps in the distribution of the COVID-19 vaccine? This is an important and interesting question in its own right. But I’ll talk about this question today because it’s an interesting example of how confusing and sometimes misleading data can be.

How do we answer this question? One is by surveying people. There are a number of surveys that ask this question, but a recent one by the Kaiser Family Foundation finds that among adults 70% of Blacks and 71% of Whites report being vaccinated. And given the sampling error possible with surveys, we would say that these are virtually identical. No racial gap! (Note: there was a racial gap when they did the same survey back in April, with 66% of Whites and 59% of Blacks vaccinated.)

But, surveys are just a sample, and perhaps people are lying. Maybe we shouldn’t trust surveys! And shouldn’t there be hard data on vaccines? Indeed, the CDC does publish data on vaccinations by race. That data shows a fairly large gap: 42.3% of Whites and only 36.6% of Blacks vaccinated. This is for at least one dose, and the percentages are of the total population (which is why it’s lower than the survey data). So maybe there is a racial gap after all!

But wait, if you look closely at the footnotes (always read the footnotes!), you’ll see something curious: the CDC admits that the race data are only available for 65.8% of the data. We don’t have the race information for over one-third of those in this data. Yikes! And given the exist disparities we know about in terms of income and access to healthcare, we might suspect that the errors are not randomly distributed. In other words, if there is probably good reason to suspect that Blacks are disproportionately reflected in the “unknown” category. But we just don’t know.

So what can we do? Since this data comes from US states, we can look at the individual state data and see if perhaps some of it is better (fewer unknowns). What does that data show us?

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GDP Losses and COVID Deaths (6 month update)

Back in March of this year, I wrote blog posts providing data on GDP losses and COVID-19 deaths for 2020, both for selected countries and US states. Since we’ve now had another 6 months of GDP data and the pandemic continues to take lives, I thought it would be useful to update that data.

I will update the data for US states in a future post, but here is the most recent data for about 3 dozen countries (mostly European and North American countries, since they have the most believe COVID data).

*indicates that the GDP data is only through the first quarter of 2021
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Would You Pay $3,000 to Not Wear a Mask?

How well do masks work at preventing disease transmission? This is a question that many of us have been asking throughout the pandemic. I have been trying to read as much about mask effectiveness as I can (for example, here’s a Tweet of mine from way back in June 2020). I think the bottom line is that, if you want really good RCTs of mask use during the COVID pandemic, there is surprisingly little evidence in any direction. But there are lots of studies, less well done but still OK, suggesting that masks do provide some protection.

I don’t want to wade into all of that research here, because Bryan Caplan has been doing that lately himself. His reading of the literature is that masks aren’t a silver bullet, but he suspects “that masks reduce contagion by 10-15%.” Still he thinks that the costs of masks (inconvenience, discomfort, and dehumanization) are large enough that they don’t pass a cost-benefit test. But this seems like a very strange conclusion given that he suspects masks reduce contagion by 10-15%! So let’s be explicit about the cost-benefit analysis.

[I am assuming that reducing contagion by 10-15% means 10-15% fewer cases and deaths. I see this as a bare minimum, since contagious disease can follow exponential growth trends, so 10-15% less contagion could mean that cases/deaths are reduced by more than 10-15%, but I’m making a simplifying assumption and the hard case.]

Quantifying the costs of the pandemic deaths is tricky, and it’s something that Bryan and I have debated before. Perhaps this is just a rehash of that debate (Bryan is highly skeptical of the VSL estimates), but I think it’s worthwhile to plug in some numbers.

What numbers should we use?

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Vaccine Lotteries: They Work!

To try and encourage vaccination during the on-going COVID pandemic, there have been many public and private incentives offered. For example, free doughnuts. Or offering $200 to state employees in Arkansas (taxable income, of course!).

But when the governor of Ohio announced on May 12, 2021 that they would be offering a $1 million lottery prize, with 5 winners, it took the incentive game to a new level (college scholarships were also a prize for 5 winners under 18).

So do the lotteries “work”? Do they get more people vaccinated? And even if they do “work,” does it pass a cost benefit test? Many expressed concern that, even if more people get vaccinated, that this is a lot of money to spend in uncertain budget times.

A new working paper by Andrew Barber and Jeremy West attempts to answer these questions. And they do so using synthetic control, one of the better methods social scientists have for attempting to identify causal relationships (which can be tricky).

What do they find? First, vaccine lotteries do work! They estimate that vaccination rates increased by 1.5% in Ohio because of the lottery. This amount is above and beyond the increase that would have been expected without the lottery (by comparing Ohio to other states that didn’t use a lottery — this is what the synthetic control method does).

But does it pass a cost-benefit test?

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COVID Deaths and Middle Age

We have known for a long time (basically since the start of the pandemic) that COVID primarily affects the elderly. Infection fatality rates are hard to calculate (since not all infections are reported), but most of the data suggest that the elderly are much more likely to die from COVID than other age groups.

For some, this has become one of the most important aspects of the pandemic. For example, Don Boudreaux emphasizes the age distribution of deaths many times in a recent episode of Econtalk, and he uses this point to argue that we addressed the pandemic incorrectly (to say the least). Boudreaux specifies that COVID is only deadly for those 70 and older. And while I won’t rehash the argument here, please also see my exchange with Bryan Caplan, where he argues that elderly lives are worth a lot less than younger lives (I disagree).

At first blush, the data seems to bear that out. The CDC reports that almost 80% of COVID-involved deaths were among those aged 65 and older (I will use the CDC’s definition of COVID-involved deaths throughout this post). In other words, of the currently reported almost 600,000 COVID deaths in the US, about 475,000 were 65 and older. Throw in the 50-64 age group, and you’ve now got 570,000 of the deaths (95% of the total).

But is this the right way to think about it? Remember, the elderly always account for a large share of deaths, around 75% in recent years. So it shouldn’t surprise us that most deaths from just about any disease are concentrated among the elderly.

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Temporary Income Shocks

As a graduate student in 2005, I took macroeconomics from Tyler Cowen. It was a fascinating class, covering not just the sweep of business cycle theories, but also just a good dose of “here is what it means to be an economist.” It was the first class in sequence, and for many incoming PhD students with no economics background (yes, this happens a lot!) it was the first economics class they took.

In that class we read a number of papers by Richard Thaler from his Anomalies series in the Journal of Economic Perspectives. We also read The Winner’s Curse in Bryan Caplan’s micro II course at GMU, the book that collected a lot of those JEP papers (for anyone that thinks the GMU PhD program is just straight Chicago school mixed with libertarianism, think again!).

One of the Thaler papers that always stuck with me was his criticism of the life-cycle theory of savings. That paper opens with a story of Thaler winning $300 in a football betting pool. Thaler, of course, used that income shock to splurge on some temporary indulgence, such as a bottle of champagne or a nice dinner. But a strictly rational agent should just use that extra income to increase their annual lifetime income by an even amount, such as about $20. That’s what the famous life-cycle hypothesis says, which is part of what Modigliani won the Econ Nobel for developing. That was in 1985. The joke is that just 5 years later, Thaler (and presumably other economists) were not personally behaving the way that economic theory says that people behave. (The meta-joke is that Thaler later wins the Econ Nobel too.)

This past week, that theory came full circle for me when Tyler Cowen awarded me an Emergent Ventures prize. It really did come as a shock, both in a real sense and an income sense. I was not expecting this prize in any way, but I am very honored and humbled to receive it. (Side note: this very blog that you are reading also received an EV grant, separate from my personal grant. Hooray for us!). The award was largely for my work on social media and this blog trying to convey good information and data during the pandemic, and to fight bad information.

The question that has been gnawing at me since receiving the award is: what should I do with it? It’s a nice problem to have. I am not complaining in any way. But it’s an especially fascinating question for an economist to think about, and to reconsider how we model human behavior.

The award also intersects with my blog post from last week on “what is income?“. The IRS most definitely considers an award like this to be “income,” and not just any income: it is self-employed income, since it doesn’t come from my employer. If I take it as a cash award, the tax bite will be quite large. However, I could also use the award for some academic purpose: purchasing equipment or software; attending a conference (perhaps one that my University would not normally pay for); or running a small workshop or conference (possibly, in the theme of the award, on how to communicate good information effectively on social media?). In those cases, I might legally avoid some taxes.

I don’t yet know what I want to do with the award. But it’s a really interesting intellectual, professional, and personal challenge to think about. Again, nice problem to have. But thank you again to Tyler, Mercatus, and Emergent Ventures for the honor. And thank you to all my readers out there for making the intellectual journey with me over the past year and a half!