Did you really think I was going to write a post this week? Sorry, this week is for far flung family and nutritionally disastrous cookies.
If you simply must have an economic observation, here you go: if you don’t gain weight during the holidays you’re probably too debt averse. Consume now, pay later. It’s worth the vig.
In August, I listed the Top EWED Posts of 2024. Here are a few more highlights. This list is roughly based on web traffic, starting with the highest number of views for 2024, since the August list.
2. Grocery Inflation Since 2019: BLS Data is Probably About Right by Jeremy-“What if we actually looked at receipts?”-Horpedahl. You can find him on Twitter/X.
3. You know it’s good when a post with such a cryptic title goes viral. Mike wrote about the topic people were thinking about, in the moment: At the moment (updated 10/22/24) Sometimes we write about the economics community and what began as a critical mass of people that used to call itself #EconTwitter. Some of those people have moved to Bluesky. You can find Mike there at @mikemakowsky.bsky.social, and most of us have accounts there. Getting social media just right is tricky. If you follow the right people and don’t waste too much time on it, then social media can be part of How to Keep Up With Economics (James).
4. Predicting College Closures: Now with Machine Learning James Bailey brings the important (unwanted) news that not all college are going to make it through the next decade, and there are signs. This follows up on what was previously listed as a top post in August: Predicting College Closures
5. Publish or Perish: A Hilarious Card Game Based on Academia My review of a new board game. If it’s not for you, it’s not for you. I played a test copy with some fellow nerds and had a great time.
6. Jeremy explains, “… fast food prices (“limited service meals”), which have definitely outpaced wages over the past 4 years, and continue to grow…” Grocery Inflation is Under Control, Fast Food Prices Aren’t
7. Jeremy asks, Did 818,000 jobs vanish?
8. Scott’s saga is perhaps attracting traffic from search engines from people with the same problem. Recovering My Frozen Assets at BlockFi 2. Scams and More Scams
9. Post-Pandemic Lumber Market Zachary Bartsch writes, “People used to talk about higher gasoline prices all the time, but never discussed with the same enthusiasm when prices fell. The same is true for lumber.” Good for teaching about supply and demand.
10. I Give Up, Standard & Poor’s Wins James lets us learn from his journey- “my stock picks underperformed the incredible 26% return the S&P has posted so far this year.” This is something most people would rather not admit, and yet for most of us it’s true.
11. James explains, “Cheapflation”: Inflation Really Does Hit the Bottom Harder. People were mad about inflation. Voters were mad about inflation. It’s worth understanding better. Some of us are in an echo chamber and need to peer out, especially if we think a lot about how (in fact) the world is getting better. Or maybe we even think about data indicating that On Average, American Wage Earners are Better Off Than They Were Four Years Ago (Jeremy).
12. Why Podcasts Succeeded in Gaining Influence Where MOOCs Failed attracted some attention. If you are being honest, would you have predicted a priori that Joe Rogan talking in a closed room FOR HOURS would outdo Ivy League professor lectures? In retrospect, it might seem obvious, but I probably would have gotten the prediction wrong. MOOCs and podcasts both launched around the same time because the internet lowered the cost of broadcasting. They both had some success. In terms of shaping culture or voting behavior, I think it’s clear that podcasts win. Until a product is launched on the market, we just don’t know what will become popular, which is a topic that came up in the podcast I recorded recently: Joy on The Inductive Economy podcast
Speaking of what I don’t predict, EWED is starting to get web traffic from LLMs like chatgpt.com. Right now, it’s very small compared to Google search. For a while, I wondered if LLMs would simply plagiarize us without giving us any credit. Maybe that’s our raison d’être. Here’s me being dramatic about it in 2022 – “Because of when I was born, I believe that something I have published will make it into the training data for these models. Will that turn out to be more significant than any human readers we can attract?”
However, writers of the world, LLMs might start giving you credit. There is some demand from users for sources and citations. (My paper on made up sources).
A little more credit to the true 2024 EWED all-stars, even though they were already listed in August: Young People Have a Lot More Wealth Than We Thought, by Jeremy Horpedahl, continues to be a top performer. And, Mike wrote about an important current event in culture: Civil War as radical literalism
While we are settling scores and doing web traffic round-ups, there is one thing I’d like to put on the record. I made one resolution last year, publicly on January 3, 2024. I have made good on this promise. The people who run the AdamSmithWorks website have informed me that I wrote their top post of the year, Would Adam Smith Tell Taylor Swift to Attend the Super Bowl?
Excel is an attractive tool for those who consider themselves ‘not a math person’. In particular, it visually organizes information and has many built-in functions that can make your life easier. You can use math if you want, but there are functions that can help even the non-math folks
If you are a moderate Excel user, then you likely already know about the AVERAGE and COUNT functions. If you’re a little but statistically inclined, then you might also know about the STDEV.S function (STDEV is deprecated). All of these functions are super easy and only have one argument. You just enter the cells (array) that you want to describe, and you’re done. Below is an example with the ‘code’ for convenience.
=COUNT(A2:A21)
=AVERAGE(A2:A21)
=STDEV.S(A2:A21)If you do some slightly more sophisticated data analysis, then you may know about the “IF” function. It’s relatively simple; if a proposition is true (such as a cell value condition), then it returns a value. If the proposition is false, then it returns another value. You can even create nested “IF”s in which a condition being satisfied results in another tested proposition. Back when excel had more limited functions, we had to think creatively because there was a limit to the number of nested “IF” functions that were permitted in a single cell. Prior to 2007, a maximum of seven “IF” functions were permitted. Now the maximum is 64 nested “IF”s. If you’re using that many “IF”s, then you might have bigger problems than the “IF” limitations.
Another improvement that Excel introduced in 2019 was easier array arguments. In prior versions of Excel, there was some mild complication in how array functions must be entered (curly brackets: {}). But now, Excel is usually smart enough to handle the arrays without special instructions. Subsequently, Excel has introduced functions that combine the array features with the “IF” functions to save people keystrokes and brainpower.
Looking at the example data we see that there is an identifier that marks the values as “A” or “B”. Say that you want to describe these subgroups. Historically, if you weren’t already a sophisticated user, then you’d need to sort the data and then calculate the functions for each subgroup’s array. That’s no big deal for small sets of data and two possible ID values, but it’s a more time-consuming task for many possible ID values and multiple ID categories.
The early “IF” statements allowed users to analyze certain values of the data, such as those that were greater than, less than, or equal to a particular value. But, what if you want to describe the data according to criteria in another column (such as ID)? That’s where Excel has some more sophisticated functions for convenience. However, as a general matter of user interface, it will be clear why these are somewhat… awkward.
Continue readingI normally like the Wall Street Journal; it is the only news page I check directly on a regular basis, rather than just following links from social media. But their “Biggest News Stories of 2024” roundup makes me wonder if they are overly parochial. When I try to zoom out and think of the very biggest stories of the past five to ten years, three of the absolute top would be the rapid rise of China and India, together with the astonishing growth in artificial intelligence capabilities.
All three of those major stories continued to play out this year, along with all sorts of other things happening in the two most populous countries in the world, and all the ways existing AI capabilities are beginning to be integrated into our businesses, research, and lives. But the Wall Street Journal thinks that none of this is important enough to be mentioned in their 100+ “Biggest Stories”.
To be fair, China and AI do show up indirectly. AI is driving the 4 (!) stories on NVIDIA’s soaring stock price, and China shows up in stories about spying on the US, hacking the US, and the US potentially forcing a sale of TikTok. But there are zero stories regarding anything that happened within the borders of China, and zero that let you know that AI is good for anything besides NVIDIA’s stock price.
Plus of course, zero stories that let you know that India- now the world’s most populous country, where over one out of every six people alive resides- even exists.
This isn’t just an America-centric bias on WSJ’s part, since there is lots of foreign coverage in their roundup; indeed the Middle East probably gets more than its fair share thanks to “if it bleeds, it leads”. For some reason they just missed the biggest countries. They also seem to have a blind spot for science and technology; they don’t mention a single scientific discovery, and only had two technology stories, on SpaceX catching a rocket and doing the first private spacewalk.
The SpaceX stories at least are genuinely important- the sort of thing that might show up in a history book in 50+ years, along with some of the stories on U.S. politics and the Russia-Ukraine war, but unlike most of the trivialities reported.
I welcome your pointers to better takes on what was important in 2024, or on what you consider to be the best news source today.
You see a lot of nostalgia for the recent past. People pining for the simpler life of the 1950s, or claims that wages have stagnated since the late 1970s or early 1980s. I’ve tried to take these arguments seriously and respond to them, such as in a paper I wrote with Scott Winship and summarized in a blog post last June. But occasionally, you find really weird economic nostalgia, like for the 1890s. Yes, the 1890s, not the 1990s.
Here’s one example: a cartoon shared on social media of workers being oppressed in the 1890s, with the caption “the problem has only gotten worse.” That post received 2 million views on Twitter, possibly because many people are criticizing it, but it also has a lot of retweets and likes.
If it was just one semi-viral social media post from an anonymous Twitter account, we could easily dismiss it. But 1890s economic nostalgia has been coming from another important place lately: President Elect Trump. Of course he is nostalgic for the policies of the 1890s. But on occasion, Trump will say things like “Go back and look at the 1890’s, 1880’s with McKinley and you take a look at tariffs, that was when we were at our richest” (emphasis added).
Really, our richest in the 1890s? Can this be true? Are the anonymous socialist Twitter accounts correct? Let’s look at the data. But the answer probably won’t surprise you: your intuition is correct, we are much better off today than the 1890s, in almost every way of looking at it economically.
Continue readingKen Alltucker at USA Today recently published a piece titled Seven reasons why Americans pay more for health care than any other nation. It starts off:
Americans spend far more on health care than anywhere else in the world but we have the lowest life expectancy among large, wealthy countries.
A lot of that can be explained by the unique aspects of our health care system. Among other things, we reward doctors more for medical procedures than for keeping people healthy, keep costs hidden from customers and spend money on tasks that have nothing to do making patients feel better.
“We spend more on administrative costs than we do on caring for heart disease and caring for cancer,” said Harvard University economist David Cutler. “It’s just an absurd amount.”
The article notes that the whole system is skewed towards high costs. It is not just profiteering insurance companies. Seven factors are listed. I will excerpt them in italics below, and close with a few of my comments.
Reason 1: Lack of price limits
U.S. hospitals have more specialists than do medical facilities in other nations. Having access to 24/7 specialty care, particularly for hospitals in major metro areas, drives up costs… Patients have more elbow room and privacy here. U.S. hospitals typically have either one or two patients per room, unlike facilities abroad that tend to have open wards with rows of beds, Chernew said. He said differences in labor markets and regulatory requirements also can pack on costs.
Of the $4.5 trillion spent on U.S. health care in 2022, hospitals collected 30% of that total health spending, according to data from the Centers for Medicare & Medicaid Services. Doctors rank second at 20%. Prescription drugs accounted for 9% and health insurance − both private health insurance and government programs such as Medicare and Medicaid − collect 7% in administrative costs.
Reason 2: Hospitals and doctors get paid for services, not outcomes
Doctors, hospitals and other providers are paid based on the number of tests and procedures they order, not necessarily whether patients get better. The insurer pays the doctor, hospital or lab based on negotiated, in-network rates between the two parties.
Critics of this fee-for-service payment method says it rewards quantity over quality. Health providers who order more tests or procedures get more lucrative payments whether the patients improve or not.
Reason 3: Specialists get paid much more ‒ and want to keep it that way
Doctors who provide specialty care such as cardiologists or cancer doctors get much higher payments from Medicare and private insurers than primary care doctors.
Some see that as a system that rewards doctors who specialize in caring for patients with complex medical conditions while skimping on pay for primary care doctors who try to prevent or limit disease.
[My comment: There is a saying in management science that your system is perfectly designed for the results you are getting. In other nations with a fixed pot of money, doled out by the government, to mainly non-profit health providers, there is (in theory, at least) an incentive system that would work towards minimizing overall health expenses. In the U.S., though, we have a mainly for-profit system, that collects more moolah the more health problems we have, and the more expensive are the treatments. Most healthcare providers try to be noble-minded and work for the good of their patients, but still the overall financial incentives are what they are. The health insurance companies are one of the few forces working against endless upward spiraling of healthcare costs. ]
Under the current system, doctors are chosen or approved by the American Medical Association to a 32-member committee which recommends values for medical services that Medicare then considers when deciding how much to pay doctors. Some have compared the idea of doctors setting their own payscale to the proverbial fox guarding the henhouse.
Reason 4: Administrative costs inflate health spending
One of the biggest sources of wasted medical spending is on administrative costs, several experts told USA TODAY….Harvard’s Cutler estimates that up to 25% of medical spending is due to administrative costs.
Health insurers often require doctors and hospitals to get authorization before performing procedures or operations. Or they mandate “step therapy,” which makes patients try comparable lower-cost prescription drugs before coverage for a doctor-recommended drug kicks in. These mandates trigger a flurry of communication and tasks for both health insurers and doctors.
Reason 5: Health care pricing is a mystery
Patients often have no idea how much a test or a procedure will cost before they go to a clinic or a hospital. Health care prices are hidden from the public. …An MRI can cost $300 or $3,000, depending on where you get it. A colonoscopy can run you $1,000 to $10,000.
Economists cited these examples of wide-ranging health care prices in a request that Congress pass the Health Care Price Transparency Act 2.0, which would require hospitals and health providers to disclose their prices.
Reason 6: Americans pay far more for prescription drugs than people in other wealthy nations
There are no price limits on prescription drugs, and Americans pay more for these life-saving medications than residents of other wealthy nations.
U.S prescription drug prices run more than 2.5 times those in 32 comparable countries, according to a 2023 HHS report…. Novo Nordisk charged $969 a month for Ozempic in the U.S. ‒ while the same drug costs $155 in Canada, $122 in Denmark, and $59 in Germany, according to a document submitted by Sanders.
[My comment: Yes, this disparity irks me greatly].
Reason 7: Private Equity
Wall Street investors who control private equity firms have taken over hospitals and large doctors practices, with the primary goal of making a profit. The role of these private equity investors has drawn increased scrutiny from government regulators and elected officials.
One example is the high-profile bankruptcy of Steward Health Care, which formed in 2010 when a private equity firm, acquired a financially struggling nonprofit hospital chain from the Archdiocese of Boston.
… Private equity investors also have targeted specialty practices in certain states and metro regions.
Last year, the Federal Trade Commission sued U.S. Anesthesia Partners over its serial acquisition of practices in Texas, alleging these deals violated antitrust laws and inflated prices for patients. …FTC Chair Lina Khan has argued such rapid acquisitions allowed the doctors and private equity investors to raise prices for anesthesia services and collect “tens of millions of extra dollars for these executives at the expense of Texas patients and businesses.”
[ This also concerns me. That anesthesia monopoly should never have been allowed, in my opinion. The reason the PE firm paid to acquire all those individual practices was so that they could raise prices while minimizing services. Duh. That is the PE gamebook. When they do a corporate takeover, they nearly always fire employees and raise prices on products, to goose profits. This would not be a problem if the business were, say, selling pet rocks, but healthcare is different.
In many metro areas now, nearly all healthcare providers (even if they seem to retain their private practices) have become part of one or two mega conglomerates that cover the area. I feel fortunate because at least on of the mega conglomerates in my area is a high-quality non-profit, but I pity those whose only choice is between two for-profits.]
Final comments: I think another factor here is in our private enterprise system, it is so costly to become a doctor that they have to charge relatively high fees to compensate. This leads to a system where there are layers and layers of admins and nurses to shield you from actually seeing the doctor. As an example, I sliced my finger a couple of years ago, and went to an urgent care facility. There was an admin at the desk who took down my insurance info and relayed my condition to the back. Some time later, an aide took me back and weighed me and took my blood pressure. I think a nurse swung by as well. Finally, The Doctor Himself sailed in, to actually patch me up. And of course there were layers of administrative paperwork between me, the care facility, and my insurance company, to settle all the charges.
In contrast, a friend told me that when he broke his arm in the UK, he went to the local clinic, which was staffed by a doctor, and no one else. The doc set his arm, charged him some nominal fee, and sent him on his way.
There are other factors, I’m sure, such as the unhealthy lifestyle choices of many Americans. Think: obesity and opioids, among others. I suspect that is to blame for the poorer health outcomes in this country, more than the healthcare system.
In favor of the current U.S. system, although we pay much more, I think we do get something in return. It seems that with a good health plan, the availability of procedures is better in the U.S. than in many other countries, though I am open to correction on that.
The reply culture on Bluesky is starting to get nasty. I know this is either ironic or churlish coming from someone who wanted more tension on Bluesky (I swear I just wanted people arguing about research and papers in a fruitful manner). Maybe I am in fact just getting what I asked for (oops). So what exactly can we do about said reaping of cursed sowing?
I don’t have any genious suggestions in the face of a very difficult exercise in tradeoffs. On the one hand we have the status quo of an open forum where we incur the cost of jerks and interlopers poisoning the conversation. On the other hand we could set up barriers to entry around the conversation, turning Bluesky into a very large #EconSky slack channel with hundreds (thousands?) of economists, policy professionals, and journalists engaging in a conversation. This sounds great at first blush, but the idea of finding new and innovative ways to make economics an even more insular club of insiders does not appeal to me. The costs go beyond that, though, because once you decide to wall something off, mechanisms have to be put in place to admit new members (and kick out misbehavers). Those mechanisms come with their own set of problems, including the costs borne by those who must see to the administering and oversight of those mechanisms.
So what’s the answer? I don’t have a silver bullet, but I am a big fan of trivial costs of entry that will only affect those attempting to enter “at scale” i.e. troll farms. Some sort of third party registration using .edu, .gov, and other profession email addresses. Maybe a google scholar or RePec connection. Basically, anything that will take 5 minutes for professionals to accomplish. Just enough that registering 100 accounts becomes costly for troll farms and repeatedly registering banned accounts becomes too much of a hassle for independent anonymous jerks. Such a thing could work for a professionally accredited jerks as well. If getting blocked by 3 people removes you from the register, then you have to go back and do the 5 minute registration over again. A tiny cost, sure, but I suspect a lot of jerks, after being removed 3 times, will simply take the hint or decide they can’t be bothered.
Yes, I know this means that the laws of ironic comeuppance will strike me down on Bluesky at some point, but if it protects the network from turning into Twitter I’ll take the hit.
Ajeya Cotra writes the following in “Language models surprised us” (recommended, with more details on benchmarks)
In 2021, most people were systematically and severely underestimating progress in language models. After a big leap forward in 2022, it looks like ML experts improved in their predictions of benchmarks like MMLU and MATH — but many still failed to anticipate the qualitative milestones achieved by ChatGPT and then GPT-4, especially in reasoning and programming.
Joy’s thoughts: A possible reason for underestimating the rate of progress is not just a misunderstanding of the technology but a missed estimate on how much money would get poured in. When Americans want to buy progress, they can (see also SpaceX).
I compare this to the Manhattan project. People said it couldn’t be done, not because it was physically impossible but because it would be too expensive.
After a briefing regarding the Manhattan Project, Nobel Laureate Niels Bohr said to physicist Edward Teller, “I told you it couldn’t be done without turning the whole country into a factory.” (https://www.energy.gov/lm/articles/ohio-and-manhattan-project)
We are doing it again. We are turning the country into a factory for AI. Without all that investment, the progress wouldn’t be so fast.
I’ve written about proteins twice before. Once concerning protein content generally and then another concerning amino acid content of animal proteins. The reason that I stuck to animal proteins initially was because I held a common and false belief: Singular vegetarian foods aren’t complete proteins. The meat-eaters gotchya claim is that meats contain complete proteins. After all, we’ve heard a million times that beans and grains are often eaten together because they form a complete protein. The native North Americans? Corn and beans. Subcontinent Indians? Rice and Lentils or chickpeas. Japan? Rice and soy. Choose your poor or vegetarian population in the world, and they combine beans and grains. We’ve always been told that it’s because the combination constitutes a ‘complete protein’.
But you know what else constitutes a complete protein? Any of those foods all by themselves. What the heck. I haven’t been lied to. But I’ve certainly been misled. Let me briefly tell you my research journey. My recommended daily intake (RDI) are from the World Health Organization and the amino acid data is from the US Department of Agriculture. Prices are harder to pin down in a representative way, but I cite those too.
Continue readingSmall, rural, private schools stand out to me as the most likely to show up on lists of closed colleges. This summer I discussed a 2020 paper by Robert Kelchen that identified additional predictors using traditional regression:
sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring
Kelchen just released a Philly Fed working paper (joint with Dubravka Ritter and Doug Webber) that uses machine learning and new data sources to identify more predictors of college closures:
The current monitoring solution to predicting the financial distress and closure of institutions — at least at the federal level — is to provide straightforward and intuitive financial performance metrics that are correlated with closure. These federal performance metrics represent helpful but suboptimal measures for purposes of predicting closures for two reasons: data availability and predictive accuracy. We document a high degree of missing data among colleges that eventually close, show that this is a key impediment to identifying institutions at risk of closure, and also show how modern machine learning algorithms can provide a concrete solution to this problem.
The paper also provides a great overview of the state of higher ed. The sector is currently quite large:
The American postsecondary education system today consists of approximately 6,000 colleges and universities that receive federal financial aid under Title IV of the federal Higher Education Act…. American higher education directly produces approximately $700 billion in expenditures, enrolls nearly 25 million students, and has approximately 3 million employees
Falling demand from the demographic cliff is causing prices to fall, in addition to closures:
Between the early 1970s and mid-2010s, listed real tuition and fee rates more than tripled at public and private nonprofit colleges, as strong demand for higher education allowed colleges to continue increasing their prices. But since 2018, tuition increases have consistently been below the rate of inflation
Most college revenue comes from tuition or from state support of public schools; gifts and grants are highly concentrated:
Research funding is distributed across a larger group of institutions, although the vast majority of dollars flows to the 146 institutions that are designated as Research I universities in the Carnegie classifications…. Just 136 colleges or university systems in the United States had endowments of more than $1 billion in fiscal year 2023, but they account for more than 80 percent of all endowment assets in American higher education. Going further, five institutions held 25 percent of all endowment assets, and 25 institutions held half of all assets
Now lets get to closures. As I thought, size matters:
most institutions that close are somewhat smaller than average, with the median closed school enrolling a student body of about 1,389 full-time equivalent students several years prior to closure
As does being private, especially private for-profit (states won’t bail you out when you lose money):
As do trends:
variables measuring ratios of financial metrics and those measuring changes in covariates are generally more important than those measuring the level of those covariates
When they throw hundreds of variables into a machine learning model, it can predict most closures with relatively few false positives, though no one variable stands out much (FRC is Financial Responsibility Composite):
My impression is that the easiest red flag to check for regular people who don’t want to dig into financials is “is total enrollment under 2000 and falling at a private school”.
They predict that the coming Demographic Cliff (the falling number of new 18-year-olds each year) will lead to many more closures, though nothing like the “half of all colleges” you sometimes hear:
The full paper is available ungated here. I’ll close by reiterating my advice from the last post: would-be students, staff, and faculty should do some basic research to protect themselves as they consider enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.
Economist Writing Every Day 