What’s Killing Men Ages 18-39?

The all-cause mortality rate in 2021 for men in the US ages 18-39 was about 40% higher than the average of 2018 and 2019. That’s a huge increase, especially for a group that is not in the high-risk category for COVID-19. What’s causing it?

Some have suggested that heart disease deaths, perhaps induced by the COVID vaccines, is the cause. This is not just a fringe internet theory by anonymous Twitter accounts. The Surgeon General of Florida has said this is true.

What do the data say? The first thing we can look at is heart disease deaths for men ages 18-39.

The data I’m using is from the CDC WONDER database. This database aggregates data from US states, using a standardized system of reporting deaths. The most important thing to know is that in this database, each death can one have one underlying cause, and this is indicated on the death certificate. Deaths can also have multiple contributing causes (and most deaths do), and the database allows you to search for those too. But for this analysis, I’m only looking at the underlying cause.

Here’s the heart disease death data for men ages 18-39, presented two different ways. First the trailing 12-month average. Don’t focus too much on that dip at the end, since the most recent data is incomplete. Instead, notice three things. First, there was a clear increase in heart disease deaths. Second, that rise began in mid-2020, well before the introduction of vaccines. Third, once vaccines started being administered to this age group in Spring 2021, the number of deaths leveled off (though it didn’t return to pre-pandemic levels).

Here’s another way of looking at the data: 12-month time periods, rather than a trailing average. I created 12-month time periods starting in March and ending in February of the following year. I’ve also truncated the y-axis to show more detail, not to trick you. But don’t be tricked! The deaths are up 2-3%, not a more than doubling as the chart appears to show.

We can see in the chart above that the rise in heart disease deaths for young males completely preceded the vaccination period. Something changed, for sure, but the change wasn’t the introduction of vaccines. Heart disease deaths (by underlying cause) are only up 2-3%, while overall deaths are up around 40%.

So, to repeat the title question, what is killing these young men?

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GDP Growth and Excess Mortality in the G7

Two weeks ago my post looked at GDP growth during the pandemic. But of course, economic growth isn’t the only important outcome to look at in the pandemic. Health outcomes are important too, and indeed I have posted about those in the past alongside GDP data.

Today, my chart looks at the G7 countries (representing roughly half of global wealth and GDP), showing both their economic performance (as measured by real GDP growth) and health performance (as measured by excess mortality through February 2022).

The US has clearly had the best economic performance. But the US also had the highest level of excess deaths per capita (not all of this is from COVID — US drug overdoses are also way up — but even using official COVID deaths, the US still tops this group).

Japan had the best health performance, in fact amazingly no cumulative excess deaths through February 2022 (this has risen very slightly since then, but I stopped in February so all countries had complete data). However, Japan also had slightly negative economic growth.

Which country ends up looking the best? Canada! Very low levels of excess deaths, and at least some positive economic growth. Not as much growth as the US, but Canada is the second best performer in the G7.

To give some context of just how low the level of deaths have been in Canada, first recognize that the US had 1.1 million excess deaths in the pandemic through February 2022. If instead our excess deaths had been roughly equal to Canada on a per capita basis, we would have only had 180,000 excess deaths in the US, saving over 900,000 lives.

Some of Canada’s COVID policy have been overly restrictive, such as the vaccine mandates that sparked protests in February 2022. But by then, Canada had already largely achieved it’s COVID victory over the US and most other G7 nations. Compare excess mortality in Canada with the US: the only big wave in Canada that came close to the US was the Spring 2020 wave. After that, Canada was always much lower.

Market Concentration & Inflation

We are living in volatile times. With covid-19, big federal legislation packages, and the Ruso-Ukrainian conflict disruptions to grain, seed oils, and crude oil, relative prices are reflecting sudden drastic ebbs of supply and demand. I want to make a small but enlightening point that I’ve made in my classes, though I’m not sure that I’ve made it here.

Economists often get a bad rap for being heartless or unempathetic. Sometimes, they are painted as ideologues who just disguise their pre-existing opinions in painfully specific terminology and statistics. Let’s do a litmus test.

Consider two alternative markets. One is a perfect monopoly, the other has perfect competition. All details concerning marginal costs to firms and marginal benefits to consumers are the same. In an erratic world, which market structure will result in greater price volatility for consumers? Try to answer for yourself before you read below. More importantly, what’s your reasoning?

Extreme Market Power

A distinguishing difference between a competitive market and a monopoly concerns prices. While firms maximize profits in both cases, the price that consumers face in a competitive market is equal to the marginal cost that the firms face. There is no profit earned on that last unit produced. In the case of monopoly, the price is above the marginal cost. Profits can be positive or negative, but the consumer will pay a price that is greater than the cost of producing the last unit.

Below are two graphs. Given identical marginal costs of production and benefits that the consumers enjoy, we can see that:

  1. The monopoly price is higher.
  2. The monopoly quantity produced is lower.

But static models only go so far. What about when there is volatility in the world?

Volatile Costs

Oil and gasoline are important inputs for producing many (most?) physical goods. Not only that, they are short-lived, meaning that they disappear once they are used, making them intermediate goods. Therefore, changes in the price of oil constitutes a change in the marginal cost for many firms. If the price of oil rises, or is volatile otherwise, then which type of market will experience greater price and quantity volatility?

Below are two figures that illustrate the same change in the marginal cost. We can see that:

  1. Monopoly price volatility is lower (in absolute terms and percent).
  2. Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).

The take-away: While monopoly does constrict supply and elevate prices, Monopoly also reduces price and output volatility when there are changes in the marginal cost.  

Volatile Demand

That covers the costs. But what about volatile demand? A large part of the Covid-19 recession was the huge reallocation of demand away from in-person services and to remote services and goods. What is the effect of market power when people suddenly increase or decrease their demand for goods?

Below are two figures that illustrate the same change in demand. We can see that:

  1. Monopoly price volatility is higher (in absolute terms, though no different as a percent).
  2. Monopoly quantity produced volatility is lower (in absolute terms, though no different as a percent).

Monopolies Don’t Cause Inflation

Economists know that inflation can’t very well be blamed on greed (does less greed beget deflation?). Another problematic story is that market concentration contributes to inflation. But the above illustrations demonstrate that this narrative is also a bit silly. Monopolistic markets cause the price level to be higher, it’s true. But inflation is the change in prices. Changing market concentration might be a long term phenomenon, but can’t explain acute price growth. If demand suddenly rises, monopolies result in no more price growth than perfectly competitive markets. If the marginal cost of production suddenly rises, monopolies result in less price growth.

All of this analysis entirely ignores welfare. Also, no market is perfectly competitive or perfectly monopolistic. They are the extreme cases and particular markets lie somewhere in between.

Did you guess or reason correctly? Many econ students have a bias that monopolies are bad. So, in any side-by-side comparison, students think that “monopolies-bad, competition-good” is a safe mantra. But the above illustrations (which can be demonstrated mathematically) reveal that economic reasoning helps to reveal truths about the world. Economists are not simply a hearty band of kool-aid drinking academics.

The Latest GDP Data: First Quarter 2022 in the OECD

Today two data releases for Gross Domestic Product were released. The first release was for the United States, giving us the third and “final” release for first quarter 2022 data. It was down 1.6% from the prior quarter (though we knew this two months ago — not much has changed since the “advance” estimate). That’s not good (but see this great Joseph Politano newsletter for some more detail).

The second release was the annual 2021 GDP data for the European Union. The release showed strong growth in 2021 (+5.4%), but that’s relative to the bad year of 2020. So compared to the pre-pandemic level of 2019, the EU was still about 0.8% below this more accurate baseline. Comparatively, the US was already 2% above 2019 with the annual 2021 release (everything in these two paragraphs is adjusted for inflation). Of course, within the EU, there is a lot of variation, but overall the US looks comparatively well.

Let’s break down that variation in the EU and include the first quarter of 2022 data to make the best comparison with the US. To bring in some more relevant comparison countries, I’ll use data from the OECD for a complete comparison. Note: I’ve excluded Ireland, because their GDP is weird. I’ve also excluded Turkey, because even though all the data here is adjusted for inflation, Turkey is in a highly inflationary environment, making the data a little difficult to interpret.

Here is the chart, which shows the change in real GDP from the 4th quarter of 2019 up through the 1st quarter of 2022 (I use the volume index, which is similar to adjusting for price inflation). I have highlighted in orange the largest economies in the OECD (anything with about $2 trillion of GDP or larger, with Spain and Canada at about that level).

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COVID Deaths, Excess Deaths, and the Non-Elderly (Revisited)

While we know that COVID primarily affects the elderly, the mortality and other effects on the non-elderly aren’t trivial. I have explored this in several past posts, such as this November 2021 post on Americans in their 30s and 40s. But now we have more complete (though not fully complete) mortality data for 2021, so it’s worth revisiting the question of COVID and the non-elderly again.

For this post, I will primarily focus on the 12-month period from November 2020 through October 2021. While data is available past October 2021 on mortality for most causes, data classified by “intent” (suicides, homicides, traffic accidents, and importantly drug overdoses) is only fully current in the CDC WONDER data through October 2021. This timeframe also conveniently encompasses both the Winter 2020/21 wave and the Delta wave of COVID (though not yet the Omicron wave, which was quite deadly).

First, let’s look at excess mortality using standard age groups. For this calculation, I use the period November 2018 through October 2019 as the baseline. The chart shows the increase in all-cause deaths in percentage terms. It is also adjusted for population growth, though for most age groups this was +/- 1% (the 65+ group was 3% larger than 2 years prior).

A few things jump out here. First notice the massive increase in mortality for the 35-44 age group (much more on this later). Almost 50% more deaths! To put that in raw numbers, deaths increased from about 82,000 to 122,000 for the 35-44 age group, and population growth was only about 1%. And while that is the largest increase, there were huge increases for every age group that includes adults.

Also notice that the 65+ age group certainly saw an increase, but it is the smallest increase among adults! Of course, in raw numbers the 65+ age group had the most excess deaths: about 450,000 of the 680,000 excess deaths during this time period. But since the elderly die at such high rates in every year, the increase was as large in percentage terms.

One related fact that doesn’t show up in the chart: while there were about 680,000 excess deaths during this time frame in the US in total, there were only about 480,000 deaths where COVID-19 was listed as the underlying cause of death. That means we have about 200,000 additional deaths in this 12-month time period to account for, or a 24% increase (population growth overall was only 0.4%).

That’s a lot of other, non-COVID deaths! What were those deaths? Let’s dig into the data.

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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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Are the COVID Vaccines Effective at Preventing Death?

A recent analysis by the Kaiser Family Foundation of CDC data suggests that about 234,000 COVID deaths in the US could have been prevented if everyone was vaccinated. That’s about 25% of all COVID deaths throughout the pandemic, and about 60% of COVID deaths since June 2021 (roughly the time when most older adults in most states had had a chance to be vaccinated).

The first way to think of that death rate is tragic, given that so many lives could have been saved. Rather than being the high-income nation with the highest COVID death rate, the US could have been more in line with countries like Italy, the UK, and France. The US actually had a lower COVID death rate than Italy and the UK when the vaccine roll-out began, and today we could be at about France’s level with better vaccination rates.

But there’s a flipside to the KFF numbers. If 60% of COVID deaths since June 2021 were preventable, that means 40% weren’t preventable. Furthermore, the same data show that about 40% of COVID deaths in January and February 2022 were fully vaccinated or had boosters. That sounds like the vaccines might not work very well! So what does this all mean? Let’s dig into the data from the CDC a little bit.

The first, and most important thing, to recognize is that most American adults are vaccinated (about 78%), so unless vaccines are 100% effective (and they aren’t, despite some public officials overenthusiastic pronouncements early in the vaccine rollout), there are still going to be a lot of COVID deaths among the vaccinated. If 100% of the population was vaccinated, 100% of the deaths would be among the vaccinated. The key question is whether vaccines lower the chance of death.

And they do. Let’s see why.

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How to Get People Vaccinated for 93 Cents

We’ve talked a lot about vaccines on this blog, including both the benefits of vaccines and how to get people vaccinated. For example, last month I posted about Robert Barro’s estimate on the number of additional vaccines needed to save 1 life. Barro put it at about 250 vaccines. Using some reasonable assumptions, I further suggested that each person vaccinated has a social value of about $20,000. That’s a lot!

But how do we convince people to get vaccinated? Lotteries? Pay them? In addition to just paying them (the economist’s preferred method), another good old capitalist method is advertising (the marketer’s preferred method). And a new working paper tries just that, running pro-vaccine ads on YouTube with a very specific spokesman: Donald Trump.

Running ads on YouTube is pretty cheap. For $100,000, the researchers were able to reach 6 million unique users. And because they randomized who saw the ads across counties, they are able to make a strong claim that any increase in vaccinations was caused by the ads. They argue that this ad campaign led to about 104,000 more people getting vaccinated, or less than $1 per person (the actual budget was $96,000, which is how they get 93 cents per vaccine — other specifications suggest 99 cents or $1.01, but all of their estimates are around a buck).

Considering, again, my rough estimate that each additional vaccinated person is worth $20,000 to society (in terms of lives saved), this is a massive return on investment. Of course, we know that everything runs into diminishing returns at some point (they also targeted areas that lagged in vaccine uptake). Would spending $1,000,000 on YouTube ads featuring Trump lead to 1 million additional people getting vaccinated? Probably not quite. But it might lead to a half million. And a half million more vaccinated people could potentially save 2,000 lives (using Barro’s estimate).

I dare you to find a cheaper way to save 2,000 lives.

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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