College Major, Marriage, and Children

The American Community Survey began in 2000, and started asking about college majors in 2009, surveying over 3 million Americans per year. This has allowed all sorts of excellent research on how majors affect things like career prospects and income, like this chart from my PhD advisor Doug Webber:

See here for the interactive version of this image

But the ACS asks about all sorts of other outcomes, many of which have yet to be connected to college major. As far as I can tell this was true of marriage and children, though I haven’t searched exhaustively. I say “was true” because a student in my Economics Senior Capstone class at Providence College, Hannah Farrell, has now looked into it.

The overall answer is that those who finished college are much more likely to be married, and somewhat more likely to have children, than those with no college degree. But what if we regress the 39 broad major categories from the ACS (along with controls for age, sex, family income, and unemployment status) on marriage and children? Here’s what Hannah found:

Every major except “military technologies” is significantly more likely than non-college-grads to be married. The smallest effects are from pre-law, ethnic studies, and library science, which are about 7pp more likely to be married than non-grads. The largest effects are from agriculture, theology, and nuclear technology majors, each about 18pp more likely to be married.

For children the story is more mixed; library science majors have 0.18 fewer children on average than non-college-graduates, while many majors have no significant effect (communications, education, math, fine arts). Most majors have more significantly more children than non-college graduates, with the biggest effect coming from Theology and Construction (0.3 more children than non-grads).

In this categorization the ACS lumps lots of majors together, so that economics is classified as “Social Sciences”. When using the more detailed variable that separates it out, Hannah finds that economics majors are 9pp more likely than non-grads to be married, but don’t have significantly more children.

I love teaching the Capstone because I get to learn from the original empirical research the students do. In a typical class one or two students write a paper good enough that it could be published in an academic journal with a bit of polishing, and this was one of them. But its also amazing how many insights remain undiscovered even in heavily-used public datasets like the ACS. We’ve also just started to get good data on specific colleges, see this post on which schools’ graduates are the most and least likely to be married.

Inflation Has Wiped Out Average Wage Gains During the Pandemic (maybe)

The latest CPI inflation report didn’t have a huge surprise in the headline number, with 8.3% being very similar to last month. But with the two most recent months of data, we can now see something very unfortunate in the data: cumulative inflation during the pandemic as measured by the CPI-U (11.6%) has now almost matched average wage growth (12.0%), as measured by the average wage for all private workers. I start in January 2020 for the pre-pandemic baseline.

What this means is that inflation-adjusted wages in the US are no greater than they were before the pandemic. They are almost identical to what they were in February 2020 (just 2 cents greater). But as regular readers will know, the CPI-U isn’t the only measure of inflation, and there’s good reason to believe it’s not the best. One alternative is the Personal Consumption Expenditures price index. Cumulative inflation for the PCE is slightly lower during the pandemic (9.0%, though we don’t have April 2022 data yet).

This chart shows average wage growth adjusted with both of these different measures of inflation, expressed as a percent of January 2020 wages. The CPI-U adjusted wages (blue line) have been falling steadily since the beginning of 2021, though the declines have accelerated in 2022. The PCE-adjusted wages (orange line) have also not performed superbly, but at least they are still 2-3% above January 2020. Still, the picture is not rosy: they’ve basically been flat since mid-2020 and have started to drop in early 2022.

Of course, average (mean) numbers can be tricky and sometimes misleading. What if instead we used median wages? Unfortunately, there is no hourly median wage data that is updated every month. The closest data that I usually look at is median weekly earnings, which is available on a quarterly basis. Here’s what that data looks like, expressed as a percent of the first quarter of 2020. I limit the data to full-time workers, since that should give us a roughly comparable number to the hourly data (hours of work may have changed, but using full-time workers should make it roughly constant).

For median weekly earnings, we can see that the picture is even less rosy. Median earnings have been declining consistently since the second quarter of 2020, regardless of which inflation adjustment we use. The decline in the PCE-adjusted measure isn’t quite as steep since early 2021, but both figures are below the pre-pandemic level, and have been for the past two quarters.

One final note: if we look at weekly earnings across the distribution, and not just at the median, we see something very interesting. Earnings at the bottom of the distribution seem to be performing better than those at the top. In fact, the 10th percentile weekly wage is the only category that is still above pre-pandemic levels. I’m only adjusting using the CPI-U here, but the patterns for the PCE-adjusted earnings would be roughly similar.

We should be cautious about interpreting this data too: if workers dropping out of the labor force are primarily at the bottom of the distribution, it will artificially push up the 10th percentile earnings level. It would be good to know how much of that is going on here. Still, I think this is an important result in the current data.

Covid-19 Didn’t Break the Supply Chains. You Did.

This is my last post in a series that uses the AS-AD model to describe US consumption during and after the Covid-19 recession. I wrote about US consumption’s broad categories, services, and non-durables. This last one addresses durable consumption.

During the week of thanksgiving in 2020, our thirteen-year-old microwave bit the dust. NBD, I thought. Microwaves are cheap, and I’m willing to spend a little more in order to get one that I think will be of better quality (GE, *cough*-*cough*). So, I filtered through the models on multiple websites and found the right size, brand, and wattage. No matter the retailer, at checkout I learned that regardless of price, I’d be waiting a good two months before my new, entirely standard, and unexceptional microwave oven would arrive. I’d have to wait until the end of January of 2021.

¡Que Ridiculo!

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Eat 20 Potatoes a Day…. For Science

Several people have tried eating an all-potato diet for a few weeks and reported losing lots of weight with little hunger or effort. Could this be the best diet out there? Or are we only hearing from the rare success stories, while all the people who tried it and failed stay quiet?

Right now we don’t really know, but the people behind the Slime Mold Time Mold blog are trying to find out:

Tl;dr, we’re looking for people to volunteer to eat nothing but potatoes (and a small amount of oil & seasoning) for at least four weeks, and to share their data so we can do an analysis. You can sign up below.

I was surprised to see that they are the ones running this, since they are best known for the “Chemical Hunger” series arguing that the obesity epidemic is largely driven by environmental contaminants like Lithium. The conclusion of that series noted:

Bestselling nutrition books usually have this part where they tell you what you should do differently to lose weight and stay lean. Many of you are probably looking forward to us making a recommendation like this. We hate to buck the trend, but we don’t think there’s much you can do to keep from becoming obese, and not much you can do to drop pounds if you’re already overweight. 

We gotta emphasize just how pervasive the obesity epidemic really is. Some people do lose lots of weight on occasion, it’s true, but in pretty much every group of people everywhere in the world, obesity rates just go up, up, up. We’ll return to our favorite quote from The Lancet

“Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures.”

That said, they did still offer some advice based on the contaminant theory that is consistent with the potato diet:

1. — The first thing you should consider is eating more whole foods and/or avoiding highly processed foods. This is pretty standard health advice — we think it’s relevant because it seems pretty clear that food products tend to pick up more contaminants with every step of transportation, packaging, and processing, so eating local, unpackaged, and unprocessed foods should reduce your exposure to most contaminants. 

2. — The second thing you can do is try to eat fewer animal products. Vegetarians and vegans do seem to be slightly leaner than average, but the real reason we recommend this is that we expect many contaminants will bioaccumulate, and so it’s likely that whatever the contaminant, animal products will generally contain more than plants will. So this may not help, but it’s a good bet. 

Overall though I think the idea here is to ignore grand theories and take an empirical approach. The potato diet works surprisingly well anecdotally, so lets just see if it can work on a larger scale. Seems worth a try; I’m sure plenty of my ancestors in Ireland and Northern Maine did 4-week mostly-potato diets and lived to tell about it. You can read more and/or sign up here. Let us know how it goes if you actually try it!

What if You Didn’t Have to File a Tax Return?

Now that we’ve all made it through the 2021 tax filing season, it’s worth thinking about a recurring question in tax policy: is it possible that most of us wouldn’t need to go through this annual ritual? Couldn’t the government just tell us how much we owe (or are due as a refund), or better yet, just deduct the correct amount from our paycheck so we’d have paid the right amount?

We need to imagine such a system: it exists in many developed nations around the world! And it’s true that, at least for many taxpayers, the IRS already has all the information on you it needs to calculate your taxes.

But how many US taxpayers would this be beneficial for? A new working paper which tries to quantify this question. In “Automatic Tax Filing: Simulating a Pre-Populated Form 1040,” the authors use a large sample of tax returns to estimate how many taxpayers a pre-filled return would work for. The results are almost split down the middle: it would work well for maybe half of US taxpayers (41-48% of taxpayers, depending on how we are defining successful). For the other half, it wouldn’t give you an accurate estimate of how much tax you owed.

And the errors can be large. For example, the authors report that “two-thirds of the cases where the lower bound approach is inaccurate, the pre-populated liability is higher than the reported liability, with a median gap of $4,200.” Note: looking at the tables, I think they mean to say “mean,” not “median” here, with the median being $1,400. Still, that’s a lot of errors in a direction that would hurt taxpayers if they didn’t fill it out on their own or pay someone to do it. And it’s not just one thing that’s causing pre-filled returns to be wrong. You might think itemized deductions are a big issue, and they are, but only for about 11% of returns (and in only 4% of returns is this the only issue). They find that 9% of returns didn’t even have the reported wages matching what the IRS showed!

Does this mean that pre-filled returns are doomed in the US? Perhaps not! They seem to work much better for younger, single filers, and as well as filers with very low income, as Figure 1 from the paper shows. Even so, the 60-80% success rate (depending on criteria) for very low income taxpayers isn’t especially encouraging. But one upshot of a pre-filled return is that there are possibly millions of taxpayers (maybe 8 or 12 million?) that don’t file a return because they aren’t legally required to (too low income), but they would benefit if they did because of refundable credits like the EITC and Child Tax Credit.

Maybe there is a compromise position. The IRS could send you a “suggested tax return,” but allow you to modify it. I suspect that, in most cases, those who are currently paying for a person or software to do their taxes would still do it. You can’t know if you are in the one-half of taxpayers where this information is accurate! The IRS could provide a list of “common reasons why you may be in the half of pre-filled tax returns that are wrong,” but we’re still shifting the burden back to the taxpayer.

I would like to suggest, instead, that there are a few changes we could make to our tax system (“simplifications,” if you will) that might make pre-filled returns much more viable.

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AS-AD: From Levels to Percent

The aggregate supply & aggregate demand model (AS-AD) is nice because it’s flexible and clear. Often professors will teach it in levels. That is, they teach it with the level of output on one axis, and the price level on the other axis. This presentation is convenient for the equation of exchange, which can be arranged to reflect that aggregate demand (AD) is a hyperbola in (Y, P) space. Graphed below is the AD curve in 2019Q4 and in 2020Q2 using real GDP, NGDP, and the GDP price deflator.

The textbook that I use for Principles of Macroeconomics, instead places inflation (π) on the vertical axis while keeping the level of output on the horizontal axis. The authors motivate the downward slope by asserting that there is a policy reaction function for the Federal Reserve. When people observe high rates of inflation, state the authors, they know that the Fed will increase interest rates and reduce output. Personally, I find this reasoning to be inadequate because it makes a fundamental feature of the AS-AD model – downward sloping demand – contingent on policy context.

At the same time, I do think that it can be useful to put inflation on the vertical axis. Afterall, individuals are forward looking. We expect positive inflation because that’s what has happened previously, and we tend to be correct. So, I tell my students that “for our purposes”, placing inflation on the vertical axis is fine. I tell them that, when they take intermediate macro, they’ll want to express both axes as rates of change. I usually say this, and then go about my business of teaching principles.

But, what does it look like when we do graph in percent-change space?

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Inflation and GDP Growth Around the World

Kalshi cofounder Tarek Mansour recently shared this graph:

In hindsight it seems like an obvious graph to make, and a good way to teach Aggregate Supply / Aggregate Demand models, but I don’t actually recall seeing much like this. One obvious improvement is to include more countries. I do so below using data from Trading Economics, showing all 182 countries that have recent data on both annual GDP growth and inflation. I also flip the axes to be more consistent with the convention in economics:

This makes clear both the costs and benefits of including all countries. We see just how extreme some outliers are: hyperinflation in Venezuela, Sudan, Lebanon, and Syria; a severe contraction in Libya; and huge growth in Azerbaijan and the Maldives (errors in the data?). But all the more typical countries blend together. So here I zoom in on the more typical countries:

This makes clear the strong aversion to deflation that most countries have. Well over a hundred countries here, many with very low inflation, but only in South Sudan does inflation actually go negative. Real GDP growth does not exhibit the same sharp divide at zero, presumably because its much harder to central banks to fine-tune. Now I try to zoom and enhance one more time:

But things are starting to just get messy, so its time to drop more countries. Here I focus on the 30 largest economies (minus Turkey, which breaks the scale on inflation):

Here we see:

  • Japan is demonstrating stagnation/ low aggregate demand / “running cold”
  • Brazil, Stagflation (negative supply shocks?)
  • Poland, high aggregate demand / running hot
  • Saudi Arabia and Israel, high growth without high inflation (positive supply shocks?)

The US is on the higher end of inflation, and I still think we should be doing more about this, but in this graph we don’t look like a huge outlier. We’re all still working through Covid-related shocks. But the very latest quarterly data today (not reflected in these graphs) showed negative GDP growth in the US, sending us toward the “Stagflation” quadrant and making the Fed’s job much harder.

Are the COVID Vaccines Effective at Preventing Death?

A recent analysis by the Kaiser Family Foundation of CDC data suggests that about 234,000 COVID deaths in the US could have been prevented if everyone was vaccinated. That’s about 25% of all COVID deaths throughout the pandemic, and about 60% of COVID deaths since June 2021 (roughly the time when most older adults in most states had had a chance to be vaccinated).

The first way to think of that death rate is tragic, given that so many lives could have been saved. Rather than being the high-income nation with the highest COVID death rate, the US could have been more in line with countries like Italy, the UK, and France. The US actually had a lower COVID death rate than Italy and the UK when the vaccine roll-out began, and today we could be at about France’s level with better vaccination rates.

But there’s a flipside to the KFF numbers. If 60% of COVID deaths since June 2021 were preventable, that means 40% weren’t preventable. Furthermore, the same data show that about 40% of COVID deaths in January and February 2022 were fully vaccinated or had boosters. That sounds like the vaccines might not work very well! So what does this all mean? Let’s dig into the data from the CDC a little bit.

The first, and most important thing, to recognize is that most American adults are vaccinated (about 78%), so unless vaccines are 100% effective (and they aren’t, despite some public officials overenthusiastic pronouncements early in the vaccine rollout), there are still going to be a lot of COVID deaths among the vaccinated. If 100% of the population was vaccinated, 100% of the deaths would be among the vaccinated. The key question is whether vaccines lower the chance of death.

And they do. Let’s see why.

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In Praise of the FRED Excel Add-in

Sometimes, large entities have enough money to throw at a problem that by sheer magnitude they produce something great (albeit at too high a cost). The iPhone app from the FRED is not that thing. But the Excel add-in is something that every macroeconomics professor should consider adding to their toolkit.

Personally, I include links to FRED content in the lecture notes that I provide to students. But FRED makes it easy to do so much more. They now have an add-in that makes accessing data *much* faster. With it, professors can demonstrate in excel their transformations that students can easily replicate. The advantage is that students can learn to access and transform their own data rather than relying on links that I provide them.

The tool is easy enough to find – FRED wants you to use it. After that, the installation is largely automatic.

Installed in excel you will see the below new ribbon option. It’s very user friendly.

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Fed Dot Plot vs Markets

After their last meeting in March, the Federal Open Market Committee released the summary of economic projections. Most of the variables they project are inherently difficult to predict: GDP, unemployment, inflation. But their forecasts of the Federal Funds rate should be pretty good, since they’re the ones that get to pick what it will be. The median FOMC member thinks the the Federal Funds rate will be just under 2% by the end of 2022.

I said in my last post that the Fed is under-reacting to inflation. Markets seem to agree, but they also think that the Fed will change. Kalshi runs prediction markets on what the Fed Funds rate will be, which they recently started to summarize using this nice curve:

So traders think that the Fed will raise rates faster than the Fed thinks they will, with rates getting to 2.5% by year end. Traders at the Chicago Mercantile Exchange see an even bigger change, with rates at 2.75% by year end, and 3.5% by July 2023 (the longest-term market they offer).

I lean toward the markets on this one; if they are wrong there is plenty of money to be made by betting so.