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!

Laboratories of Democracy in Pandemic

You’ve probably heard the phrase that US states are often “laboratories of democracy.” The phrase comes from a Supreme Court case. It’s well known enough that it has a short Wikipedia page. The basic idea is simple: states can try out different policies. If it works, other states can copy it. If it doesn’t work, it only hurts that state.

The 2020-21 pandemic has provided a number of possibilities for the “states as laboratories” concept. Here’s three big ones I can think of (please add more in the comments!):

  1. Do states that impose stricter pandemic policies (“lockdowns”) have better or worse outcomes? This could be about health, the economy, both, or some other outcome.
  2. Do states that end unemployment benefits sooner have quicker labor market recoveries? Or are these not the main drag on the labor market?
  3. Do states that offer incentives for vaccination have higher vaccination rates? And what sort of incentives work best?

These are all good questions, but let me throw some cold water on this whole concept: we might not be able to learn anything from these “experiments”! The primary reason: the treatments aren’t randomly assigned. States choose to implement them.

Let’s think through the potential problems with each of these three areas:

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Old Lives Matter

Bryan Caplan has kindly responded to my latest blog post, which was in turn a response to his blog post on the relative value of human lives by age. Caplan has always been kind in his responses, even when responding to pesky graduate students — kind in both his approach and the time he dedicates to responding thoughtfully. So I appreciate his taking the time to respond to me, and I will offer a few more thoughts on the matter.

To briefly summarize: Caplan believes that young lives (10 year olds) are worth 100-1,000 as much as old lives (80 year olds). I contend that they are closer to roughly equally valued. My disagreement with Caplan can be broken down into two categories:

  • A. Caplan’s three reasons why young lives are worth more (a lot more!) than old lives. I didn’t respond to that directly, but I will do so here. I think Caplan is narrowing the goalposts.
  • B. A disagreement over the shape of the VSL curve over the lifetime, specifically whether an inverted-U-shaped curve makes sense. I’ll say more about this too, but Caplan doesn’t just have a beef with me, but with almost everyone in the VSL literature!

Let’s start with Caplan’s three reasons, which he calls “iron-clad”: young people have more years to live, those years are generally healthier, and young people will be missed more when they are gone. The first in undeniably true on average, the second is probably true almost all the time, and I’m not sure on the third, but I’m willing to admit it’s not a slam dunk either way.

So how can I disagree? These are only three things. There are many other considerations, and we can imagine other reasons that old lives are valued as much or more than younger lives! I’ll call mine 4-6 to go with Caplan’s 1-3:

  1. Old age spending is the largest component of public budgets in developed countries (and this is unlikely mostly due to rent seeking or the self interest of younger generations).
  2. The elderly possess wisdom which is highly valuable and that the young benefit from.
  3. The last years of your life are, on average, worth a lot more — you are usually very wealthy, have no employment obligations, you have grandchildren you love (without the responsibilities of parenting), and are (until the very end) generally healthy too.

Taken as a whole, I think these three reasons present a strong counterargument to Caplan’s three reasons. And I think we could certainly come up with more! My point being that Caplan has picked three areas where clearly young lives have the advantage, but ignored all the good reasons why old lives are more valuable. These is what I mean by we shouldn’t rely on our intuitions. Neither of our lists are exhaustive, but let me elaborate on a few of these.

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The Value of Life, Again

Bryan Caplan argues that the life of a 10-year-old is worth 100-1,000 times that of an 80-year-old. But he suggests the modal answer people would give is that the two lives are equally valued.

I’m not sure if he is right about what the modal answer would be that they are exactly equal (though see below for an attempt to answer this question). Surprisingly, though, roughly equally valuing all lives is actually the answer that a normal economic calculation, willingness-to-pay for risk reduction, would give you! Or at least roughly. I haven’t seen an estimate for a 10-year-old, but estimates of the Value of a Statistical Life for 20-year-old is roughly equal to an 80-year-old. I’ve written about this before, and here’s a summary of a working paper by Aldy and Smyth that I am drawing on. Middle age lives are worth more, using this method, though perhaps just 2-3 times more.

Caplan doesn’t directly connect his hypothetical to the COVID pandemic, but in the comments Don Boudreaux does make that connection and says that “surely the correct level of precaution to take against a disease that kills X number of old people is lower [than a disease that kills the same number of young people].” I find this a very interesting statement because Don Boudreaux, and many others, have been against just about any precaution (other than asking the elderly to isolate) in the current pandemic. Would he and others support more caution if they believed the VSL estimate to be true?

So who is right? Caplan’s intuition? Or the modeled VSL calculations? For surely these are miles apart, and they can not both be correct.

As an economist, I have a strong preference in favor of willingness-to-pay over our intuitions. Indeed, Caplan himself as defended the VSL approach quite forcefully!

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The Impact of the Pandemic on US States: GDP and Deaths

Following up on my recent post on country GDP growth rates and mortality in 2020, we now have the first look at state GDP growth rates for 2020 from the BEA.

As with the national data, I would look to caution against over-interpreting this data. I’m presenting it here to give a picture of how 2020 went for states (including a few months of 2021 for morality data). One thing you will notice is that there appears to be little correlation with the raw data between GDP declines and mortality. Lots of important factors (policy, behavior, demographics, weather, luck) aren’t controlled for here. Still, I think it’s useful to see all the data in one picture, given how much many of us have been following the daily, weekly, and monthly releases.

Here is the data. Below I’ll explain more how I created this chart, especially the excess mortality data.

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GDP Growth in 2020

Last year was a historically bad year for many reasons, but to economists that badness is most visible in our widest measure of the economy: Gross Domestic Product. All issues with GDP aside, especially as a perfect measure of relative living standards, the annual real (inflation-adjusted) growth rate of GDP gives us a good picture of how much national economies were harmed by the pandemic, private behavior changes, and government restrictions (disentangling these three effects is hard — I will leave that to the academic journals rather than a blog post).

While GDP is reported with a lag of several months and is subject to revision, many countries have now reported full GDP data for 2020. For those that don’t follow GDP very closely, for a developed country an annual rate of growth of about 2% is pretty normal and respectable. For further context, in the US recent recessions had declines of -2.5% in 2009, -0.1% 1991, and -1.8% in 1982 (the 2001 recession never had an annual decline, only a few quarterly declines). While it is unusual for countries to go more than 10 years without a decline, it does happen. For example, Australia’s last annual decline was in 1991, when it declined -1.3%. But that’s unusual.

This chart shows the 2020 GDP growth rates (mostly negative, with one exception — Taiwan) for 2020 for most countries were I could find data. What this number shows us is the total amount of economic activity in 2020 compared with the total amount of economic activity in 2019 (adjusted for inflation, of course). I believe this is a better measure than others you might see, such as data that compares the level in the 4th quarters of 2020 and 2019 (a country could have had a terrible 2nd quarter but still gotten back close to the prior year level, and a simple Q-over-Q measure would miss that decline). As I did for the 3rd quarter data, this chart also plots the cumulative COVID-19 death rates on the vertical axis.

GDP data comes from government statistical agencies and media reports. COVID-19 death data is from Our World in Data.

What can we learn from this data?

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