Author: Jeremy Horpedahl

What’s the Worst Tax?

Jeremy Horpedahl2 Comments

It’s the most wonderful time of the year, when we start to get all those little documents in the mail and electronically showing how much you earned in the past year. The purpose of these little documents, of course, is to complete your federal and state income tax returns. While many Americans dislike paying income taxes, there is another tax that is rated as even worse in surveys: the property tax.

Why do Americans dislike the property tax so much? One popular explanation is that people don’t like the idea that “you never really own your property.” In other words, even after you have paid off your mortgage, you must continue to pay property taxes, which feels like a form of “rent” that you pay to the government. Of course, that “rent” does pay for a variety of public services, primarily K-12 education in most locations, but this still seems to rub many Americans the wrong way. The libertarian phase “taxation is theft” conveys a similar sentiment for income taxes, that you never “really own” your own labor if you must pay taxes on your earnings.

But there is also an economic explanation for the hatred of the property tax: it is very salient, especially to taxpayers that no longer have a mortgage. While those of us that still have a mortgage on our home pay property taxes through our normal monthly mortgage payment, Americans that have paid off their mortgage typically write a check (or two) to pay the full amount of their property tax bill. An interesting paper by Cabral and Hoxby finds that jurisdictions with more taxpayers using escrow for their property taxes (meaning they have a mortgage) also have higher property tax rates. And furthermore, they “find that owners with tax escrow report their taxes much less accurately than those without tax escrow” (look at Figure 2 in the paper to see the huge differences).

Income taxes, on the other hand, are not salient for most Americans. Payroll withholding means that the taxes are taken out before we even get our paycheck, and you’ll only notice them if you look at your pay stub. And about three-quarters of US taxpayers get a tax refund at the end of the year. For most Americans, the only salient part of the income tax system is a check they receive as a refund, rather than writing a check for their property taxes.

What does all this mean? Should income taxes be made more salient? Should property taxes be made less salient? A simple answer could be that all taxes should be equally salient. Or if you view one tax as superior in some way, maybe that tax should be less salient, so there is less opposition to it.

I don’t have the answers to these questions. But I do have a question for readers: do you know your own income tax rate? Specifically, what is the marginal rate on your federal income taxes? I invite readers to write down their guesses, then look up the correct answer. How close were you? Please leave a comment, and be honest!

Excess Mortality in 2020

Jeremy Horpedahl7 Comments

My last post of 2020 tried to end the year on an optimistic note: the rapid innovation of a new vaccine was truly a marvel. But I also warned you that I would have a post in the new year talking about the deaths of 2020 during the pandemic. And here it is.

Throughout 2020, I have tried to keep up with the most recent data, not only on officially coded COVID-19 deaths, but also on other measures. An important one is known as excess mortality, which is an attempt to measure the number of deaths in a year that are above the normal level. Defining “normal” is sometimes challenging, but looking at deaths for recent years, especially if nothing unusual was happening, is one way to define normal. The team at Our World in Data has a nice essay explaining the concept of excess mortality.

One thing to remember about death data is that it is often reported with a lag. The CDC does a good job of regularly posting death data as it is reported, but these numbers can be unfortunately deceptive. For example, while the CDC has some death data reported through 51 weeks of 2020, but they note that death data can be delayed for 1-8 weeks, and some states report slower than others (for reasons that are not totally clear to me, North Carolina seems to be way behind in reporting, with very little data reporting after August).

So there’s the caution. What can we do with this data? Since 2019 was a pretty “normal” year for deaths, we can compare the deaths in 2020 to the same weeks of data in 2019. In the chart at the right, I use the first 48 weeks of the year (through November), as this seems to be fairly complete data (but not 100% complete!). The red line in the chart shows excess deaths, the difference between 2019 and 2020 deaths. From this, we can see that there were over 357,000 excess deaths in 2020 in the first 11 months of the year, or about a 13.6% increase over the prior year.

Is 13.6% a large increase? In short, yes. It is very large. I’ll explain more below, but essentially this is the largest increase since the 1918 flu pandemic.

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Vaccine Innovation: A Marvel of Modern Science and Modern Markets

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We’ve already talked about different methods for distributing the vaccine in the face of limited supply on this blog (see my post and Doug Norton’s post). But today I want to talk about something different: the speed at which this vaccine was developed. It is truly amazing.

Timeline showing a comparison of vaccine development timescales from Typhoid fever in 1880 to SARS-CoV2 in 2020.

This chart from Nature (adapted from the fantastic Our World in Data) dramatically shows just how quickly the COVID-19 vaccine was developed compared with past vaccines. What used to take decades or even a century was done in mere months (yes, even with all the regulatory barriers today).

Exactly how we developed this vaccine so quickly is a complex story that involves the advanced state of modern science, incentives offered by concerned governments, and the harnessing of the profit motive to advance the public good. We don’t know all the details yet, and likely won’t for a long time since, like a pencil, no one person knows how to make and distribute a vaccine.

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Logrolling: An Efficient Institution

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Along with the colorful phrase “pork barrel” spending, logrolling is a term used to describe the process of vote trading in elected legislative bodies. The process has long been maligned by political scientists, pundits, and the general public. It’s also come up in the debate about the proposed Budget/COVID Relief Bill.

President Grant tried to stop logrolling. He failed.

What’s bad about logrolling? I think there are two general lines of argument. First, it just seems immoral. Citizens can’t legally trade their votes, and many see any attempt to do so as wrong. You get one vote, and one vote only. For someone to have more votes than others rubs our intuitions the wrong way, similar to the ability for wealthy individuals or corporations to essentially have more votes by influencing politicians through campaign contributions.

More pragmatically, logrolling gets a bad name because it could lead to wasteful spending, particularly the “pork barrel” type that Americans really hate (unless it is coming to their district, of course). If you vote for my bill, I will vote for yours, even though I might not care about your bill. Maybe even I think your bill is kinda bad, but I think my bill is really good, so I am willing to hold my nose and vote for your bill, if it gets me what I want.

Buchanan and Tullock (1962) turned this logic on its head. Logrolling is efficient because it allows members to express their preferences, specifically the intensity of their preferences. Moreover, it allows legislative bodies to get things done that are beneficial for society, even if none of those things would pass in a simple referendum.

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Allocating the vaccines: central planning or the free market?

Jeremy Horpedahl5 Comments

In the short term, there are only a few million doses of the COVID vaccines available, but well over 100 million adults in the US that want to take the vaccine if offered for free to the consumer. There are also billions worldwide that would like the vaccine.

So who should get it first? In practice in the US, the allocation method has already been determined politically: the federal government will allocate vaccines to the states, and states will allocate them to individuals based on a priority list: health workers and the most vulnerable first, then teachers, etc. The NY Times has a tool that shows you your probable place in line.

But essentially the allocation method being used is central planning.

John Cochrane has proposed a “free market” solution: sell the vaccine to the highest bidder. Or at least, sell some doses to the highest bidder.

As an economist, there is always some appeal in thinking about a free market solution. But there is a problem in this case: there are positive externalities from taking the vaccine. It not only benefits me, but it also benefits others. My willingness to pay only reflects the benefit to me, the private benefit. The social benefit is mostly ignored by a simple auction, and in the aggregate for a vaccine most of the benefits are likely to be social benefits. But positive externalities don’t imply we need to use central planning!

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Reflections on Teaching in Fall 2020

Jeremy Horpedahl2 Comments

As the Fall semester comes to close on college campuses, it’s a good time to reflect on and assess how the past semester went. Many universities went to almost exclusively virtual learning, but other schools tried to make Fall 2020 as normal as possible given the circumstances of the COVID-19 pandemic.

My school, the University of Central Arkansas, chose the route of trying to have things as normal as possible — by which I mean students live on campus, classes are mostly in-person — while still accommodating students and faculty that preferred a more physically distant atmosphere. For example, UCA increased the number of fully online courses available, roughly trying to meet faculty and student demand. I normally teaching one online course per semester anyway, and I continued that this semester. Other faculty had more online classes than usual, or moved their class to be partially online.

So what was my experience?

First, the students, the most important part of the teaching process. Overall, I would say my students did very well. At least in the classroom, they complied with all the rules the University set forth: wearing masks, physical distancing in classrooms (seen in the image below), even the one-way entrances and exits to the building. There were only 3-4 times I can recall this semester when a student entered my classroom without a mask, and they immediately asked me for one upon realizing their mistake (I kept a pack of surgical masks with me).

My classroom at the University of Central Arkansas, with chairs blocked off for physical distancing.

As far as academic performance of students, I was very pleased with the students. For those students that were able to stick with the class and keep up, which was most students, they perform as well or better than previous semesters. Some students, due to personal circumstances, had trouble keeping up. I tried as much as possible to accommodate students in these situations, by being flexible with deadlines, offering additional resources, and generally just trying to listen to them and empathize. It was hard for everyone.

On my end, I tried to make the teaching atmosphere of the classroom as normal as possible. I usually do have some interactive aspects of the classroom, where students work in small groups, talk to their neighbors, etc. Most of those activities didn’t happen, unfortunately. But otherwise, the classroom atmosphere operated as usual.

As my students did, I also wore a mask in the classroom while I lectured. For students that had to miss class due to quarantine, isolation, or other reasons, we were asked to record every lecture and have an option for students to watch the lecture virtually if needed. Making sure that the video was properly recording and the I had set up the Zoom link for students that needed to be remote added an extra element to think about at the beginning of each class, but it was the kind of thing that once you get used to it, it just became normal.

I will say that I often felt very exhausted after teaching each day. The mental load of making sure everything was working right in the classroom, combined with the constant sense of doom in the world around us, made this a challenging semester mentally. I’m sure this was even more true for some of my students. But, we made it.

Finally, how about the administration of my University. I’ll bite my tongue a little here: I am up for tenure this year! But really, I don’t have anything major to complain about. Guidance was communicated well, although sometimes big changes were rolled out a bit more quickly than the faculty liked. UCA provided isolation and quarantine dorms for students, though these never came close to capacity. Weekly updates on testing, cases, and related data were provided to everyone (and made publicly available, so I’m not revealing any secrets here).

Testing data for UCA students. This data excludes athletes, since they were required to get tested regularly, which could have skewed the data.

As you can see above, the general student body at UCA did report positive COVID cases every week. And some weeks the positive test rate was a little higher than I was comfortable with! But we never had a large spike in cases, and the University held firm to its commitment to offer in-person classes for everyone that wanted them, as long as the campus was generally safe.

All in all, I think it was the best semester we could have had under the circumstances. The only thing really weighing on my mind: we are going to do it all over in the Spring semester. And we’ll do it as well as we can.

“Firearms and Violence Under Jim Crow”

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A new working paper by Mike Makowsky and Patrick Warren finds that “firearms offered an effective means of Black self-defense in the Jim Crow South.” By this the authors mean that greater access to firearms by Blacks decreased the likelihood of being lynched.

That headline finding is sure to be provocative in both debates over gun control and the history of Jim Crow. And with good reason. What I found most interesting is how they measured Black access to firearms. Since they did not have direct access to any good sources measuring Black access to firearms, they proxy access with the percent of Black suicides committed with firearms. Increased access to firearms would also mean a higher proportion of suicides were committed using firearms.

That’s a “grisly” way of measuring Black access to firearms, as Makowsky put it in a Twitter thread summarizing the paper. But also a very creative one.

Here’s the full abstract:

We assess firearm access in the U.S. South by measuring the fraction of suicides committed with firearms. Black residents of the Jim Crow South were disarmed, before re-arming themselves during the Civil-Rights Era. We find that lynchings decrease with greater Black firearm access. During the Civil-Rights Movement, both the relative Black homicide and Black “accidental death by firearm” rates decrease with Black firearm access, indicating frequent misclassification of homicides as accidents. In the contemporary era, greater firearm access correlates with higher Black death rates. We find that firearms offered an effective means of Black self-defense in the Jim Crow South.

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

Jeremy Horpedahl5 Comments

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

Jeremy Horpedahl1 Comment

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.

Two Papers on the 1966 Minimum Wage Increase

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Continuing on the theme of last week’s minimum wage increase in Florida, there are two interesting papers recently accepted for publication that both cover the 1966 Fair Labor Standards Act. This law extended the federal minimum wage to a number of previously uncovered. Crucially, the newly covered industries employed a large number of African-American workers.

The two papers agree on some points, such as that African Americans saw large wage gains following the increase. But was there a disemployment effect? Here is where the papers differ.

Ellora Derenoncourt and Claire Montialoux’s paper “Minimum Wages and Racial Inequality” is forthcoming in the Quarterly Journal of Economics. Here is what they find: “We can rule out significant disemployment effects for black workers. Using a bunching design, we find no aggregate effect of the reform on employment.”

Martha J. Bailey, John DiNardo, and Bryan A. Stuart’s paper “The Economic Impact of a High National Minimum Wage: Evidence from the 1966 Fair Labor Standards Act” is forthcoming in the Journal of Labor Economics. They find “some evidence shows that disemployment effects were significantly larger among African-American men, forty percent of whom earned below the new minimum wage in 1966.”

So who is right? Let me clearly state here that both of these papers are very well done, both in their methods and in their assembling of historical data. But I think there is a key difference in the samples they analyze: Derenoncourt and Montialoux’s paper only includes workers aged 25-55. Bailey and co-authors use a broader age range, 16-64, which importantly includes teenagers (this is discussed in Section D of their online appendix).

Since teenagers and other young workers are the ones we suspect are going to be most impacted by the minimum wage (much of the literature focuses on teenagers), the exclusion of workers under 25 seems like a curious omission, and a reason I tempted to believe the results of Bailey and co-authors. But Derenoncourt and Montialoux do try to justify their choice of age group: 1. workers under 21 were subject to a different minimum wage; and 2. workers under 25 were subject to the draft for the Vietnam War.

So once again, you might ask, who is right? I will admit here that I don’t know. Standard economic theory suggests that disemployment effects will result from a legal minimum wage (I fully acknowledge the emerging literature on monopsony power, but I maintain this is still not the standard analysis), and especially so for teenagers and young workers. So I am skeptical of any analysis which excludes these workers, whatever other merits it may have.

Here’s my take: we probably can’t tell much about how the minimum wage will impact young workers today based on these studies. If Derenoncourt and Montialoux’s reasons for removing young workers are indeed sound, then we aren’t really testing the question most economists are interested in today (so I would caution against their attempt to apply the results to labor markets today). But that doesn’t mean these aren’t interesting papers to read on an important change in the history of minimum wage laws in the US!