Yikes! If true, these are serious charges against a profession in decline.
But hang on. What’s really going on with the award for David Card? Again, Tabarrok sums it up nicely: “what really made the paper great was the clarity of the methods that Card and Krueger used to study the problem.” What was this clarity?
Are there racial gaps in the distribution of the COVID-19 vaccine? This is an important and interesting question in its own right. But I’ll talk about this question today because it’s an interesting example of how confusing and sometimes misleading data can be.
How do we answer this question? One is by surveying people. There are a number of surveys that ask this question, but a recent one by the Kaiser Family Foundation finds that among adults 70% of Blacks and 71% of Whites report being vaccinated. And given the sampling error possible with surveys, we would say that these are virtually identical. No racial gap! (Note: there was a racial gap when they did the same survey back in April, with 66% of Whites and 59% of Blacks vaccinated.)
But, surveys are just a sample, and perhaps people are lying. Maybe we shouldn’t trust surveys! And shouldn’t there be hard data on vaccines? Indeed, the CDC does publish data on vaccinations by race. That data shows a fairly large gap: 42.3% of Whites and only 36.6% of Blacks vaccinated. This is for at least one dose, and the percentages are of the total population (which is why it’s lower than the survey data). So maybe there is a racial gap after all!
But wait, if you look closely at the footnotes (always read the footnotes!), you’ll see something curious: the CDC admits that the race data are only available for 65.8% of the data. We don’t have the race information for over one-third of those in this data. Yikes! And given the exist disparities we know about in terms of income and access to healthcare, we might suspect that the errors are not randomly distributed. In other words, if there is probably good reason to suspect that Blacks are disproportionately reflected in the “unknown” category. But we just don’t know.
So what can we do? Since this data comes from US states, we can look at the individual state data and see if perhaps some of it is better (fewer unknowns). What does that data show us?
Bryan Caplan recently wrote about public goods theory, how we teach it, and the unrealistic nature of how we classify goods as either/or, rather than on a continuum. I explored similar themes in a blog post that I wrote back in January, but Caplan brings up another important point about public goods theory that I forget.
In a short 2002 paper, and then in a 2003 book with the same title, Foldvary and Klein proposed the idea of “the half-life of policy rationales.” In brief, the justification for many market failure arguments is contingent on the current state of technology. They apply this to concepts such as natural monopoly and information asymmetries, but for public goods theory the most important application is to the concept of excludability.
Here’s the basic idea: it is costly to exclude non-payers for using some goods. If it is so costly that it would not be profitable for a private enterprise to produce the good in question, it won’t be produced privately. But it still may be efficient for government to produce the good, if the benefit from the good exceeds the cost of raising the revenue to pay for it (likely out of general revenue, since we have already admitted it is infeasible to charge the users directly).
But here’s the Foldvary and Klein point: all of the above paragraph is dependent on the current state of technology! Take roads for example. When you had to pay someone to physically take a few coins for a toll road, plus force all motorists to slow down to a complete stop to pay the toll, it was probably cost prohibitive to operate limited-access private toll roads. But technology changes. We now have the technology for electronic tolling done at highway speed (and even coin buckets were slightly faster than handing some dude your change). The argument for government provision of highways, which was strong when technology was ancient, is significantly weakened now that technology has reached its modern state.
(There may be lots of other reasons you think that roads should be publicly provided, such as equity, but these are separate questions and distinct from the argument made in standard public goods theory.)
Foldvary and Klein go through many more examples in their book, but we can already see the key insight. And I think this is extremely important for teaching public goods to undergraduates. It’s normal for us to say that goods are either excludable (in which private provision is best) or non-excludable (in which there is a strong case for some government intervention). But this either/or framing is wrong (a continuum is a better way to think about it), and crucially it can change over time depending on technological changes. Excludability is not some inherent feature of a good or service, it is a function of the state of technology.
If you have ever been through the process of applying to colleges, you have almost certainly heard the term “selective colleges.” If you haven’t the basic idea is that some colleges are harder to get into, for example as measured by what percentage of applicants are accepted to the school. The assumption of both applicants and schools is that a more selective college is “better” in some sense than a less selective college. But is it?
In a new working paper, Mountjoy and Hickman explore this question in great detail. The short version of their answer: selective colleges don’t seem to matter much, as measured by either completion rates or earnings in the labor market. That’s an interesting result in itself, but understanding how they get to this result is also interesting and an excellent example of how to do social science correctly.
Here’s the problem: when you just look at outcomes such as graduation rates or earnings, selective colleges seem to do better. But most college freshmen could immediately identify the problem with this result: that’s correlation, not causation (and importantly, they probably knew this before stepping onto a college campus). Students that go to more selective colleges have higher abilities, whether as measured by SAT scores or by other traits such as perseverance. It’s a classic selection bias problem. How much value is the college really adding?
Here’s how this paper addresses the problem: by only looking at students that apply to and are accepted to colleges with different selectivity levels, but some choose to go to the less selective colleges. What if we only compare this students (and of course, control for measurable differences in ability)?
Now this approach is not a perfect experiment. Students are not randomly assigned to different colleges. There is still some choice going on. But are the students who choose to attend a less selective college different in some way? The authors try to convince us in a number of ways that they are not really that different. Here’s one thing they point out: “nearly half of the students in our identifying sample choose a less selective college than their most selective option, suggesting this identifying variation is not merely an odd choice confined to a small faction of quirky students.”
Perhaps that alone doesn’t convince you, but let’s proceed for now to the results. This chart on post-college earnings nicely summarizes the results (see Figure 3 in the paper, which also has a very similar chart for completion rates)
The title question may seem obvious. “We” care about inflation because, ultimately, any dollars we have saved will purchase fewer real goods and services. Additionally, we might worry that our incomes are not keeping pace with the increase in the prices of good and services that we want to purchase.
But the answer to that question is a little more nuanced. “We” also care about why prices are increasing. I keep putting “we” in quotation marks because who the we is crucial for answering the question. For example, individuals and families primarily care about inflation for the reasons I stated in the first paragraph.
But central bankers care about inflation for different reasons. In broad terms, monetary policy is an attempt to smooth out the fluctuations in the economy, especially to make recessions shorter and less deep. But monetary officials want to know: is the policy they are putting in place leading to prices rising in general? If so, especially if inflation gets above certain target levels, it may mean that monetary has been “too loose.”
However, if particular prices are rising, say the price of cars (due to a lack of computer chips), central bankers don’t really care about this: it gives them no indication of whether they’ve done “too much” or “too little” with regards to stimulating the economy. Similarly, if gasoline prices rise, consumers really care about this. Central bankers, not so much: it doesn’t really tell them much about their goal (stimulating the economy with stimulating it too much).
And because some prices are so volatile, historical context is important for understanding what a recent increase or decrease means. For example, gasoline prices are up 45% in the past 12 months. That’s a lot! But it’s an increase from a very low base, and the historical reality is that gasoline prices today (around $3.00/gallon on average) are at similar levels to what they were way back in 2006, and are lower than they were for almost all of 2011-2014. And these are all in nominal terms, median household income has gone up a lot since 2006 (up 40% in nominal terms) and even since 2014 (up 25%).
All of this is important background for thinking about the latest release of the CPI-U data this week. The headline inflation number of 5.3% is indeed startling, similar to last month. We haven’t touched that level since mid-2008, and that was only for a few months. If consumer price inflation were to stay at around 5% for a sustained period of time, it would be a new, harsh reality for most consumers today: we haven’t had a year with 5% inflation since 1990, and for the past decade the average has hung around 2%.
So will it stay this high? Sadly, I have no crystal ball and I will just reiterate what I said last month: the picture is just too muddled right now to say anything concrete. Perhaps by the end of the year we will have a better picture. But is there anything we can say right now even with the muddled picture? I continue to like this chart from the Council of Economic Advisors:
Bottom line: if we strip out the unusual supply chain disruptions to automobiles as well as airline/hotel prices making up for lost ground during the pandemic, inflation is at completely normal levels. It’s almost exactly 2%
But is this cheating? Can we really strip out the things that are increasing at rapid rates?
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).
I have seen it many times. It comes from this Washington Post article, but it seems to go viral on Twitter about every 6 months or so.
The implication of the chart seems to confirm what many young people feel in their bones: Boomers had it much easier, and it’s getting harder and harder for later generations to catch up and build wealth. For many the graph… explains a lot, as one recent viral Tweet put it (in the weird world of social media, 5 short words and a recycled chart are all it takes for 20,000 retweets).
But wait. A few questions probably come to mind. For example, when Boomers were young they comprised a much larger share of the population. The original article makes an attempt to adjust for this, by calculating a few ratios towards the end of the article. However, there’s a much more straightforward way to adjust for this, which also nicely fits into a chart: put wealth in per capita terms!
If we do that, here’s the chart we get (also, of course, adjusted for inflation).
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.
It’s time to head back to school! Which means it’s time for college students to once again ask the question: How am I going to pay for this?
It’s common knowledge that college is expensive and getting more expensive every year. A Google search for “skyrocketing tuition” produces almost 60,000 results. But whenever a fact is so commonly accepted, it’s worth asking if it’s really true.
Here’s one way to think about: are college tuition and fees increasing faster than the overall rate of inflation? For much of recent history, the answer has been most definitely “yes.” I start the series here in 2006, because there were some methodological changes to the index just before 2006. Cumulatively, college tuition and fees (as measured in the CPI) have increased by 78%, while prices overall have only increased by about 38%.
But for the very recent history, since 2017, the answer is “no.” College tuition and fees have often been increasing at slower rates than overall prices in the CPI, and the difference is especially dramatic in 2021. Since 2017, overall prices have increased by about 12.4%, but college tuition and fees has only increased by 7.8%.
However, even this data overstates how much tuition and fees have gone up for undergraduates in the US!
The latest inflation data for the US has been released, and the headline CPI-U annual increase of 5.4% is once again raising worries that high inflation could be a permanent part of the landscape for the near future.
My personal opinion is that the picture is much too muddled now, between temporary supply issues and low bases for 2020 prices, to say much about the medium-term picture. I think we’ll have a better picture by the end of the year. Still, it’s worth drilling down into the data, as we have done in the past on this blog, to understand some things about economics, prices, and how price changes are impacting real people.
Certainly the prices of some goods are rising at alarming rates. Many of these are related to automobiles and transportation generally, but some categories of food have rose a lot in the past year too (though groceries overall are only up 2.6%).
But I want to talk about two categories of consumption: beer and hot dogs.
Actually, my co-blogger Zachary has already written about beer. And using the producer price index, he found that canned beer is actually cheaper than it was a year ago. If you like canned beer, rejoice! And for all beer at home, the CPI shows only a 1.8% increase since last year, after a similar small 1.6% increase last July (not much of a base effect… a clue for later!).
But not all Americans consumer alcohol. So let’s talk about that most American food product: the hot dog.