Chesterton Views on Work in 20th Century America

One hundred years ago, the British writer G.K. Chesterton traveled to the United States for a lecture tour. He published his observations of America in What I Saw in America (1922). In an essay titled “The American Businessman”, Chesterton notes with surprise how passionate Americans appear about their professional work.

Chesterton recognizes this enthusiasm for work as more than mere greed.

This is the intro to my latest article for the OLL Reading Room. I discuss the American work ethic and Chesterton’s prescient insight into American economic dynamism compared to Britain. (Relatedly, Alex on British stagnation this week.)

Here’s a fun bit of the book that I didn’t include in the OLL article. Chesterton wrote this about seeing New York City for the first time:

But there is a sense in which New York is always new; in the sense that it is always being renewed. A stranger might well say that the chief industry of the citizens consists of destroying their city; but he soon realises that they always start it all over again with undiminished energy and hope. At first I had a fancy that they never quite finished putting up a big building without feeling that it was time to pull it down again; and that somebody began to dig up the first foundations while somebody else was putting on the last tiles. This fills the whole of this brilliant and bewildering place with a quite unique and unparalleled air of rapid ruin. 

Interested New Yorkers can find the rest online at Project Gutenberg. Delightful throughout if you like history. Amazon link to the book here.

Online Reading Onpaper

We have six weekly contributors here at EWED and I try to read every single post. I don’t always read them the same day that they are published. Being subscribed is convenient because I can let my count of unread emails accumulate as a reminder of what I’ve yet to read.

Shortly after my fourth child was born over the summer, I understandably got quite behind in my reading. I think that I had as many as twelve unread posts. I would try to catchup on the days that I stayed home with the children. After all, they don’t require constant monitoring and often go do their own thing. Then, without fail, every time that I pull out my phone to catch up on some choice econ content, the kids would get needy. They’d start whining, fighting, or otherwise suddenly start accosting me for one thing or another – even if they were fine just moments before. It’s as if my phone was the signal that I clearly had nothing to do and that I should be interacting with them. Don’t get me wrong, I like interacting with my kids. But, don’t they know that I’m a professional living in the 21st century? Don’t they know that there is a lot of good educational and intellectually stimulating content on my phone and that I am not merely zoning out and wasting my time?

No. They do not.

I began to realize that it didn’t matter what I was doing on my phone, the kids were not happy about it.

I have fond childhood memories of my dad smoking a pipe and reading the newspaper. I remember how he’d cross his legs and I remember how he’d lift me up and down with them. I less well remember my dad playing his Game Boy. That was entertaining for a while, but I remember feeling more socially disconnected from him at those times. Maybe my kids feel the same way. It doesn’t matter to them that I try to read news articles on my phone (the same content as a newspaper). They see me on a 1-player device.

So, one day I printed out about a dozen accumulated EWED blog posts as double-sided and stapled articles on real-life paper.

The kids were copacetic, going about their business. They were fed, watered, changed, and had toys and drawing accoutrement. I sat down with my stack of papers in a prominent rocking chair and started reading. You know what my kids did in response? Not a darn thing! I had found the secret. I couldn’t comment on the posts or share them digitally. But that’s a small price to pay for getting some peaceful reading time. My kids didn’t care that I wasn’t giving them attention. Reading is something they know about. They read or are read to every day. ‘Dad’s reading’ is a totally understandable and sympathetic activity. ‘Dad’s on his phone’ is not a sympathetic activity. After all, they don’t have phones.

They even had a role to play. As I’d finish reading the blog posts, I’d toss the stapled pages across the room. It was their job to throw those away in the garbage can. It became a game where there were these sheets of paper that I cared about, then examined , and then discarded… like yesterday’s news. They’d even argue some over who got to run the next consumed story across the house to the garbage can (sorry fellow bloggers).

If you’re waiting for the other shoe to drop, then I’ve got nothing for you. It turns out that this works for us. My working hypothesis is that kids often don’t want parents to give them attention in particular. Rather, they want to feel a sense of connection by being involved, or sharing experiences. Even if it’s not at the same time. Our kids want to do the things that we do. They love to mimic. My kids are almost never allowed to play games or do nearly anything on our phones. So, me being on my phone in their presence serves to create distance between us. Reading a book or some paper in their presence? That puts us on the same page.

Steal My Paper Ideas!

Since early in graduate school I’ve kept a running list of ideas for economics papers I’d like to write and publish some day. I’ve written many of the papers I planned to, and been scooped on others, but the list just keeps growing. As I begin to change my priorities post-tenure, I decided it was time to publicly share many of my ideas to see if anyone else wants to run with them. So I added an ideas page to my website:

Steal My Paper Ideas! I have more ideas than time. The real problem is that publishing papers makes the list bigger, not smaller; each paper I do gives me the idea for more than one new paper. I also don’t have my own PhD students to give them to, and don’t especially need credit for more publications. So feel free to take these and run with them, just put me in the acknowledgements, and let me know when you publish so I can take the idea off this page.

Here’s one set of example ideas:

State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.

You can find more ideas on the full page; I plan to update to add more ideas as I have them and to remove ideas once someone writes the paper.

Thanks to a conversation with Jojo Lee for the idea of publicly posting my paper ideas. I especially encourage people to share this list with early-stage PhD students. It would also be great to see other tenured professors post the ideas they have no immediate plans to work on; I’m sure plenty of people are sitting on better ideas than mine with no plans to actually act on them.

Counting Jobs

Last week I wrote about the challenges of counting deaths. But surely in economics, we can count better, especially when it comes to something concrete like the number of people working. Right?

Maybe not. If you follow the economic data regularly, you’ll know that once per month, the Bureau of Labor Statistics releases data on the employment situation of the nation’s economy. And if you are familiar with this report, you will probably know that it is based on two separate surveys, one of businesses and one of households. And furthermore, it gives us two separate measures of employment, the number of people working for pay.

Joseph Politano has been tracking the employment situation reports, and he writes that the two measures of employment have “completely diverged since March of [2022], with the establishment survey showing payroll growth of nearly 2.7 million and the household survey showing employment growth of 12,000.” The surveys are tracking the labor market differently, so it’s not surprising that they won’t be exactly the same (they rarely are), but this sort of discrepancy is huge. Even accounting for most of the differences between the surveys, there is still a gap of about 2 million jobs.

Today, the BLS released yet another measure of employment, this one comes from the Business Employment Dynamics series. The BED is not released as quickly as the data in the employment situation report — the BED data released today is for the 2nd quarter of last year. But that’s because this data is much more comprehensive, and it’s actually the same data underlying the employment measure from businesses in the monthly employment report (it comes from unemployment insurance records, which covers most of the workforce).

What did the BED find for the 2nd quarter of 2022? A net loss of 287,000 jobs. The BED is only looking at private-sector jobs, and it is also seasonally adjusted to smooth out normal quarterly fluctuations. If we look back at the monthly data on employment, what did it look like in the 2nd quarter of 2022? Using the seasonally adjusted, private-sector jobs number to match the BED, it showed a gain of 1,045,000 jobs. In other words, we have a discrepancy of 1.3 million jobs in a single quarter. This is huge.

Perhaps some of this could be attributed to different seasonal adjustment factors, but even using the unadjusted data there is still a gap: 3,089,000 jobs added in the monthly payroll survey (private sector only), but only a net gain of 2,432,000 private-sector jobs in the BED data. That discrepancy is smaller, but it is still a difference of over 600,000 jobs. Note here that there was job growth in the second quarter in the BED measure, just not enough job growth that on a seasonally adjusted basis that it showed net growth. Another way to think of this: there is almost always growth in the 2nd quarter, but we expected it to be a bit stronger than this data shows.

If you aren’t confused enough yet, BLS produces yet another measure of employment, called the Quarterly Census of Employment and Wages. Really this is the broadest measure of jobs and is using the same underlying data as the BED and monthly nonfarm jobs in the business survey. But like the BED, it is also released with a significant lag. What does it show? A gain of 2,338,000 jobs in the 2nd quarter of last year (this includes public sector employment too). That number isn’t seasonally adjusted and compares with the CES (monthly nonfarm employment) number of 2,702,000, a discrepancy of 364,000 jobs (note: the CES will later be revised and benchmarked with the QCEW data).

What can we learn from all these different estimates of jobs? And which is right? The short answer to the second question is: they are all right, but measuring different things. The big takeaway is that there was indeed job growth in the 2nd quarter of 2022 (even the household survey shows job growth), but based on more complete data the monthly business survey probably overstated job growth, and it may have actually been pretty weak job growth compared to what we would normally expect in that quarter in the private sector (but of course, we aren’t in normal times).

Decline in Consumer Use of Cash Is Offset by Criminal Usage of Benjamins

We have all seen the decline in consumer usage of physical currency. The trend has been going on for some years, with folks finding it more convenient to whip out a credit card or just wave their phone in order to make a purchase. The drop in cash use was dramatically accelerated during COVID when we avoided physical contact with anyone and anything outside our homes, preferring contactless payments or just ordering stuff on line.

The Federal Reserve has since 2016 run an annual survey of households to track trends in payments. This data set shows the big drop in cash use in 2020, with a corresponding increase in payments by credit cards and mobile apps:

Share of payments use for all payments, from Federal Reserve’s “Diary of Consumer Choice” , 2022 edition.

Similar trends hold for the U.K.; the main alternative to cash there seems to be debit cards:

Source: BBC

Cash use continues to decline but the rate of decline seems to be slowing. Among other things, some twenty-somethings have been inspired by social media discussions to practice budgeting by using physical envelopes of physical cash for specified categories of spending.

Our discussion so far has mainly dealt with retail purchases by consumers. However, there is another dimension of cash use. As pointed out by Andy Serwer, there has been a steady surge in international demand for the largest denomination of U.S. currency, which is the $100 bill. This chart from the Fed shows that the dollar value of U.S. dollars in circulation has roughly doubled in the past decade:

Nearly all this rise is due to the insatiable demand for $100 bills, and the vast majority of that new demand is from overseas. Some of those Benjamins may be innocently sitting in foreign central bank vaults, but it is understood that many (perhaps most) of them are used by arms and drug dealers and other criminals.  Cash is used way more than cryptocurrencies for criminal activity. According to Serwer:

A million dollars in $100 bills, in case you’re wondering, weighs about 22 pounds, they say. A double stack would be about 21.5 inches high by 12.28 inches by 2.61 inches. You could carry it in a big briefcase, or as I suggested, a satchel.

The consequences of minting the trillion dollar coin

A group of congressmen are (again) opposing raising the US debt ceiling, which (again) threatens to put the US government into default on a portion of the US debt. There is some uncertainty about the magnitude of the consequences of a US default, varying between very bad and globally catastrophic. Phrases like “taking hostage” and “political extortion” are thrown around too casually in the discourse when opportunities for politically leverage are taken advantage of, but in this case I think the scale of consequences makes it completely appropriate. A threat to force a US debt default through the mechanics of a mistake made when legislating bond issuance rules during World War I is an act of political extortion that holds the global economy hostage.

The obvious solution is to eliminate the debt ceiling, but we have failed to do so because of the same political incentives underpinning our problems today. Some economists and economics-adjacent folks have suggested a policy solution, itself similarly born of an unintended legislative loophole: the trillion dollar coin.

As far as specialty areas go, I’m about as far from a monetary specialist as an economist can get, so I’m not going to litigate here whether putting the coin on the balance sheets of the Federal Reserve would be inflation neutral or compromise the independence of the Fed. What I want to consider is the Lucas Critique.

Specifically, the Lucas Critique applied to political economy after minting a trillion dollar coin. In briefest of terms, the Lucas Critique says a model of the world generated from past data to forecast a policy’s effects is wrong as soon as that policy changes the rules. We (rightfully) do not like the status quo as created by the current rules, but it is extremely difficult to predict the consequences of a big rule change, via loophole exploitation, made to fix the status quo because the underlying data generating process has been fundamentally altered.

I don’t know if minting a trillion dollar coin is a good idea or a bad idea. What I do know it is that we should be humble when trying to forecast the consequences of shifting the power to radically impact the balance sheets of the Federal Reserve from a elected body of 435 congressmen and 100 senators to a cabinet member appointed by a singular elected President.

Let’s ask two questions. I like to ask myself a version of these two questions when evaluating change in political options or rules:

  1. Why is the opposition reacting the way it is?
  2. What would Trump have done ?

The first is because it forces me to consider what the underlying incentives and strategies really are. The Republicans, as it stands, do not seem to view the trillion dollar coin as a policy outcome to be avoided. They’re, historically, the anti-inflation party. They represent a lot of bond holders. Hyper inflation should terrify them, so maybe they agree with the prediction of inflation neutrality. On the other hand, they also know that electoral college favors them and, with the growing aspiration within the party to win over Latino voters for the next few decades, maybe they like the idea of shifting more power away into the executive branch.

The second question is important because it forces me to acknowledge when I’m relying on norms to produce the outcome I prefer. Say what you will about Trump, the man was never concerned with norms, traditions, or the consequences for anyone but himself. This question also allows me to consider obviously ludicrous things that no one could get away because he got away with exactly such things. So, let me ask you this: if the Secretary of the Treasury can order the minting of a trillion dollar commemorative coin and deposit it in the Federal Reserve balance sheet, what other ways could the Treasury reallocate funds on US balance sheets? What if we stopped assuming it would only be used in the most benign, inflation neutral way possible? Why can’t they use it to loan money to Russia or pay for the balance of global debt held by a small country that specializes in off-shore banking? Or, stepping back from the brink of “The President stole a trillion dollars”, what are the ways in which a President could trigger an economic or constitutional crisis by appropriating the power to significantly increase M1? What are the ways this new option would be internalized in the political marketplace and equilibrium of power?

The point is this: political norms, especially those constraining power at the highest level, are more fragile than we sometimes appreciate. Nothing exposes this more than big changes to the rules of governance. Game theory and mediocre movie plots now considered, let’s return to the Lucas Critique. A political compromise made to expedite bond issuance under the pressures of The Great War produced an political lever that has been exploited for decades. This was an unintended consequence. As a current wing of the Republican party has put more and more weight on this lever, the opposition is now considering exploiting a loophole, itself an unintended consequence of the otherwise innocuous coinage act. It’s hard to forecast the effect of such a fundamental shift in the rules and distribution of power because it immediately renders obsolete the model currently informing our expectations.

Cards on the table, if we’re at the zero hour and it’s either a) mint the coin or b) default on US debt, I think we should mint the coin. Defaulting on the debt of the country that provides what is without question the currency tying together the global economy scares me enough that some sort of workaround gambit becomes a necessary risk. But what will be the unintended consequences of minting a trillion dollar coin? I don’t know.

And neither do you.

ChatGPT Cites Economics Papers That Do Not Exist

This discovery and the examples provided are by graduate student Will Hickman.

Although many academic researchers don’t enjoy writing literature reviews and would like to have an AI system do the heavy lifting for them, we have found a glaring issue with using ChatGPT in this role. ChatGPT will cite papers that don’t exist. This isn’t an isolated phenomenon – we’ve asked ChatGPT different research questions, and it continually provides false and misleading references. To make matters worse, it will often provide correct references to papers that do exist and mix these in with incorrect references and references to nonexistent papers. In short, beware when using ChatGPT for research.

Below, we’ve shown some examples of the issues we’ve seen with ChatGPT. In the first example, we asked ChatGPT to explain the research in experimental economics on how to elicit attitudes towards risk. While the response itself sounds like a decent answer to our question, the references are nonsense. Kahneman, Knetsch, and Thaler (1990) is not about eliciting risk. “Risk Aversion in the Small and in the Large” was written by John Pratt and was published in 1964. “An Experimental Investigation of Competitive Market Behavior” presumably refers to Vernon Smith’s “An Experimental Study of Competitive Market Behavior”, which had nothing to do with eliciting attitudes towards risk and was not written by Charlie Plott. The reference to Busemeyer and Townsend (1993) appears to be relevant.

Although ChatGPT often cites non-existent and/or irrelevant work, it sometimes gets everything correct. For instance, as shown below, when we asked it to summarize the research in behavioral economics, it gave correct citations for Kahneman and Tversky’s “Prospect Theory” and Thaler and Sunstein’s “Nudge.” ChatGPT doesn’t always just make stuff up. The question is, when does it give good answers and when does it give garbage answers?

Strangely, when confronted, ChatGPT will admit that it cites non-existent papers but will not give a clear answer as to why it cites non-existent papers. Also, as shown below, it will admit that it previously cited non-existent papers, promise to cite real papers, and then cite more non-existent papers. 

We show the results from asking ChatGPT to summarize the research in experimental economics on the relationship between asset perishability and the occurrence of price bubbles. Although the answer it gives sounds coherent, a closer inspection reveals that the conclusions ChatGPT reaches do not align with theoretical predictions. More to our point, neither of the “papers” cited actually exist.  

Immediately after getting this nonsensical answer, we told ChatGPT that neither of the papers it cited exist and asked why it didn’t limit itself to discussing papers that exist. As shown below, it apologized, promised to provide a new summary of the research on asset perishability and price bubbles that only used existing papers, then proceeded to cite two more non-existent papers. 

Tyler has called these errors “hallucinations” of ChatGPT. It might be whimsical in a more artistic pursuit, but we find this form of error concerning. Although there will always be room for improving language models, one thing is very clear: researchers be careful. This is something to keep in mind, also, when serving as a referee or grading student work.

If You Get Too Cold, I’ll Tax the Heat

Public utilities are funny things. The industry is highly capital intensive and many argue that it makes for natural monopolies. At the same time, access to electricity and water (and internet) are assumed as given in any modern building. Further, utility providers are highly, highly regulated at both the state and federal levels of government. Many utilities must ask permission prior to changing anything about their prices, capital, or even which services they offer.

Don’t get me wrong. Utility companies have a sweet deal. They are protected from competition, face relatively inelastic demand for their goods, and they have a very dependable rate of return. I just can’t help feeling like state governments are keeping hostage a large firm with immobile fixed business capital. For that matter, given what we know about the political desire for opaque taxation, I also have a suspicion that many states might tax their populations by using the utility companies as an ingenious foil. “Those utility companies are greedy, don’t you know. It’s a good thing that they are so highly regulated by the state.”  

There are two types of utility taxation. 1) Gross receipts taxes are like an income tax. From the end-user’s perspective, the tax increases with each unit consumed. 2) A utility license tax is like a fee that the utility must pay in order to operate in the state. From the user’s perspective, well… This tax may not even appear on the monthly bill. But if it does, then the tax per household falls with each additional household that the utility serves. Either way, state governments can get their share of the economic profits that protection affords. Below is map which shows the 2021 cumulative utility tax per resident in each state.

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Waxing Crescent: New Orleans 2013-2023

The scars of Hurricane Katrina were still obvious eight years afterward when I moved to New Orleans in 2013. Where I lived in Mid-City, it seemed like every block had an abandoned house or an empty lot, and the poorer neighborhoods had more than one per block. Even many larger buildings were left abandoned, including high-rises.

Since then, recovery has continued at a steady pace. The rebuilding was especially noticeable when I spent a few days there recently for the first time since moving away in 2017. The airport has been redone, with shining new connected terminals and new shops. The abandoned high-rise at the prime location where Canal St meets the Mississippi has been renovated into a Four Seasons. Tulane Ave is now home to a nearly mile-long medical complex, stretching from the old Tulane hospital to the new VA and University Medical Center complex. There are several new mid-sized health care facilities, but most striking is that Tulane claims to finally be renovating the huge abandoned Charity Hospital:

Old Charity Hospital, January 2023

The new VA hospital opened in 2016 as mostly new construction, but they’ve now managed to fully incorporate the remnants of the abandoned Dixie Beer brewery:

VA Hospital incorporating old Dixie Beer tower, January 2023

Dixie beer itself opened a new beer garden in New Orleans East, and just renamed itself Faubourg Brewery. Some streets named for Confederates have also been renamed, though you can still see plenty of signs of the past, like the “Jeff Davis Properties” building on the street renamed from Jefferson Davis Parkway to Norman C Francis Parkway.

Other big additions I noticed are the new Childrens’ Museum and the greatly expanded sculpture garden in City Park:

Of course, even with all the improvements, many problems remain, both in terms of things that still haven’t recovered from the hurricane, and the kind of problems that were there even before Katrina. The one remaining abandoned high-rise, Plaza Tower, was actually abandoned even before Katrina.

My overall impression is that large institutions (university medical centers, the VA, the airport, museums, major hotels) have been driving this phase of the recovery. The neighborhoods are also recovering, but more slowly, particularly small business. Population is still well below 2005 levels. I generally think inequality has been overrated in national discussions of the last 15 years relative to concerns about poverty and overall prosperity, but even to me New Orleans is a strikingly unequal city; there’s so much wealth alongside so many people seeming to get very little benefit from it.

The most persistent problems are the ones that remain from before Katrina: the roads, the schools, and the crime; taken together, the dysfunctional public sector. Everywhere I’ve lived people complain about the roads, but I’ve lived a lot of places and New Orleans roads were objectively the worst, even in the nice parts of town, and it isn’t close. The New Orleans Police Department is still subject to a federal consent decree, as it has been since 2012. The murder rate in 2022 was the highest in the nation. Building an effective public sector seems to be much harder than rebuilding from a hurricane.

As much as things have changed since 2013, my overall assessment of the city remains the same: its unlike anywhere else in America. It is unparalleled in both its strengths and its weaknesses. If you care about food, drink, music, and having a good time, its the place to be. If you’re more focused more on career, health, or safety, it isn’t. People who fled Katrina and stayed in other cities like Houston or Atlanta wound up richer and healthier. But not necessarily happier.

On Counting and Overcounting Deaths

How many people died in the US from heart diseases in 2019? The answer is harder than it might seem to pin down. Using a broad definition, such as “major cardiovascular diseases,” and including any deaths where this was listed on the death certificate, the number for 2019 is an astonishing 1.56 million deaths, according to the CDC. That number is astonishing because there were 2.85 million deaths in total in the US, so over half of deaths involved the heart or circulatory system, at least in some way that was important enough for a doctor to list it on the death certificate.

However, if you Google “heart disease deaths US 2019,” you get only 659,041 deaths. The source? Once again, the CDC! So, what’s going on here? To get to the smaller number, the CDC narrows the definition in two ways. First, instead of all “major cardiovascular diseases,” they limit it to diseases that are specifically about the heart. For example, cerebrovascular deaths (deaths involving blood flow in the brain) are not including in the lower CDC total. This first limitation gets us down to 1.28 million.

But the bigger reduction is when they limit the count to the underlying cause of death, “the disease or injury that initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury,” as opposed to other contributing causes. That’s how we cut the total in half from 1.28 million to 659,041 deaths.

We could further limit this to “Atherosclerotic heart disease,” a subset of heart disease deaths, but the largest single cause of deaths in the coding system that the CDC uses. There were 163,502 deaths of this kind in 2019, if you use the underlying cause of death only. But if we expand it to any listing of this disease on the death certificate, it doubles to 321,812 deaths. And now three categories of death are slightly larger in this “multiple cause of death” query, including a catch-all “Cardiac arrest, unspecified” category with 352,010 deaths in 2019.

So, what’s the right number? What’s the point of all this discussion? Here’s my question to you: did you ever hear of a debate about whether we were “overcounting” heart disease deaths in 2019? I don’t think I’ve ever heard of it. Probably there were occasional debates among the experts in this area, but never among the general public.

COVID-19 is different. The allegation of “overcounting” COVID deaths began almost right away in 2020, with prominent people claiming that the numbers being reported are basically useless because, for example, a fatal motorcycle death was briefly included in COVID death totals in Florida (people are still using this example!).

A more serious critique of COVID death counting was in a recent op-ed in the Washington Post. The argument here is serious and sober, and not trying to push a particular viewpoint as far as I can tell (contrast this with people pushing the motorcycle death story). Yet still the op-ed is almost totally lacking in data, especially on COVID deaths (there is some data on COVID hospitalizations).

But most of the data she is asking for in the op-ed is readily available. While we don’t have death totals for all individuals that tested positive for COVID-19 at some point, we do have the following data available on a weekly basis. First, we have the “surveillance data” on deaths that was released by states and aggregated by the CDC. These were “the numbers” that you probably saw constantly discussed, sometimes daily, in the media during the height of the pandemic waves. The second and third sources of COVID death data are similar to the heart disease data I discussed above, from the CDC WONDER database, separated by whether COVID was the underlying cause or whether it was one among several contributing causes (whether it was underlying or not).

Those three measures of COVID deaths are displayed in this chart:

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