Astral Codex Ten

Wall Street

52 min read

Wall Street

Article

Wall Street is a recurring place in the Astral Codex Ten archive, appearing 16 times across 16 issues between February 12, 2021 and January 16, 2026. The archive places it in contexts such as “Wall Street developed a financial instrument that let them short ConTrackeds”; “I would trust Wall Street to be able to apply the techniques it’s learned playing the stoc”; “jumping out of Wall Street windows”. It most often appears alongside Japan, US, Twitter.

Metadata

  • Category: Places
  • Mention count: 16
  • Issue count: 16
  • First seen: February 12, 2021
  • Last seen: January 16, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

List Of Fictional Cryptocurrencies Banned By The SEC
February 12, 2021 · Original source
Banned because: Wall Street developed a financial instrument that let them short ConTrackeds, then tried really hard to prevent bridges from being built.
Mantic Monday: Scoring Rule Controversy
March 01, 2021 · Original source
Or here's a more optimistic way to think about this: if we can get good short-term and medium-term prediction markets, we'll learn a lot about forecasting and who's good at it. Then we can ask those same people to make long-term predictions pro bono, or for a flat fee. Even if there were some industry that (for some reason) couldn't issue stock or participate on the stock market, I would trust Wall Street to be able to apply the techniques it's learned playing the stock market to predict trends in that industry too. In the same way, whatever the prediction market's version of Wall Street is will probably be good at things beyond just playing prediction markets.
Book Review: A Brief History Of Neoliberalism
May 04, 2021 · Original source
Previous phases of capitalist history—one thinks of 1873 or the 1920s—when a similarly stark choice arose, do not augur well. The upper classes, insisting on the sacrosanct nature of their property rights, preferred to crash the system rather than surrender any of their privileges and power. In so doing they were not oblivious of their own interest, for if they position themselves aright they can, like good bankruptcy lawyers, profit from a collapse while the rest of us are caught most horribly in the deluge. A few of them may get caught and end up jumping out of Wall Street windows, but that is not the norm. The only fear they have is of political movements that threaten them with expropriation or revolutionary violence. While they can hope that the sophisticated military apparatus they now possess (thanks to the military industrial complex) will protect their wealth and power, the failure of that apparatus to easily pacify Iraq on the ground should give them pause. But ruling classes rarely, if ever, voluntarily surrender any of their power and I see no reason to believe they will do so this time. Paradoxically, a strong and powerful social democratic and working-class movement is in a better position to redeem capitalism than is capitalist class power itself. While this may sound a counter-revolutionary conclusion to those on the far left, it is not without a strong element of self-interest either, because it is ordinary people who suffer, starve, and even die in the course of capitalist crises (examine Indonesia or Argentina) rather than the upper classes. If the preferred policy of ruling elites is après moi le déluge, then the deluge largely engulfs the powerless and the unsuspecting while elites have well-prepared arks in which they can, at least for a time, survive quite well.
Already nearly one-third of stock assets on Wall Street and nearly half of US Treasury bonds are owned by foreigners, and the dividends and interest flowing out to foreign owners are now roughly equivalent to, if not more than, the tribute that US corporations and financial operations are extracting from abroad (Figure 7.1). This balance of benefits will turn more strongly negative the more the US borrows, and it is now borrowing from abroad at a rate approaching $2 billion per day. Furthermore, if US interest rates rise (as at some point they must) then what happened to Mexico after the Volcker interest rate increase in 1979 starts to loom as a real problem. The US will soon be paying out far more to service its debt to the rest of the world than it brings in. This extraction of wealth from the US will not be welcome domestically. The perpetual increases in debt-financed consumerism that have been the foundation of social peace in the US since 1945 would have to stop.
Your Book Review: The Accidental Superpower
May 21, 2021 · Original source
Then there’s something like the CNBC effect. Why did stock prices increase/decrease on any random day? Intelligent people don’t think like this, but there is demand for “analysis” of such questions. Some people go so far as to say that it’s the same reason that Wall Street provides market research; the demand is there, so someone will sell it. It reminds me of a story I heard from Reason columnist Ron Bailey. When he was selling The End of Doom, an agent asked him to change his thesis, because optimism doesn’t sell, whereas doom could make him a very rich author. I very much doubt that Zeihan created his analysis in order to arrive at the conclusion that we’re going to see a global Disorder over the next decade, 11 but once you see structural incentives it’s hard to banish them from your thoughts (see also, Meditations on Moloch).
Inline links: 11, Meditations on Moloch
The Passage Of Polymarket
February 07, 2022 · Original source
Easy to create your own subsidized markets “Real money” should be self-explanatory. Metaculus and Manifold are both very nice, but so far they’re limited to a small group of enthusiasts playing in their spare time. I value them both, but neither is the killer app that makes prediction markets as central to everyday life as stock markets or polls or whatever. “Easy to use” is kind of self-explanatory, but with some caveats. A big part of ease-of-use is liquidity; you can get that from a big user base or from clever deployment of automated market makers. A market that requires crypto knowledge is harder to use than one that doesn’t; one that’s inaccessible from the US is harder to use than one that isn’t. Also all the normal things like UI and search. “Easy to create your own markets” is where we’ve gotten stuck so far. Prediction markets are absolutely on top of questions about whether Donald Trump will win various elections. This is a solved problem. What I really wanted last year (and would have subsidized!) was a market about whether Alameda County, California, would permit indoor gatherings of 50 people on January 8th 2022 (ie would I be forced to cancel my wedding). But I also would have appreciated the ability to put a few questions to prediction markets before starting my psychiatry practice, or my grants program, or any of a dozen other things I did. A friend has gone further, and half-jokingly said they want to create conditional prediction markets about whether they’re compatible with various women in our friend group, to be paid out six months after the first date. Some of these applications are attempts to route around the principal-agent problem. Maybe I have some question about whether a certain grant would succeed, I’m not sure who to ask, and even if someone gives me a “Bob Smith, Grant Evaluator” business card, I don’t know if he’s any good. A prediction market takes all the pain out of searching for information - if I subsidize it enough, it’ll attract people with the relevant skill set who will solve my problem for me. Probably some of these ideas wouldn’t work, but probably other ideas I can’t even think of now would. I don’t know what the killer app for prediction markets will be. But we’re not going to find out unless people can create their own subsidized markets and play around. Polymarket took some baby steps towards this before the settlement: they had a Discord server where anyone could propose questions, and a lot of those questions became markets. But they still had to be general interest, not “let Alice’s five friends predict her dating life”. And there’s a big difference between “talk it over with company representatives on a Discord server” and “press a button”. Imagine if you could only tweet by emailing Jack Dorsey and convincing him that your comment was a good thing to have on Twitter. Even if Jack had good judgment and approved most requests, this would be a long way from the limbic system < — > Send Tweet loop that real Twitter users know and love. I asked some people in the business why they won’t do this. They said most people are bad at writing good resolution criteria. They don’t want their employees to get stuck resolving incredibly dumb questions about people’s dating lives, hunting down inaccessible or conflicting information, and making a bunch of people mad whichever way they decide. As far as I can tell, Manifold Markets solved that problem with their “proposer decides the resolution, caveat emptor” strategy. But Manifold is US-based and can’t use real money, so there’s still no way to subsidize a market effectively. (This is why I’m pessimistic about Kalshi. They could potentially do a lot of good in the “will Afghanistan collapse?” types of markets the Nobel laureates want, though even there I think some of their betting limits will give them trouble - $25,000 is good money, but not quite good enough to incentivize founding the prediction market equivalent of a Wall Street trading firm. But even if they solve this, I can’t imagine the regulators giving them permission to host “will this grant work out?” or “how will my dating life go?” markets; it’s just too weird, and the CFTC is too conservative. I don’t know, maybe their connections will come through and pull it off, but I don’t even know if they’re ambitious enough to want this, and I hate having to rely on one organization.) Right now my hopes are, in ascending order of likelihood: Manifold figures out some kind of weird crypto thing that isn’t real money from a legal perspective, but is real money from a “people really want it and will put a lot of effort into getting it” perspective.
Inline links: Metaculus, Manifold
Information Markets, Decision Markets, Attention Markets, Action Markets
March 28, 2022 · Original source
Banned because Wall Street developed a financial instrument that let them short ConTrackeds, then tried really hard to prevent bridges from being built.
Your Book Review: Viral
July 30, 2022 · Original source
This is especially frustrating when the random guy on the internet turns out to be right. When people talk about “trusting the experts”, I think they mean trusting people with technical expertise over people without technical expertise. This makes sense a lot of the time. Probably almost all the time. If you need your car fixed, have a weird rash on your skin, or have a leaking pipe in your house, you consult a mechanic, a dermatologist, or a plumber because they have the technical expertise you need on those issues. You don’t ask a random guy on Twitter for help. But what if you have a question about investment banking on Wall Street, and how it should be regulated. Should you put the question to a bunch of investment bankers? After all, they do have the most technical expertise on this subject, right? They probably know more about investment banking than you or me, or a lot of the people pushing for more financial regulations. Now we’ve run into an issue: they do have technical expertise, but it’s bundled together and intertwined with a bunch of incentives that could lead to biased judgment, so we can’t take what they’re saying as some pure, objective truth. Of course, their technical expertise is still valuable, so we shouldn’t necessarily throw out everything they say either. The proper response is to listen to what they’re saying and weigh the information accordingly after considering the incentives they’re facing, and possible biases. I guess what I’m trying to say here is that scientific institutions, though probably not as bad as Wall Street, are still made up of human beings who are susceptible to all kinds of cognitive biases, including group think, confirmation bias, and the good ol’ Not Wanting To Be Wrong. So what should we do about this? Well, the easy option is to just become an insane person, like Alex Jones, and assume the experts are lying all the time about everything. This strategy has the advantage of letting us feel edgy and rebellious, but it’s not very helpful if we actually want to figure these issues out. On the other hand, if we want to seriously try to discern truth from expert claims on controversial topics, that’s a messy challenge that involves considering their technical expertise, as well as potential biases they might have, as well as our own potential biases. Conclusion 3: Some optimism about science I know this has probably been a bit of a depressing post to read, but my final conclusion is actually one of optimism about the state of science. What differentiates science from other ways of knowing is its self-correction mechanisms. It’s all about changing our minds and reevaluating our beliefs based on new evidence and clearer understanding of things. This is basically what we’ve seen in the way the scientific community has changed positions on the lab leak hypothesis. Harsh critics might refer to this as a “flip flop”, or point out that the lab leak hypothesis never should have been dismissed in the first place, but I see it as a commendable error correction. What’s even cooler is that much of this reevaluation was the result of amateurs and semi-amateurs making discoveries based on freely accessible genomic sequence data, and open source online sequence analysis tools. Plus the fact that, despite their lack of official credentials, their analysis was taken seriously (eventually), when it became evident that they were making good points. This is a credit to the scientific community. Further sources to check out Natural Origins Proponents The most comprehensive post I’ve found making the case for natural origins is Philipp Markolin’s Substack post, which attempts to apply Bayesian reasoning to the question. Definitely recommend.
Inline links: The most comprehensive post I’ve found making the case for natural origins is Philipp Markolin’s Substack post
The Psychopharmacology Of The FTX Crash
November 16, 2022 · Original source
Here’s a report by a Wall Street trader who got addicted to cocaine - another dopaminergic drug:
Inline links: Here’s a report
I think you could make an argument that dopaminergic drugs shift various complicated risk curves in the brain. But a lot of Wall Street is on stimulants of one sort or another, and most of them don’t act like FTX did. Emsam is a little stranger than the usual Wall Street stimulants, and combining it with other stimulants might amplify the effects. But I still would think in terms of “how much are we moving the risk curves, and is it really that much further than a lot of other things do all the time?” rather than “does this switch you into uncontrollable pathological gambling mode?”
Links For March 2023
March 10, 2023 · Original source
24: Related: The Mensa Fallacy. Many older studies show that very-high-IQ people are mentally imbalanced, bad at real-world tasks, or unambitious. Newer, more representative studies don’t show this. The most likely explanation: the older studies got their very-high-IQ samples from Mensa, the most convenient place to find lots of very-high-IQ subjects. But within the population of very-high-IQ people, Mensa is adversely selected for non-IQ traits; geniuses who are mentally healthy and ambitious are probably inventing quantum computers at MIT or making bank on Wall Street; it’s the geniuses who can’t make it in regular society who are going to every Mensa to boast about how quickly they can solve Rubik’s Cubes. Any study that uses a Mensa sample to study very-high-IQ people and conclude that they’re defective should be considered suspicious.
Inline links: The Mensa Fallacy
Highlights From The Comments On Housing Density And Prices
May 10, 2023 · Original source
Makeshift housing in a North Dakota oil boom town (source) If each person creates half a job, the original 1,000 oilmen attract 500 service workers, those 500 attract another 250, and so on until population stabilizes at 2,000 people. In this model, if there are fewer than 2,000 houses in the town, demand exceeds supply (no matter what is going on in the rest of the country), but if there are more than 2,000, supply exceeds demand. So if we imagine Google’s presence as an oil-like resource, the extra demand for housing in the Bay should gradually decline: at some point, you will have finished housing the Google workers and the service workers who support them. But this isn’t right either, because Google isn’t a natural resource - it’s a company founded by Bay Area residents. If you got more Bay Area residents, you would (with some delay) get more Googles. Or: Austin gets lots of jobs from Tesla. Tesla wasn’t founded by Austinites. But it moved to Austin when it became a known “tech hub”, ie a place with lots of tech companies and tech employees. It wouldn’t have moved to Austin if Austin was still an uninhabited plain or a one-horse town. So as Austin got bigger, it attracted more tech companies. So in both the Bay Area case and the Austin case, having more people attracted more tech companies, either because the residents themselves found the company or because the company gets attracted to this newly bustling city. Potential counterargument: Each new Bay Area resident gives the Bay another lottery ticket to found the next Google. If having the first Google gets it an extra 1 million people, but there are 300 million people in the US, then those extra 1 million only give it a 1/300 chance of winning the next lottery. So even though the Bay Area won the lottery once, and this made it have high demand, this doesn’t mean the high demand will cause it to win more lotteries. If you win the lottery once, spend all your winnings on more lottery tickets, and keep doing this forever, you haven’t invented an infinite money printing machine, eventually you’ll just lose. Potential counter-counter-argument: the Bay got Google, and Facebook, and Apple, and . . . so these can’t all be separate lotteries. I think you should probably model it as a high-level lottery to become the next hub of a tech-sized industry, plus many low-level lotteries where once you’re the tech hub, you’re attracting lots of techies, and each techie gives you a ticket in a lottery where the denominator is the number of techies to found the next big tech company. And the Bay might have half the US’s techie population. So maybe here there is a self-sustaining lottery-winning cycle, at least until tech plays itself out and nobody wants any more tech companies. And that might take a long time. Tom (author of Tom Thought) writes: The primary drivers of demand for living in NYC are the specific opportunities available in NYC. It is true that on long time horizons, one of the reasons these opportunities have tended to collect in NYC is that it is a dense place. But those aren't the only reasons - NYC is much more important than other, bigger cities in other parts of the world for complex historical reasons. Even if a catastrophe were to wipe out half the city, there would still be a great deal of demand to live near important institutions like Broadway, Wall Street, Port of NY & NJ, Columbia, etc (assuming those institutions survived the catastrophe). Increasing the number of housing units has a very mechanical impact on how many people can live in the place. But it has only a second-order impact on the types of institutions that drive demand to live in the city. People don't just generically crave to live near other people for the most part (a handful of urbanist freaks like myself excepted). The Bay Area is a great example of this. It is much less populated than other much cheaper cities. Density isn't why people want to live there - it's access to a specific culture and specific institutions. Demand for that is not simply a function of density - some people want to be part of Bay Area culture and others don't. Adding more units will induce some demand as a second-order effect, but will bring prices down as a first-order effect. To relate this to your model: we might be able to say that the country has a certain number of abstract "culture points" that have been allocated to different cities by various historical forces. Each culture point a city has increases demand to live in that city by a certain amount. Adding more people to the city may allow it to generate additional culture points over time, or acquire culture points from other cities, but this doesn't happen right away, and is determined by a host of factors other than just density. Under this model, we expect a place like NYC to always cost much more than North Dakota (since NYC possesses a large number of culture points), but we would also expect that adding additional housing units to NYC would bring costs down (since there are now additional housing units per culture point). Perhaps this process will over time allow NYC to steal away some culture points from Chicago, Boston, or other cities, but this is a secondary effect. This just seems to be passing the buck. Yes, people move to New York because it has Broadway, Columbia University, and Wall Street. Why does it have those things? Because one in every X New York citizens founds a good artistic/educations/financial institution, and New York has a large population of employees to work at those institutions and customers to patronize those institutions. If Conanicut Island had a population of 10 million people instead of Manhattan, there would be lots of great institutions on Conanicut and it would have more culture points. I don’t think it’s a culture-point game and population/density just sort of occasionally redistributes culture points, I think to a first approximation culture points just track population/density. Maybe they track the population/density of upper class people better than the total population/density, but I don’t think this is a big enough distinction to sink the argument. 3. Comments About Chinese Ghost Cities Some people brought these up as a good natural experiment: the Chinese really did try building millions of houses on their equivalent of a North Dakota plain. What happened? Jeremiah Johnson (author of Infinite Scroll) writes: You currently seem like you're at the stage of understanding the thought experiments pretty well, but not understanding them on a DEEP level. For example with your hypothetical, this has actually happened before! Kind of. China built a bunch of 'ghost cities' basically out of nothing, and while there was an initial craze of speculation and tons of investment and building... nobody went to live in those cities most of the time. And now they're deeply distressed assets worth basically nothing. When nobody actually lives in the ghost city, it doesn't matter that they have super dense housing. There's no demand. (the only reason they might be worth something is that the CCP very, very much does not want to pop their huge housing bubble and is likely to bail out some of the parties involved) Parmenides (author of Last House On The Left) writes: I think your mixing up the agglomeration effects of density, which is what induces the demand, and the housing supply. You can't just build a city and expect people to move in, China has tried that. But if you have the agglomeration effects of density and shortage of housing due to artificial constraints, which we have all across the US, then you get dense areas with high housing costs. sdwr writes: Think of China's ghost cities / apartment blocks. Prices surely can't be that high there. Maybe the answer is that developers are good at their job, and build supply where theres demand for it? But several other people object that although the Western press made a big deal about Chinese ghost cities a few years ago, it mostly just took a couple of years for people to move in, and now at least some of them seem to be thriving. For example, Michael quotes the Wikipedia article, Under-occupied Developments In China: Reporting in 2018, Shepard noted that "Today, China’s so-called ghost cities that were so prevalently showcased in 2013 and 2014 are no longer global intrigues. They have filled up to the point of being functioning, normal cities". Ash Lael writes: I'm sceptical of the Chinese "ghost city" phenomenon. I haven't explored the issue rigorously but my impression is that in areas that were previously dismissed as "ghost cities" like Ordos Kangbashi, the population is now large and growing. I think we in the west are so used to infrastructure bottlenecks and short sightedness and anti-construction policies that the idea of it being possible to build the housing and infrastructure to accomodate expected demand ten years in the future is completely foreign to us. Perhaps building brand new cities before they are even needed is what the YIMBY utopia looks like. See also Bloomberg: China’s Ghost Cities Are Finally Stirring To Life After Years Of Empty Streets. This wasn’t trivial. It looks like the Chinese government had to put in some work to make people move in, including opening good schools and universities there. Probably if they had just built apartments in the middle of the desert and nothing else, they would have stayed empty. But that’s even more of a reductio ad absurdum than the original ghost city plan. Kangbashi, China’s most famous ghost city. What are housing prices like in the ghost city? Again from Bloomberg: Sitting on the southern outskirts of Inner Mongolia’s Ordos City (population 2.2 million), Kangbashi was the archetypal ghost city 10 years ago, with barren boulevards and empty buildings standing forlornly in the desert. Local officials are adamant that things have changed. They say 91% of homes in the district are occupied. In fact, after a yearslong construction freeze, the government approved six housing projects in 2020 and expects 3,000 homes to be built by the end of this year. Apartments in a new development are selling for 9,500 yuan per square meter, and downtown they go for 15,000 to 16,000 yuan, according to Liu Yueyue, 28, a salesman at a new residential development in the district’s northeast. “Would houses in a ghost town sell at such high prices?” asks Liu. Half of his customers come from outside Kangbashi, and most are parents who want to send their children to the well-regarded local schools, he says. Looking at this list of real estate prices across Chinese cities, Kangbashi seems squarely in the middle - for example, Wuhan and Xian are also in the 15,000 - 16,000 range. I claim this supports my argument: surely twenty years ago, houses in this particular deserted corner of Inner Mongolia would have been dirt cheap (if any even existed). But if you build a city there, it becomes just as expensive as any other city! Here it’s very obvious that the density caused the high prices instead of the other way around. Still, the Chinese housing market is weird, with significant vacancies even in expensive, well-developed cities. Paul Botts: No official vacancy rates are published in China and no specific definition of it exists there. Various think tanks and researchers both within that country and elsewhere have published estimates ranging from as low as 11 percent to as high as 24 percent. Those estimates have been for varying samples of Chinese cities, have used various definitions of housing vacancy rate, etc. The best (as in most systematic) estimate yet produced has come from researchers at a university in Liaoning. They used night-time urban lightsheds captured by a new (2018 launch) Chinese satellite having a new level of light sensing technology which allows separating out light from parks and plazas. They covered a large sample (49 cities), and made their sample representative of city type, city size, regions within China, etc. They also crossed-referenced with local housing data to ensure accurate balancing of their sample and to confirm that the satellite was successfully identifying light coming from housing blocks. They found vacancy rates of just under 20 percent in China's Tier 1 cities, and found rates above 20 percent in 40 of the 49 cities. They found the highest vacancy rates in western and northeastern cities, which are also the newest ones; that finding is consistent with the hypothesis of significant numbers of recently-built ghost cities. https://www.researchgate.net/publication/345092218_Housing_Vacancy_Rate_in_Major_Cities_in_China_Perspectives_from_Nighttime_Light_Data And Phil H (author of the blog Tang Poetry) writes: The price of housing in China has skyrocketed over the past few decades, as all those extra apartments have been built. I live in a pleasant but unremarkable southern city, and I paid London prices (about 4.5m yuan/$650k for a 1,300 sq ft flat). That seems to match Scott's hypothesis that high density leads to high prices. House prices here have risen much faster than incomes. They've risen in rural areas, too, but the increases in price in cities have been stratospheric. 4. Comments Accusing Me Of Not Considering Tokyo, Even Though I Included A Section In The Post On Why I Didn’t Think Tokyo Was Relevant I won’t name and shame people, but for example: You excluded Tokyo from your dataset. Tokyo has much higher density than SF and much lower price per sqft. Tokyo just kills this. Tokyo is bigger than New York and has significantly lower rent because they build more housing! This is in a wealthy country with even lower interest rates than the US. I don't think you have justified excluding non-US metros, like Tokyo, or Auckland. Doesn't this lead to the natural conclusion that there is a sufficient level of housing to build, and that the problem is that the USA's many metros are structured to prevent housing? It seems like you're just arguing that US metros are bad at building housing, which is also what Matt Yglesias is arguing. "Change my mind about housing, but don't mention Tokyo" is like saying "Change my mind about gun possession, but don't mention Switzerland." You can't test the effect of allowing new housing unless you're willing to look at cities that do, in fact, allow it. Tokyo and NYC both attract tons of new residents But Tokyo's housing rents have been stable, while NYC rents keep rising. Why? Tokyo has permissive housing construction laws. NYC makes building new housing almost illegal. Yes, dense cities are attractive, and that makes them get more dense over time. But it only makes them more expensive if you forbid new housing to keep up with the new residents. Tokyo! But I’m like the 10th person to bring it up… As I wrote on the original post (not even edited in! it’s been there the whole time!): I worry someone will bring up Tokyo as a counterexample. But I think Tokyo managed to build its way to low housing prices in the context of the rest of Japan also having good housing policy. Even if that isn’t true, Tokyo on its own is a quarter of the Japanese market, so it might be able to exhaust the entire pool of Japanese house-seekers by itself! That is, yes, you’re all correct that cities are only expensive in the context of more demand for city housing than the (NIMBY-constrained) city housing market can currently supply. You are all correct that if this problem were solved at the national level, then city housing would be cheap, and every additional city house would make it cheaper. My claim is that marginal changes - like Oakland building an extra 10,000 units, but everyone else staying the same - will most likely increase Oakland prices. Yes, if Oakland unilaterally built 50 million units, that would soak up the entire excess demand and probably lower prices everywhere (including Oakland). Yes, if the entire US switched to good housing policy at the same time, that would probably lower prices everywhere (including Oakland). But if we don’t do any of that stuff, and just build another 10,000 houses in Oakland, I think it would probably increase prices in Oakland. Some other people brought up that Japan has a declining population, and it’s much easier to have low house prices when your population is declining (compared to some previous time when number of houses presumably matched number of people), but ddd pointed out that people continue to migrate from the Japanese countryside to Tokyo, so its population continues to increase. Also, Mike (I’m stitching together two comments here): In a country with a declining population, you would expect that fewer homes are being built per capita because there's little to no competition for existing homes. But it's exactly the opposite! Japan builds far more homes per capita than the US does, despite their declining population […] As a result, the average Japanese home is very new and the average house is torn down and replaced after a relatively short 30 years. They're living in nice new homes for cheaper. 5. Comments Accusing Me Of Not Understanding Economics Maximum Limelihood Estimator writes: I think you're making a very common mistake here of confusing supply/demand with *quantity* supplied or quantity demanded. (This is very common! we teach students about this in micro 101 because it's so easy to make!) What you're seeing is that the quantity supplied is correlated with housing prices (true!). But this is very different from establishing that the supply curve--i.e. the amount of housing that would be produced at any given price, and what moves up/down when we regulate/deregulate supply--is positively correlated with price. Figuring out what supply curves look like is a lot less intuitive and requires some high-grade econometrics, which is why economists had to set up a whole commission just to study this particular problem (the Cowles Commission). In terms of resources for understanding how these concepts are different, a micro 101 textbook will cover this distinction. For the econometrics side of this, I've heard good things about Scott Cunningham's *Causal Inference Mixtape*, although I haven't personally used it. My claim is that increasing density within a city shifts the demand curve for housing within that city, because of increasing desirability. MLE later gets more on point: The effect you're discussing here is kind of real in a sense. When the marginal utility of housing increases for *other* people, density arguably becomes more desirable for me, which is kind of like the demand curve shifting up. These are called bandwagon goods and discussed here: http://econfac.bsu.edu/research/workingpapers/bsuecwp200804gisser.pdf In theory, the bandwagon effect could be so strong that parts of the demand curve are upward-sloping. Solutions like this are not, technically, prohibited by the laws of mathematics, just the laws of economics. (And arguably of physics--see paper for conditions where these kinds of bandwagon effects imply the amount of housing in the city would have to be negative). In practice, this effect exists but just can't overcome the normal, non-weird economics that says "making more of a good makes the prices fall." Again, I claim the existence of Manhattan vs. Conanicut shows that sometimes it does. I cannot find the words “housing”, “real estate”, or “land value” anywhere in that paper. Alex Poterack writes: There's two things going on here: confusing shifts in demand with movement along the demand curve, and getting causation backwards. You're assuming density causes prosperity, rather than prosperity causing density. There are ways the former can happen, but the bigger thing is that, for a wide range of historical reasons, you can make a lot of money in NYC and SF, so lots of people want to live there, so they get very dense. This is the prosperity shifting demand right, so at any given price, more people want to live there; this drives prices up, and they go higher the more fixed supply is. If you built a bunch of housing in Oakland, lots of people would move there because it's cheaper, which is movement along the demand curve; it's still the same number of people who want to live there at any price. Now, it's possible that the increased number of people living there makes the city more prosperous (this is the phenomenon of induced demand), which would shift demand right, but there are way more differences between NYC/SF and Oakland than just the density, so I don't think it would shift demand enough to offset this. In particular, if it's just a small increase in small, it's also a small increase in density, so there's almost no shift in demand (but there is movement along the curve). I still think this is missing my point, but I present it here in case anyone else is enlightened by it and wants to try further to convince me I’m making this mistake. 6. Comments By Famous People Who Potentially Have Good Opinions Scott Sumner is an economist and blogger; he writes: It is certainly the case that building more housing can make a city more desirable, and that this effect could be so strong that it overwhelms the price depressing impact of a greater quantity supplied. But studies suggest that this is not generally the case. Texas provides a nice case study. Among Texas’s big metro areas, Austin has the tightest restrictions on building and Houston is the most willing to allow dense infill development. Even though Houston is the larger city, house prices are far higher in Austin: Houston pretty much describes the “Oakland with more housing” outcome that Alexander views as somewhat far-fetched. Only in this case, it’s Austin with more housing. Alexander seems too quick to accept the, “If you build it they will come” idea—that you can build more housing and thereby boost demand so much that prices actually rise. I started the post with a graph of about 50 cities, showing a positive correlation between density and price. I’m having trouble seeing how Sumner’s point isn’t just “if you remove 48 of those cities and cherry-pick two, the relationship is negative”. My attempt to place Austin and Houston on the original graph, using Sumner’s data plus a few other things available online. Why weren’t they on there already? Maybe because the graph is metro areas and Sumner was talking about Austin and Houston as cities, but I’m not sure and agree this is confusing. Everyone knows Austin is more expensive than Houston because Austin is a trendy tech and culture hub and Houston isn’t (and relatedly, because Austin’s median family income is 50% higher than Houston’s). Unless someone wants to claim that its failure to build housing helped turn it into a trendy tech and culture hub, I don’t think there’s much point to this comparison. It’s true that Houston’s bigger size didn’t let it leapfrog over Austin to become a trendy tech and culture hub, which goes against some of what I claimed in the first part of this post. But I never claimed there would be a perfect 1-1 correlation between city size and trendiness, or that you could never find a pair of cities where one was bigger but the other was more trendy. Just that there would be a correlation. Moving on: Here’s the problem with this argument. It mixes up population change due to economic effects such as the benefits of agglomeration, with population changes due to regulatory changes such as less strict zoning. If you look at things this way, then the stylized facts work against Alexander’s argument. Over the past 50 years, increasingly strict zoning has reduced housing construction on big cities like New York and San Francisco. As a result, their populations have increased by less than in cities with less strict zoning, such as Houston. If Alexander were correct, then the price gap between the tightly controlled cities on the coast and the more laissez-faire cities of Middle America should have shrunk over time. Instead, the price gap has widened. New York and San Francisco were always more expensive than other cites, but with tighter zoning and less new construction the gap has become far wider. During the last fifty years, there was also deindustrialization and demographic sorting. This is just the Austin vs. Houston story all over again. Alexander is implicitly viewing this outcome as a “problem” for the city that builds more housing. They must sacrifice so that the rest of the country can gain. But in his scenario, Oakland is better off. Indeed if it were not better off, then why would more people choose to live in Oakland? In order for it to be true that building more housing boosts housing prices, it must also be true that the quality of existing houses (including neighborhood effects) rises by more than enough to offset the increase in supply. That means the new housing construction must make Oakland such a desirable place to live that the amenity effect overwhelms the quantity effect [...] Of course, economic change always has winners and losers. Here’s how I would describe the impact of allowing more housing construction in Oakland, in the unlikely event that this did raise housing prices: 1. America would benefit. 2. Oakland would benefit. 3. Poor people in America would benefit, in aggregate. 4. Affluent people in America would benefit, in aggregate. 5. Homeowners in Oakland would benefit. 6. Some renters in Oakland would benefit (from a more economically dynamic city.) 7. Some renters in Oakland would suffer from higher rents. In the much more likely case where new housing construction would lower prices, the impact described in #5 and #7 might reverse. Either way, there is no defensible argument for not building more housing in Oakland, regardless of the impact on price. If building more housing reduces its price, then there is a strong argument for allowing more housing construction. If building more housing raises its price, then the argument for more construction is even stronger. I agree with all this. Jeremiah Johnson is a co-founder of the Center for New Liberalism, host of the Neoliberal Podcast, and a YIMBY activist (not to be confused with Jeremiah “Liver-Eating” Johnson, who killed 300 Native Americans and ate their livers). He writes: Here's why you're wrong in a single sentence: Demand causes high prices, not new units. Prices are high in SF and NYC because those are desirable places to live for a huge number of people. People all over the country and the world would live there if they could, and prices reflect that. The fact that the densest cities are the most expensive is true. But the high prices are not caused by density - rather, the density and the high prices are both a consequence of crushingly high demand […] There's a feedback loop, but what matters here is the elasticity, which is less than one. We can measure this empirically. New housing lowers prices via the mechanism of adding supply, which is basic economics and how we expect markets to work. New housing could raise prices if it also made the city a more desirable place to live and shifted people's preferences, such that there was more demand to live there after the new housing is built. If you think it's unclear which of these effects would dominate, luckily we have empirical data that over and over and over shows adding housing supply does indeed lower prices on a local level. This is a fairly well established result that replicates well. edit: I'm actually thinking about drawing out the weighted DAG graphs here to make the conceptual stuff easier, but it would be pretty long. I'd love to do this as a guest post. I’m skeptical of the empirical results because they don’t match the much stronger “Manhattan vs. Conanicut island” empirical results, and if I try to think about why, the best explanation I can think of is that the Manhattan experiment has been going on longer (ie long enough for Manhattan’s extra residents to found businesses and institutions that attract new people). I’ve told him he can try pitching this guest post to me; in either case, I would be interested in seeing the graphs. Several other people also posted this graph that Johnson helped make famous: Hopefully by now you can predict my objection: the places in the southeast corner are mostly unfashionable red state Sun Belt cities; the places in the northwest corner are mostly trendy liberal coastal cities. My conclusion is that trendy liberal coastal cities are both more NIMBY and more desirable, and if you use this to draw any conclusions about housing policy you’ll just end up confused. But maybe I should take this same lesson to heart myself. Dense cities are mostly trendy liberal coastal cities; uninhabited tundra in North Dakota isn’t. Maybe the demand is just for trendy liberal coastal cities, and once you attain that status, extra density doesn’t matter that much. Maybe Oakland has already maxed out its “trendy liberal coastal city” status, and even if it became Manhattan-sized, it wouldn’t get any trendier, or would get trendier only with a long time lag. There are a few very trendy small coastal villages in California (think eg Sea Ranch); maybe these (rather than North Dakota) are the natural control group for San Francisco. I think they are still cheaper than SF, but maybe not by very much. Cameron Murray is a housing economist whose work some other commenters recommended; he also writes the blog Fresh Economic Thinking. He very kindly showed up and wrote: I think you are in general right that agglomeration effects are real, which is why bigger cities have higher value to residents. I agree that people move locations. But I think you can go a step further. If one city is growing faster and densifying, surely those people are not demanding homes in other cities and those cities build slower. This is part of the spatial equilibrium story that further makes claims about “build density and get cheap homes” less plausible. 7. My Final Thoughts + Poll Thanks to everyone who commented on this post and helped me refine my thoughts. I’m willing to concede the following points: It might be that only attracting the sort of educated people who found companies, universities, etc will make housing prices go up. Less educated people will take more jobs than they create and not ratchet up the city’s desirability level. (I’d previously told commenters talking about “gentrification” that it was irrelevant to the mechanism I was talking about here, but maybe it isn’t - maybe “gentrifiers” are the people creating more jobs and institutions than they consume, and so homes that attract them in particular will increase demand more than they increase supply? Maybe this discussion does reduce to the gentrification discussion?)
Inline links: source, Tom Thought, https://substackcdn.com/image/fetch/$s_!86gT!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5788a8dd-c5ee-4111-be67-9c1533f7410b_335x172.png, Infinite Scroll, writes, Last House On The Left, writes, writes, quotes, Under-occupied Developments In China, writes, China’s Ghost Cities Are Finally Stirring To Life After Years Of Empty Streets., https://substackcdn.com/image/fetch/$s_!AAuI!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d6a6159-a16f-45e3-b15a-e62cb2c73336_1200x900.jpeg, this list of real estate prices across Chinese cities, Paul Botts, https://www.researchgate.net/publication/345092218_Housing_Vacancy_Rate_in_Major_Cities_in_China_Perspectives_from_Nighttime_Light_Data, Tang Poetry, writes, ddd pointed out, Mike, Maximum Limelihood Estimator, gets more on point, http://econfac.bsu.edu/research/workingpapers/bsuecwp200804gisser.pdf, writes, writes:, far higher in Austin, https://substackcdn.com/image/fetch/$s_!tAF8!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F03f2b338-da84-4d85-b71c-0ce2e02e10e1_685x559.png, https://substackcdn.com/image/fetch/$s_!mLwn!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9cf942da-5716-49e8-a177-20140681e7fc_573x421.png, Center for New Liberalism, the Neoliberal Podcast, Jeremiah “Liver-Eating” Johnson, Johnson helped make famous, https://substackcdn.com/image/fetch/$s_!tg5t!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc490a60a-3e97-4b73-9806-79bb967b5172_608x550.png, Sea Ranch, some other commenters, Fresh Economic Thinking, wrote
Building new housing in certain cities with specific windfalls (eg Wall Street in NYC, tech in the Bay) might absorb the windfall faster than it produced new windfalls (eg building new houses in SF might make prices lower by successfully housing all existing Google employees, without necessarily producing new Googles). This depends on global factors like how hard it is to make the next Google, how many new Googles the world economy has room for, and how much of an advantage San Francisco has over Cleveland or China in being the most likely location for the next Google.
Your Book Review: Lying for Money
May 26, 2023 · Original source
This is the quintessence of a market crime; all Clarence did was buy shares at the going price, and everyone who dealt with him did so willingly and transparently. He did not deceive anyone; he literally took out advertisements in the newspapers saying what he was doing. But the market wanted to protect itself, and his conduct was disruptive to a set of economic institutions that other people rely on. So tough luck, Clarence. … he died a lot poorer and less happy than if he’d never borrowed $10 million to teach Wall Street a lesson.
23: Room Temperature Superforecaster
August 01, 2023 · Original source
Is this a realistic fear? There are already so many people who have very very very strong opinions about who should win US elections that adding some gamblers to the mix won’t matter much. The maximum bet a normal person (as opposed to a big Wall Street firm) can make is $250,000. There are already thousands of businesses and millions of individuals who have more than $250,000 riding on the outcome of US elections, just because politicians sometimes make laws that affect the economy. But also, have you seen people lately? People have so so many reasons to want to rig US elections.
Big Wall Street firms can bet more on Kalshi, up to $100 million, but big Wall Street firms already have hundreds of millions of dollars at stake in elections based on who passes the next Sarbanes-Oxley or whatever. In fact, the whole reason for Wall Street to gamble $100 million on an election is to hedge the risk that Bernie Sanders will get elected and cost them $100 million. Allowing election bets makes Wall Street less interested in elections, not more!
In response to the lawsuit, the CFTC rescinded their original cancellation of the no-action letter, then cancelled it a second time in a way that put more effort into complying with the regulations around agency action. But the Appeals Court ruled that they were going to rule on CFTC’s original bad action , and not this later better action, and let other courts figure out what to do about the better action. I don’t know whether this means things are looking good for PredictIt, or whether this means the CFTC will start a new case with its better action and the court will agree that it is better. The decision didn’t seem to move the market on overall lawsuit success. Although I like the result of this decision, I’m worried about the ruling that no-action letters constitute binding commitments whose amendment or cancellation requires careful agency action with every t crossed and every i dotted. Why would any government agency ever give a no-action letter now? The Mantis Of Wall Street I ran into some finance people at the NYC meetup this week and asked why they weren’t using more advanced forecasting technology - prediction markets, superforecaster tournaments, calibration training, that kind of thing. A common reply was “who says we aren’t using it?” When I asked for details, the two types of answers I got were: We’re using in-house proprietary software, we won’t tell you anything about it, and even if we did, we’ve optimized for making it hard to explain so that any accidental leaks don’t risk threatening our competitive edge.
Inline links: then cancelled it a second time, the market on overall lawsuit success
Here's Why Automaticity Is Real Actually
August 31, 2023 · Original source
There might be exceptions in certain unnatural pastimes like investing in the stock market. Probably past generations of stock traders discovered some of these biases by accident, and try to pass them down to new Wall Street interns. But there haven’t been a hundred generations of stock traders, so the knowledge is still fragmentary and inconsistent. Maybe cognitive science has reached a point where it can supplement or codify this kind of wisdom - or maybe it hasn’t reached that point yet.
Does Capitalism Beat Charity?
January 04, 2024 · Original source
RIP …so this doesn’t support the “invest in whatever companies give the best rate of return” narrative either. What’s left is strategy 3: Do something like donating to charity, but the donation should go to charities that promote capitalism somehow, or be an investment in companies doing charitable things (impact investing) I find this promising, but I don’t know what a good charity along these lines would be. There are some charities that send economists (or other professionals) to developing countries and advise them on how to do more capitalism. This kind of development aid has been roundly criticized and did especially badly in Russia. I’ve supported some of these that seem especially careful in the past, and would be willing to support them more if someone found a very good one with a strong track record. (also, I’m concerned that even though rich countries got rich because of capitalism, it’s no longer that easy for poor countries to get rich with the same type of capitalism - existing rich countries will outcompete them - and we’re not entirely sure how to help poor countries get rich now, although probably good institutions are always better than bad institutions) I am partial to Charter Cities Institute, which helps advise developing countries on creating charter cities that have better governance and less corruption than the rest of the region. But EA evaluator group Rethink Priorities has a report on why they don’t think this is quite as valuable as traditional charity (they’re not sure special economic zones consistently make areas develop faster, and they think this finding should be applied to charter cities too). Here’s CCI’s counterargument (they think SEZs aren’t a good reference class for the charter cities they want). I think both sides make good points but I’m currently more convinced by Rethink Priorities’ (although I do still donate to CCI sometimes). Finally, you could invest in developing-world projects and companies that seem unusually likely to make an overall economic difference there. I’m nervous about this because of China’s Belt and Road initiative, which did this at huge scale for infrastructure, but doesn’t seem to have done much good (and might have done some bad). Also, I’m not smart money, which means I’m exposed to adverse selection - if there’s a company that can’t raise enough money to build a dam in Kenya and needs your charity dollar to make the budget work, why hasn’t Wall Street come through for them? One plausible answer is “because it’s a bad company with a bad plan”. Admittedly another plausible answer is “because it has a 5% RoI, the next Instacart has a 6% RoI, and so Wall Street would prefer the next Instacart but you as a charitable individual should prefer the Kenyan dam.” I would potentially be willing to believe this if some smart charity evaluator would tell me which projects were good. But $1 million only gets you a fraction of a dam, and does get tens of thousands of clean water dispensers, so I would also want someone to present the specific case for why the dam would be better (not just the heuristic “capitalism is always better than charity”). I’m willing to believe that some capitalist charities - whether these are development aid think tanks, or investment in developing-world projects - could potentially be better than usual charities. The reason I’m not donating to these is that nobody’s done the hard work of identifying these and calculating their expected value, and I don’t feel qualified to do that work myself. I have a high prior that any nonprofit that hasn’t been rigorously shown to be good is probably bad, and the potential advantage of capitalism over normal charity usually isn’t enough to overcome my decreased certainty in its efficacy2. UPDATE: I respond to your comments and counterarguments here. 1Instacart is worth $10 billion and has 10 million customers, so naively you might say that it cost $1000 in investment per customer. But successful companies are worth more than the amount of investment it took to create them. I don’t know how much has ever been invested in Instacart total, but this also seems like the wrong question. You, today, can’t invest in “the next Instacart” - everyone wants to invest in the next successful company, but nobody can be sure which one it will be. All you can do is invest in a basket of promising-looking startups: most will fail but some will succeed. Because of this, I thought the best way to represent “the amount of investment money it originally took back when Instacart was founded in 2012 to create Instacart today” as the current value of $10 billion discounted by the rate of return a good VC gets on their investments, which I think is about 7.5%. That suggests it took about $5 billion of investment in 2012 to create the amount of value represented by Instacart today, ie 10 million customers getting a good deal on grocery delivery. That means $500 in investment per customer. Because most charities can’t take $5 billion in new funding, I chose to represent this as per million dollars, so 2,000 customers per $1 million. I understand this is a very shaky estimate and I’m hoping that all the comparisons I’m going to make are so order-of-magnitude different that nobody really cares about the specifics. There’s one thing that confuses me here, which is that Instacart has 10 million customers and makes $2.5 billion in revenue per year, suggesting each customer spends $250. But you can get a yearly subscription to Instacart for $100, after which the service is free. So either customers are overwhelmingly being stupid, not buying the subscription, and paying much more than it should cost - or I’m missing something here and the numbers are wrong. Again, I’m hoping all of this is done across so many orders of magnitude that it doesn’t matter. 2Doesn’t this principle also mean I shouldn’t do ACX Grants, where I donate to fledgling projects with no evidence of efficacy? Maybe, and every year I debate whether I should really do this. I think the arguments for a distinction are: ACX Grants go to charities where my donation potentially has a very high upside, so I’m not as concerned about the high prior on failure.
Inline links: roundly criticized, in Russia, no longer that easy, how to help poor countries get rich now, Charter Cities Institute, has a report, CCI’s counterargument, Belt and Road initiative, 2, here, 1, 2
Verses On Five People Being Killed By A Falling Package Of Foreign Aid
March 14, 2024 · Original source
Or to creep in like accountants Using gain and loss as anchors As cunning as the serpents And as cold as Wall Street bankers
The Dilbert Afterlife
January 16, 2026 · Original source
Silicon Valley, where hustle culture has reached its apogee, has an additional consideration: why don’t you found a startup? If you’re so much smarter than your boss, why not compete against him directly? Scott Adams based Dilbert on his career at Pacific Bell in the 80s. Can you imagine quitting Pacific Bell in the 80s to, uh, found your own Pacific Bell? To go to Michael Milken or whoever was investing back then, and say “Excuse me, may I have $10 billion to create my own version of Pacific Bell, only better?” But if someone were to try to be Dilbert today – to say, earnestly, “I hate my job because I am smarter than my boss and could do it better than him,” that would be the obvious next question, the same way “I am better at picking stocks than Wall Street” ought to be followed up with “Then why don’t you invest?”