Data

Excess Mortality and Vaccination Rates in Europe

Jeremy HorpedahlLeave a comment

Much ink has been spilled making cross-country comparisons since the start of the COVID-19 pandemic. I have made a few of these, such as a comparison of GDP declines and COVID death rates among about three dozen countries in late 2021. I also made a similar comparison of G-7 countries in early 2022. But all such comparisons are tricky to interpret if we want to know why these differences exist between countries, which surely ultimately we would like to know. I tried to stress in those blog posts that I was just trying to visualize the effects, not make any claims about causation.

Here’s one more chart which I think is a very useful visualization, and it may give us some hint at causation. The following scatterplot shows COVID vaccination rates and excess mortality for a selection of European countries (more detail below on these measures and the countries selected):

The selection of countries is based on data availability. For vaccination rates, I chose to use the rate for ages 60-69 at the end of 2021. Ages 60-69 is somewhat arbitrary, but I wanted a rate for an elderly age group that was somewhat widely available. There is no standard source for an international organization that published these age-specific vaccine rates (that I’m aware of), but Our World in Data has done an excellent job of compiling comparable data that is available.

Note: I’m using the data on at least one dose of the vaccine. OWID also has it available by full vaccine series, and by booster, but first dose seemed like a reasonable approach to me. Also, I could have used different age groups, such as 70-79 or 80+, but once you get to those age groups the data gets weird because you have a lot of countries over 100%, probably due to both challenging denominator calculations and just general challenges with collecting data on vaccination rates. By using 60-69, only one country in my sample (Portugal) is over 100%, and I just code them as 100%. Using the end of 2021, rather than the most current data, is a bit arbitrary too, but I wanted to capture how well early vaccination efforts went, though ultimately it probably wouldn’t have mattered much.

Also: dropping the outliers of Bulgaria and Romania doesn’t change things much. The second-degree best fit polynomial still has an R2 over 0.60 (for those unfamiliar with these statistics, that means about 60% of the variation is “explained” in a correlational sense).

The excess mortality measure I use comes from the following chart. In fact, this entire post is inspired by the fact that this chart and others similar to it have been shared frequently on social media.

The chart comes from a Tweet thread by Paul Collyer. The whole thread is worth reading, but this chart is the key and summary of the thread. What he has done is shown the average and range of a variety of ways of calculating excess mortality. Read his thread for all the details, but the basic issues are what baseline to use (2015-2019 or 2017-2019? A case can be made for both), how to do the age-standardized mortality, and other issues. I won’t make a claim as to which method is best, but averaging across them seems like a fine approach to me.

For the y-axis in my chart, I just used the average for each country from Collyer’s chart. There are 34 countries in his chart, but in the OWID age-specific vaccination rates, only 22 countries were available the overlapped with his group. Unfortunately, this means we drop major countries like Italy, Spain, the UK, and Germany, but you work with the data you have.

For many sharing this and similar chart (such as charts with just one of those methods), the surprising (or not surprising) result to them is that Sweden comes out with almost the lowest excess mortality rate. Some approaches even put Sweden as the very lowest. Sweden!

Why is Sweden so important? Sweden has been probably the most debated country (especially by people not living in the country in question) in the COVID pandemic conversation. In short, Sweden took a less restrictive (some might say much less restrictive) approach to the pandemic. This debate was probably the most fevered in mid-to-late 2020, when some were even claiming that the pandemic was over in Sweden (it wasn’t). The extent to which Sweden took a radically different approach is somewhat overstated, especially in relation to other Nordic countries. And as is clear in both charts above, the Nordic countries all did relatively very well on excess mortality.

The bottom line from my first chart is that what really matters for a country’s overall excess mortality during the pandemic is how well they vaccinated their population. There seems to be a lot of interest on social media to rehash the debates about whether lockdowns (and lighter restrictions) or masks worked in 2020. But what really mattered was 2021, and vaccines were key. A scatterplot isn’t the last word on this (we should control for lots of other things), but it does suggest that a big part of the picture is vaccines (you can see this in scatterplots of US states too). It’s frustrating that many of those wanting to rehash the 2020 debates to “prove” masks don’t work, or whatever, either ignore vaccines or have bought into varying degrees of anti-vaccination theories. It’s completely possible that lockdowns don’t pass a cost/benefit test, but that vaccines also work very well (this has always been my position).

Why did Sweden have such great relative performance on excess mortality? Vaccines are almost certainly the most important factor among many that matter to a much smaller degree.

What About the US?

Note: for those wondering about the US, we don’t have the vaccination rate for ages 60-69 that I can find. Collyer also didn’t include the US in his analysis, it was only Europe. So, for both reasons, I didn’t include them in this post. The CDC does report first-dose vaccinations for ages 65+ in the US, though they top-code states at 95%. As of the end of 2021, here are the states that were below 95%: Mississippi, Louisiana, Tennessee, West Virginia, Indiana, Ohio, Wyoming, Georgia, Arkansas, Idaho, Alabama, Montana, Alaska, Missouri, Texas, Michigan, and Kentucky. These states generally have very high age-adjusted COVID death rates. Ideally we would use age-adjusted excess mortality for US states, but in the US that is horribly confounded by the rise in overdoses, homicides, car accidents, and other causes that are independent of vaccination rates (though they may be related to 2020 COVID policies — this is still a matter of huge debate).

Self-Conception, Relative Prices, & Confabulation

Zachary Bartsch7 Comments

We all like to think that we are individuals. We like to think that we grow and that our tastes develop and mature. We begin to appreciate different things in life, and among other behaviors, our spending habits change.

But what would you say if I told you that your maturing tastes didn’t cause your maturing consumption patterns? Indeed, what if it’s the other way around? Maybe, you’re just a bumbling ball bearing bouncing about and pinging off of various stimuli in a very predictable fashion. What if the prices that you face changed over the course of the past two decades, adjusting your optimal bundle of consumption, and then you contrived reasons for your new behavior in an elegant post-hoc fashion.

Have you *really* taken a liking to whole wheat bread and pasta over the past decade because your tastes have developed? Or maybe it’s because you found that scrumptious New York Times recipe that turned you away from potatoes and toward rice. Whether it’s a personal experience, a personal influence, or a personal development, we like to think about ourselves as complex organisms with a narrative that makes sense of the way in which we interact with the world.

On the other hand, we have price theory. Price theory still accepts that you are special and that you have preferences. Then, it asserts that your preferences remain fixed and that your changes in behavior are merely responses to changing costs and benefits that you perceive in the world. Maybe you’re not any more inclined to eat healthily than you were previously, but the price ratio of whole wheat bread to white bread is 10% less than it use to be. Maybe your east-Asian inspired recipe didn’t cause you to spurn potatoes, but instead the price ratio of rice to potatoes fell by 20%.   

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The Economics of Brushing Teeth and the Tooth Fairy

Jeremy Horpedahl2 Comments

There are many papers with titles in the style of “The Economics of X” with X covering a wide variety of topics, some deadly serious (“Economics of Suicide“) and others more trivial or unintentionally hilarious (“The Economics of Sleep and Boredom” comes to mind). There is a related genre of papers on “The Political Economy of Y,” once again with papers that are both serious and occasionally silly (or sometimes deadly serious papers with silly-sounding titles, such as “The Political Economy of Coffee, Dictatorship, and Genocide“).

But perhaps the best paper of this sort is a 1974 article on the Journal of Political Economy by Alan Blinder, titled “The Economics of Brushing Teeth.” It is, as you might guess, a paper that is somewhat tongue-in-cheek (tongue-in-teeth?), but the paper carefully follows the formal style you would expect from a JPE paper in 1974. I recommend reading the paper in full, and I can assure you that it is not at all like pulling teeth. But if you prefer not to look a gift horse in the mouth, here are a few favorite parts.

The paper is, of course, full of tooth-related puns, even in the footnotes, such as this acknowledgment: “I wish to thank my dentist for filling in some important gaps in the analysis.”

There are also plenty of jokes about human capital theory, jokes that only an economist could love, such as: “The basic assumption is common to all human capital theory: that individuals seek to maximize their incomes. It follows immediately that each individual does whatever amount of toothbrushing will maximize his income.”

Another section manages to poke fun at both sociologists and economists. In reference to a fake paper (no, there is no Journal of Dental Sociology), Blinder chastises the fake sociologist for misattributing a change in brushing patterns (assistant professors brush more) to advancing hygiene standards over time. No! It must be about maximizing income: “To a human capital theorist, of course, this pattern is exactly what would be expected from the higher wages received in the higher professorial ranks, and from the fact that younger professors, looking for promotions, cannot afford to have bad breath.”

And what good is a paper without a formal model of teeth brushing? This is the kind of model that many young economists cut their teeth on in graduate school.

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Twenty Years of Animal Protein Affordability

Zachary Bartsch2 Comments

Have you heard the hubbub about eggs? People say that they’re expensive. My wife told me that if she’s going to pay an arm and a leg, then she may as well get the organic, pasture raised eggs. Absolutely. That’s what the substitution effect predicts. As the price ratio of low-quality to high-quality eggs rises, we’re incentivized to consume more of the high-quality version. It has to do with opportunity costs.

Consider a world in which the low-quality eggs cost $2 and the high-quality eggs cost $6 per dozen. Every high-quality egg costs 3 low-quality eggs. You might still choose the high-quality option, but you know that you’re giving up a lot by doing so. Consider the current world where low-quality eggs are priced on par with high-quality eggs. Now, the opportunity cost of consuming the fancy, pasture-raised eggs has fallen. When consuming one high-quality egg costs you one low-quality egg, it’s much easier to opt for the high-quality version. You’re not giving up as much when you purchase it.

For vegetarians, the recent price swing has probably been rough. Not eating meat, they’re facing the price squeeze more so than their omnivorous counterparts. Through the magic of math, median wages, and average retail prices, the figure below charts the affordability of eggs and dairy products.* The median person has been facing falling egg affordability for two decades. Indeed, it’s only been the past few years, punctuated by the Covid crisis, that consumers experienced more affordable eggs.

Dairy products, however, have become much more affordable. The median American can now afford 50% more of their namesake cheese. Further, we can afford 20-25% more whole milk and cheddar cheese. So, the vegetarians are not so poorly off after all.

But how do meatier sources of protein compare?

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Behavioral Risk Factor Surveillance System Survey: Now in Stata and CSV formats

James Bailey1 Comment

The BRFSS Annual Survey is now available in Stata DTA and Excel-friendly CSV formats at my Open Science Foundation page.

The US government is great at collecting data, but not so good at sharing it in easy-to-use ways. When people try to access these datasets they either get discouraged and give up, or spend hours getting the data into a usable form. One of the crazy things about this is all the duplicated effort- hundreds of people might end up spending hours cleaning the data in mostly the same way. Ideally the government would just post a better version of the data on their official page. But barring that, researchers and other “data heroes” can provide a huge public service by publicly posting datasets that they have already cleaned up- and some have done so.

That’s what I said in December when I added a data page to my website that highlights some of these “most improved datasets”. Now I’m adding the Behavioral Risk Factor Surveillance Survey. The BRFSS has been collected by the Centers for Disease Control since the 1980s. It now surveys 400,000 Americans each year on health-related topics including alcohol and drug use, health status, chronic disease, health care use, height and weight, diet, and exercise, along with demographics and geography. It’s a great survey that is underused because the CDC only offers it in XPT and ASC formats. So I offer it in Stata DTA and Excel CSV formats here.

Let me know what dataset you’d like to see improved next.

The Minimum Wage and Crime

Jeremy Horpedahl2 Comments

The minimum wage is one of the most studied topics in economics, and also something that is frequently discussed on this blog from many different angles. For someone that isn’t an expert in this area, it can be hard to keep track of all the most recent, cutting-edge research on the topic.

Here’s a brand-new paper in the literature with an important finding: raising the minimum wage increases crime. Specifically, in “The Unintended Effects of Minimum Wage Increases on Crime” the authors find that 16-to-24-year-olds commit more property crimes after a minimum wage increase. For every 1% increase in the minimum wage, there is a 0.2% increase in property crime. That implies a doubling the minimum wage would increase property crimes for this age group by 20%. Here’s a figure from the paper showing this increase in crime:

What is the mechanism by which the rising minimum wage increases crime? Here the authors move into examining one of the central questions of the empirical minimum wage debate: the labor market. The authors do find evidence that employment decreases for this same age group following an increase in the minimum wage. Again, a figure from the paper:

The results in this paper add one more element to the cost-benefit calculus of the minimum wage. But I think the results are also interesting because they seem to point in the opposite direction of a paper co-authored by fellow EWED blogger Mike Makowsky. His paper “The Minimum Wage, EITC, and Criminal Recidivism” found that increasing the minimum wage made it less likely that former prisoners would commit another crime. I would be interested to hear Mike’s thoughts on this paper!

The Murky Macro Picture

James Bailey2 Comments

Last June I wondered if we were seeing the peak of inflation, and by at least one major measure I called the peak exactly:

At the moment, though, I’m feeling more confused than prophetic. The big question a year ago was how long it would take the Fed to get inflation down to reasonable levels, and how much collateral damage they would do to the real economy in that effort. Today most current indicators make it look like they pulled off the miraculous “soft landing”. Inflation over the last 12 months is still high, but over the last 6 months we’re nailing the Fed’s 2% annualized target. This has hit a few sectors of the real economy hard, with housing slowing dramatically and tech doing mass layoffs, but the overall picture is great: GDP growth was around 3% the last 2 quarters, and the 3.4% unemployment is the lowest since 1969.

What’s confusing about this is that we have a hard time believing we really got this lucky. Its like your plane lost power, you diverted course for an emergency crash landing, and once you touch down and find yourself seemingly unharmed you look around and wonder if the plane is about to explode. Consumer sentiment is worse than it was in the depths of Covid; business sentiment has been falling for over a year and is almost down to March 2020 levels. Betting markets forecast a 50% chance of a recession in 2023, and the yield curve is strongly inverted (one of the best predictors of a recession, though the guy who first noticed this says it might not work this time):

Finally, M2 money supply is shrinking for the first time since at least 1960, and I believe the first time since the Great Depression. This bodes well for inflation continuing to moderate, but its also one more indicator of a potential recession.

To sum up, most of the indicators of the current state of the economy look great, while most indicators of its near-term future look awful. So which do we trust?

My guess is that we avoid recession in 2023, but honestly this is mostly the gut feeling of an optimist. There’s no one knock-down piece of data I’d point to in support; its more that things are currently going well, and usually the best prediction is that tomorrow will be like today unless you have a good reason to think otherwise. The main reason people expect a slowdown is because of the Fed’s actions to fight inflation. The Fed itself predicts that they will cause a slowdown, but not a recession. Their most recent summary of economic projections from December predicts GDP growth slowing to 0.5% in 2023 and unemployment rising to 4.6%.

I think the “so what” outlook is also murky. Stocks have already fallen a lot from their highs and a recession already seems somewhat ‘priced in’, so even if I thought one was coming I wouldn’t necessarily sell stocks. On the flip side US stocks are still quite expensive by historical standards, so I don’t want to buy more on the assumption that they’ll rise more on good economic news this year. You might want to lock in decent rates on long-term bonds if you think the Fed will cut rates in response to a recession, but the inverted yield curve shows this is already somewhat priced in. 1-year bonds yielding almost 5% seems decent in either scenario, I have some and I’ll probably buy more, but 5% returns are nothing to get excited about. I’d like to hear suggestions but to me the small direct betting market on a potential recession is the clearest “so what” for anyone who does have a confident view about this year’s macro picture.

The Growth of Black Families Income

Jeremy Horpedahl4 Comments

Black families are the poorest major racial or ethnic group in the US. With a median income of $59,541, a Black family only has about 59 percent of the income of a White, Non-Hispanic family. That’s the same proportion, 59 percent, as was true in 1972, the earliest date that we have comparable data. (For most of the data in this post, I will be using Table F-23 from the Census Bureau’s Historical Income Tables.) That’s almost 50 years with no closing of the racial gap in total money income for families.

Of course, what this also means is that family incomes of both Black and Whites grew at the same rate from 1972 to 2021. They both are about 50 percent larger than in 1972, and that’s after accounting for inflation (using the CPI-U-RS). As a first point of optimism, this very much goes against the typical narrative of income stagnation since the early 1970s.

To be sure, some of this growth is because families have more earners today, but even so: they have a lot more income. Having two earners does mean that you must spend more on some consumption categories, such as daycare when kids are young, possibly more on dining out or prepared meals. But even with those additional expenses, these families will have significantly more disposable income than their 1970s counterparts.

There is an ever more optimistic fact that we need to point out for Black families today: there are many, many more rich Black families today than in 1972. There are more rich families in absolute terms and as a proportion of the total. Here is the basic data from the Census Bureau (it goes back to 1967, the earliest date available for Blacks).

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The Social Drug of Prohibition

Zachary Bartsch1 Comment

Why does the average drinker consume alcohol? There are plenty of reasons, one of which is social. Alcohol, while inhibiting clarity, precision, and discretion, is a social lubricant. If you’re one of those drinking, then it’s enjoyable to be around other drinkers. Also, people build the habit of drinking *something* while socializing. We all know that prohibition resulted in bootlegging and tainted cocktails. But what were the legal alternatives? One was that you could purchase grape juice and make your own wine (that’s a story for another time). Another is to switch to another drug.

Alcohol is a depressant and arguably the most popular one in the US. It’s not a clear substitute for alcohol in terms of its direct effects on the body. However, it’s a liquid, safe, and tasty. That make is a good candidate for satisfying the physical urge to imbibe. But, importantly, it is also a social drug. People would get so hopped up on coffee and feed off of one another’s high that Charles the II of England banned coffee houses in order to prevent seditious fomentation. This brings us to an important characteristic of coffee. It’s a stimulant. You’d think that a stimulant would not be a substitute for alcohol. If anything, one might think that they are complements. Coffee helps to provide that kick in the pants after having an enjoyable night. But, the social feature makes coffee a good candidate to substitute alcohol, should the times be dire.

Illegal activity aside, people wanted an outlet for their physical and social proclivities. They wanted intoxication. Coffee provided exactly that. Conveniently, the continental US didn’t grow any of its own coffee. That means that imports and domestic consumption have a tight relationship.

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Economic Recovery from the Pandemic

Jeremy Horpedahl3 Comments

How well have countries recovered from the declines in the pandemic? It’s actually a bit difficult to answer that question, because it depends on how you measure it. Even if we agree that GDP is the best measure, how do we measure recovery? One possibility is to simply ask whether the country has exceeded its pre-pandemic GDP level. Exactly which quarter to use as the baseline is debatable, but here is a chart that Joseph Politano made for G7 countries using the 3rd quarter of 2019 as the baseline.

But we know that absent the pandemic, most countries would have continued growing (absent a recession for some other reason), so just getting back to pre-pandemic levels isn’t necessarily a full recovery. But how much growth should we have expected? It’s a hard question, but here’s a chart along those lines from the Washington Post, using the CBO’s measure of “potential GDP” as what growth might have looked like.

Using either of these approaches, it appears that the US has recovered pretty well, although it would be nice to have a comparison across countries using the same approach as the Washington Post chart does. While there is no consistent measure similar to CBO’s potential GDP figure for all countries, a simple approach is to project growth forward using the average pre-pandemic growth rate. I have done so for a number of countries, using the average growth rate from 2017-2019. In the following charts, the blue line is actual GDP levels, and the orange line is projecting the 2017-2019 growth rate forward. Sorry that I can’t easily fit all these into one chart, so here come the charts!

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