Author: James Bailey

Abnormal Times Call for Abnormal Policies

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The Fed made two mistakes during the Great Recession of 2007-2009: being too slow and weak in their initial reaction to the financial crisis, and being too hurried in their attempts to return to a ‘normal’ policy stance. The first mistake turned what could have been a minor road bump into the worst recession in decades, and the second mistake meant it took a full decade from the start of the crisis in 2007 for unemployment to return to pre-crisis levels.

The rapid recovery from the Covid recession shows that the Fed learned from its first mistake in 2007. In 2020, the Fed acted quickly and decisively, so that despite the worst pandemic in a century the US experienced a recession that lasted only months, and it took unemployment barely 2 years to return to pre-Covid levels. But the Fed’s talk about cutting rates this year makes me worry they did not learn the second lesson. Despite all their talk of being “data driven”, I don’t see how a dispassionate look at current inflation, labor market, or financial data could lead them to be considering rate cuts; if anything it currently suggests rate hikes.

Why then is the Fed talking rate cuts? Of course you can dig and find a few data points to support cuts, but I think the driving factor is simply a feeling that interest rates are currently above “normal”. They are digging to find data points to support cuts because they want to return rates to “normal”, just as in the early to mid 2010’s they were digging for reasons to raise rates to “normal”. Rather than being consistently too hawkish or too dovish, they are consistently too eager to return rates to “normal” when circumstances are still abnormal.

This is not simply out of a social and political desire to avoid appearing “weird”, though that is definitely a factor. There is also a long academic tradition of measuring the stance of monetary policy by comparing current interest rates to a neutral, “natural” rate of interest, r*. But this tradition has problems. The “natural” rate of interest is always changing, and at any given time we can’t really know for sure what it is. The current Fed Funds rate may be higher than it has been in recent years, but that doesn’t necessarily mean it is above the current natural rate of interest; the natural rate itself could have risen too. This is why interest rates aren’t a great way to measure the stance of monetary policy. At times Chair Powell himself has made the same point, saying that trying to set policy by comparing to the “natural” rate of interest r* is like “navigating by the stars under cloudy skies”.

Lacking such celestial guidance, I can only hope the Fed will make good on their promise to be data-driven and navigate by the guideposts they can see around them: measures like current inflation and unemployment, or market-based forecasts of such measures.

When Will the Fed Raise Rates?

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Everyone else keeps asking when the Fed will cut rates, and yesterday Chair Powell said they will likely cut this year. Either they are all crazy or I am, because almost every indicator I see indicates we are still above the Fed’s inflation target of 2% and are likely to remain there without some change in policy. Ideally that change would be a tightening of fiscal policy, but since there’s no way Congress substantially cuts the deficit this year, responsibility falls to the Federal Reserve.

Source: https://fiscaldata.treasury.gov/americas-finance-guide/national-deficit/

Lets start with the direct measures of inflation: CPI is up 3.1% from a year ago. The Fed’s preferred measure, PCE, is up 2.4% from a year ago. Core PCE, which is more predictive of where inflation will be going forward, is up 2.8% over the past year. The TIPS spread indicates 2.4% annualized inflation over the next 5 years. The Fed’s own projections say that PCE and Core PCE won’t be back to 2.0% until 2026.

The labor market remains quite tight: the unemployment rate is 3.7%, payroll growth is strong (353,000 in January), and there are still substantially more job openings than there are unemployed workers. The chattering classes underrate this because they are in some of the few sectors, like software and journalism, where layoffs are actually rising. Real GDP growth is strong (3.2% last quarter), and nominal GDP growth is still well above its long-run trend, which is inflationary.

I do see a few contrary indicators: M2 is still down from a year ago (though only 1.4%, and it is up over the past 6 months). The Fed’s balance sheet continues to shrink, though it is still trillions above the pre-Covid level. Productivity rose 3.2% last quarter.

But overall I am still more worried about inflation than about a recession, as I was 6 months ago. Financial conditions have changed dramatically from a year ago, when the discussion was about bank runs and a near-certain recession. Today the financial headlines are about all time highs for Bitcoin, Gold, Japan, and US stocks, with an AI-fueled boom (bubble?) in tech pushing the valuation of a single company, Nvidia, above the combined valuation of the entire Chinese stock market. All of this screams inflation, though it could also indicate a recession in a year or so if the bubble pops.

At least over the past year I think fiscal policy is more responsible than monetary policy for persistent inflation. But I can’t see Congress doing a deficit-reducing grand bargain in an election year; the CBO projects the deficit will continue to run over 5% of GDP. That means our best chance for inflation to hit the target this year is for the Fed to tighten, or at least to not cut rates. If policy continues on its current inflationary path, our main hope is for a deus-ex-machina like a true tech-fueled productivity boom, or deflationary events abroad (recession in China?) lowering prices here.

Pistol Squats Complete the Home Workout

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A good strength workout includes a push, a pull, and legs. When I can get to the gym I like to alternate bench press and incline press for the push; rows and pulldowns for the pull; and squats and deadlifts for the legs. But with a baby to take care of at home, its been hard to find time for the gym. Between driving, waiting for equipment, and the actual lifts, the gym takes an hour. Doing a similar workout at home can take just 10 minutes, and has the advantage that you can watch a baby while doing it.

But the big challenge with home workouts was finding a good leg exercise. Pushes are easy: just do pushups. Pulls are pretty easy: just buy a $15 pullup bar to hang over a door. But how to do a good leg workout without costly barbells and plates that take up lots of space? Enter the pistol squat.

The idea is simply to start from a stand and lower yourself down almost to the ground on a single leg, then come back up on one leg, with the other leg out front for balance:

Source: Snapshot from this video, which shows how to do the standard pistol plus many variations

I find this to be about as difficult as doing a traditional two-legged barbell squat with 1x bodyweight on the bar. The traditional squat has two legs lifting 2x bodyweight (your body itself, plus 1x bodyweight on the bar); the pistol squat has one leg lifting 1x bodyweight (just your body itself), which is about equal. This was perfect for me because I was doing about 3 sets of 5 reps of squats with 1x bodyweight on the bar, so I just do the same number of pistol squats. But what if you’re not exactly at that weight?

Going lighter is easy– just put one hand on something sturdy nearby like a table and lean on it until it takes enough of your weight that you can do the squat. This helps with balance too if that is an issue. Going heavier is harder, but you could carry something heavy in your hands, turn the rise into more of an explosive jump, or just do more reps.

I’d still rather be at the gym, but the complete home workout seems like a good application of the Pareto Principle– you get most of the benefits of the gym while paying only a small fraction of its time and money costs.

The Best Personal Finance Books

James Bailey5 Comments

Last week Scott offered a very negative review of one popular personal finance book, Rich Dad Poor Dad. My own take on the book is less negative, but I still wouldn’t recommend it to most people. That still leaves the question of which personal finance books are worthwhile. I gave my answer back in 2020 in a post on my personal blog. You can read the full reviews there, but I’ll give my short answers here:

I Will Teach You to Be Rich

Despite the title, the book is really about the basics of how to get out of debt, save for retirement, and manage credit. The material is stuff most people will figure out on their own by their 30’s or 40’s, but it’s a nice presentation all in one place and can save people from learning lessons the hard way. Perfect for a college student, someone at their first real job, or someone older who feels like they missed the memo on how all this works. His big idea is that once you set and meet good savings goals, you don’t need to feel guilty about the things you do spend money on.

The Millionaire Next Door

This book is built around surveying millionaires and finding the commonalities in what they did to get wealthy. The core idea is that Americans with millions saved tend to have moderately high incomes but very high savings rates. Even someone with a normal income can become a millionaire- income is different from wealth. The key is to live frugally and let the compound returns on your savings work for you. The original version of the book is inspiring, but has out of date numbers; the author’s daughter recently updated it (The Next Millionaire Next Door) with more current numbers.

There are many more books about how to invest, but for broad takes on personal finance overall these are the best two I have found, and the ones I recommend to students. Still interested to hear your thoughts on more recommendations.

Go East, Young Man

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Americans have moved westward in every decade of our history. But after over 200 years, that trend may finally be ending.

A new report from Bank of America notes that the share of Americans who live in the West has been falling since 2020:

The absolute population of the West is still growing slightly, but the Southeast is growing so quickly that it makes every other region of the country a smaller share by comparison:

I think this has a lot to do with the decline in housing affordability that Jeremy discussed yesterday. Americans always went West for free land, or cheap land, or cheap housing. Or in more recent decades on the Pacific coast, they went for nice weather and good jobs with non-insane housing prices. But now all that is gone, and if anything housing prices are pushing people East.

I see some green shoots of zoning reform with the potential to lower housing costs in the West. But I worry that this is too little too late, and that 2030 will confirm that our long national trek Westward has finally been defeated by our own poor housing policy.

Medicaid Cuts Mean Credit Card Debt

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My paper “Missouri’s Medicaid Contraction and Consumer Financial Outcomes” is now out at the American Journal of Health Economics. It is coauthored by Nate Blascak and Slava Mikhed, researchers at the Federal Reserve Bank of Philadelphia. They noticed that Missouri had done a cut in 2005 that removed about 100,000 people from Medicaid and reduced covered services for the remaining enrollees. Economists have mostly studied Medicaid expansions, which have been more common than cuts; those studying Medicaid cuts have focused on Tennessee’s 2005 dis-enrollments, so we were interested to see if things went differently in Missouri.

In short, we find that after Medicaid is cut, people do more out-of-pocket spending on health care, leading to increases in both credit card borrowing and debt in third-party collections. Our back-of-the-envelope calculations suggest that debt in collections increased by $494 per Medicaid-eligible Missourian, which is actually smaller than has been estimated for the Tennessee cut, and smaller than most estimates of the debt reduction following Medicaid expansions.

We bring some great data to bear on this; I used the restricted version of the Medical Expenditure Panel Survey to estimate what happened to health spending in Missouri compared to neighboring states, and my coauthors used Equifax data on credit outcomes that lets them compare even finer geographies:

The paper is a clear case of modern econometrics at work, in that it is almost painfully thorough. Counting the appendix, the version currently up at AJHE shows 130 pages with 29 tables and 11 figures (many of which are actually made up of 6 sub-figures each). We put a lot of thought into questioning the assumptions behind our difference-in-difference estimation, and into figuring out how best to bootstrap our standard errors given the small number of clusters. Sometimes this feels like overkill but hopefully it means the final results are really solid.

For those who want to read more and can’t access the journal version, an earlier ungated version is here.

Disclaimer: The results and conclusions in this paper are those of the authors and do not indicate concurrence by the Agency for Healthcare Research and Quality or the US Department of Health and Human Services. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Historical State GDP Data

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Data on Gross State Product prior to 2017 has disappeared from the main page of the Bureau of Economic Analysis. It is also gone from some third party hosts like FRED. It turns out BEA is in the middle of revising how they calculate state GDP; they have the new version done back to 2017, and took down the older inconsistent estimates until they can recalculate them. After that, they tell me they will repost pre-2017 state Gross Domestic Product:

In the mean time, they offer some messy and seemingly incomplete versions of pre-2017 GDP here, and you can find 1980-2021 state GDP (along with many other nice variables) in a nice panel from the University of Kentucky Center for Poverty Research’s National Welfare Data.

You can find more details on the actual changes BEA is making to how they calculate GDP here. Most changes seem relatively minor for states, but might have more impact on the measured relative size of industries. For instance, “equity REITs will be reclassified from the funds, trusts, and other financial vehicles industry to the real estate industry, while mortgage REITs will remain classified as funds, trusts, and other financial vehicles”.

Does More Health Spending Buy Better Outcomes for States?

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When you look across countries, it appears that the first $1000 per person per year spent on health buys a lot; spending beyond that buys a little, and eventually nothing. The US spends the most in the world on health care, but doesn’t appear to get much for it. A classic story of diminishing returns:

Source: https://twitter.com/MaxCRoser/status/810077744075866112/photo/1

This might tempt you to go full Robin Hanson and say the US should spend dramatically less on health care. But when you look at the same measures across US states, it seems like health care spending helps after all:

Source: My calculations from 2019 IHME Life Expectancy and 2019 KFF Health Spending Per Capita

Last week though, I showed how health spending across states looks a lot different if we measure it as a share of GDP instead of in dollars per capita. When measured this way, the correlation of health spending and life expectancy turns sharply negative:

Source: My calculations from 2019 IHME life expectancy, Gross State Product, and NHEA provider spending

Does this mean states should be drastically cutting health care spending? Not necessarily; as we saw before, states spending more dollars per person on health is associated with longer lives. States having a high share of health spending does seem to be bad, but this is more because it means the rest of their economy is too small, rather than health care being too big. Having a larger GDP per capita doesn’t just mean people are materially better off, it also predicts longer life expectancy:

Source: My calculations from 2019 IHME life expectancy and 2019 Gross State Product

As you can see, higher GDP per capita predicts longer lives even more strongly than higher health spending per capita. Here’s what happens when we put them into a horse race in the same regression:

The effect of health spending goes negative and insignificant, while GDP per capita remains positive and strongly significant. The coefficient looks small because it is measured in dollars, but what it means is that a $10,000 increase in GDP per capita in a state is associated with 1.13 years more life expectancy.

My guess is that the correlation of GDP and life expectancy across states is real but mostly not caused by GDP itself; rather, various 3rd factors cause both. I think the lack of effect of health spending across states is real, between diminishing returns to spending and the fact that health is mostly not about health care. Perhaps Robin Hanson is right after all to suggest cutting medicine in half.

Where is Health Care The Biggest Part of the Economy?

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State health care spending usually gets reported in terms of dollars per capita, leading to maps like this that show Alaska as the highest-spending state and Utah as the lowest:

Source: https://www.kff.org/other/state-indicator/health-spending-per-capita/

But states differ greatly in how rich they are and how much they have to spend. I wanted to know the states where health care takes up the largest and smallest share of the economy, so I got the data:

Health Care Spending as Share of State Gross Domestic Product in 2019:

Source: I divided 2019 National Health Expenditure Provider data on total health spending by 2019 Gross State Product data.

You can see that health spending as a share of GDP looks pretty different from health spending in raw dollars. We’ve gone from a high-spending North and low-spending South to more of a mix. Health spending is now highest in West Virginia, where it makes up more than a fourth of the economy; and lowest in Washington State and Washington D.C., where it makes up less than one ninth of the economy.

The biggest change when considering things this way is in Washington D.C., which has the highest spending in $ terms but the lowest as a share of GDP because it has an enormous GDP per capita. Many other states that spend a lot in $ also fall a lot in the rankings due to high GDP per capita, including Alaska, New York, and Massachusetts. The states that rise the most in this ranking are poor states like Arkansas, Alabama, and Mississippi. Mississippi rises the most, gaining 37 spots in the rankings of highest-spending states when we go from $ per capita to share of GDP.

I share the data here so you can do your own comparisons:

Continue reading →

Industries Without Investors

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Venture-capital backed startups almost all cluster in the same handful of industries, mostly various types of software. This leaves a variety of large and economically important sectors with almost no venture-capital backed startups. That means those industries see fewer new companies and new ideas; they must rely on either growth from existing firms, which are unlikely to embrace disruptive innovation, or on startups that bootstrap and/or finance with debt, which tend to grow slowly.

Venture capital firm Fifty Years has done a nice job cataloging exactly which industries see the most, and least, investment relative to their size. Here is their picture of the US economy by industry market size:

Now their picture of which industries get the investment (though unfortunately, they aren’t very clear about their data source for it):

They use this to create an “Opportunity Ratio”- current market size divided by current startup funding:

They call the industries with the largest Opportunity Ratios the “Top Underfunded Opportunities”:

I don’t necessarily agree; some industries face shrinking demand, prohibitive regulation, or other fundamental issues making them bad candidates for investment. Conversely, investors haven’t just focused on software randomly or through imitation; they see that it is where the growth is.

Still, herding by investors is real, and I always like the strategy of finding a new game instead of trying to win at the most competitive games, so I do think there is something to the idea of investing in an unsexy industry like paper. Growing up in Maine and watching one paper mill after another close, I always wondered how they managed to lose money in a state that is 90% trees, and whether anyone could find a way to reverse the trend. Perhaps related technology like mass timber or biochar will be the way to take advantage of cheap lumber.

Thanks again to Fifty Years for releasing the data.