Selectivity and Selection Bias: Are Selective Colleges Better?

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)

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Why Do We Care About Inflation?

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:

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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?

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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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Who is the Wealthiest Generation?

Have you seen this chart?

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).

Data is for 1989-2021 from the Federal Reserve’s Distributional Financial Accounts, but only the first quarter is available right now for 2021. For 1989, it is the average of the third and fourth quarters. Population data comes from Census single-year of age estimates for various years. 2020 and 2021 population estimated using growth rate from 2010-2019.
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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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Back to School! But what’s up with college pricing?

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!

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Beer, Hot Dogs, and Inflation

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.

Why should we care about hot dogs? Read on.

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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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The Recession Is Over! (15 months ago)

Lately there has been lots of both good and bad news about the pandemic and its impact on the economy. But here’s once piece of good news you might have missed: the recession which began in February 2020 ended in April. And not April 2021… it ended in April 2020. At least, that’s according to the NBER Business Cycle Dating Committee, which made the announcement last week.

The 2020 recession of just 2 months is by far the shortest on record. NBER maintains a list of recessions with monthly dates going back to 1854 (there are annual business cycles dates before that, including important modern revisions of the original estimates, but the monthly series starts in 1854). In that timeframe, there have been 7 recessions in the 6-8 month range, but nothing this short. Still, it was mostly definitely a recession, as unemployment briefly spiked to levels not seen since the Great Depression. But only for 2 months. Keep in mind that the first part of the Great Depression last 43 months.

Unemployment Rate, 1948-present

But how can this be? Is the recession really over? There are still about 6-7 million fewer people working than before the pandemic began. Lots of businesses are still hurting. The unemployment rate is still 2 full percentage points above pre-pandemic levels. How in the world can we say the recession ended 15 months ago?

To answer that question, it helps to know what NBER and most macroeconomists mean by a “recession” — essentially, it is used interchangeably with “contraction.” It means the economy, by a broad array of measures (NBER uses about 10 measures), is shrinking — or we might say, going in the wrong direction. The only other option, at least in the NBER chronology, is an expansion — when the economy is going in the right direction.

Does an economic expansion mean that everything is fine the economy?

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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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