I’m not using all of it, but it’s very helpful to see what other instructors have come up with to make teaching monetary policy more fun and more effective. You have to sign up to access it, using your official instructor email address.
It can feel relatively easy to talk to students about their role in the economy as consumers. It is relatively hard to lecture about central banking, because it is less relatable to everyday life. These exercises help us get into the “mind” of a bank.
Thank you to Econiful and Marginal Revolution University for making these resources available. There will probably be an equivalent for fiscal policy produced in the future.
When discussing the median voter theorem with my public policy class, I went on an informative and educational tangent about ranked choice voting.
We gave an example in which we would go out to eat, each pay our own way, but we must all go to the same restaurant in town. We went through the multiple rounds of voting, eliminating least popular alternatives, and came to a conclusion. The winning restaurant was Tropical Smoothie. If you are not familiar, it is nothing to write home about. However, it is also inoffensive and they provide what they say that they will.
The students quite enjoyed the exercise and the process drove the point home that there are perfectly reasonable alternatives to the typical one – man – one – vote status quo.
Entirely separate
Last weekend, my family purchased a new beta fish. There are six people in our family with four children, ages ranging from one to six years old. Thanks to an offhand comment by my wife, I realized that it was such a beautiful opportunity to teach the kids about ranked choice voting. Everybody in the family suggested a name for the fish. The options were: Hibiscus, Jack Sparrow, Bubbles the 2nd <3, sparkels, camouflage, and ‘no’. Which do you prefer?
Daniel Kahneman, the psychologist who won a Nobel prize in economics and wrote the best-selling book “Thinking Fast and Slow“, died yesterday at age 90. Others will summarize his biography and the substance of his work, but I wanted to highlight two aspects of his style that I think fueled his unusual success among both the public and economists.
Daniel Kahneman’s new book amazes me. Not so much due to the content, though I’m sure that will blow your mind if you haven’t previously heard about it through studying behavioral economics or psychology or reading Less Wrong. It is the writing style: Kahneman is able to convey his message succinctly while making it seem intuitive and fascinating. Some academics can write tolerably well, but Kahneman seems to be on a level with those who write popularly for a living- the style of a Jonah Lehrer or Malcolm Gladwell, but no one can accuse the Nobel-prize-winning Kahneman of lacking substance.
This made me wonder if it is simply an unfair coincidence that Kahneman is great at both writing and research, or causation is at work here. True, in more abstract and mathematical fields great researchers do not seem especially likely to be great writers (Feynman aside). But to design and carry out great psychology experiments may require understanding the subject intuitively and through introspection. This kind of understanding- an intuitive understanding of everyday decision-making- may be naturally easier to share than other kinds of scientific knowledge, which use processes (say, math) or examine territories (say, subatomic particles) which are unfamiliar to most people. Kahneman says that he developed the ideas for most of his papers by talking with Amos Tversky on long walks. I suspect that this strategy leads to both good idea generation and a good, conversational writing style.
But how did a psychologist get economists to not just take his work seriously, but award him the top prize in our field? One key step was learning to speak the language of our field, or coauthor with people who do. For instance, summarizing the results of an experiment as showing indifference curves crossing where rationally they should not:
Finally, something that helped Kahneman appeal to all parties was that he avoided the potential trap of being the arrogant behavioral economist. Most economists have a natural tendency toward arrogance, kept somewhat in check by our belief that most people are fundamentally rational. Behavioral economists who think most people are irrational can be the most arrogant if they think they are the only sane one, and should therefore tell everyone else how to behave. But Kahneman avoided this by seeming to honestly believe he is just as subject to behavioral biases as everyone else.
Christians across the world are observing the season of Lent right now, concluding this week. This important period of religious observance involves personal sacrifice of some sort, and for Western Christians a common form of sacrifice is abstaining from consuming meat on Fridays during Lent. But there is one exception: most Christians allow consumption of fish on Fridays, in lieu of other kinds of meat.
But abstaining from meat on Fridays was not always a practice reserved for Lent. Catholics used to abstain from meat for the entire year prior to a 1966 decree by Pope Paul VI. This decree relaxed the rules on fasting and decentralized them. In the US, Catholic Bishops chose to eliminate meatless Fridays, except during Lent.
No doubt this was an important religious change, but it was also an important economic change. And the first question an economist would ask is: how did this impact the price of fish? In our simple supply and demand framework, this should result in a decrease in demand, which would lower the price of fish. Did that happen?
In 1968, economist Frederick Bell asked just that question in an article published in the American Economic Review titled “The Pope and the Price of Fish.” The short answer is that yes, the price of fish did indeed decline!
Surely you have heard by now that a solar eclipse is coming. As the April 8 date approaches, the media/social media coverage will likely rise to a roar. I think we all know that the experience of being in the path of a total solar eclipse is eerie and memorable – – birds and insects can fall silent as night-like darkness falls, and a noticeable chill may be felt in the air.
Maps abound of the eclipse path across North America. For the U.S., it starts in Texas around 1:30 Central time, traverses southern Indiana and northern Ohio around 3:10 Eastern and ends in northern Maine about 3:30. Here is a snip I took from this NASA map, where I zoomed in the Midwest/Northeast section, and traced in red the lines of 90% totality:
If you really want the 100% experience, and if you want it to last the full four minutes, you must be in a relatively narrow strip. And if you want to have good chance of not having clouds obscure the fun, you may need to fly to central Texas. Buffalo, New York is in the middle of the eclipse path, but it is a notoriously overcast place.
But lots of folks, including residents of Chicago, Toronto, and the Boston-Washington corridor, live within the zone of (nearly) 90% totality, where you can see the moon sliding across most of the sun’s disk over the course of a few minutes, and experience significant darkening. The next solar eclipse to touch the U.S. will not be until 2044, and that will be barely visible from three less-populated states, Montana, North Dakota, and South Dakota.
So, I suggest you take the opportunity to enjoy this one to the max. This absolutely entails using special glasses with filters designed for safe viewing of the sun. Do not even think of looking at the sun without such glasses, and be alert lest children pick up the wrong cues and try to look at the sun.
The good news is that eclipse glasses are still available. I ordered some from Amazon a couple days ago that arrived two days later, and I saw them for sale in Lowe’s today. I got some extra to share with random friends and strangers. This can be a great chance to interact with neighbors and children.
The price per pair of glasses varies a lot, so do comparison shop. I look for ones that say “CE and ISO Certified” like these. Be safe and have fun!
I mentioned this in conversation yesterday and they found it of interest, so here is the prospective usecase for blockchain/crypto that is the main reason I am bullish and things like ethereum:
Artificial Intelligent agents will eventually get to the point where we are comfortable letting them act autonomously on our behalf. For them to maximize their value to us, however, they will need to be able to contract with other AI agents without human middlemen slowing down the process. This means they need a way to form contracts outside of the traditional legal system, particularly since we are unlikely to grant them personhood or power of attorney any time soon. Tokens and the blockchain offer an immutable ledger that will serve as a form of credible contracting for agents absent any legal institutions in real time. I expect they legal human agents will remain necessary for early stage formation and late stage ex post adjudication of disputes, but the micro (nano) contracting facilitated in real time will allow for an allocation (and arbitraging) of personal private capital not previously accessible to any but the largest personal and corporate wealth agglomerations.
There you go. That’s why I own a little bit of ethereum and plan on holding it for a few more decades. Don’t know if it will end up being worth anything, but that’s why I own it. NB: I didn’t google it, so I’m not sure if this is a standard usecase or not.
This is a transcript of Lex Fridman Podcast #419 with Sam Altman 2. Sam Altman is (once again) the CEO of OpenAI and a leading figure in artificial intelligence. Two parts of the conversation stood out to me, and I don’t mean the gossip or the AGI predictions. The links in the transcript will take you to a YouTube video of the interview.
(00:53:22) You mentioned this collaboration. I’m not sure where the magic is, if it’s in here or if it’s in there or if it’s somewhere in between. I’m not sure. But one of the things that concerns me for knowledge task when I start with GPT is I’ll usually have to do fact checking after, like check that it didn’t come up with fake stuff. How do you figure that out that GPT can come up with fake stuff that sounds really convincing? So how do you ground it in truth?
Sam Altman(00:53:55) That’s obviously an area of intense interest for us. I think it’s going to get a lot better with upcoming versions, but we’ll have to continue to work on it and we’re not going to have it all solved this year.
Lex Fridman(00:54:07) Well the scary thing is, as it gets better, you’ll start not doing the fact checking more and more, right?
Sam Altman(00:54:15) I’m of two minds about that. I think people are much more sophisticated users of technology than we often give them credit for.
Sam Altman(00:54:21) And people seem to really understand that GPT, any of these models hallucinate some of the time. And if it’s mission-critical, you got to check it.
Lex Fridman(00:54:27) Except journalists don’t seem to understand that. I’ve seen journalists half-assedly just using GPT-4. It’s-
Sam Altman(00:54:34) Of the long list of things I’d like to dunk on journalists for, this is not my top criticism of them.
As EWED readers know, I have a paper about ChatGPT hallucinations and a paper about ChatGPT fact-checking. Lex is concerned that fact-checking will stop if the quality of ChatGPT goes up, even though no one really expects the hallucination rate to go to zero. Sam takes the optimistic view that humans will use the tool well. I suppose that Altman generally holds the view that his creation is going to be used for good, on net. Or maybe he is just being a salesman who does not want to publicly dwell on the negative aspects of ChatGPT.
I also have written about the tech pipeline and what makes people shy away from computer programming.
Lex Fridman(01:29:53) That’s a weird feeling. Even with a programming, when you’re programming and you say something, or just the completion that GPT might do, it’s just such a good feeling when it got you, what you’re thinking about. And I look forward to getting you even better. On the programming front, looking out into the future, how much programming do you think humans will be doing 5, 10 years from now?
Sam Altman(01:30:19) I mean, a lot, but I think it’ll be in a very different shape. Maybe some people will program entirely in natural language.
Someday, the skills of a computer programmer might morph to be closer to the skills of a manager of humans, since LLMs were trained on human writing.
In my 2023 talk, I suggested that programming will get more fun because LLMs will do the tedious parts. I also suggest that parents should teach their kids to read instead of “code.”
The tedious coding tasks previously done by humans did “create jobs.” I am not worried about mass unemployment yet. We have so many problems to solve (see my growing to-do list for intelligence). There are big transitions coming up. Sama says GPT-5 will be a major step up. He claimed that one reason OpenAI keeps releasing intermediate models is to give humanity a heads up on what is coming down the line.
I teach one hour-forty minute classes on Tuesdays and Thursdays. And I allot only sixty minutes for exams. While student enjoy having the unexpected spare time after an exam, that’s a lot of learning time to miss. Therefore, after my midterms, we do an in-class activity that is a low-stakes, competitive game (and, entirely voluntary).
I call this game “The Extent of the Market” and it has three lessons. Here’s how the game works:
I have a paper handout, a big bag of variety candy, and a URL. The handout is pictured below-left and lists the types of candy. Each student rates their preference with zero being the least preferred candy. Whether they keep their preferences a secret is up to them. Next, I distribute two pieces of candy to each of them. Importantly, their candy endowment is random and they don’t get to choose or trade (yet). Finally, the URL takes them to a Google sheet pictured below-right where they can choose an id and enter there ‘value score’ under Round 0 by summing the candy ratings of their endowment.
Round 1 is where they get to make choices. I tell students that their goal is to maximize their score and that there is a prize at the end. They are now permitted to trade with anyone at their table or in their row. It doesn’t take long since their candy preferences compose of only the short list, their endowments are small, and the group of potential trade partners is small. When trading is finished, they enter there new scores under round 1.
Lesson #1: Voluntary trade makes people better off.
For each transaction that occurred, someone’s score increased. And in most cases two people’s scores increased. Not everyone will have traded and not everyone will have a higher score. But no one will have a lower score, given the rules and objective of the game. Importantly, the total amount and variety of candy in the little classroom economy hasn’t changed. But the sum of the values in Round 1 increased from Round 0. Trade helps allocate resources where they provide the most value, even if the total amount of physical stuff remains fixed. If it’s a microeconomics class, then this is where you mention Pareto improvements.
Round 2 follows the same process, but this time they may trade with anyone in their quadrant or section of the room. After trading concludes, they enter their scores at the URL under round 2.
Lesson #2: More potential trade partners increases the potential gains from trade.
Again, the variety and total amount of candy in the room remains constant. The only thing that increased was the size of the group of people with whom students could trade. And, they again earn higher scores or, at least, scores that are no lower. People have diverse resources and diverse preferences, and the more of them that you can trade with, the more opportunities to find complementary gains. Clearly, this means that increasing the size of the pool of trading partners is beneficial. One among the many reasons that the USA has had great economic success is that we are a large country geographically with diverse resources and a population of diverse preferences. This means that we have a large common market with many opportunities for mutually beneficial trade. The bigger that we make that common market, the better. Clearly, the implications run afoul of buy-local and protectionist inclinations.
Round 3 proceeds identically with students able to trade with anyone in the room and they enter their scores. At this time the game is finished. It’s important to identify the cumulative class scores across time and to reemphasize lessons #1 & #2. Often, the cumulative value-score will have doubled from Round 0, despite the fixed recourses, making no one worse off. If trading with a row, and then a section, and then the whole class results in gains, then there is an analogy to be drawn to a state, country, and the globe.
Lesson #3: Trade changes the distribution of resources.
Despite an initial distribution of resources, voluntary trade changed that distribution. While no one is worse off and plenty of students are better off, measured inequality may have been affected. Regardless, once a voluntary trade occurs, the distribution of candy and of scores changes. This has implications for redistributive policies. If income or wealth is redistributed in order to achieve some ideal distribution, then the ability to freely trade alters that distribution. The only way to achieve it again would be for another intervention to change the candy distribution by force or threat thereof. Consider that sports superstar Lebron James became rich by playing basketball for people who like to watch him. If we redistribute his income, and then permit him the freedom to voluntarily play basketball again, then the income distribution will change as he again trades and increases his income. Similarly, giving money to a low marginal product worker can provide some short-term relief. But, if the worker resumes their prior behavior and productivity, then the same determinants and resulting income persist.
It’s a fund game and students enjoy it. There are some important limitations. #1: There is no production in this game nor incentives for production. This is a feature for the fixed resources aspect of the game. But this is a bug insofar as students think about US jobs vs international jobs. I can assert that the supply side works similarly to the demand side, but students see it less clearly (it helps to draw these parallels throughout the semester). #2: While there is a maximum possible score in the game, the value created in reality is unbounded. There is no highest possible score IRL. #3: There are no feedback dynamics. Taxes associated with income redistribution cause workers to require higher pay, worsening pre-tax inequality. People respond to incentives, and the tax/subsidy component that determined the initial distribution of candy is absent.
It’s a fun game. If you try it, then please let me know how it goes or leave suggestions in the comments.
*By default, Google Sheets anonymizes users. You could have them sign in or use an institutional cloud drive to remove problems that might be associated anonymity.
**If your student can’t handle choosing their own id, then you can just list your students.
***Ideally, each increased trade-group is a superset of the prior round’s potential trading partners.
****You can do more than 3 rounds, but the principle doesn’t change
*****More trade will occur with more students, a greater variety of possible candies, and with more candies endowed per person. You can alter these as needed depending on the classroom limitations.
I’ve always told my health economics students that Medicaid is both better and worse than all other insurance in the US for its enrollees.
Better, because its cost sharing is dramatically lower than typical private or Medicare plans. For instance, the maximum deductible for a Medicaid plan is $2.65. Not $2650 like you might see in a typical private plan, but two dollars and sixty five cents; and that is the maximum, many states simply set the deductible and copays to zero. Medicaid premiums are also typically set to zero. Medicaid is primarily taxpayer-financed insurance for those with low incomes, so it makes sense that it doesn’t charge its enrollees much.
But Medicaid is the worst insurance for finding care, because many providers don’t accept it. One recent survey of physicians found that 74% accept Medicaid, compared to 88% accepting Medicare and 96% accepting private insurance. I always thought these low acceptance rates were due to the low prices that Medicaid pays to providers. These low reimbursement rates are indeed part of the problem, but a new paper in the Quarterly Journal of Economics, “A Denial a Day Keeps the Doctor Away”, shows that Medicaid is also just hard to work with:
24% of Medicaid claims have payment denied for at least one service on doctors’ initial claim submission. Denials are much less frequent for Medicare (6.7%) and commercial insurance (4.1%)
Identifying off of physician movers and practices that span state boundaries, we find that physicians respond to billing problems by refusing to accept Medicaid patients in states with more severe billing hurdles. These hurdles are quantitatively just as important as payment rates for explaining variation in physicians’ willingness to treat Medicaid patients.
Of course, Medicaid is probably doing this for a reason- trying to save money (they are also trying to prevent fraud, but I have no reason to expect fraud attempts are any more common in Medicaid than other insurance, so I don’t think this can explain the 4-6x higher denial rate). This certainly wouldn’t be the only case where states tried to save money on Medicaid by introducing crazy rules hassling providers. You can of course argue that the state should simply spend more to benefit patients and providers, or spend less to benefit taxpayers. But the honest way to spend less is to officially cut provider payment rates or patient eligibility, rather than refusing to pay providers as advertised. In addition to being less honest, these administrative hassles also appear to be less efficient as a way to save money, probably because they cost providers time and annoyance as well as money:
We find that decreasing prices by 10%, while simultaneously reducing the denial probability by 20%, could hold Medicaid acceptance constant while saving an average of 10 per visit.
Medicaid is a joint state-federal program with enormous differences across states, and administrative hassle is no exception. For administrative hassle of providers, the worst states include Texas, Illinois, Pennsylvania, Georgia, North Dakota, and Wyoming:
Source: Figure 5 of A Denial a Day Keeps the Doctor Away, which notes: “The left column shows the mean estimated costs of incomplete payments (CIP) by state and payer. The right column shows the mean CIP as a share of visit value by state and payer. “
Lately on Twitter this chart has been going around:
The chart comes from Bloomberg journalist Justin Fox, who always puts together interesting economic data. You can read his interpretation of the data at Bloomberg, but the folks posting it on Twitter all seem to have the same shock and awe: Detroit was the richest big city in 1949. And of course we all know that today it isn’t. Still, the Detroit MSA has done OK since 1949, even though it is no longer anywhere near the top.
How well has Detroit done? Despite industrial decline and many other major problems, median household income of the Detroit MSA was around $71,000 in 2022 according to the Census Bureau. How does this compare to the $3,627 median income in 1949? It’s about double in real terms: you can multiply it by about 10 using the Census’ preferred inflation adjustment for household incomes since 1949 (the C-CPI-U since 2000, and the R-CPI-U-RS before that).
Economist Writing Every Day 
