Purchasing Drinks with Push-ups.

Early in the summer of 2021, I was having fun. The semester was ended and traveling was on the horizon. Due to changes at my wife’s job I began driving to work instead of making the 20 minute trek by foot. And there was plenty of time to be social. And social I was — several days of the week. And, inevitably, drinks would be served. I was doing a lot less walking and a lot more drinking alcohol (responsibly).

I was footloose and fancy free. Until… The bathroom scale reminded me that I had surpassed the age of 30 years old. Being sedentary and drinking were starting to add up.

Right then and there, I made a decision. I would disincentivize my drinking, but I would also make drinking beneficial rather than detrimental to my waist line.

I made a deal with myself. For each drink that I had, I would have to ‘pay’ 25 push-ups. No exceptions. And, no borrowing from myself. Push-ups *had* to come first. None of this “I owe myself push-ups” nonsense (it’s a trap!). I could *save* for the future, however. Knowing that a social event was approaching, I’d build myself a nice little balance. And the exchange rate was constant: 25 push-ups per drink – always.

Who held me accountable? Me, myself, and I… And some incentive compatible approbation.

I wasn’t shy about any of it. At a outdoor beer garden with my wife and her cousin, I had prepared by banking 50 push-ups. But round 3 was impending… and I’m no square. So, over to the side, quite out of the way on the outdoor patio, I knocked out a quick 25. Round 4 came after still another 25.

Now let’s talk incentives. Requiring push-ups of myself increased my physical activity, so I felt better about my body. Further, if I hadn’t banked push-ups ahead of time, paying prior to each drink limited how many push-ups I could comfortably do. Once push-ups became uncomfortable, I stopped drinking.

That’s all great. But the social incentives were pivotal in keeping me dedicated. Upon seeing the push-ups in action, female friends would talk to my wife who quickly developed a well-crafted dialogue for each new observer, complete with convincingly spontaneous gesticulations and eye-rolls. I can’t say that I didn’t enjoy the attention.

Other men provided direct positive approval. I combined 3 activities that were already ‘manly’ when separate: muscle building, drinking, and dispassionate self control. Men would praise me immediately and similarly feel compelled to do there own sets of push-ups in my presence — as if being sedentary in my presence convicted them as guilty of something. At least one wife sent me a text after we had left town that included a picture of her husband knocking out some of his own pre-drink push-ups (Is this what it feels like to be an influencer??).

Aristotle would be proud.

I was very consistent for months. Being the summer and seeing a lot of friends and family, I did a lot of push-ups. But, as time passed, the exercise habit stuck even as the drinking began to pass by the wayside. What began as an arbitrary, self-imposed rule soon became a legit change in behavior. And then, that change in behavior became a practice. Did that practice improve my temperance and fortitude through habituation? Idk. But wouldn’t that be nice?

YLL or VSL? Cost-Benefit Analysis in the Year of COVID

How do we conduct cost-benefit analysis when different policies might harm some in order to help others? This question has become increasingly important in the Year of COVID.

In particular, it is possible that some interventions to prevent the spread of COVID may save the lives of the vulnerable elderly, but have the unfortunate effect of causing other harms and potentially deaths. For example, increased social isolation could lead to increased suicides among the young (we don’t quite have good data on this yet, but it’s at least a possibility).

If you don’t think any public policies will reduce COVID deaths, then the post isn’t for you. It’s all cost, no benefit!

But for those that do recognize the trade-offs, a common way to do the cost-benefit analysis is to look at “years of life lost” or YLL. This is a common approach on Twitter and blogs, but I’ve seen it in academic papers too. In this approach, you look at the age of those that died from COVID, and use an actuarial life table to see how long they would have been expected to live. For example, an 80-year-old male is expected to live about 8 more years. Conversely, a 20-year-old males is expected to live another 56 years.

So, here’s the crude (and possibly morbid) YLL calculus: if a policy saves six 80-year-olds, but causes the death of one 20-year-old, it’s a bad policy. Too much YLL! (Net loss of 8 years of life.) However, if the policy saves eight elderly and kills just one young person, it’s a good policy. A net gain 8 years of life. (Of course, we can never know these numbers with precision, but that’s the basic idea.)

But I think this approach is fundamentally flawed. Not because I oppose such a calculation (though maybe you do, especially if you are not an economist!), but because it’s using the wrong numbers. Briefly: we shouldn’t value every year of life equally.

The superior approach for this calculation is to use an approach called the “value of a statistical life” (VSL). In this approach, we assign a value to human life (the non-economists are really cringing now) based on revealed preferences of various sorts. Timothy Taylor has a nice blog post summarizing how this value can be estimated, which is much better than how I would explain it.

In short, the average VSL in the US is around $10-12 million, depending on how you calculate it. You might be skeptical of this figure (I was at first too!), but what really convinced me is that you get roughly this number when you do the calculation using very different approaches. It just keeps coming up.

So how does VSL apply to our COVID calculation? What’s really interesting about VSL is that it varies with age. And not perhaps as you might expect, as a constantly declining number. It’s actually an inverted-U shape, with the highest values in the middle of the age distribution. Young and old lives are roughly equally valued! Once we realize this, I think we can see how the YLL approach to analyzing COVID trade-offs is flawed.

Kip Viscusi has been the pioneer in establishing the VSL calculation. If you’ve heard that “a life is worth about $10 million” and scratched your head, Viscusi is the man to blame. Over the weekend, Viscusi gave his Presidential Address to the Southern Economic Association (he actually delivered it in-person at the conference in New Orleans, but to a very small crowd since the conference was over 90% virtual).

As you might have guessed given his area of research, Viscusi used this address to estimate the costs of COVID, both mortality and morbidity (the talk is partially based on this paper). He didn’t talk much about the policy trade-offs, but we can use his framework to talk about them. Here’s a very relevant slide from the presentation.

Notice here we see the inverted-U shaped VSL curve. You may not be able to read it very well, but Viscusi helps us with a bullet point: VSL at age 62 is greater than at age 20. Joseph Aldy, a frequent co-author of Viscusi, has extended the curve even further up to age 100 which you can see in this column. Aldy and Smyth use a slightly different approach, but the short version is that the VSL for a 62-year-old is much greater than a 20-year-old (roughly double). The 20-year-old VSL is roughly equal to that of an 80-year-old.

So let’s go back to the above YLL calculation, which told us that if a policy intervention only saves six 80-year-olds but results in the death of one 20-year-old, it’s bad policy. Too many YLL!

However, using the VSL calculation, this policy is actually good, since 20- and 80-year-olds have roughly equally valued lives. The policy only becomes bad if it kills more 20-year-olds than elderly folks. This may seem strange, given the short life left for the 80-year-old, but it is where the VSL calculus leads us.

I will admit, this calculations are morbid in some sense. But we live in morbid times. Death is all around us, and we need to some clear method for assessing trade-offs. YLL seems like the wrong approach to me. VSL seems better, but if we take a third approach, something like All Lives Matter (and matter equally), we end up with the same calculation when comparing a 20- and 80-year-old.

In the end, we should also be looking for policy interventions that have low costs and don’t result in additional deaths. For example, I think there is now good evidence that wearing masks slows the spread of viruses, which will lower deaths without any major costs. But if we are going to talk about trade-offs, let’s do it right.

(Final technical note: there is an approach that combines YLL and VSL, called “value of a statistical life year” [VSLY]. Viscusi discusses VSLY in the paper that I linked to above. I won’t get into the technicalities here, but suffice it to say VSLY involves more than simply adding up the years of life lost.)

Third Quarter Check-In: COVID and GDP

How have countries around the world fared so far in the COVID-19 pandemic? There are many ways to measure this, but two important measures are the number of deaths from the disease and economic growth.

Over the past few weeks, major economies have started releasing data for GDP in the third quarter of 2020, which gives us the opportunity to “check in” on how everyone is doing.

Here is one chart I created to try to visualize these two measures. For GDP, I calculated how much GDP was “lost” in 2020, compared with maintaining the level from the fourth quarter of 2019 (what we might call the pre-COVID times). For COVID deaths, I use officially coded deaths by each country through Nov. 15 (I know that’s not the end of Q3, but I think it’s better than using Sept. 30, as deaths have a fairly long lag from infections).

One major caution: don’t interpret this chart as one variable causing the other. It’s just a way to visualize the data (notice I didn’t try to fit a line). Also, neither measure is perfect. GDP is always imperfect, and may be especially so during these strange times. Officially coded COVID deaths aren’t perfect, though in most countries measures such as excess deaths indicate these probably understate the real death toll.

You can draw your own conclusions from this data, and also bear in mind that right now many countries in Europe and the US are seeing a major surge in deaths. We don’t know how bad it will be.

Here’s what I observe from the data. The countries that have performed the worst are the major European countries, with the very notable exception of Germany. I won’t attribute this all to policy; let’s call it a mix of policy and bad luck. Germany sits in a rough grouping with many Asian developed countries and Scandinavia (with the notable exception of Sweden, more on this later) among the countries that have weathered the crisis the best (relatively low death rates, though GDP performance varies a lot).

And then we have the United States. Oddly, the country we seem to fit closest with is… Sweden. Death rates similar to most of Western Europe, but GDP losses similar to Germany, Japan, Denmark, and even close to South Korea. (My groupings are a bit imperfect. For example, Japan and South Korea have had much lower death tolls than Germany or Denmark, but I think it is still useful.)

To many observers, this may seem strange. Sweden followed a mostly laissez-faire approach, while most US states imposed restrictions on movement and business that mirrored Western Europe. Some in the US have advocated that the US copy the approach of Sweden, even though Sweden seems to be moving away from that approach in their second wave.

Counterfactuals are hard in the social sciences. They are even harder during a public health crisis. It’s really hard to say what would have happened if the US followed the approach of Sweden, or if Sweden followed the approach of Taiwan. So I’m trying hard not to reach any firm conclusions. To me, it seems safe to say that in the US, public policy has been largely bad and ineffective (fairly harsh restrictions that didn’t do much good in the end), yet the US has (so far) fared better than much of Europe.

All of this could change. But let’s be cautious about declaring victory or defeat at this point.

Coda on Sweden Deaths

Are the officially coded COVID deaths in Sweden an accurate count? One thing we can look to is excess deaths, such as those reported by the Human Mortality Database. What we see is that Swedish COVID deaths do almost perfectly match the excess deaths (the excess over historical averages): around 6,000 deaths more than expected.

Some have suggested that the high COVID deaths for Sweden are overstated because Sweden had lower than normal deaths in recent years, particularly 2019. This has become known as the “dry tinder” theory, for example as stated in a working paper by Klein, Book, and Bjornskov (disclosure: Dan Klein was one of my professors in grad school, and is also the editor of the excellent Econ Journal Watch, where I have been published twice).

But even the Klein et al. paper only claims that “dry tinder” factor can account for 25-50% of the deaths (I have casually looked at the data, and these seems about right to me). Thus, perhaps in the chart above, we can move Sweden down a bit, bringing them closer to the Germany-Asia-Scandinavia group. Still, even with this correction, Sweden has 2.5x the death rate of Denmark (rather than 5x) and 5x the death rate of Finland (rather than 10x, as with officially coded deaths).

As with all things right now, we should reserve judgement until the pandemic is over (Sweden’s second wave looks like it could be pretty bad). The “dry tinder” factor (a term I personally dislike) is worth considering, as we all try to better understand the data on how countries have performed in this crisis.