Certificate of Need laws require healthcare providers to obtain government approval before building new facilities or expanding services. The original intent of the laws was that less building would mean less spending, but critics worry that it means less competition and higher prices leading to higher spending. Our paper finds that the critics are right, as states repealing their Certificate of Need laws see their healthcare spending fall relative to those that maintain them.
Longtime readers may recall that back in 2022 I posted about another paper of mine with a similar result, and even that wasn’t the first word on the subject. So what’s new in this paper? Besides newer data, we measure health spending as a percentage of the state economy rather than in raw dollars, to try to account for how CON changes the true burden of healthcare spending. We also use newer regression techniques that are probably more accurate but definitely help make prettier graphs.
Here’s what we found happening to health spending when states repeal Certificate of Need:
Full published paper is here, ungated working paper version here.
The Center for Modern Health just released a report “Paring Down Coverage Mandates To Reduce Health Insurance Premiums“. It builds on one of my papers to estimate how much states could drive down insurance premiums by reducing the number of things health insurers are required to cover:
Estimated savings for Family Plans (Table 2 of CMH Report)
It’s nice to see that someone in the health policy world remembers that health insurance benefit mandates exist and can be a significant cost driver. I’m also glad to see that someone still cares about my 2014 paper (the first one I ever got accepted at a journal), though I wish there were more up to date estimates available for them to cite.
Why has no one developed more recent estimates? Partly for data reasons- the organizations that had been putting out counts of mandates by state each year (the Council for Affordable Health Insurance & Blue Cross Blue Shield Association) stopped doing so. Partly because of the ACA, which changed how mandates worked in complex ways.
That said, there there seems to be some more recent data from 2017 that could be used for a slight update, and this post and my ideas page detail the many things we still don’t know about how mandates operated prior to 2014:
State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? How does all this change in light of the ACA? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.
Adam Smith kicked off modern economics by asking what causes nations to become wealthy. We’ve spent centuries improving our answers to the question and we still have lots to learn, but I think Smith’s original answer holds up remarkably well:
Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things. All governments which thwart this natural course, which force things into another channel, or which endeavour to arrest the progress of society at a particular point, are unnatural, and to support themselves are obliged to be oppressive and tyrannical
The overall correlation of GDP per capital and economic freedom is strong, but I find the extremes even more striking. Economic freedom is necessary for wealth: every country with a GDP per capita above $50,000 has above-average economic freedom (getting even close without high economic freedom requires having lots of oil). Economic freedom is sufficient: every country with an economic freedom rating over 8 has a GDP per capita over $50,000.
I periodically see someone claim that a country “did everything right” in terms of policy but still wound up poor, usually implying that the country is doomed by their geography or genes, or arguing that “neoliberal policy” failed. Whenever I check in on these countries, I inevitably find them to be far from “doing everything right”, with economic freedom scores typically far below 8.
It’s true that these are “just correlations” and that economic freedom alone isn’t a perfect predictor. It’s worth looking into the outliers and wondering- if a country is above the trend line without oil wealth (like China), what else are they doing right? If a country is well below the overall trend line (like Guatemala), does it mean they are have something else working against them to counteract the benefits of economic freedom? Or does it mean they are about to turn the corner and see high economic growth?
Overall though I don’t think it’s a big oversimplification to say that Adam Smith was right, and economic freedom is enough to bring prosperity. If you’re looking for economic growth, start with Smith’s peace, easy taxes, and tolerable administration of justice, or the Economic Freedom of the World’s small government, strong property rights and legal system, sound money, and freedom to trade at home and abroad.
We noted that “People Are Paying For Music Again” in 2024, showing that streaming was partly making up for the drop in sales of physical recordings, while live music sales were setting record highs. Thus,
When you combine live and recorded sales, total spending on music has now passed the 1999 peak; this is the biggest the market for music has ever been.
But I didn’t have a chart showing the music market as a whole. I meant to make on for a followup post, but still hadn’t got around to it when I saw this from Joey Politano’s Apricitas Substack:
He uses BEA data instead of the music industry sources I was using, which means a wider range of years is available, and the colors tell the story nicely. This chart still shows a late ’90s peak because it is measuring music as a percentage of all consumer spending; but total real consumer spending is way up since the ’90s, so the real dollar peak of money flowing to the music industry is today. Here’s my version of the chart using real dollars:
This chart tells a more optimistic story. But it’s worth reading the entirety of Politano’s post, which suggests that AI is already significantly reducing overall employment in the arts. He also notes that money moving from recorded to live music has changed which artists are winning. I’ve noted something of a ‘rich get richer’ phenomenon, with ticket prices for top artists shooting higher while it becomes harder for regular musicians to stay full time.
My semi-serious solution is to bring back hipsters. Make it once again cooler to spend $20 each weekend on an obscure band’s show or vinyl than to spend $1000 on a VIP ticket to a Taylor Swift-level show once a year. Hipsters might be annoying, but the hipster music strategy is an efficient way to support more people putting in the time to make music- and I think that would be a good thing in a field where talent is fairly widely distributed. The difference between full-time musicians and semi-pros who do a few gigs a year (or the best amateurs) is often not musical talent but luck, connections, and the willingness and ability to push through early years with little income. We’re a richer society than we were in 1999 and we can afford to support more people giving music a real try.
Certificate of Need (CON) laws require healthcare providers to obtain state approval before opening new facilities, expanding existing facilities, or introducing certain healthcare services or equipment. We’ve covered them frequently here, and I’ve written several papers on them.
But my research evaluating the effects of CON on healthcare facilities, spending, and outcomes (along with everyone else’s) always had the drawback that we relied on fairly crude measures of CON- often just a binary measure of whether a state had any CON requirements at all. The problem with this is that different states have wildly different approaches to CON- some states like Vermont require CON for as many as 27 separate types of health facilities, services, or equipment, including major ones like hospitals, while other states like Ohio require CON for only a single type (nursing homes). Previous attempts to catalog this variation tended to produce single-year snapshots (e.g. Institute for Justice, Cicero, Mercatus, NCSL). These are helpful for policymakers wanting to see how their state compares to others, but not so useful to researchers trying to measure the effects of CON, who would typically prefer many years of historical data.
The dataset us CON researchers have always wanted is now here!
It tracks 31 different types of healthcare facilities, services, and equipment that are sometimes regulated by CON, showing which ones required a CON in each state in every year back to 1990 (going even further back for some states). This means our national panel has more than 55,000 data points.
Number of Certificate of Need Requirements Per State, 2025
I’ve spent the last 2+ years working on this with a large team of coauthors (Sriparna Ghosh, Conor Norris, and Justin Leventhal) and research assistants, with support from Providence College and the Knee Regulatory Research Center at WVU. It involved reading decades of old state statutes on HeinOnline and Westlaw. We’ve released a paper, Certificate of Need: A New Comprehensive Panel, explaining the dataset in more detail and sharing ideas for how researchers could use it.
Change In Certificate of Need Requirements Per State, 1990 to 2025Change In Number of States Requiring Each Type of CON From 1990 to 2025
The dataset is public and free for everyone to use- we just ask that people cite us (though feel free to ask any of the dataset’s creators if you do want us as coauthors on your paper using the data). I’d love to hear your ideas for how you might use it, or how we can improve it- this is Version 1.0 but we plan to maintain and improve it going forward so that it can become the standard for the field.
So many things are happening that it’s hard to decide which to write about. So I’ll briefly cover several that would each deserve a full post on a normal week:
1. The Fed voted unanimously to raise rates to 4%, showing that the Warsh Fed still has some independence from the President.
2. The 10-year Treasury bond hit 5%. The CBO’s long-term budget forecasts assume 4% rates for the 10-year, and looked unpleasant even with that rosy assumption. The UK dumped Prime Minister Liz Truss over their 10-year hitting 4.5%.
3. Jacob Coxon resigned from Anthropic over AI safety concerns and set off a surprisingly large firestorm over it.
4. The Trump administration reacted by insisting we move forward with AI, with the Department of War blaming Effective Altruists for trying to slow it down:
I suspect this could lead many people to get into Effective Altruism by promoting it from ‘weird thing for nerds‘ to ‘something you can do to oppose Trump’. If that interests you, this is a good place to start.
5. EA hero Michael Kremer (whose work inspired many donations to deworming) was just appointed Chief Economist of the World Bank.
6. But AI progress keeps rolling. Quantum theorist Scott Aaronson declares the Singularity is here:
I recoil from the neverending shell game where you say “oh sure, of course AI can now [escape from its sandbox / solve Millennium Problems / whichever dramatic thing it most recently did], no one ever denied that [I did deny it], wake me up when AI does [thing AI hasn’t yet done but is going to do next year], that’s when I’ll reevaluate my whole worldview [no I won’t].” Where no matter how fast the rollercoaster accelerates, even after your whole familiar world has vanished behind you, you’re still inventing reasons why it doesn’t count.
My position on AI is merely the conservative, skeptical position of 2006, updated with intellectual honesty for the reality of late 2026. And that position, if you need me to spell it out, is as follows:
It seems to me that the Singularity has already started; it’s just wildly unevenly distributed.
I’m honestly surprised we have yet to see an upsurge of millenarianism. Y2K concerns came and went 2000 years after Jesus’ birth. But see how much people worry about AI now, and realize it will only get more powerful as we approach 2000 years after his death- 2033, or if you trust the revisionists, 2030.
The Center for Modern Health and the Knee Regulatory Research Center just released an index of how free residents in each state are to provide and pursue health care as they see fit. Their summary map looks like this:
The index was created by averaging measures of freedom in 54 separate categories, summarized into the 5 broad areas of Professional, Institutional, Patient, Payment, and Delivery Freedom. A report with maps for each of the 54 underlying measures is here, and a spreadsheet with all the data is here.
This project represents a major effort on an important issue, but I have to say my favorite part is just how unusual the final map of the overall ranking looks. I’ve created many maps of the states based on data and seen many more, but almost all of them (no matter the underlying variable they represent) end up falling into a handful of looks. They are either secretly maps of population density, or North vs South, or East vs West, or the South + Appalachia (high poverty, low health and education, et c). But the Health Freedom Index groups states in a way I’ve never seen before, putting Maine and New Hampshire with Mountain West states as the most free, while South Carolina and Louisiana join California and much of the Northeast among the least free.
Some of the Index’s creators will be presenting it online on September 18th.
Note: I’m affiliated with the Knee Regulatory Research Center at WVU, but I wasn’t directly involved with this project. I’m working on a different data project with Knee I hope to discuss here soon.
Governors serve for 4 years in most US states. You can find many rankings of state economies out there, but they tend not to measure how states have done over the last 4 years- instead they either use measurements based on levels (things like mean income which were mostly determined by events before the current governor’s term), or one year of growth (which has a lot of randomness), or they don’t explain what period it is based on at all.
But if you want to know how a state’s economy has performed under an incumbent governor (for instance, to inform your vote on whether to re-elect them), the best way is to measure it’s economic growth over the period of their term- most commonly, 4 years. A governor currently up for re-election following their first term would typically have taken office in January 2023. Below I map how two of the most commonly used economic measured have fared by state from January 2023 to the most recent available data (what is available differs by measure):
Source: My calculations from BEA current-dollar GDPSource: My calculations from BLS data on total employment
Overall South Carolina looks best and Wyoming looks worst. There might be other economic measures you prefer, like poverty rates or median income- but whatever measure you prefer and whatever politician you are evaluating, I encourage you to check how that measure has changed since their term started and how that ranks compared to other similar regions.
This post was inspired by the mailers that would-be Rhode Island Governor Foulkes’ campaign keeps sending me suggesting I vote for her in the primary against incumbent Rhode Island Governor Dan McKee because “we are last in the country for our economy”, citing this CNBC ranking. Looking into the source, CNBC says:
The United States has long answered this question “Yes- they need 3+ years of retraining unless they are from Canada”. But that has been changing rapidly since Tennessee began allowing foreign doctors to practice without years of retraining in 2023.
Doctors can’t start practicing independently right after getting their MD- they need 3 to 6 additional years of supervised on-the-job “residency” training first (even graduates of US medical schools). Most developed countries have similar systems of on-the-job supervised training for junior doctors. But until recently the US didn’t recognize training from any country besides ourselves and Canada. Doctors were required to re-do a US residency before practicing here even if they had been successfully practicing for decades in one of the dozens of countries with life expectancies higher than the US.
I’ve been pointing this out to my health economics classes for years as one of many quirks of the US system that keeps healthcare expensive and hard to access. But since Tennessee offered a faster pathway for trained foreign doctors with HB 1312 in 2023, dozens of states have rapidly follow suit:
I think the impact of this change has been limited by the fact that states still tend to have other requirements for foreign doctors, like spending a year or two under supervision at ACGME-accredited programs. This still serves as a shorter quasi-residency, and these programs are exactly the ones that tend to have plenty of doctors anyway- big urban hospitals, not small rural clinics. Immigration rules present their own high and rising barriers; most foreign doctors can’t come here in the first place.
That said, I’m still happy to see policy experiments from states attempting to address our doctor shortage. These state laws waiving residency retraining requirements are so new and so numerous that the present an excellent opportunity for research on their effects; I’m adding this to my ideas page.
I noticed the rapid expansion of these laws when doing research for a talk I’m giving at Creighton University at 4pm today (if you’re in Omaha, maybe see you there); thanks to Creighton for the inspiration.
Before recording and broadcasting technologies, there was no way for people all over the world to hear the same music performance- you had to be in the same room as the performer. Even when I was growing up, distributing your worldwide meant getting a record deal, something few could do. But the modern internet allows for music to easily be uploaded from anywhere, to be listened to anywhere.
For artists, this means to problem shifts from just being available, to standing out to listeners among the sea of available music. How best to do this- other than making great music- is a complex and detailed question of marketing. But one crucial early step- getting on the right streaming platforms where artists make their money– is now surprisingly simple and easy.
Amateur artists tend to start with what is free- uploading their music directly to Youtube or Soundcloud. But huge numbers of listeners are on streaming platforms like Spotify, Amazon Music, or Apple Music. These don’t allow artists to upload music directly, which can make listeners or new artists think that getting on streaming platforms is a bit like getting a record deal in the old days, where an industry insider needs to approve the artists. In fact, approval for streaming now mostly runs automatically through third-party music distributors.
Adding third parties into the mix sounds complicated, but in fact they are easy for artists to work with, and a single distributor can get your music onto all major streaming platforms at once. Here’s how the page for uploading a song starts at the largest distributor, Distrokid:
There are many distributors; Spotify currently recommends 21 that work directly with artists. Distributors charge artists, but not very much. Some charge by the song or album (CD Baby does this, starting at $10/song or $15/album), others let artists upload unlimited songs for an annual fee (DistroKid does this, starting at $25/yr). Distributors also collect royalties from streams on behalf of artists. To me this looks like a no-brainer for any semi-pro musician who is out there playing some gigs, but also makes sense for stronger amateur artists looking to expand their reach* (for professionals with record deals, of course, the label will do this for you).
For an academic comparison, getting your music out there is now less like hoping an insider likes you or your work enough to invite you to NBER, and more like just uploading to SSRN (if SSRN charged a small fee).
*At least if you write your own songs- buying the rights to distribute covers costs extra (e.g. $12 per song per year for DistroKid), and on streaming people can listen to the original artist just as cheaply and easily, so there’s not the same niche for cover bands that there is in live music.
