Human Capital is Socially Contingent

The Deaf community is interesting.

Before I did research, I thought that deaf people simply could not hear. After seeing the Spiderman episodes that featured Daredevil, I believed that it was plausible and likely that deaf people had some sort of cognitive or sensory compensatory skill.

But it wasn’t until recently that I learned of the Deaf Studies field. There is an entire field that’s dedicated to studying deaf people. It’s related to, but not the same as Disability Studies. In fact, there are some sharp divisions between the two fields.

Continue reading

Why Many Substance Use Treatment Facilities Don’t Take Insurance

According to the latest data, about one in four facilities doesn’t accept private insurance or Medicaid, and more than half don’t accept Medicare. This makes substance use treatment something of an outlier, since 91% of all US health spending is paid for through insurance. Still, there are many reasons to prefer being paid in cash: insurance might reimburse at low rates, impose administrative hassles, and generally try to tell you how to run things.

Providers generally put up with the hassles of insurance because they see the alternative as not getting paid. But if demand for their services gets high enough that they can stay busy with patients paying cash, they will often try going cash-only. Some try to generate high demand by providing excellent service. Sometimes high demand comes from a growing health crisis, as with opioids.

Demand can also be high relative to supply because supply is restricted. US health care is full of supply restrictions, but in this case I wondered if Certificate of Need laws were playing a role. As we’ve written about previously, CON laws require health care providers in 34 states to get the permission of a government board to certify their “economic necessity” before they can open or expand. But there’s a lot of variation from state to state in what types of services are covered by this requirement; acute hospital beds and long-term care beds are most common. 23 states require substance use treatment facilities to obtain a CON before opening or expanding.

States with Substance Use–Treatment CON Laws in 2020. Created using data from Mitchell, Philpot, and McBirney

How do these laws affect substance use treatment? We didn’t really know- only one academic article had studied substance use CON, finding it led to fewer facilities in CON states. But I’ve studied other types of CON, so I joined forces with Cornell substance use researcher Thanh Lu and my student Patrick Vogt to investigate. The resulting article, “Certificate-of-need laws and substance use treatment“, was just published at Substance Abuse Treatment, Prevention, and Policy. Here’s the quick summary:

We find that CON laws have no statistically significant effect on the number of facilities, beds, or clients and no significant effect on the acceptance of Medicare. However, they reduce the acceptance of private insurance by a statistically significant 6.0%.

Overall I was surprised that CON didn’t significantly affect most of the outcomes we looked at, and appears to be far from the main reason that treatment facilities don’t take insurance. Still, repealing substance use CON would be a simple way to improve access to substance use treatment, particularly since CON doesn’t appear to bring much in the way of offsetting benefits.

Going forward I aim to investigate how these laws affect health outcomes like overdose rates, and to dig more into the text of state laws and regulations to determine exactly what is covered by substance use CON in different states. As the article explains, we identified several errors in the official data sources we were using. This makes me worry there are more errors we didn’t catch, and there are certainly things the sources just don’t specify, like in which states the laws apply to outpatient facilities. So I hope we (or someone else) will have even better work to share in the future, but for now this article is as good as it gets, and we share our data here.

Crypto Drama: $40 Billon Vaporized as Terra “Stablecoin” and Luna Implode; Bored Ape NFTs Break Ethereum

Last month I posted on “The Different Classes of Crypto Stablecoins and Why It Matters “.  The main point there is that some so-called stablecoins (e.g., USDC) maintain their peg to the dollar by holding a dollars’ worth of securities (preferably U.S. treasury notes) for each dollar’s worth of stablecoin. This mechanism requires some centralized issuer to administer it. As long as said issuer is honest and transparent, this should work fine.

Crypto purists, however, prefer decentralized finance (de-fi), where there is no central controlling authority. Hence, clever folks have devised stablecoins which maintain their dollar peg through some settled algorithm which operates more or less autonomously out on the web; various other coins or assets are automatically bought or sold, or created/destroyed in order to keep the main stablecoin value more or less fixed versus the dollar. We warned that this type of stablecoin is “potentially problematic”; it is the sort of thing which works until it doesn’t.

In 2018 the Terra project was launched by Do Kwon and others.  The Terra stablecoin (UST) was designed to “maintain its peg through a complex model called a ‘burn and mint equilibrium’. This method uses a two-token system, whereby one token is supposed to remain stable (UST) while the other token (LUNA) is meant to absorb volatility.” Terra grew very rapidly, to become something like the fourth largest stablecoin at over $30 billion in capital value. As the supply of Terra increased, the market value for LUNA also increased. Many investors bought into LUNA and for a while were making big bucks as its value soared. A headline from February read, “LUNA shines with a 75% surge in February as $2.57 billion is delisted.”  Woo-hoo! And this headline from May 10  proclaimed, “Terra Ecosystem is the strongest growing ecosystem in 2021.”

However, just as that laudatory article was hitting the internet, Terra/Luna blew up. I am not clear on the exact sequence of events, especially on whether the catastrophe was a result of just some accidental market fluctuation or of deliberate dumping by some party who was positioned to benefit. In any event, the value of Terra quickly dropped from $1.00 to around $0.61, which triggered the issuing of vast amounts of LUNA, which cratered its value by some 98%. Since Luna was mainly what backed Terra, this was a positive feedback death spiral. This is same way the $2 billion IRON stablecoin imploded in June, 2021: a “stablecoin” was backed by an in-house crypto token whose value depended on more people buying into the system. Ponzi scheme, anyone?

Both Terra and LUNA got delisted from major exchanges for several days. As of today, the value of Terra (UST) is about ten cents.  Poof, there went some $40 billion  of investors’ money, just like that. Do Kwon is under police protection in Seoul after a man who lost $2.3 million in Terra/Luna tried to break into his home to demand an apology.

And this just in today: “DEI becomes latest algorithmic stablecoin to lose $1 peg, falling under 70 cents  “. Ouch. Looks like the federal regulators will be swarming the stablecoin space, or at least we may get some grandstanding Senate hearings out of it.

In other news, transactions connected to the insanely (I chose that word deliberately) popular and costly Bored Ape Yacht Club NFTs overwhelmed the Ethereum transaction network about two weeks ago; this is kind of a big deal because a whole lot of de-fi and other blockchain applications depend on Ethereum as the backbone of their transactions:

When Bored Ape Yacht Club creators Yuga Labs announced its Otherside NFT collection would launch on April 30, it was predicted by many to be the biggest NFT launch ever. Otherside is an upcoming Bored Ape Yacht Club metaverse game, and the NFTs in question were deeds for land in that virtual world. Buoyed by the BAYC’s success — it costs about $300,000 to buy into the Club — the sale of 55,000 land plots netted Yuga Labs around $320 million in three hours.

It also broke Ethereum for three hours.

Users paid thousands of dollars in transaction fees, regardless of whether those transactions succeeded. Because the launch put load on the entire blockchain, crypto traders were unable to buy, sell or send coins for hours. The sale highlights the growing profitability of the NFT market but also the uncertainty around whether blockchains are robust enough to handle the attention.

… Because the Otherside mint impacts the whole Ethereum blockchain, people doing completely unrelated things like selling ether or trading altcoins would also have to pay huge fees and wait hours for their transactions to clear. Someone tweeted a picture of them trying to send $100 in crypto from one wallet to another, showing it required $1,700 in gas fees.

Children Are Not 3/55ths of a Person

In the past several years there has been increasing salience and support of pronatalist policies. Several people have turned to the IRS income tax code, which already includes some incentives regarding children. The Child Tax Credit (CTC), which lowers a person’s tax liability on a dollar-per-dollar basis, is the most obvious item that addresses children. The other tax credit is for child care expenses, but I won’t be focusing on that here.

Below are the 2021 marginal tax rate brackets and the standard deductions.  The standard deduction reduces the taxable income, and then the tax rates are applied.

After the tax liability is calculated, it’s reduced by any tax credits, such as the CTC. In 2021, households earned a credit of $3,600 for every child under 6 years old and $3,000 for every child under 18 years old.  Median household income in 2020 was $67,521.  That means that the tax liability was reduced by 5.3% – or 3/55ths – of median gross income. But, I have a problem with that.

Continue reading

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!

Continue reading

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.

Continue reading

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?

Continue reading

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.