Author: James Bailey

Monetary and Fiscal Policy Is Still Easy

James Bailey4 Comments

The last post where I attempted a macro prescription was in April 2022, when I said the Fed was still under-reacting to inflation. That turned out right; since then the Fed has raised rates a full 500 basis points (5 percentage points) to fight inflation. So I’ll try my luck again here.

Headline annual CPI inflation has fallen from its high of 9% at the peak last year to 3.7% today. Core PCE, the measure more closely watched by the Fed, is at a similar 3.9%. Way better than last year, but still well above the Fed’s target of 2%. Are these set to fall to 2% on the current policy path, or does the Fed still need to do more?

The Fed’s own projections suggest one more rate hike this year, followed by cuts next year. They expect inflation to remain a bit elevated next year (2.5%), and that it will take until 2026 to get all the way back to 2.0%. They expect steady GDP growth with no recession.

What do market-based indicators say? The yield curve is still inverted (usually a signal of recession), though long rates are rising rapidly. The TIPS spread suggests an average inflation rate of 2.18% of the next 5 years, indicating a belief the Fed will get inflation under control fairly quickly. Markets suggest the Fed might not raise rates any more this year, and that if they do it will only be once. All this suggests that the Fed is doing fine, and that a potential recession is a bigger worry than inflation.

Some of my other favorite indicators muddy this picture. The NGDP gap suggests things are running way too hot:

M2 shrank in the last month of data, but has mostly leveled off since May, whereas a year ago it seemed like it could be in for a major drop. I wonder if the Fed’s intervention to stop a banking crisis in the Spring caused this. Judging by the Fed’s balance sheet, their buying in March undid 6 months of tightening, and I think that underestimates its impact (banks will behave more aggressively knowing they could bring their long term Treasuries to the Fed at par, but for the most part they won’t have to actually take the Fed up on the offer).

The level of M2 is still well above its pre-Covid trend:

Before I started looking at all this data, I was getting worried about a recession. Financial markets are down, high rates might start causing more things to break, the UAW strike drags on, student loan repayments are starting, one government shutdown was averted but another one in November seems likely. After looking at the data though, I think inflation is still the bigger worry. People think that monetary policy is tight because interest rates have risen rapidly, but interest rates alone don’t tell you the stance of policy.

I’ll repeat the exercise with the Bernanke version of the Taylor Rule I did in April 2022. Back then, the Fed Funds rate was under 0.5% when the Taylor Rule suggested it should be at 9%- so policy was way too loose. Today, the Taylor Rule (using core PCE and the Fed’s estimate of the output gap) suggests:

3.9% + 0.5*(2.1%-1.8%) + 0.5%*(3.9%-2%) + 2% = 7%

This suggests the Fed is still over 1.5% below where they need to be. Much better than being 9% below like last April, but not good. The Taylor rule isn’t perfect- among other issues it is backward-looking- but it tends to be at least directionally right and I think that’s the case here. Monetary policy is still too easy. Fiscal policy is still way too easy. If current policy continues and we don’t get huge supply shocks, I think a mild “inflationary boom” is more likely than either stagflation or a deflationary recession.

New Center for the Restoration of Economic Data

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Regular readers will know that we love not only economics, but also history and data. We especially love it when “data heroes” take data that was difficult or impossible to access and make it easily available to everyone. The Federal Reserve Bank of Philadelphia just announced a project that brings together all of these things we love, their new Center for the Restoration of Economic Data:

Our mission is to advance research in topics related to regional economics and consumer finance by making economic data available in readily accessible, digital form. CREED combines state-of-the-art machine learning technology with deep subject matter expertise to convert natively unstructured data (information in books, images, and other undigitized formats) into readily accessible digital data.

The CREED research team shares the original analog or unstructured data as well as the code used to recover and clean these data, which are aggregated for use in novel economic research. Our collection features volumes of old, often overlooked, and frequently inaccessible data, which have been mined, restored, and converted into unstructured digital and analytically usable formats.

Their first project is to map all of the racially restrictive covenants in the city of Philadelphia. Until the U.S. Supreme Court declared such covenants to be unenforceable in 1948, they often barred properties from being sold to non-whites or non-citizens. After 1948 redlining took different forms, some of which may still persist today.

CREED shares the underlying data used to build the map here, and they say much more is one the way. I love it when economic historians (and regular historians) digitize old paper records and share the resulting data, and hope to see more examples like this to share in the coming years.

Disclaimer: I am a visiting scholar at the Federal Reserve Bank of Philadelphia but I was not involved with this project

OpenAI wants you to fool their AI

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OpenAI created the popular Dall-E and ChatGPT AI models. They try to make their models “safe”, but many people make a hobby of breaking through any restrictions and getting ChatGPT to say things its not supposed to:

Source: Zack Witten

Now trying to fool OpenAI models can be more than a hobby. OpenAI just announced a call for experts to “Red Team” their models. They have already been doing all sorts of interesting adversarial tests internally:

Now they want all sorts of external experts to give it a try, including economists:

This seems like a good opportunity to me, both to work on important cutting-edge technology, and to at least arguably make AI safer for humanity. For a long time it seemed like you had to be a top-tier mathematician or machine learning programmer to have any chance of contributing to AI safety, but the field is now broadening dramatically as capable models start to be deployed widely. I plan to apply if I find any time to spare, perhaps some of you will too.

The models definitely still need work- this is what I got after prompting Dall-E 2 for “A poster saying “OpenAI wants you…. to fool their models” in the style of “Uncle Sam Wants You””

Bond King Doesn’t Like Bonds

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Bill Gross grew PIMCO into a trillion dollar company by trading bonds, earning the epithet “Bond King“. But in an interview with Odd Lots this week, he disclaims both bonds and his title. He wasn’t the king:

My reputation as a bond king was first of all made by Fortune. They printed a four page article with me standing on my head doing yoga, and I was supposedly the bond king, and that was good because it sold tickets. But I never really believed it. The minute you start believing it, you’re cooked.

Who is the real bond king? The Fed:

The bond kings and queens now are are at the Fed. They rule, they determine for the most part which way interest rates are going.

Who still isn’t the bond king? Any other trader, especially Jeff Gundlach:

To be a bond king or a queen, you need a kingdom, you need a kingdom. Okay, Pimco had two trillion dollars. Okay, DoubleLine’s got like fifty five billion. Come on, come on, that’s no kingdom. That’s like Latvia or Estonia whatever. Okay, and then then look at his record for the last five, six, seven years. How does sixtieth percentile smack of a bond king? It doesn’t.

Why he doesn’t believe in long-term bonds right now:

We have a deficit of close to two trillion. The outstanding treasury market is about 33 trillion… about thirty percent of the existing outstanding treasuries, so ten trillion have to be rolled over in the next twelve months, including the two trillion that’s new. So that’s that’s twelve trillion dollars. Where the treasuries that have to be financed over the next twelve months, and who’s going to buy them at these levels? Well, some people are buying them, but it just seems to be a lot of money. And when you when you add on to that, Powell is doing quantitative tightening, as you know, and that theoretically is a trillion dollars worth of added supply, I guess. And so it just seems like a very dangerous time based on supply, even if inflation does comedown.

By revealed preference I agree with Gross, in that I don’t own any long-term bonds. Their yields are way up from 2 years ago, making them somewhat tempting, but I can get higher yields on short-term bonds, some savings accounts, and some stocks. So I see no reason to go long term, especially given the factors Gross highlights. If he’s right, better long-term yields will be here in a year or two. If he turns out to be wrong, I think it would be because of a severe recession here or in another major economy, but I don’t expect that. So what is Gross buying instead of bonds? He likes the idea of real estate:

 All all my buddies at the country club are in real estate, and they’ve never paid a tax in their life…. I’ve paid a lot of taxes.

He landed on Master Limited Partnerships, common in the energy sector, as an easier way to avoid taxes, and has 40% of his wealth there. Those are yielding more like 9% and have the tax benefits, though they are risker than treasury bonds. The rest of his portfolio he implies is in stocks, describing some merger arbitrage opportunities. I am a bit tempted by bonds because they’ve done so badly recently (and so have gotten much cheaper), but like Gross I think we’re still not to the bottom.

Cool the Schools

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Short post today because I’m busy watching my kids, who had their school canceled because of excessive heat, like many schools in Rhode Island today.

I thought this was a ridiculous decision until my son told me he heard from his teacher that his elementary school is the only one in town that has air conditioning for every classroom. Given that, the decision to cancel given the circumstances is at least reasonable, but the lack of AC is not.

It’s not just that hot classrooms are unpleasant for students and staff, or that sudden cancellations like this are a major burden for parents. Several economics papers have found that air conditioning significantly improves students’ learning as measured by test scores (though some find not). Park et al. (2020 AEJ: EP) find that:

Student fixed effects models using 10 million students who retook the PSATs show that hotter school days in the years before the test was taken reduce scores, with extreme heat being particularly damaging. Weekend and summer temperatures have little impact, suggesting heat directly disrupts learning time. New nationwide, school-level measures of air conditioning penetration suggest patterns consistent with such infrastructure largely offsetting heat’s effects. Without air conditioning, a 1°F hotter school year reduces that year’s learning by 1 percent.

This can actually be a bigger issue in somewhat Northern places like Rhode Island- we’re South enough to get some quite hot days, but North enough that AC is not ubiquitous. Data from the Park paper shows that New York and New England are actually some of the worst places for hot schools:

This is because of the lack of AC in the North:

The days are only getting hotter…. it’s time to cool the schools.

Long Covid is Real in the Claims Data… But so is “Early Covid”?

James Bailey4 Comments

I’ve seen plenty of investigations of “Long Covid” based on surveys (ask people about their symptoms) or labs (x-ray the lungs, test the blood). But I just ran across a paper that uses insurance claims data instead, to test what happens to people’s use of medical care and their health spending in the months following a Covid diagnosis. The authors create some nice graphics showing that Long Covid is real and significant, in the sense that on average people use more health care for at least 6 months post-Covid compared to their pre-Covid baseline:

Source: Figure 5 of “Long-haul COVID: healthcare utilization and medical expenditures 6 months post-diagnosis“, BMC Health Services Research 2022, by Antonios M. Koumpias, David Schwartzman & Owen Fleming

The graph is a bit odd in that its scales health spending relative to the month after people are diagnosed with Covid. Their spending that month is obviously high, so every other month winds up being negative, meaning just that they spent less than the month they had Covid. But the key is, how much less? At baseline 6 months prior it was over $1000/month less. The second month after the Covid diagnosis it was about $800 less- a big drop from the Covid month but still spending $200+/month more than baseline. Each month afterwards the “recovery” continues but even by month 6 its not quite back to baseline. I’m not posting it because it looks the same, but Figure 4 of the paper shows the same pattern for usage of health care services. By these measures, Long Covid is both statistically and economically significant and it can last at least 6 months, though worried people should know that it tends to get better each month.

I was somewhat surprised at the size of this “post Covid” effect, but much more surprised at the size of the “pre Covid” or “early Covid” effect- the run-up in spending in the months before a Covid diagnosis. For the month immediately before, the authors have a good explanation, the same one I had thought of- people are often sick with Covid a couple days before they get tested and diagnosed:

There is a lead-up of healthcare utilization to the diagnosis date as illustrated by the relatively high utilization levels 30–1 days before diagnosis. This may be attributed to healthcare visits only days prior to the lab-confirmed infection to assess symptoms before the manifestation or clinical detection of COVID-19.

But what about the second month prior to diagnosis? People are spending almost $150/month more than at the 6-month-prior baseline and it is clearly statistically significant (confidence intervals of months t-6 and t-2 don’t overlap). The authors appear not to discuss this at all in the paper, but to me ignoring this lead-up is burying the lede. What is going on here that looks like “Early Covid”?

My guess is that people were getting sick with other conditions, and something about those illnesses (weakened immune system, more time in hospitals near Covid patients) made them more likely to catch Covid. But I’d love to hear actual evidence about this or other theories. The authors, or someone else using the same data, could test whether the types of health care people are using more of 2 months pre-diagnosis are different from the ones they use more of 2 months post-diagnosis. Doctors could weigh in on the immunological plausibility of the “weakened immune system” idea. Researchers could test whether they see similar pre-trends / “Early Covid” in other claims/utilization data; probably they have but if these pre-trends hold up they seem worthy of a full paper.

The Least Terrible Car Safety Sites

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I’m looking for a new car now and would like to know what the safest reasonable option is. There are lots of ways to get some information about this, but none are very good.

The government provides safety ratings based on crash tests they perform. This is better than nothing but the crash tests only test certain things and don’t necessarily tell you how a car performs in the real world. They also have a habit of just giving their top rating (5 stars) to tons of vehicles so it doesn’t help you pick between them, and they only compare cars to other cars in the same “class”, ignoring that some classes are safer than others. On top of all the problems with the ratings themselves, they also don’t provide any lists of their ratings, instead making you search one car at a time.

Several other sites improve on the government ratings by using real-world data on how often cars actually crash (much of which comes from the government, which as usual is great at collecting data but not so great at presenting it in helpful user-friendly ways). The Auto Professor grades cars using real-world data but otherwise has the same problems as the government (NHTSA) site. Cars get letter grades rather than a rank or meaningful number, so it’s not actually clear which car is best, or how much better the good cars are than the average or bad cars. You can search the grades for one car at a time but they don’t just list the safest cars anywhere, including on their page labelled “safest cars list“.

The Insurance Institute for Highway Safety uses real world data and provides actual numbers of fatality rates for different vehicles. This is great because you don’t have the problem of “dozens of cars all have 5-star / A, which is best?” or the problem of “how much better is 5 star than 4 star, or A than B?”. But they don’t include data from the 2 most recent years, and they only post their ratings for a handful of cars. Not only do they not present a complete list, they seem to have no search function whatsoever for their real-world data (they do for their NHSTA-style crash test data). Some 3rd party sites seem to have posted more complete versions of their data, but it still doesn’t show data for most car models.

The least-terrible car safety site I have found is Real Safe Cars. The good: they use real-world safety data, they apply reasonable-sounding corrections and controls do it, they present meaningful quantitative measures like “vehicle lifetime fatality chance” and “vehicle lifetime injury chance”, and they present the data using both a search function and lists of “safest vehicles”. For 2020 you can see that the #1 car, the 2020 Audi e-tron Sportback, has a vehicle lifetime fatality chance of 0.0158%. Compare this to the #100 car, which is about average overall- the 2020 Acura TLX has a vehicle lifetime fatality chance of 0.0435% (almost 3x the safest). The site makes it hard to find the very worst car but near the bottom is the 2020 Hyundai Accent, which “has a vehicle lifetime fatality chance of 0.0744%”.

The lists could be better; the only list that includes all vehicle classes is restricted to only 2020 makes. Meanwhile when you search a car it ranks it only relative to cars in the same year, though you can make comparisons across years yourself using the quantitative “fatality chance” and “injury chance” measures. I’m not totally convinced of the ratings themselves, given how well many smaller sedans do. Their front page explains how taller cars are generally safer, but also lists the Mini Cooper as the #18 safest car of 2020 across all classes. But Real Safe Cars seems like the current best site to me (maybe I’m biased since one of its creators is an economics professor).

I hope these sites will address some of the weaknesses I identified here, though I’m not optimistic about most of them, because other than Real Safe Cars the “bad” decisions seem to be clearly driven by incentives like keeping car companies happy or SEO.

I also think there’s still room for another effort by economists or other quantitatively-skilled people to make another site. The underlying crash data is public and the statistical problems are not especially hard; I think a single economist could run the numbers in about the time it takes to write a typical economics paper (weeks to months for a 1st draft), and a decent website could be built off that quickly as well. You could probably make a decent amount of money off the site, though perhaps not if you do the right thing and publicly post all the data and code. Posting the data would make it easy for others to copy you and make their own sites. You could fight that with copyright, but given the huge public good aspect here and the lives at stake it might make more sense to get grant funding up front and then make the data and code totally public. A sane world would have done this already; NHTSA’s annual budget is over $1 billion, with $35 million of that going to research and analysis. I think any decent funder should be able to do at least as well as the sites above with under $200k, or anyone with good data chops could do it out of the goodness of their heart in a few months. I don’t have a few months right now but perhaps one of you could take this up or start applying for grants to do it.

For everyone who just wants to know about which cars are safe, for now I think Real Safe Cars is the best bet, though I’d also like to hear if you think I missed anything.

US Stocks Are Expensive, These Countries Are Not

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While we have stepped back from the meme stock craziness of 2021, US stocks remain quite expensive by historical standards, with our Cyclically Adjusted Price to Earnings (CAPE) ratio at almost twice its long-run average:

Source

Even at a high price, US stocks could still be worth it, and I certainly hold plenty. But I also think it it a good time to consider the alternatives. US Treasury bond yields are the highest they’ve been since 2007. But there are also many countries where stocks are dramatically cheaper than the US- and not just high-risk basket-cases, but stable “investable” countries.

There are several reasonable ways to measure what counts as “expensive” for stocks in addition to the CAPE ratio I mention above. The Idea Farm averages out four such measures to determine how expensive different “investable” (large, stable) country stock markets are. Here is their latest update:

MSCI Investable Market Indices:

Source: The Idea Farm Global Valuation Update

You can see that US stocks are expensive not only relative to our own history, but also relative to other countries, lagging only India and Denmark. That means that much of the world looks like a relative bargain, with the cheapest countries being Colombia, Poland, Chile, Czech Republic, and Brazil.

Of course, sometimes stocks, just like regular goods and services, are cheap for a reason: they just aren’t that good. They might be cheap because investors expect slow growth, or a recession, or political risk. But if you don’t share these expectations about a cheap stock (or country), that’s when to really take a look. I certainly did well buying Poland after I saw they were the cheapest in last year’s global valuation update and thought there was no good reason for them to stay that cheap.

I like that the chart above provides a simple ranking of investable markets. But if you wish it included more valuation measures, or small frontier markets, you can find that from Aswath Damodaran here. Some day I hope to provide a data-based, rather than vibes-based, analysis of which countries are “cheap/expensive for a reason” vs “cheap/expensive for no good reason”, featuring measures like industry composition, population growth, predictors of economic growth, and economic freedom. For now you just get my uninformed impression that Poland and Colombia seem like fine countries to me.

Disclosure: I’m long stocks or indices in several countries mentioned, including EPOL, FRDM, PBR.A, CIB, and SMIN. Not investment advice.

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Comprehensive Cancer Centers: Expensive But Fast

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An article I coauthored, “Comparing hospital costs and length of stay for cancer patients in New York State Comprehensive Cancer Centers versus nondesignated academic centers and community hospitals“, was just published in Health Services Research. We find that:

Inpatient costs were 27% higher (95% CI 0.252, 0.285), but length of stay was 12% shorter (95% CI −0.131, −0.100), in Comprehensive Cancer Centers relative to community hospitals.

In other words, these cutting-edge hospitals that tend to treat complex cases are more expensive, as you would expect; but despite getting tough cases they actually manage a shorter average length of stay. We can’t nail down the mechanism for this but our guess is that they simply provide higher-quality care and make fewer errors, which lets people get well faster.

What are Comprehensive Cancer Centers? Here’s what the National Cancer Institute says:

The NCI Cancer Centers Program was created as part of the National Cancer Act of 1971 and is one of the anchors of the nation’s cancer research effort. Through this program, NCI recognizes centers around the country that meet rigorous standards for transdisciplinary, state-of-the-art research focused on developing new and better approaches to preventing, diagnosing, and treating cancer.

Our paper focuses on New York state because of their excellent data, the New York State Statewide Planning and Research Cooperative System Hospital Inpatient Discharges dataset, which lets us track essentially all hospital patients in the state:

We use data on patient demographics, total treatment costs, and lengths of stay for patients discharged from New York hospitals with cancer-related diagnoses between 2017 and 2019.

You know I’m all about sharing data; you can find our data and code for the paper on my OSF page here.

My coauthor on this paper is Ryan Fodero, who wrote the initial draft of this paper in my Economics Senior Capstone class last Fall. He is deservedly first author- he had the idea, found the data, and wrote the first draft; I just offered comments, cleaned things up for publication, and dealt with the journal. I’ve published with undergraduates several times before but this is the first time I’ve seen one of my undergrads hit anything close to a top field journal. You can find a profile of Ryan here; I suspect it won’t be the last you hear of him.

Knowing When To Sell: Portfolio Review

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90 plus per cent of people, they spend all their time on the buy decision and then they figure it out as they go along on when to sell and we say that’s crazy. You need to establish sell criteria, even if it’s just rebalance, even if it’s a trailing stop, whatever it may be on all your public market positions, because otherwise it gets emotional and that creates huge problems.

Meb Faber

Last week I explained why I buy individual stocks. This week I’ll share how I think about when to sell individual stocks, as I go through my portfolio and decide what to hold and what to sell. This is the first time I’m doing this exercise, though I should have done it long ago; until now I’ve unfortunately been on the wrong side of the above Meb Faber quote.

I actually think that most people are correct not to put much thought into what to sell, because I still agree with Buffett and most economists that most people should just buy and hold diversified index funds. Thinking about selling too much might lead people to sell everything whenever they get worried, sit in cash, and miss out on years of gains. But the important truth in Faber’s point is that if you are buying stocks or active funds for any reason other than “its a great company/idea that I’d like to hold indefinitely”, it makes sense to put as much thought into when/whether to sell as when/whether to buy.

People buy stocks all the time based on short-term arguments like “this banking crisis is overblown”, or “I think the Fed is about to cut rates”, or “this IPO is going to pop”, or “I think the company will beat earnings expectations this quarter”. These might be good or bad arguments to buy but they are all arguments about why it makes sense to hold a certain stock for weeks or months, not for years or indefinitely.

But people often buy a stock for short-term reasons like these, then hold on to it long term- either out of inertia, or because they grow attached to it, or because it lost money and they want to hold until it “makes it back” (sunk cost fallacy). None of these reasons really make sense; they might work out because buying and holding often does, but at that point you might as well be in index funds. If you’re going to be actively trading based on ideas, it makes sense to sell once you know whether your idea worked or not (e.g., did the company you thought would beat earnings actually do it) to free up capital for the next idea (unless you genuinely have a good new idea about the same stock, or you think it makes sense to hold onto it a full year to hit long-term capital gains tax). Its also always fair to fight status quo bias and ask “would I buy this today if I didn’t already own it?” (especially if its in a non-taxable account).

Maybe this is obvious to you all, and writing it out it sounds obvious to me, but until now I haven’t actually done this. For instance, I bought Coinbase stock at their IPO because I thought it would trade up given the then-ongoing crypto / meme stock mania. I was correct in that the $250 IPO started trading over $300 immediately; but then I just held on for years while it fell, fell, fell to below $100. The key difference I’m trying to get at here is the one between ideas and execution: its not that I thought Coinbase had such good fundamentals that it was a good long term buy at $250 and my idea was wrong; instead I had a correct short-term idea of what would happen after the IPO, but incorrectly executed it as if it were a long-term idea (mostly through inertia, not paying attention, and not putting in an immediate limit sell order at a target price after buying).

So if you buy stocks for short- or medium-term reasons, it makes sense to periodically think about which to sell. I’ll show how I I think about this by going through some examples from my own current portfolio below (after the jump because I think the general point above is much more important that my thinking on any specific stock, which by the way is definitely not investment advice):

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