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”.
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:
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.
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:
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:
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:
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.
Is this the year the world’s largest economics conference settles into its new normal? ASSA 2024 starts in San Antonio today.
Like most conferences, the Allied Social Science Association took a big hit during the pandemic. Unlike most other conferences, a big fraction of this hit appears to be permanent. Part of what made ASSA so popular was that it was the site of most 1st-round job interviews for economists, but the pandemic made this shift to remote interviews. The American Economic Association decided the job market was better that way, so they made the arrangement permanent.
This shrunk their conference by about half compared to pre-2020; overall I thought it was still fine last year, but that the transition creates a problem:
The big problem with attendance falling to 6k is that they’ve planned years worth of meetings with the assumption of 12k+ attendance. Getting one year further from Covid and dropping mask and vaccine mandates might help some, but the core issue is that 1st-round job interviews have gone remote and aren’t coming back. The best solution I can think of is raising the acceptance rate for papers, which in recent history has been well under 20%.
I suspect the AEA is starting to take my advice. Acceptance rates ticked up slightly in 2023 (from 7% to 9% for individual papers, and from 16% to 30% for complete sessions). They have yet to release full information on acceptance rates this year, but my own experience indicates that this summer they realized they had a problem. I got a rejection email in July that said:
We were able to accept less than one third of the more than 1,150 submissions for paper or poster sessions.
This was followed by something I’ve never seen from an economics conference before- a rejection of the rejection:
You have probably already received an email saying your paper which you submitted for the American Economic Association program at the meeting in San Antonio, TX in January 2024 was not accepted. However, the AEA has decided to select a few more papers for the poster session.
I am pleased to inform you that your paper entitled
Certificate of Need and Self-Employment
which you submitted for the American Economic Association program, has been selected to be part of the AEA’s poster session.
This sums up my relationship to the core of the profession nicely: I’m exactly on the margin of it. But this time, just barely on the right side of it, helping them fill up a newly-oversized hotel block.
The odds aren’t what they were in the mega-conference days before 2020, but I expect I’ll still see some of you in San Antonio.
Information on the internet was born free, but now lives everywhere in walled gardens. Blogging sometimes feels like a throwback to an earlier era. So many newer platforms have eclipsed blogs in popularity, almost all of which are harder to search and discover. Facebook was walled off from the beginning, Twitter is becoming more so. Podcasts and video tend to be open in theory, but hard to search as most lack transcripts. Longer-form writing is increasingly hidden behind paywalls on news sites and Substack. People have complained for years that Google search is getting worse; there are many reasons for this, like a complacent company culture and the cat-and-mouse game with SEO companies, but one is this rising tide of content that is harder to search and link.
To me part of the value of blogging is precisely that it remains open in an increasingly closed world. Its influence relative to the rest of the internet has waned since its heydey in ~2009, but most of this is due to how the rest of the internet has grown explosively at the expense of the real world; in absolute terms the influence of blogging remains high, and perhaps rising.
The closing internet of late 2023 will not last forever. Like so much else, AI is transforming it, for better and worse. AI is making it cheap and easy to produce transcripts of podcasts and videos, making them more searchable. Because AI needs large amounts of text to train models, text becomes more valuable. Open blogs become more influential because they become part of the training data for AI; because of what we have written here, AI will think and sound a little bit more like us. I think this is great, but others have the opposite reaction. The New York Times is suing to exclude their data from training AIs, and to delete any models trained with it. Twitter is becoming more closed partly in an attempt to limit scraping by AIs.
So AI leads to human material being easier for search engines to index, and some harder; it also means there will be a flood of AI-produced material, mostly low-quality, clogging up search results. The perpetual challenge of search engines putting relevant, high-quality results first will become much harder, a challenge which AI will of course be set to solve. Search engines already have surprisingly big problems with not indexing writing at all; searching for a post on my old blog with exact quotes and not finding it made me realize Google was missing some posts there, and Bing and DuckDuckGo were missing all of them. While we’re waiting for AI to solve and/or worsen this problem, Gwern has a great page of tips on searching for hard-to-find documents and information, both the kind that is buried deep down in Google and the kind that is not there at all.
2023 continues to be a dangerous year for eminent economists. We have once again lost a Nobel laureate who was influential even by the standard of Nobelists, Robert Solow:
I’m sure you will soon see many tributes that discuss his namesake Solow Model (MR already has one), or discuss him as a person. I never got to meet him (just saw him give a talk) and the Solow Model is well known, so I thought I’d take this occasion to discuss one of his lesser-known papers- “Sustainability: An Economists Perspective“. What follows comes from my 2009 reaction to his paper:
The government continues to be great at collecting data but not so good at sharing it in easy-to-use ways. That’s why I’ve been on a quest to highlight when independent researchers clean up government datasets and make them easier to use, and to clean up such datasets myself when I see no one else doing it; see previous posts on State Life Expectancy Data and the Behavioral Risk Factor Surveillance System.
National Health Expenditure Accounts Historical State Data: The original data from the Centers for Medicare and Medicaid Services on health spending by state and type of provider are actually pretty good as government datasets go: they offer all years (1980-2020) together in a reasonable format (CSV). But it comes in separate files for overall spending, Medicare spending, and Medicaid spending; I merge the variables from all 3 into a single file, transform it from a “wide format” to a “long format” that is easier to analyze in Stata, and in the “enhanced” version I offer inflation-adjusted versions of all spending variables. Excel and Stata versions of these files, together with the code I used to generate them, are here.
A warning to everyone using the data, since it messed me up for a while: in the documentation provided by CMMS, Table 3 provides incorrect codes for most variables. I emailed them about this but who knows when it will get fixed. My version of the data should be correct now, but please let me know if you find otherwise. You can find several other improved datasets, from myself and others, on my data page.
Some economists love to write about sports because they love sports. Others love to write about sports because the data are so good compared to most other facets of the economy. What other industry constantly releases film of workers doing their jobs, and compiles and shares exhaustive statistics about worker performance?
To take an extreme example, suppose an average high-school athlete got thrown into a professional football or basketball game; a fan asked to evaluate them could probably figure out that they don’t belong there within minutes, or perhaps even just by glancing at them and seeing they are severely undersized. But what if an average high school coach were called up to coach at the professional level? How long would it take for a casual observer to realize they don’t belong? You might be able to observe them mismanaging games within a few weeks, but people criticize professional coaches for this all the time too; I think you couldn’t be sure until you see their record after a season or two. Even then it is much less certain than for a player- was their bad record due to their coaching, or were they just handed a bad roster to work with?
The sports economics literature seems to confirm my intuition that coaches are difficult to evaluate. This is especially true in football, where teams generally play fewer than 20 games in a season; a general rule of thumb in statistics is that you need at least 20 to 25 observations for statistical tests to start to work. This accords with general practice in the NFL, where it is considered poor form to fire a coach without giving him at least one full season. One recent article evaluating NFL coaches only tries to evaluate those with at least 3 seasons. If the article is to be believed, it wasn’t until 2020 that anyone published a statistical evaluation of NFL defensive coordinators, despite this being considered a vital position that is often paid over a million dollars a year:
Companies and non-profit organizations tend to be managed day-to-day by a CEO, but are officially run by a board with the legal power to replace the CEO and make all manner of changes to the company. But last week saw two striking demonstrations that corporate boards’ actual power can be much weaker than it is on paper.
The big headlines, as well as our coverage, focused on the bizarre episode where OpenAI, the one of the hottest companies (technically, non-profits) of the year, fired their CEO Sam Altman. They said it was because he was not “consistently candid with the board”, but refused to elaborate on what they meant by this; they said a few things it was not but still not what really motivated them.
Technically it is their call and they don’t have to convince anyone else, but in practice their workers and other partners can all walk away if they dislike the board’s decisions enough, leaving the board in charge of an empty shell. This was starting to happen, with the vast majority of workers threatening to walk out if the board didn’t reverse their decision, and their partner Microsoft ready to poach Sam Altman and anyone else who left.
After burning through two interim CEOs who lasted two days each, the board brought back ousted CEO Sam Altman. Formally, the big change was board member Ilya Sutskever switching sides, but the blowback was enough to get several board members to resign and agree to being replaced by new members more favored by the workers (including, oddly, economist Larry Summers).
A similar story played out at IZA last week, though it mostly went under the radar outside of economics circles. IZA (aka the Institute for Labor Economics) is a German non-profit that runs the world’s largest organization of labor economists. While they have a few dozen direct employees, what makes them stand out is their network of affiliated researchers around the world, which I had hoped to join someday:
Our global research network ist the largest in labor economics. It consists of more than 2,000 experienced Research Fellows und young Research Affiliates from more than 450 research institutions in the field.
But as with OpenAI, the IZA board decided to get rid of their well-liked CEO. Here at least some of their reasons were clear: they lost their major funding source and so decided to merge IZA with another German research institute, briq. Their big misstep was choosing for the combined entity to be run by the the much-disliked head of the smaller, newer merger partner briq (Armin Falk), instead of the well-liked head of the larger partner IZA (Simon Jaeger). Like with OpenAI, hundreds of members of the organization (though in this case external affiliates not employees, and not a majority) threatened to quit if the board went through with their decision. Like with OpenAI, this informal power won out as Armin Falk backed off of his plan to become IZA CEO.
Each story has many important details I won’t go into, and many potential lessons. But I see three common lessons between them. First is the limits to formal power; the board rules the company, but a company is nothing without its people, and they can leave if they dislike the board enough. Second, and following directly from this, is that having a good board is important. Finally, workers can organize very rapidly in the internet age. At OpenAI nearly all its employees signed onto the resignation threat within two days, because the organizers could simply email everyone a Google Doc with the letter. Organizers of the IZA letter were able to get hundreds of affiliates to sign on the same way despite the affiliates being scattered all across the world. In both cases there was no formal union threatening a strike; it was the simple but powerful use of informal power: the voice and threatened exit of the people, organized and amplified through the internet.
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