San Francisco is a recurring place in the Astral Codex Ten archive, appearing 91 times across 91 issues between February 02, 2021 and April 06, 2026. The archive places it in contexts such as ""A trip from San Francisco to Milan.""; "housing crisis in San Francisco"; "San Francisco". It most often appears alongside California, New York, Los Angeles.
- Article page
- San Francisco
- Mention count
- 91
- Issue count
- 91
- First seen
- February 02, 2021
- Last seen
- April 06, 2026
- http://web.archive.org/web/20221104130431/https://stevekirsch.substack.com/p/1m-bet-rules
- http://web.archive.org/web/20221129133112/https://blog.rootclaim.com/rootclaim-accepts-500000-challenge-on-covid-vaccine-safety-efficacy/
- http://web.archive.org/web/20221224061743/https://www.skirsch.com/covid/SaarWilf.pdf
- http://www.cjcj.org/news/12756
- https://archive.ph/pY4gF#selection-663.103-683.190
- https://beverlypress.com/2024/05/uptick-in-graffiti-paints-a-bleak-picture/
- https://biblehub.com/greek/3646.htm
- https://bjs.ojp.gov/data-collection/ncvs
- https://en.wikipedia.org/wiki/Sturmabteilung
- https://governsf.substack.com/p/introducing-governing-san-francisco
- https://hsh.sfgov.org/wp-content/uploads/2017/02/San-Francisco-PIT-Homeless-Count-2013-Final-February-13-2014.pdf
- https://insidebigdata.com/2023/09/25/protestors-to-meta-ai-sharing-model-weights-is-fundamentally-unsafe/
- Riddle Of The Sphinx II: Sustained Release Riddlin'
- Ezra Klein On Vetocracy
- Book Review: Fussell On Class
- Your Book Review: Progress And Poverty
- Contra Smith On Jewish Selective Immigration
- Adumbrations Of Aducanumab
- If You're So Smart, Why Aren't You Governor Of California?
- Whither Tartaria?
- Please Don't Give Up On Having Kids Because Of Climate Change
- Links For November
- Does Georgism Work? Part 1: Is Land Really A Big Deal?
- Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?
- ACX Grants ++: The Second Half
- 22
- California Gubernatorial Candidates From Z to Z
- Book Review: San Fransicko
- What Caused The 2020 Homicide Spike?
- Highlights From The Comments On San Fransicko
- Open Thread 231
- Highlights From The Comments On The 2020 Homicide Spike
- Bay Area Meetups This Weekend
- Open Thread 242
- Why Is The Central Valley So Bad?
- Highlights From The Comments On The Central Valley
- 22
- Highlights From The Comments On My California Ballot
- The Psychopharmacology Of The FTX Crash
- Book Review: First Sixth Of Bobos In Paradise
- Sorry, I Still Think I Am Right About The Media Very Rarely Lying
- Links For February 2023
- Half An Hour Before Dawn In San Francisco
- Change My Mind: Density Increases Local But Decreases Global Prices
- Highlights From The Comments On Housing Density And Prices
- Your Book Review: Public Citizens
- Contra The xAI Alignment Plan
- What Can Fetish Research Tell Us About AI?
- Meetups Everywhere 2023: Times & Places
- Highlights From The Comments On Fetishes
- 23: California Dreamin'
- Open Thread 293
- Highlights From The Comments On Last Week's Model Cities Post
- Pause For Thought: The AI Pause Debate
- Open Thread 298
- My Left Kidney
- Seen In The Bay
- In The Long Run, We're All Dad
- Links For January 2024
- Spring Meetups Everywhere 2024
- Links For April 2024
- Open Thread 325
- Your Book Review: Autobiography Of Yukichi Fukuzawa
- Highlights From The Comments On Mentally Ill Homeless People
- Links for July 2024
- Meetups Everywhere 2024: Times & Places
- Open Thread 345
- Interview Day At Thiel Capital
- Ballots Everywhere: Call For Organizers, Times, & Dates
- Ballots Everywhere: Times And Places
- SB 1047: Our Side Of The Story
- ACX Local Voting Guides
- Notes From The Progress Studies Conference
- The Case Against California Proposition 36
- Open Thread 354
- Mantic Monday: Judgment Day
- Book Review: The Rise Of Christianity
- 25
- Open Thread 369
- Meetups Everywhere Spring 2025: Times & Places
- Book Review: Arguments About Aborigines
- Press Any Key For Bay Area House Party
- Highlights From The Comments On Liberalism And Communities
- Meetups Everywhere 2025: Times and Places
- Links For September 2025
- Your Review: Project Xanadu - The Internet That Might Have Been
- Sources Say Bay Area House Party
- Open Thread 401
- ACX Grants Results 2025
- Non-Book Review Contest 2025 Winners
- 25
- What Happened To SF Homelessness?
- Open Thread 409
- Vibecession: Much More Than You Wanted To Know
- You Have Only X Years To Escape Permanent Moon Ownership
- Open Thread 415
- The Dilbert Afterlife
- Links For February 2026
- Record Low Crime Rates Are Real, Not Just Reporting Bias Or Improved Medical Care
- Crime As Proxy For Disorder
- Open Thread 424
- Meetups Everywhere Spring 2026: Times & Places
- Open Thread 428
"Hmmmmm," I said. Then it came to me. "A trip from San Francisco to Milan. Here, check out the Kayak.com results." I held up my cell phone:
Nobody who’s ever looked into the housing crisis in San Francisco will disagree here, but it raises some complicated questions that need some sorting out.
Where then may a member of the top classes live in this country? New York first of all, of course. Chicago. San Francisco. Philadelphia. Baltimore. Boston. Perhaps Cleveland. And deep in the countryside of Connecticut, New York State, Virginia, North Carolina, Pennsylvania and Massachusetts. That's about it. It’s not considered good form to live in New Jersey, except in Bernardsville and perhaps Princeton, but any place in New Jersey beats Sunnyvale, Cypress, and Compton, California; Canton, Ohio; Reno, Nevada; Cheyenne, Wyoming; Albuquerque, New Mexico; Columbus, Georgia, and similar army towns.
Although 2021 seems better than 1879 in absolute material terms, George's complaint still rings true: healthcare and higher education are increasingly unaffordable, inequality is as bad as it ever was, and The Rent Is Too Damn High. And even if all of these measures had improved as well, we still have to contend with a fundamental complaint: how can human civilization have piled up an amount of wealth best described as absolutely banana pants insane, and yetstill have poverty, oppression and cyclical recessions? Yes, greed, evil, and human nature will always be with us, but isn't it weird that we haven't eliminated these economic problems the same way we've eliminated Smallpox, Scurvy, and having to write your scathing polemics about Thomas Jefferson by candlelight with a goose feather? Giving the mic back to George, he closes the chapter with this haunting quote, first written 142 years ago: If there is less deep poverty in San Fran Francisco than in New York, is it not because San Francisco is yet behind new York in all that both cities are striving for? When San Francisco reaches the point where New York now is, who can doubt that there will also be ragged and barefooted children on her streets? I'll just leave this here: Number of Homeless Children in U.S. At All-Time High; California Among Worst States. I. Wages and Capital George insists sloppy terminology leads to sloppy thinking. Naturally, he spends an entire chapter beating words to death to correct this. The Meaning of the Terms Let's start with Wealth. The common usage, both then and now, is "anything with an exchange value." George doesn't like how this mixes dissimilar things. By George, what is wealth? Wealth is produced when Nature's bounty is touched by human labor resulting in a tangible product that is the object of human desire. Labor is required, but the amount and type doesn't matter - George offers the example of simply picking a berry off a bush as an act that transforms nature's gifts into human wealth. Note particularly that human desire is an important requirement of wealth; it doesn't matter how much work someone put into something, if it doesn't gratify human needs or desires in some way, it's not wealth. Speaking of human desire, let's talk about Value. Where does a thing's value come from? The prevailing theory of the day was the Labor Theory of Value which originated with Adam Smith and David Ricardo, which says that Labor is the source of value. The early formulations were a bit ambiguous, here's Smith in Wealth of Nations for instance: The value of any commodity ... is equal to the quantity of labor which it enables him to purchase or command. Labor, therefore, is the real measure of the exchangeable value of all commodities. So... is a thing's value how much labor it takes to make the thing, or how much labor someone's willing to exchange for the thing? Nowadays Labor Theory of Value is most commonly associated with Marx. Marx picks a lane and says the value of something is tied to the amount of "socially necessary labor" required to produce it. George goes the other way: It is never the amount of labor that has been exerted in bringing a thing into being that determines its value, but always the amount of labor that will be rendered in exchange for it. - Henry George, The Science of Political Economy, p. 253 In other words, "a thing's value is whatever someone is willing to pay for it." This is in line with the so-called marginal revolution (the movement, not the blog) and modern theories of value. Labor Labor is the exertion of human beings. It's possible to labor to no avail (try punching a concrete wall), but typically humans labor towards an end, such as gaining wealth. But whether or not we accomplish anything with our efforts, George calls them labor. Labor isn't just making things, by the way – it's also moving or exchanging them. Production Production is labor applied "to the production of wealth." You know, productively. This is all human exertion that isn't punching a concrete wall and rewards you for your efforts with something that fits the definition of wealth. Said wealth is the "product of labor." Wages whatever is received as the result or reward of exertion is "wages." No distinction here is made between blue-collar work and white-collar work – whether one is called "hourly pay" and the other is called "annual salary," George calls them both "wages." It doesn't matter whether you receive them from your boss, from customers, or from nature. If you do work and get something from it, you have received "wages." With those basics under our belt, let's circle back to Wealth: What are some examples of wealth? By George, Gold is wealth. Teddy bears are wealth. Tesla roadsters and candy canes and young adult vampire romance novels are wealth. The same goes for fish you've caught, deer you've hunted, and cool looking rocks you've picked up on your morning walk. The value of these things may differ, but as long as they're tangible, originate in nature, someone ever did a lick of work to make or acquire them, and a human being somewhere desires them for any reason, they're wealth. It gets a little clearer when we ask what isn't wealth. And by George, Money isn't wealth. Articles of gold are wealth because they're tangible things that have been dug up, crafted, and fulfill certain human desires. But paper currency, digital currencies, and other things that aren't inherently valuable but merely represent value are not wealth (outside of putting their physical articles in coin collections or making paper airplanes, and so forth). Now don't get the man wrong, these things are certainly valuable. They're just not wealth. They are certificates that represent claims on wealth. For any computer programmers in the audience, money is a pointer to wealth. Likewise Stocks and Bonds and other financial instruments are not wealth. These are also just claims on wealth. A creditor's title to Debt isn't wealth, either, it's just a claim on the debtor's (typically future) wealth. And, writing as he was not long after the Civil War, George points out that Slaves are not wealth either but, represent "merely the power of one class to appropriate the earnings of another class." Wealth, thus defined, is the terminal "ground truth" bits of the economy, and all the financial layers on top are fancy IOUs that just encode various claims on it. George offers a thought experiment to test if something is wealth: if you produce a pile of gold, fish, or Lego bricks, you've clearly increased the amount of wealth in the world. But if you produce a giant pile of IOUs that just records who owns what and who owes what to whom, it doesn't matter how many of them you pile up or how long the chains of ownership get, you still haven't increased the amount of real wealth in the world. Again, this isn't saying the IOUs aren't valuable, they are. But they're only valuable because they ultimately point to real wealth. If you magically transported everyone over to a hypothetical Earth 2, carrying over all of Earth 1's money and financial instruments but none of Earth 1's tangible wealth, the value of all those IOUs would instantly evaporate. Now what about digital goods? Leaving things like Bitcoin aside for the moment, let's consider the case of a digital image file: By George, this is wealth. Digital though it may be, it's physically encoded on a storage device somewhere, and is thus tangible (it's not a pure abstract concept flitting about in Platonic heaven) and has its origins in nature. Human exertion built the computer that encodes it, and clicking the button that saves it to disk or displays it on your screen is labor. Finally, it directly satisfies human desires (mine, at the very least). It's value may be negligible, but it's wealth. By contrast, the digital bit sitting in some database that says I own a particular eBook or mp3 is just a digital IOU – a claim on the wealth that are the physical bits on my local storage device or remote server that digitally encodes the files. The fact that digital files don't seem particularly physical, and that they can be trivially and endlessly copied, doesn't mean that Henry George, magically transported to today, wouldn't regard them as wealth. Okay, so is there anything else that's not wealth? By George, Bitcoin isn't wealth, in case you were wondering. It's just a (very fancy) financial instrument, a digital claim on wealth. And that goes for most crypto assets – a token on some blockchain that says I own a painting by Banksy is just another IOU, regardless of the technical sophistication of its distributed trustless ledger. What about intellectual property? Copyrights, patents, and trademarks are all different forms of Monopoly – the exclusive, government-granted legal right to do a particular thing (publish a certain book, manufacture a certain product, use a certain name in business, etc). The exclusive right to do or produce a thing, valuable as it may be, is not the thing itself. By George, Monopoly is not wealth. But there is something big that is wealth – the C-word. Capital. By George, Capital is "wealth devoted to procuring more wealth", and it's the next thing he insists everyone is hopelessly confused about. He quotes Adam Smith, agreeing with him thus far: That part of a man's stock which he expects to afford him revenue is called his capital. ...and also gives us a short etymology lesson on the origin of the term: The word capital, as philologists trace it, comes down to us from a time when wealth was estimated in cattle, and a man's income depended upon the number of head he could keep for their increase. ("Per capita" being the Latin for "by head") By George, all capital is wealth, but not all wealth is capital. George notes capital is often described as being "stored up labor", and endorses this view – but what it really means, is capital is stored up production. It's not literally the labor that's stored up but the wealth generated by it, set aside and then dedicated to the purpose of getting more wealth. George insists that it is the owner's intention that transforms wealth into capital. If you buy an old factory to throw parties in for your hipster friends, it's just wealth. But the minute you decide to put it to work to make something useful (or start charging your hipster friends a cover charge at the door), it becomes capital. George therefore further insists that a laborer's daily bread and the clothes on their back do not count as capital, because a person has to eat and wear clothes whether they work or not. The laborer's tools (and arguably their steel-toed work boots) can however be counted as capital, because their purpose is to assist the laborer in getting more wealth by working for wages, and the laborer wouldn't acquire, use, and maintain those things otherwise. George has more exclusions: We must exclude from the category of capital everything that may be included either as land or labor. Human exertion (labor) by itself can never be capital. The products of human labor become capital when they are stored up and set to the purpose of getting more wealth. To muddle this distinction defeats the point of having separate terms for those things at all, and prevents us from reasoning meaningfully about how they relate to one another. Labor is not capital, and neither is labor by itself wealth, it produces wealth – and if it ain't wealth, it ain't capital. And that brings us to land. Land, land, land. By George, land is not wealth. And it's definitely not capital. The unique specialness of land is George's entire schtick and the very core of his philosophy. The term land embraces, in short, all natural materials, forces, and opportunities That means that a field or a meadow is "land", as is a mountain. But so are the fish in the sea, the clouds in the sky, veins of gold in the earth's crust, and the oil deep under ground. These things aren't yet wealth – not until human beings both a) desire them and b) touch them with labor. So... land is not wealth. But... how come? I mean, look: land is tangible, it "comes from nature", humans are always productively applying their labor to it, and it certainly seems capable of gratifying human desires. George sees this reasoning as understandable, but insists it's the root mistake that leads other political economists astray – because for George, land just is nature itself. Come again? Land is the ultimate source of all wealth, but it's most useful to think of it as a generator, acompletely separate entity from the wealth that human labor and desire draws from it. Players of Magic: the Gathering and Settlers of Catan should already have a solid grasp of this distinction: In modern times, George would grant electromagnetic spectrum and orbital real estate for satellites the same status of "land" that already applies to farmland and terrestrial real estate. We don't even need to speculate about whether he'd attach this status to sunlight because he straight-up predicted solar power: Even the lack of rain which makes some parts of the globe useless to man, may, if invention ever succeeds in directly utilizing the power of the sun's rays, be found to be especially advantageous for certain parts of production. (That's from Protection or Free Trade, footnote 19) The important thing to grasp about land is that it comes before everything humans do or make, and is itself a thing no human can make. Okay, smarty-pants, what about the Netherlands? They've been making land for centuries! Well, land in the Georgist sense doesn't refer simply to "dry land", but also the sea bed, the oceans, and the skies above. The "new land" in the Netherlands counts as an improvement to land that already existed. The seabed was always there, but by filling it in so you can walk around on it, now it's more useful to us (George has a lot to say about improvements to land, which we'll get to later). Okay, what is land not? nothing that is freely supplied by nature can be properly classed as capital By George, land is not wealth. And since it's not wealth, it's not capital. Okay, we get it. Land is very special to Mr. George and we must never put it in the same category as wealth, labor, capital, wages, production, money, or anything else. Why exactly is this so damn important? Well, by George, if you treat land the same way you would a bar of pig iron, an hour of work, or a dollar bill, before you know it you'll get poverty paradoxically advancing alongside progress, inexplicable bouts of industrial depression, literal genocides and holocausts (he's dead serious about this), and The Rent Being Too Damn High. With terminology now firmly established, George moves on to the relationship between wages and capital. 3-for-1 special on Wages, Capital, and Labor I'm condensing three chapters here because they all deal with the same basic thing. The question George wants to answer is: Why, in spite of increase in productive power, do wages tend to a minimum which will give but a bare living? The conventional wisdom of George's time is that wages are governed by a fixed ratio between the number of laborers and the amount of capital devoted to their employment, because "the increase in the number of laborers tends naturally to follow and overtake any increase in capital." So it doesn't matter how much capital you throw at employing workers, it'll just attract even more workers splitting it up, so although wages might temporarily wiggle a bit in the long term they'll always settle back to a "natural" minimum. (As we'll see in the next section, this argument stems from Malthusianism). George spends some time methodically poking holes in the theory (it's predictions don't line up with the facts he observes), and then sets out to prove his replacement theory (emphases mine): wages, instead of being drawn from capital, are in reality drawn from the product of the labor for which they are paid. He pulls a G.K. Chesterton to make his point: During the time [the laborer] is earning the wages he is advancing capital to his employer, but at no time, unless wages are paid before work is done, is the employer advancing capital to him. He starts by identifying the source of confusion: Because wages are generally paid in money, and in many of the operations of production are paid before the product is fully completed, or can be utilized, it is inferred that wages are drawn from pre-existing capital I mean, the old theory seems sensible: the employer has capital and uses it to pay wages. But however you slice it, capital's investment gets paid back by production when it takes its cut, so does it even make a difference to talk about where wages are "drawn" from? Value goes out, value comes in, isn't it all a wash? By George, it isn't: in the old theory, because capital "must come first", it follows that "industry is limited by capital - that capital must be accumulated before labor is employed", which leads to a reductio ad absurdum – We are told that capital is stored-up or accumulated labor – "that part of wealth which is saved to assist future production." If we substitute for the word "capital" this definition of the word, the proposition carries its own refutation, for that labor cannot be employed until the results of labor are saved becomes too absurd for discussion. George anticipates the following rejoinder – Well, when we say 'labor is paid out of capital' we don't mean it as an absolute statement for all stages of human development (or else we have a chicken-and-the-egg problem and civilization could never have begun), we just mean it applies to, say, every civilization that's left the stone age. George will have none of it and spends three entire chapters relentlessly beating to death the idea that wages are drawn from capital instead of from production. He starts with the simple case where wages are paid in the form of direct, concrete wealth, then moves on to the more complex case where people are paid in money and other instruments. Laboring for wages: Imagine a fishing village where nobody cooperates – each person digs their own bait and catches their own fish. Then they discover labor specialization and realize they can catch more fish together if one specializes in digging and the other in catching. So the digger digs, the catcher catches, and they share the fish. The digger really contributes as much to the catch as the one who physically pulls the fish off the hook even though the digger never directly "caught" a fish, and the fish he gets for his work is directly paid out of his contribution to the total production. Later, our fisherfolk invent canoes, and one stays home making and repairing canoes. This increases the haul of the digger and catcher, and the canoe-er gets paid out of her contribution to the increased production. And so it goes as society continues to advance. The work the specialist puts in causes more fish to be caught, and that person's wages is drawn from the growing pile of fish. As George puts it: "Earning is making." George gives another example: If I take a piece of leather and work it up into a pair of shoes, the shoes are my wages – the reward of my exertion. Surely they are not drawn from capital – either my capital or any one else's capital – but are brought into existence by the labor of which they become the wages; and in obtaining this pair of shoes as the wages of my labor, capital is not even momentarily lessened one iota... As my labor goes on, value is steadily added, until, when my labor results in the finished shoes, I have my capital plus the difference in value between the material and the shoes. And another: If I hire a man to gather eggs, to pick berries, or to make shoes, paying him from the eggs, the berries, or the shoes that his labor secures, there can be no question that the source of the wages is the labor for which they are paid. George goes on to say it doesn't matter if you're paid in money or directly in wealth, because the money is a direct claim on the underlying wealth. It also doesn't matter if you get paid on commission. Imagine a whaling ship where each crewman gets paid a share out of whatever the ship catches. When the ship sails back into port with a hold full of whale oil and bone, the crew gets paid in money, the owner simultaneously adds to his capital oil and bone. The crew's money directly represents their share of the concrete wealth that is the oil and bone. The owner's capital hasn't decreased, and the workers drew their wages directly from the production. So let's get to the point, Mr. George – wages aren't drawn from capital but instead from production. Great, let's grant that – so what? George hammers away at this because thinking wages are drawn from capital leads to a false conclusion, namely that "labor cannot exert its productive power unless supplied by capital with maintenance." "Maintenance?" Well, workers need food and clothing and they get paid by their employers, so you could imagine capital as a limiting factor on labor. But by George, food and clothing isn't capital, it's just wealth, as we said before. And with regard to wages, the point is that the employer always gets "paid" first, because the second the laborer produces value, the employer's capital increases: As in the exchange of labor for wages the employer always gets the capital created by the labor before he pays out capital in the wages, at what point is his capital lessened even temporarily? Okay, but what if I'm just a terrible businessman and I pay somebody $500 an hour to smash Ming vases, then sell the fragments as aggregate to a construction crew for a few pennies a pound, all at a tremendous loss? Surely then the laborer's wages must be drawn from my capital, because there's not enough productive value generated by the labor to draw them from! George says okay, sure, but only because I'm an idiot and will soon be out of business: Yet, unless the new value created by the labor is less than the wages paid, which can be only an exceptional case, the capital which he had before in money he now has in goods – it has been changed in form, but not lessened. Fair enough, Mr. George, but what if I'm building some enormously expensive multi-decade project, like a dam or a nuclear power plant or a cathedral? The kind of thing we call a "capital-intensive" project? What do you have to say to that? George points out that as laborers labor, they progressively add value to whatever they're producing. Take the case of a shipwright building ships for an employer – even if the boss can't sell a half-finished ship, it still holds value (for one, it costs less to finish a half-finished ship then no ship at all). And with every stroke of the laborer's work, the employer who owns the shipyard gets an incremental increase in his stock of capital. It is not the last blow, any more than the first blow, that creates the value of the finished product – the creation of value is continuous, it immediately results from the exertion of labor. A pedant would point out that the "last hit" that finishes the product which makes it ready for market adds disproportionate value, but George's point is just to establish that value is continuously created, and doesn't magically come into being allat once right at the end. George further points out that if you look at things like agriculture you'll see the market directly acknowledging his theory: As a plowed field will bring more than an unplowed field, or a field that has been sown more than one merely plowed... It is tangible in the case of orchards and vineyards which, though not yet in bearing, bring prices proportionate to their age. George freely admits that capital can be required for certain kinds of work, but he disagrees with what its purpose is. It's not a pool that wages get paid out of. He goes on for another chapter on "The Maintenance of Laborers Not Drawn From Capital" but I think we can safely skip it and move on. TL:DR – George hammers to absolute death the idea that Laborers derive their own maintenance (food/shelter/clothing/etc) from their wages, with George insisting it is drawn from production and... you guessed it, not from capital. At least some of George's ideas will not seem so radical to modern readers (especially those already critical of capitalism or neoclassical economics), but it's important to understand that at the time almost everything he was saying was considered deeply radical and shocking. Capital was the fundamental driving force of the economy and labor was utterly dependent on it, and the Malthusian theory of overpopulation was the accepted explanation for why wages were low and workers were starving. Political Cartoon literally demonizing Henry George – Puck magazine Oct. 20, 1886 The Real Functions of Capital Okay, Mr. George. You've spent three whole chapters beating me over the head with what the functions of capital aren't. So what are the functions of capital? Capital "increases the power of labor to produce wealth." How? By enabling labor to apply itself more effectively (power tools go brrrr)
Source: can't find the author of this image, closest I can get to its origin is this blog Okay, but is this still relevant in the modern age, with the internet and work-from-home? Obsessing about land just feels so 19th century. Well, in Silicon Valley rents are famously off the charts, and those and all other rents seep into the economy at every level. Workers priced out of living close by have to spend more time and money commuting longer distances to work, and businesses must devote an increasingly larger share of their production to landowners who aren't actively contributing anything to productivity. What else could explain how a family of four making $100,000 in San Francisco is considered to be living below the poverty line? Here, take a look at this chart (source): I found this in a tweet by Thomas Piketty, and it shows the breakdown of personal assets in Spain over the last 100+ years. The bulk of the value of personal assets is from landownership. This is still the case even though the chart includes "financial assets" – which are just IOUs that ultimately have something real (e.g. land or wealth) underpinning their value. If we exclude those, the true portion of overall value represented by land is even higher than this graph first implies. And this isn't just Spain. Here's a graph Nate Blair made for the UK, excluding all financial instruments and only looking at real assets: Based on data from the United Kingdom National Accounts: The Blue Book 2017. Published Oct 31, 2017. Revision Period: Beginning of each time series. Date of next release: July 2018. The "privileges" in "Land and privileges" are things like taxi medallions and patents, that were worth "almost zero" according to Nate. No matter how hard you try, "there is no occupation in which labor and capital can engage which does not require the use of land." Whenever anyone does labor, the owner of some piece of land – whether it's the farm in the middle of Kansas that grows your food, the lot upon which the server farm sending you these bytes sits, or the ground that right now sits beneath your feet – is sticking their finger in the pie. George reminds us that labor and capital will have to share whatever landowners take off the top of production in rent: As Produce = Rent + Wages + Interest, Therefore, Produce - Rent = Wages + Interest So... what happens when the productivity of land goes up? Let's go back to Lot A and Lot B, both 100-util fields. Let's say they belong to different landlords, and I'm a tenant on Lot B. I improve the soil of the field I'm working on so now it's worth 110 utils. What happens? My landlord raises the rent, of course! The only way wages (the return to labor) and interest (the return to capital) can go up as productivity increases, is if land values fail to rise at the same rate. The Law of Interest George wants to find the fundamental reason capital is able to produce wealth and justly claim a fair share of production. Remember that capital is wealth devoted to getting more wealth. So if capital is wealth that begets wealth, it makes sense that if I lend it out to you, I miss out on the potential for it to grow while it's out of my hands. George says I am justly entitled to ask for more back than I originally gave you. Let's say I loan you some corn seeds for a season. Had I not leant them to you, in a season's time I could have grown my own crop of corn and been left with more seed than I started with. So in a perfectly square deal, you need to give me back what I started with and what I could have expected to gain from natural increase (less the value of the labor required to get things started). Likewise with any other article of capital – say bricks or lumber. In the time I've spent without it while it was in your possession, I could have found someone else who had a better use for it than I did and exchanged it for something of theirs that I had a better use for, leaving me with capital of greater value. George says the act of progressively exchanging things in a way that increases subjective value for all involved is analogous to the natural forces of nature that make living capital (like corn and cows) grow over time. Remember, "subjective value" is real value. In a game of Settlers of Catan, if I have two bricks and you have two lumber, neither of us can build anything. The simple act of trading one brick for one lumber means both of us are better off because each of us can now build a road. The amount of bricks and lumber in the world didn't increase, but the amount of roads (or potential roads) did, and that represents a real increase in wealth. Interest thus springs from the "reproductive" powers of capital, whether that's biological reproduction, or the more abstract reproductive force of exchanging things so that you have a more valuable distribution of capital than you started with. As for how it relates to the other two returns to production – the more powerful the "power of increase" the capital has, the greater return interest can claim compared to wages. If you're ploughing a field and I lend you a tractor which makes you ten times as productive, I can justly claim more compensation for that than if I lend you a mule that only makes you twice as productive. However, rent still holds the whip hand, so the margin of cultivation determines how much return is left over to divvy up between interest and wages. This is because the net "reproductive" value of capital goes down given rent is a general tax on overall productivity. The amount I would have gained by using the thing productively over the period of time it was out on loan (the amount I can justly charge in interest) is reduced by how much I have to pay in rent. The Law of Wages Wages, like interest, are limited by the margin of production. Within that limit there's not much to understand about how wages work except that people seek to satisfy their desires "with the least exertion," which is a fancy way of saying people don't like to get ripped off. If two bosses offer the same exact job, but one offers higher pay, I'm taking that gig. If two bosses pay the same, but one is asking for twice as much work, I'll tell that boss where he can stick it. Wages depend upon the margin of production, or upon the produce which labor can obtain at the highest point of natural productiveness open to it without the payment of rent. So with all three laws established George sums it up like so: Where land is free and labor is unassisted by capital, the whole produce will go to labor as wages. Where land is free and labor is assisted by capital, wages will consist of the whole produce, less that part necessary to induce the storing up of labor as capital. Where land is subject to ownership and rent arises, wages will be fixed by what labor could secure from the highest natural opportunities open to it without the payment of rent. Where natural opportunities are all monopolized, wages may be forced by the competition among laborers to the minimum at which laborers will consent to reproduce. This is the reason George says that wages are so high in "new countries" where there's more land available than in countries where it's been locked up for centuries. Here's how it all fits together: Though neither wages nor interest anywhere increase as material progress goes on, yet the invariable accompaniment and mark of material progress is the increase of rent – the rise of land values. And: where the value of land is highest, civilization exhibits the greatest luxury side by side with the most piteous destitution IV. Effect of Material Progress upon the Distribution of Wealth As a society undergoes material progress, the rent goes up. Why? Let's break it down. Three things contribute to material progress: Increasing population
In 2000, the median household income of US Jews was $72,000. The median household income of people who lived in New York City was $40,000; in San Francisco, $55,000. So I don't think you can explain Jewish success by saying they are more likely to live in places like New York City and San Francisco.
A new program in 1992 allowed for “accelerated approval” on the basis of surrogate markers, which are indirect measures of a drug’s benefit, assessed via laboratory or imaging tests, that stand in for more meaningful outcomes such as life expectancy. But the implementation of these accelerated processes was criticized by some scientists and patients, even at the time. In 1994, for example, The New York Times cited skeptics who worried that “no one can tell if the drugs work.” Eight months later, the AIDS activist organization ACT UP San Francisco called Anthony Fauci a “pill-pushing pimp” for supporting CD4 immune-cell counts and viral loads as surrogate markers. They were completely invalid, the activists wrote, and nothing more than “a marketing exec’s wet dream.”
I think this is a pretty typical experience a lot of people have dealing with the police, especially in the Bay (unofficial motto: “San Francisco - Where Crime Is Legal”). A friend had a really scary stalker, and kept reporting him to the police, and the police’s answer, phrased only slightly uncharitably, was “Have you, as of now, already been murdered by this person? No? Then stop wasting our time.” My friend was left with the feeling that the police could have been a little stricter or more proactive.
Californians love long-shot bets. Actors trying to make it big in LA, tech founders chasing unicorns in San Francisco, cult leaders trying to found religions in Pasadena. In Silicon Valley, VCs turn the long-shot bet into an art: if some new startup has a 5% chance of making a billion, that's $50 million in expectation. Just a whole state full of people looking for weird opportunities.
I have tried to be as fair as possible here. The first pair is the formal dress of the highest-status person in China in each time period. The second is an architecturally-celebrated building from Milan in each period (the university won the World Building Of The Year award for the the year it was constructed). The third pair is the receiving room of the mansion of a rich person from each period. For the last pair, I used a famous old public sculpture, and searched for the most-celebrated public sculpture from San Francisco, the nearest big city to where I live. Older art tends to have bright colors, ornate details, realistic representations, technical skill, and be instantly visually appealing to the average person. Newer art tends to be more abstract, require less obvious skill, and have less direct appeal. Although it doesn't fit in meme format, I would carry the analogy to poetry (cf. The Fairie Queene vs. William Carlos Williams) and certain pieces of high status music (cf. Mozart vs. Philip Glass). Obviously these are broad generalizations vulnerable to cherry-picking; I'm mostly relying on your common sense here.
I want to focus on sea level rise because it’s easy to quantify and display. Sea levels have already risen about a quarter of a meter since 1880. This has flooded some low-lying islands, damaged some coastal cities, and devastated some wetlands and other habitats. But the average person in the First World hasn’t noticed. The IPCC predicts sea levels will probably rise another half a meter to a meter by 2100, so maybe 2-4x as much as they’ve risen already. This also won’t be very noticeable to average people. Here’s San Francisco now (top picture), after 1m of sea level rise ie the IPCC’s worst-case scenario for 2100 (middle picture) and after 3m of sea level rise ie the worst-case scenario for 2200 (bottom picture):
Boris Johnson (left) is 5’9, so the guy in the middle must be gigantic. Who is he? Looks like it’s Milo Djukanovic, President of Montenegro, who’s 6’6 (198 cm). Is he the tallest world leader? It seems like he’s tied with his colleague across the border, Serbian president Aleksandar Vucic. Why are Balkan leaders so tall? As usual, the answer is “genetics”. This article says: It has been noted that men from Herzegovina are taller on average than men in other places—the average male height is just over six feet...Putting all the data together, researchers concluded that the most likely cause of larger-than-average height of Herzegovinian men is lifestyle during the Paleolithic—men hunted large animals such as mammoth for survival—such a diet, heavy in protein, combined with small population densities, would have provided ideal conditions for height selection, resulting in increasingly taller men who passed the trait down through their I-M170 chromosome to future generations. Some sources note that they manage to beat the Dutch despite the latter country’s much higher human development index. The Dutch are probably tall through a combination of nature and nurture; Balkan people are tall through nature alone. 7: Eliezer Yudkowsky doesn’t need more ego boosts, but an idea he had a couple of years ago - using strings of bright lights to provide a better and brighter experience for Seasonal Affective Disorder sufferers than regular light boxes - spread from him to the rationalist community to the wider world, and has finally gotten tested in a formal study (see Acknowledgments section). Results seem vaguely positive: "SAD symptoms of both groups improved similarly and considerably...exploratory analyses indicate that a higher illuminance is associated with a larger symptom improvement in the BROAD light therapy group" 8: Percent of people who choose woke options on polls very tentatively and preliminarily seems to be going down post-Trump (h/t Richard Hanania). 9: Twitter conspiracy theories 10: Did you know: all those reconstructions of “how classical art would have looked with the original paint” are probably inaccurate. There is no reason to think the Greeks and Romans used garish technicolor hues on their statues; what evidence we have suggest they were good at shading, and the statues were probably colored very tastefully. 11: Complaints about how Karl Friston uses the term “Markov blanket” 12: Trevor Klee on the claim that cyclosporine patients don’t get dementia. Apparently there was a big study where basically nobody on the immunosuppressant cyclosporine ever got dementia, and there are some theoretical reasons why cyclosporine might prevent neurodegeneration. But another study found people on cyclosporine got dementia at the usual rate. I think in a situation like this you should have a really high prior on “the people who got the crazy result bungled their study somehow”, but I’m interested in hearing what other people think. 13: Also from Trevor: a history of fluvoxamine treatment for COVID. 14: To tide you over until the next book review contest, here is awanderingmind’s review of The Conquest Of Bread. 15: Claims: cnbc.com/2021/11/05/sam…\nft.com/content/dcb75a… (better article, but paywalled)","username":"moskov","name":"Dustin Moskovitz","profile_image_url":"","date":"Fri Nov 05 15:49:46 +0000 2021","photos":[],"quoted_tweet":{},"reply_count":0,"retweet_count":184,"like_count":1188,"impression_count":0,"expanded_url":{"url":"https://www.ft.com/content/dcb75a56-ca23-439c-96db-56483979bf34","image":"https://bucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com/public/images/80a58c96-c72f-4301-b571-aa9384f132bd_2400x1350.jpeg","title":"Subscribe to read | Financial Times","description":"News, analysis and comment from the Financial Times, the worldʼs leading global business publication","domain":"ft.com"},"video_url":null,"belowTheFold":true}" data-component-name="Twitter2ToDOM"> 16: Big trial on Vitamin D for depression finds null result. Peter Attia tries to tear it apart here, but I am unconvinced, especially in the context of Vitamin D never working for any of the things people say it does besides the most boring aspects of bone health. 17: “California is actively considering the adoption of flawed and inequitable guidance on math curricula based on misleading data and inaccurate success metrics reported by San Francisco Unified School District (SFUSD)...Based on our review of the data, we found misleading, unsupported, and cherry-picked assertions of success for the new math program. We noted that overall test scores are down and enrollments in UC-approved advanced math classes have dropped as well.” It looks like San Francisco is trying the good old “lower standards, then when more kids meet the standards, claim your school reform plan worked” trick again. 18: A new study claims that self-reported “Long COVID” symptoms are more associated with believing you’ve had COVID than with actually having it (as measured by serologic testing), which sounds like pretty strong evidence that it’s psychsomatic. Expert reactions are mixed-to-negative, although the only one of these that doesn’t sound like excuse-making is Dr. Rossman’s about the unreliability of the tests. I haven’t confirmed test reliability stats but Philippe Lemoine also thinks this is a plausible confounder. 19: Noahpinion: What If Xi Jinping Just Isn’t That Competent? I appreciated this for making me think, and for underlining the extent of the difference between the Deng/Jiang/Hu era and what Xi’s doing. I especially appreciated this line, which I’d never thought about before: Xi presided over the end of China’s hypergrowth. To some extent this is not his fault. No country can grow at 10% forever, and there were many structural forces pushing downward on China’s numbers — the end of the demographic dividend, the exhaustion of rural surplus labor (the Lewis Turning Point), the saturation of export markets, and so on. But China is also slowing down earlier than South Korea, Taiwan, or Japan did in their day. China’s per capita GDP (at PPP) is still only about 1/3 that of a developed country, so if they stop catching up at about half of developed-country levels, that will not be a great showing. A big lesson of the past twenty years has been “actually liberal democracy isn’t necessary to reach developed-country status”, so it would be quite the twist if it turned out you needed liberal democracy to reach developed-country status. This gets pretty close to the great mystery of why some less-developed countries “catch up” and others don’t; whatever happens in China is going to be a really useful data point. 20: Variations on the fable of The Frog And The Scorpion. 21: You’ve probably heard about the University of Austin, the new project by a bunch of wokeness-critical academics to start a new university that won’t cancel people or force conformity (New York Post article, Politico article - these were the two least “you need to be super-outraged about this right now” articles I could find). Tyler Cowen and Larry Summers are involved; Steven Pinker was supposed to be but left for unclear reasons. My thoughts, in no particular order: Even forgetting the political aspect, attempts to start new universities are always welcome.
Here's the same map but for land share–the percentage of a property's value that's due solely to the land. If you build a shack in the desert, nearly 100% of the property's value will come from the shack, because the land is worthless. But if you build a shack in San Francisco, nearly all of the property's value will come from the land. Notice how the land share gets closer to 100% as you move towards big cities along the coast.
In 2020, all real estate in NYC was worth about $2.7 trillion, according to LendingTree (the land + the buildings). But let's go ahead and see for ourselves. You can run a quick spot check by going on Zillow or Redfin in a major city like New York or San Francisco. First, search for a vacant lot for sale in the heart of downtown, and note the asking price. Then look for a similarly-sized lot with a building on it that has sold within the last few years, situated next to the empty lot. The last selling price should be available. You can subtract one price from the other to get a rough estimate of the land share of the plot with the building on it.
Here's a listing for a vacant lot in the heart of San Francisco (personal information redacted). They're asking for $1.99 million dollars, and, judging from other listings and sales records in the area, they'll probably get it.
Well managed, transparent, and adequately funded mass appraisal procedures Everyone is in further agreement about the three basic "approaches" to value estimation: the market approach, the cost approach, and the income approach. The Market Approach This is the most common approach. You gather a bunch of information about comparable properties, look at past selling prices and rents, and make adjustments for differences. This is greatly aided by modern computerized databases, as well as Geographic Information System (GIS) mapping and visualization tools. Remember those spot checks I did in Part I to estimate the value of the land under a building in San Francisco using a nearby, similarly-sized empty lot? That was me (crudely) using the market approach. The Cost Approach In this approach, you estimate the cost of the buildings minus depreciation. Professionals that value residential and commercial buildings often rely on Marshall & Swift's Valuation Service. This is a fancy calculator where you plug in all the different characteristics of your building, and it spits out a cost estimate. You can think of it as a Kelley Blue Book for buildings. Once you have the cost of your building, you apply certain widely-accepted depreciation formulas based on its age. The cost approach has two chief limitations. The first is that it requires a lot of detailed information about the building. The second is that the cost to build something isn't necessarily the same as what it would sell for in today's market. Therefore, this approach tends to overestimate building values and underestimate land values, as discussed in detail in Part I. The Income Approach In this approach, you look at the net income (rent - expenses) that a commercial or residential property generates and then use the prevailing capitalization rate of the area to get the property value. You typically use this formula: Value = Income / Rate This gives you the total property value, and from there, you can use one of the other two approaches to separate land value from building value. Crucially, any observed land or property tax needs to be factored into the observed "income" portion. Even if the state is collecting the tax, it's part of the flow that originates from the property, and thus affects the full untaxed market value of the property. Naively you might expect a 100% Land Value Tax to drive itself to zero because it also drives down the purchase price of the land to approximately nothing. To avoid this, you figure out the capitalized value of the LVT that's already been applied to get the untaxed land value. These are the basic methods that we've used to value properties "by hand" over the last century, and there are many who claim that these are good enough. As for separating land from buildings, Ted Gwartney prefers to estimate the value of land directly whenever possible and derive the building value as a residual. He claims it's easier to assess land than buildings, because in most cases, the value of land is derived almost entirely from the location. Land doesn't have as many fiddly variables, like how much damage your roof took from the last hailstorm and whether you've remodeled your bathroom in the past five years. But let's dive deeper. 2. Assessing the Assessments Okay, so once you've made all your assessments, how do you ensure they're accurate? You test them. We have two main signals: ongoing transaction data from the market, and complaints from property owners about the assessed values. The typical way you compare yourself against market transactions are "Ratio Studies", which you can read more about in this IAAO paper on the subject. As for complaints, you'd think property owners would always complain out of pure self-interest, but apparently, only a minority do, and assessors actually build in an expectation for a certain number of complaints as a chief source of feedback. If complaints are below a certain threshold (2% according to Hefferan and Boyd), that's apparently a sign that you're doing well. During Ted Gwartney's seminar, someone asked him about what tends to drive objections: ATTENDEE: Can you tell us what fraction of property owner who request a lower assessment argue that their land assessment is too high? GWARTNEY: A very small number. Almost all of the adjustments that are made are made because of improvements. Most of the arguments when you go to an appeal is about the building, it’s condition, or what’s in it or whatever. Generally the land is accepted by people, they realize it’s fair by looking at what other parcels are assessed for and most people don’t argue it. They might say he has a better view than I do or whatever, but usually [the objection is] because there’s some physical difference or condition in the structure. So if the public accepts your valuations, and new market signals match your assessments, then they can be said to be accurate. But how precise do they need to be? Here's Gwartney's opinion: ATTENDEE: How accurate do assessments have to be to get the benefits of Georgism? GWARTNEY: You have a lot of wiggle room. It doesn’t have to be perfectly precise. The idea is to improve on what’s already being done. You get immediate feedback that what you’re working on is making good results. This is a part I'd like to know more about. Is plus or minus 5% of the true land value "good enough?" What about 15%? Or 1%? If land is under-assessed, then we basically have the same problem as the status quo, and we're not really any worse off. But if land is over-assessed, we might drive people off of it, which is bad. So it seems our main problem is not over-assessing the value of land. Georgists often talk about "100% LVT," but during practical discussions, it seems that their wildest dream is just to get as high as 85%. That would leave a pretty big safety margin for not over-taxing the land, even if you over-assessed it. Here's a graph. If you under-assess a property's land by 15%, the assessed value is 85% of the true value. Take 85% of that and now you're collecting 72.25% of land rents. If you over-assess a property's land by 15%, the assessed value is 115% of the true value. If you take 85% of that, you get 97.75%. Collect all that and you're still leaving 2.25% of the land rents on the table, but you're not going over. This is comforting, but frankly, all the evidence I've seen so far suggests that we're chronically and consistently under-assessing the value of land. But even if we can assess things accurately, it's a moot point if we can't afford to hire enough assessors to do the job thoroughly. 3. How Many Assessors do you need? Another critique about assessment is that you're going to need an army of property assessors peeking inside windows at all hours of the night, and that it's all going to be ruinously expensive. Here's a slide from Gwartney's presentation, which is itself taken from an IAAO conference. Gwartney says that when he was the assessment commissioner and chief executive officer in British Columbia, he had a staff of 690, and that this number has not changed significantly since then. British Columbia has a population of about 5 million, so that's 1 assessment officer for every 7,250 British Columbians. For context, the IRS has a staff size of 74,454, or about one IRS agent for every 4,425 Americans. I don't have data on how many property tax assessors the USA has in total, but the above slide suggests British Columbia's figure is on the high end. As for how you actually do assessments, sure, you can send out an army of assessors to value each and every property in your jurisdiction by hand. However, not only is that labor-intensive, it's also a recipe for inconsistency. Whatever method you're using to value properties needs to be consistent and standardized across all properties, so you don't have sharp discontinuities on the assessment map that are due solely to differences between Assessor Fred and Assessor Sally's personal methodologies. Thankfully, we're living in the modern age, and we have some fancy new tools at our disposal. 4. Modern Technology Georgists were doing split-rate assessments to allegedly good success long before the rise of the computer, such as J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911. Today, we have spreadsheets, property value databases, GIS mapping visualizations, regression analysis, machine learning...the works. According to Gwartney, the Canadian province of British Columbia has revalued all its land and all its property on an annual basis simply by using computers and market analysis, ever since he first helped them set up their system back in 1975. Not every jurisdiction revalues their land this thoroughly and this often, but Gwartney says there is no significant technical or staffing barrier standing in the way. Gwartney has been retired for some time, so his seminar didn't cover all the latest cutting-edge techniques that have come out in the last few years. Let's look at some recent papers and see what new tools assessors have to play with. The first on my list is Land Value Appraisal Using Statistical Methods by Kolbe, Schulz, Wersing, and Werwatz (2019). This is a study on mass appraisal techniques using real estate transaction data from Berlin, Germany. It claims that not only are the results cheaper and faster to generate than those done by conventional property assessment methods, but they are also no less accurate than those done "by hand" by experts. Kolbe et al. assert that, provided you have access to high quality market transaction data, you can perform accurate and efficient mass appraisals of land values. They chose Berlin because it "has a very effective system of property transaction data collection and storage," in contrast to other parts of Germany. They cite some prior work by Almy (2014) studying Canada, the Netherlands, and the United States, suggesting that the assessment cost per property can be brought down to 20 Euros–25 times cheaper than what some other people (Fuest, et al. (2018)) assert. Given an average tax receipt of 2,000 Euros per property, this means that the assessment cost should represent only about 1% of the funds raised. Is that good? Let's take this assertion at face value for the moment and compare it to the cost of the IRS. Federal tax receipts in 2020 were $3.42 trillion, and operation costs for the IRS were $12.3 billion, or 0.36%. However, the IRS outsources most of the labor of tax preparation to the taxpayers themselves, with compliance costs estimated between $200 billion and $400 billion a year, to the delight of Intuit. Add that up and the total cost of federal tax collection to the economy is anywhere between 6-12% of the amount it raises. And what about sales tax? According to a 2006 report by PriceWaterHouseCoopers: The study finds that the national average annual state and local retail sales tax compliance cost in 2003 was 3.09 percent of sales tax collected for all retailers, 13.47 percent for small retailers, 5.20 percent for medium retailers, and 2.17 percent for large retailers So a compliance cost of 1% would be way more efficient in terms of cost collection than the other two most common forms of taxation, and taxpayers don't even have to do anything themselves, other than pay the bill. Alrighty, how about the accuracy? The authors cite two international examples, Australia and Lithuania, as among the few countries in the world that have both a Land Value Tax and statistical methods for mass appraisals. Hefferan and Boyd (2010) assert that objections to assessments from property owners in Australia are less than 1%. I'm willing to buy the improved efficiency claims just by taking a look at some methodologies. It seems reasonable that computerized records and algorithms can cut costs significantly; the real question is if you're trading off accuracy. The other papers I found on the subject are Bencure, et al (2019) in BayBay City, Philippines, Kilić, et al (2019) in Croatia, Yalpir & Unel (2017) in Konya, Turkey, and Raslanas et al. (2014) in Vilnius, Lithuania. Let's dive in and examine some methods. 5. Mass Appraisal Methods Here are some of the latest mass appraisal methods cribbed from the research papers listed above. All of these are based on taking market transaction data, plotting them out on a map, and running computations over them to estimate valuations for the properties you don't have known values for. Furthermore, all of these methods are able to value land and building values separately. Multiple Regression Analysis This paper by Yalpir and Unel out of Turkey gives a straightforward example of using Multiple Regression Analysis for land valuation. For those of you who didn't study math, let me explain regression analysis. This is a family of mathematical models where you basically take a data set, ask the question "what mathematical formula would best fit this data," choose a basic equation model, and then have a computer search for a set of coefficients that "best fit" that curve to the data with the least amount of error. The simplest example is using linear regression on a scatterplot of observed data points to fit a trend line. This is a common exercise in freshman physics and statistics classes. You can use more complicated versions of this numerical method to take a big bag of observations (real estate sales) and use "multiple regression" to tease out dependent variables (land value and improvements value) based on the independent variables (size, location, age, number of bedrooms) of your observations. In this case the team identified about a hundred different factors that can affect the price of a property: Then you create an entry for each property, fill in the values for each of those characteristics, and run it through the regressor. Take note of how many of these factors start with the words "proximity to." Each of these can be calculated automatically just by knowing where the property is on a map, and each of them is an independent contributor to the value of the property's location. The next step is to generate individual "index maps" that combine various related features into combined heat maps. Then you run everything through and see if it works. You can get the land share of the final value by combining the contributions of all the individual factors that you associate with "land," such as proximity to important things. In the verification section the authors say: As a result of the analysis, since the significance level (0.000) p <.05, corresponding to the F values in the ANOVA test, indicates that the regression analysis is appropriate and the models are significant. The criteria that make up the model account for about 85% of the market value and 15% cannot be explained for reasons such as economic, non-existent data and unearned income. Unfortunately, they don't say anything about how accurate their model is for assessing land values specifically. Otherwise, this is a pretty good example of using the Multiple Regression method for estimating the individual contributions of various factors to overall property values. Gwartney says Multiple Regression Analysis was a standard method he typically used, of which this specific paper is just one example. Nonparametric kernel regression This will be a method familiar to the programmers in the audience who have any experience with image processing algorithms. Here's an example from this old Gamasutra article: The basic idea here is to take a matrix of numbers, called a "kernel", and run that over every pixel in a source image. The kernel tells you how strongly to weight all of the source pixel's neighbors to compute a final result for that position. A simple "box blur" is a kernel where every value is 1 (meaning it averages the values of all neighboring pixels within a range). The more subtle gaussian blur illustrated above uses a two-dimensional normal distribution of values so that each pixel is most affected by those nearest to it. So let's apply the same principle to land valuations. If you have a map with lots of transaction data of pure land sales–defined as sales of either vacant land or teardown properties (where the building value is essentially zero)–then you can use a special kernel filter to smoothly interpolate land values across the region. So you basically have a smooth curve that mostly favors close-by points, tapers off a bit, and then disregards anything outside a certain distance entirely. The big assumption here is that land values change smoothly and do not change suddenly across very short distances. There are, in fact, locations with sharp jumps in value (any town with an "other side of the tracks," for instance). But for cases where we know a priori that land values change smoothly, this method is appropriate. No other prior restriction is placed on the form of the land value map, however, and this is why it's called "nonparametric." Here's an illustration. The outer box is the entire search distance that the kernel considers, and the circles represent the falloff of the curve itself. The size of the box is called the "bandwidth" and is set by the user. Everything outside of it will have zero influence on the kernel's output at any given location. This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes
This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes
For San Francisco and most other major cities, we would forecast 1.5-2x lower probability (12-16 micromorts). We focused on London as it seems to be at high risk and is a hub for the effective altruism community, one target audience for this forecast.
Bradley Zink is fed up. The terminally-delayed high speed railway from Los Angeles to San Francisco has been a boondoggle. As governor, his first act would be to cancel it, saving $40 billion. What would he do with the money? From his Facebook page:
The San Francisco Chronicle accuses him of being “obsessed with stoicism”, and I don’t know whether this is some kind of attack or if he is actually obsessed with the philosophy. He does recommend Victor Frankl books to people on Twitter:
The latest step in his intellectual evolution is his book San Fransicko: Why Progressives Ruin Cities, which points out all the rampant crime and drug use and homelessness and garbage in SF and says maybe some of these things are bad (the New York Times wrote a negative review here).
In the spring of 2021 two colleagues and I went to San Francisco. We first went to check in on the open-air drug scenes in the Tenderloin and United Nations Plaza. It was the usual scenes of people sitting against buildings and injecting drug needles into their necks and feet. There was garbage, old food, and feces everywhere. After a couple of hours, we decided to go out to eat in the Mission. Work was over. We were all looking forward to a relaxing dinner. We were eating ice cream and walking along Valencia Street when a psychotic man, perhaps about thirty years old, began following us and screaming obscenities. When we turned around to look at him, he screamed at us, “What are you looking for, huh! WHAT. ARE. YOU. LOOKING. FOR!” and started walking faster toward us. We walked faster until the man found other people to verbally assault.
Things haven’t always been like this. San Francisco used to be one of the safest and most beautiful cities in the world. Shellenberger opens with the story of a 1970s SF candidate who campaigned on a message of public cleanliness, promising stronger punishments for owners who failed to pick up after their dogs. The message was a hit, so much so that the increased community and social pressure was enough to reverse the dog-poop-in-parks problem with minimal police enforcement.
In 2018, San Francisco’s mayor, London Breed, held a walking tour with television cameras and newspaper reporters in tow. “I will say that there’s more feces on the sidewalks than I’ve ever seen,” said Breed. “Growing up here, that was something that wasn’t the norm.”
In my review of San Fransicko, I mentioned that it was hard to separate the effect of San Francisco’s local policies from the general 2020 spike in homicides, which I attributed to the Black Lives Matter protests and subsequent police pullback.
I asked experts and advocates, “How do we know that the homeless population won’t replace itself if provided with housing?” Said Randy Shaw, the Tenderloin permanent supportive housing provider, “The question you’re raising is one that never gets discussed. Somehow, there’s this sense that San Francisco is under the obligation that anyone who comes here we have to suddenly house. There is an underlying logic that San Francisco doesn’t really ever want to talk about.”
But the stats I found were that 70% of SF homeless lived in SF before becoming homeless, 22% were elsewhere in California, and 8% were from other states. From this report, I gather most of the Californians were elsewhere in the Bay Area or nearby, and this is more like homeless people in Palo Alto going to San Francisco because it’s the nearest big city with a shelter, rather than people opportunistically seeking places with good social services. So while the opportunistic thing does happen, it doesn’t seem to be responsible for very much of the homeless presence.
Overall these numbers make me a little less worried about this concern. San Francisco is already quite generous, and even if some new policy doubled the absolute number of people who came from elsewhere, it would only increase the total number 10-20%.
2: Isaak Freeman asks me to signal-boost the Future Forum, from August 4-7 in San Francisco, featuring speakers including Sam Altman, Anders Sandberg, Patrick Collison, and Tyler Cowen. They are bringing together 250 people from EA, Silicon Valley, and related communities to “arm the world's brightest minds with the tools they need to tackle global problems” and to potentially offer funding and mentoring. Apply at the link above.
2. Police respond to changes in risk. Police are evaluated on whether what they do is "reasonable" - a rule which should allow for good-faith mistakes, but which is also subjective. When an officer who is fired or prosecuted for something that is clearly unreasonable (like Derek Chauvin, whom all cops agreed was guilty) police don't worry about it. However the trend now is to prosecute officers even for things which are arguably unreasonable - Chesa Boudin just lost an excessive force case against a cop in San Francisco. If you can't convince a San Francisco jury that a cop used excessive force, that case never should have been brought. Even when officers don't end up in prison - nobody wants to end up the target of a massive media smear campaign, like the officer in the Jacob Blake case.
3. Many large cities and states are now making it very clear that they literally do not want police to do proactive police work. Philadelphia, for example, banned police from doing traffic stops. Baltimore's prosecutor said she won't prosecute any cases for drug possession. Boudin refused to use evidence San Francisco police gathered during "pretextual" traffic stops. When political leaders tell officers that they should stop doing police work, police stop doing it.
San Francisco, at 11 AM on Sunday 9/18, “in the Panhandle, between Ashbury and Masonic, with an ACX sign”
Shouldn’t we have planned meetups further apart for people who wanted to go to multiple of them? Yes, and this is directly my fault, up to and including rescheduling to avoid the San Jose one . . . right on to the same day as the San Francisco one. Sorry, I’ll try to do better next time.
I looked for photos of the Central Valley to illustrate this article, but none of them were quite as I remember it. This one from Sacramento Bee is the closest I could find. But imagine it through a layer of haze, and also you can’t see well because you are in the process of dying from heatstroke. Of large Central Valley cities, Sacramento has a median income of $33,565 (but it’s the state capital, which inflates it with politicians and lobbyists), Fresno of $25,738, and Bakersfield of $30,144. Compare to Mississippi, where the state capital of Jackson has $23,714, and numbers 2 and 3 cities Gulfport and Southhaven have $25,074 and $34,237. Overall Missisippi comes out worse here, and none of these seem horrible compared to eg Phoenix with $31,821. Given these numbers (from Google), urban salaries in the Central Valley don’t seem so bad. But when instead I look directly at this list of 280 US metropolitan areas by per capita income, numbers are much lower. Bakersfield at $15,760 is 260th/280, Fresno is 267th, and only Sacramento does okay at 22nd. Mississippi cities come in at 146, 202, and 251. Maybe the difference is because Google’s data is city proper and the list is metro area? Still, it seems fair to say that the Central Valley is at least somewhat in the same league as Mississippi, even though exactly who outscores whom is inconsistent. III. What do the people who live in the Valley think went wrong? What The Hell Is Wrong With California’s Central Valley?, starting around 9:30, interviews a local conservative realtor (most people in the Valley are conservative; I haven’t found a liberal equivalent). He says that the farms in the Central Valley used to be manned by migrant workers, who would come from Mexico, work for a season, then go back to Mexico and live off their earnings for the rest of the year. Later, policies shifted to welcoming them and granting them citizenship, so many of them came over and brought their families. But around the same time there was a drought, the farm industry crashed, the remaining farms mechanized, all the immigrants were left without work, they got on welfare, and they weren’t able to get off of it. He doesn’t say exactly when this happened, but he says times were good when he was a child, and he looks like he’s in his 30s or 40s. So if he’s 35 and things started going bad when he was 10, that would mean he thinks things started going bad around 1995 to 2000. Here’s a story in the LA Times from 1999, which talks about how things are starting to get bad. It admits that Californians like to poke fun at the Central Valley, but it seems to be just that - poking fun - and not freaking out about poverty and dysfunction the way articles about the Valley do now. But it ends by saying that things are getting worse: To be honest, living in the Central Valley takes some getting used to, especially if you’re from the coast. It’s an acquired taste. Oppressive heat in summer. Depressing tule fog in winter. Sure, fall and spring are OK. But where aren’t they? First-rate culture is scarce. The state capital doesn’t even have a symphony. One of the attractions--it’s almost a local joke--is the ability to get away, particularly from Sacramento. It’s 90 minutes to San Francisco in one direction, or skiing in another; two hours-plus to the ocean or Tahoe […] Still, earthquakes aren’t a menace to most people. And it doesn’t take long before you begin to appreciate certain benefits--indeed, to understand that some Central Valley burgs, especially the capital, are among California’s best kept secrets. Or, at least, they have been. Continuing: When I moved here nearly 40 years ago--the first of three times--summer skies were blue and the stars bright. Fishing was easy in the rivers and pheasant hunting was 10 minutes from town--in fact, where I now live. All this good life, however, has been changing. Sacramento is now the sixth smoggiest area in the country. A gloomy, beige pall greets motorists as they descend from the Sierra. Even worse is the San Joaquin Valley, from Stockton to Bakersfield. It’s rated the nation’s fourth smoggiest region […] And this brings us to the root problem: a population explosion, fed notably by commuters spilling over the Grapevine from L.A. into Bakersfield, and from the Bay Area into the northern San Joaquin Valley, turning farms into houses and freeways into parking lots. In Sacramento, high-tech industry is generating jobs and sprawl. Up and down the valley, people without job skills are having babies and going on welfare. Many are immigrants from Mexico and Southeast Asia. “The population is growing at a faster pace than the economy,” notes Dan Whitehurst, a former Fresno mayor who is running again. “Livability is becoming more of an issue. But the biggest issue still is jobs.” That’s because, aside from Sacramento, the Central Valley has not cashed in on California’s economic boom. Unemployment in the San Joaquin Valley is roughly double the state average. It’s smoggy. Traffic’s getting worse. Farms are disappearing. There aren’t enough jobs. And, says pollster Mark Baldassare, people are “myopic” about their plight. It finishes: “We have a huge problem. ‘No way L.A.’ has been our slogan. But if we build nonstop houses, we’ll be worse than L.A. because we’ll have destroyed our [farm] economic base. . . . There’s no regional leadership. More state officials need to decide this area matters and poke their heads up out of the fog.” The fog and the smog. If not, one day there’ll be no getting used to the place. This is a weird article. It seems to confirm that things used to be better - nobody would call the Central Valley “the good life” now. But its concerns are smog, sprawl, and decreasing share of agriculture. These seem like the problems of somewhere that’s growing - local NIMBYs complaining that too many people want to move in. Today the problem is more that everyone in the Central Valley wants to leave. The piece sort of touches on poverty - “people without job skills are having babies and going on welfare” and “the population is growing at a faster pace than the economy” - but it’s still a weird emphasis, and one that makes me think of this as supporting the “problems were starting in the 90s” view. But by 2012, things were clearly very bad - here’s an article about how Census Shows Central Valley Areas Among Poorest In Nation. It says: Experts say the poverty problem in the nation’s agricultural powerhouse is deeply ingrained. The most important barrier is the valley’s lack of economic diversity. There are simply too few good nonagricultural jobs around and jobs in agriculture tend to be low-wage ones — except for those who run agribusinesses. “It’s a pretty ag-heavy region, so the inequality of wages and the opportunity to earn better wages is really skewed,” said Caroline Farrell, executive director of the Delano-based Center on Race, Poverty & the Environment. “If you own a farm, you’re apt to earn more wealth, while if you’re a farmworker, don’t earn very much.” The valley has not been able to bring or retain many new companies partly because it lacks a qualified workforce, said Atonio Avalos, associate professor of economics at Fresno State University. “We have an issue of skills mismatch,” Avalos said. “Companies may be offering jobs, but the skills of people in the valley are not ones they are looking for.” Students who want to get a college degree face many barriers, he said, and public funding for education is being slashed. Those who do graduate leave to find jobs elsewhere. The valley also doesn’t offer attractive amenities and has serious problems such as air pollution that have gone unaddressed. “If you’re a doctor or engineer, there are other places where you can make good money and live in better conditions,” Avalos said. “Many people don’t come here or leave because of the high incidence of asthma and other respiratory problems.” This sounds like things were already pretty bad in 2012, maybe bad enough that they must have been getting worse for longer than 10 or 15 years, I don’t know. IV. What do the data say? Here are some economic time series. I couldn’t find any good long-term ones; the least bad one comes from this unsourced report: Here it looks like things got worse from 1975 - 1985, and then depending on county there was a slower-to-imperceptible decline thereafter. FRED only has data since 1989, but agrees that things haven’t gotten worse since then. Here’s unemployment: Is this just because people got discouraged (or on welfare) and stopped seeking employment, and so stopped showing up in the statistics? Here’s a graph of Total Employed Persons: In 1990, 303,000 people were employed out of a population of 354,000. In 2022, 430,000 people were employed out of a population of 542,000. So labor participation rate went from 86% to 79%. But national labor force participation decreased by about the same amount during that time, so I don’t think we should overemphasize that. And here are some other graphs I found useful: Fresno housing prices: Racial demographics: Source: Wikipedia. Central Valley cities like Fresno and Bakersfield aren’t really more Hispanic than other parts of California or Arizona, so if immigration or racial issues played a part it must have been more complicated than just numbers. Number of immigrants in California over time: Factors of productivity in agriculture: V. So why is the Central Valley so bad? It’s an agricultural region, but lots of places are agricultural. It got lots of immigrants, but no more than many other places. It’s polluted - but so was LA, and LA rebounded. This is just a weak guess, but I think it starts with their crops. The Midwest grows mostly corn and wheat. The Central Valley is more fruits, vegetables, and nuts. Corn and wheat are easier to harvest, so middle-class farmers can own the farm and buy a mechanical harvester or something. Fruits, vegetables, and nuts benefit from intensive manual picking, so farm owners hire outside labor. According to Carolina Demography: There are about 3 million farmworkers in the United States: about two million are family farmworkers and another one million are hired farmworkers…nationally, about three-fourths of hired farmworkers are foreign-born; most (69%) were born in Mexico; 6% were born in Central America; and 1% were born in another country. Given that these are mostly Mexican immigrants, we’re probably not talking about people who are hired to grow corn in Kansas. I think plausibly the majority of US hired farmworkers live in California’s Central Valley. This makes it a sort of plantation agriculture system, which naturally tends towards landowners taking all the gains and workers ending up as an underclass. In the mid-20th century, the local plantation underclass was made of Okies (cf. The Grapes of Wrath). In the later 20th century, many immigrants moved in, lowering wages. Although immigrants don’t usually lower wages, this is because there are usually lots of industries for people to branch out into, but the Central Valley only has agriculture. Also, agribusinesses were becoming better at mechanizing their operations. Although technology doesn’t usually lower wages, again, this requires lots of diverse industries, and the Central Valley only had agriculture. All of this corresponds to the 1975-1985 period on the graphs where wages were going down. But it sounded from some of the testimonials above like the Central Valley didn’t become truly miserable until the late 90s. I’m not sure why this is. It could be the immigrants switching from being migrant laborers to raising families, and those families were impacted by poverty and inequality in a way the original migrants weren’t. It could be worsening drug problems as new drugs get invented and go down in price. (I’m not sure if NIMBYism and rising house prices also played a part. House prices do seem to have risen, a lot, but I was under the impression that building things in the Central Valley was easy and most of a house’s price there is construction rather than land. I’m not sure why house prices would have gone up so much since 1990 if this were true, though.) Other things that the articles I read emphasized: There’s a severe drought in the Central Valley right now. This is probably partly climate change, partly bad luck, and partly California diverting water to hydrate growing coastal cities. This has made everything worse (but then why isn’t that reflected in worsening economic statistics?)
Sometimes well-off residents of California coastal cities get houses in the Central Valley and commute. It’s about 2 hours from LA to Bakersfield, or 1.5 from Stockton to San Francisco, so it’s not worth it for most people. But Central Valley houses cost between 25% and 50% the cost of coastal houses, so I guess it’s worth it for some. I don’t know whether this is good (because these people bring money in and create jobs) or bad (because these people bid up land values).
I think there's a story here the data aren't telling. Give me Fresno over San Francisco any day.
That same day, the client drove me to see what he considered desperate poverty: a neighborhood in the shadows of an agricultural feed mill. It was smelly and industrial, the houses were small and older, BUT THE PEOPLE WERE LIVING IN HOUSES. I kept waiting for the kind of brutal conditions you see on every street in San Francisco--there is some of that in Fresno, but nothing like CA's coastal cities.
[…] I am a big fan of our fine author here, but this post drove me mad. Imagine looking out at some of the most productive farmland in the world from the hellhole of San Francisco (former decade-long SF resident here!) and asking what's wrong with them!
Several additional plaintiffs I can’t find good information about You can find the complaint here. The plaintiffs write: The [CFTC’s action], without explanation or other indication of reasoned decisionmaking, without “written notice of the facts or conduct which may warrant” the Revocation, and without providing anyone “an opportunity to demonstrate or achieve compliance” with the terms of No-Action Relief or other requirements, violates the Administrative Procedure Act. 5 U.S.C. §§ 558, 706. Among other things, the Revocation is “arbitrary, capricious, an abuse of discretion, [and/or] otherwise not in accordance with law” and occurred “without observance of procedure required by law.” The Court should “hold unlawful and set aside” the Revocation, including its command that contracts that would otherwise turn on events occurring after February 2023 be prematurely liquidated. 5 U.S.C. § 706. The Court also should enter a preliminary and then permanent injunction against the prescriptions in the Revocation requiring the liquidation of contracts by February 2023, including contracts that concern the 2024 elections, well before they would ordinarily mature. I am not a lawyer, but it sounds kind of like they’re saying “the decision was bad, and the Administrative Procedure Act says regulators shouldn’t do bad things”. I am split between the part of me which hates government regulators doing bad things, and the part of me which feels like this is how you get a cover-your-ass-ocracy that never does anything at all without fifteen layers of paperwork and ten trillion dollars per action. Whatever. At least this time it’s in my favor. Of course there are prediction markets about it: Source: Insight Prediction Nuclear Warcasting, Part 2 Samotsvety Forecasting is a team made of top prediction market players and tournament winners, vaguely affiliated with effective altruism, who make predictions in the public interest. Earlier this year, they got attention for forecasting the risk of nuclear war - in particular, they said there was an a 0.01% per month chance of London getting nuked this spring. Since then, most of the fear has crystallized into a specific scenario. Suppose Russia is losing very badly in Ukraine. Putin, fearing a coup or revolution at home if he gives up, decides to use a tactical nuclear weapon, ie a “small” nuke more suited to winning battles than destroying cities. He nukes a Ukrainian battlefield position. The West is enraged at this violation of the nuclear taboo and feels like it needs to respond decisively - maybe by nuking something on Russia’s side, or through some other act of extreme escalation. Then Russia feels like they need to respond, and eventually it escalates to strikes on major cities and global nuclear war. There are reasons for doubt. Tactical nukes wouldn’t really be useful in Ukraine; the battle lines are too spread out and there’s no single place where a nuclear explosion could take out a substantial portion of Ukraine’s forces. In the past, nuclear powers have accepted lost wars gracefully rather than turning to nukes. And the Russians deny it, and saying this is all just Western propaganda intended to scare people. Amid this uncertainty, Samotsvety has published an update: now they are at 16% chance that “Russia uses any type of nuclear weapon in Ukraine in the next year”, and 0.02% per month of a strike on London. Although they didn’t mention it this time, they previously said the risk of a strike on San Francisco was a little over half that of London; I don’t know if that’s changed. See also Dan Keys’ comment here for some skepticism of Samotsvety’s process. Swift Centre is a lot like Samotsvety; they’re a collection of top forecasters brought together by EA to make important predictions. They also took a swing at the nuclear question, and said 9.1% chance of a hostile nuclear detonation in Europe in the next six months. They didn’t calculate the risk that this would spread to global war, but they did discuss how different scenarios would bring the risk up or down: One of my hopes for forecasting is that it eventually becomes so well-validated that decision-makers can take these kinds of considerations into account: “Should we sent ATACMS missiles to Ukraine? It would have such-and-such benefits, but also increase the risk of nuclear escalation by 3.6%, is it worth it?” We can’t directly compare Samotsvety and Swift because they’re predicting over different time periods. But assuming that there’s more risk in the next six months than in the six months after that, I think Samotsvety is a little higher but they’re not embarrassingly far off. Metaculus is a bit more optimistic than either, believing there’s only a 4% chance of detonation in Ukraine in 2023 and a 7% chance of any use in the next ~year. Max Tegmark is going much higher than anyone else and says 16% chance of global nuclear war. Kalshi Applies For Election Markets Kalshi is a regulated and fully-legal prediction market with good lobbyists and a compliance team. This means the CFTC probably won’t randomly shut them down one day. But it also means they can only create new markets with CFTC permission. In July, Kalshi asked the CFTC for permission to make midterm election prediction markets - specifically, which party will win control of the House and Senate. The CFTC has said they will make a decision by October 28 (which doesn’t leave much time for predicting to happen before the November 8 election, but I guess it sets a precedent). September was the Request For Comment period, when the CFTC solicited comments from stakeholders about what they should do. Kalshi tried really hard to get lots of people to send in positive assessments - I know this because of how many people asked me “why is the CEO of Kalshi emailing me about this thing?” Their strategy seems to have worked; among the people who wrote to the CFTC in support were: A managing director at JP Morgan
…it's got some big problems. The study there only compares tobacco sales in a single city (San Francisco) before and after a ban on menthol cigarettes. However, because there's no comparison to other cities, it's essentially worthless; tobacco sales throughout the US dropped at this time, and I don't know how this compares.
I generally really like our current mayor, Libby Schaaf. She has generally had good ideas, prevented Oakland from becoming quite as bad as San Francisco, and a bunch of BLM protesters were harassing her in really awful ways for not defunding the police during the George Floyd protests but she stood firm and won my respect / good will. I also like SF mayor London Breed for being a YIMBY and being willing to call out some of the problems with her city. Both of them have endorsed Loren Taylor. Taylor is a biomedical engineer and businessman, which makes it seem like he’s smart and has some experience with the real world that will make him less than maximally socialist. The YIMBYS also endorse him. Generally seems like the best we’re going to get.
Conflict of interest notices: I was friends with an FTX/Alameda employee a few years ago. I support the effective altruist movement, which FTX donated money to. I briefly worked at the same San Francisco clinic as Dr. Lerner, a psychiatrist mentioned in this piece - but I’m so introverted at work that I never actually met him.
In 1952, most freshmen at Harvard were products of . . . the prep schools of New England (Andover and Exeter alone contributed 10% of the class), the East side of Manhattan, the Main Line of Philadelphia, Shaker Heights in Ohio, the Gold Coast of Chicago, Grosse Pointe of Detroit, Nob Hill in San Francisco, and so on. Two-thirds of all applicants were admitted. Applicants whose fathers had gone to Harvard had a 90% admission rate. The average verbal SAT score for the incoming men was 583, good but not stratospheric. The average score across the Ivy League was closer to 500 at the time.
The US branch of St. Hubertus was founded at Bohemian Grove, a secret society of elites (past members include Nixon, Reagan, Kissinger, and Hearst) who meet in a private redwood forest near San Francisco and do undisclosed things there.
Why is the San Francisco Bay Area always like this? (image source here, they claim it’s a photo but I’m doubtful) For some reason, conspiracy theorists find this concerning, and have been fretting over it for the past hundred years or so. Anyway, this is where some people decided to found a US branch of the Order of St Hubertus. All of this is attested to by the Washington Post article linked above, Wikipedia, and a bunch of other sources; as far as I know nobody seriously denies it.
This seems really low - this article says California needs an extra 2.5 million homes over the next decade just to avoid things getting worse. I think this is because most cities plan to “voluntarily” bring themselves into compliance by building more homes. It looks like San Francisco has come into compliance by “voluntarily” agreeing to build 82,000 homes; San Franciso is about 2.5% of California’s population, but this probably applies more to denser cities, so maybe this suggests a total number of homes in the 1 million range?
43: Related: claims that Newsom threatened to withhold state money from San Francisco unless they cleaned up their streets, they immediately and successfully cleaned up their streets, and now people can feel safe and happy walking outside again. I don’t go to SF enough to know if this is true, but people on Twitter say there’s been a dramatic change.
I try to avoid San Francisco. When I go, I surround myself with people; otherwise I have morbid thoughts. But a morning appointment and miscalculated transit time find me alone on the SF streets half an hour before dawn.
Somewhere to the south, Ray Kurzweil walks into his office at Google. Twenty years ago, he conjectured that all human history - no, all evolutionary and geologic history - was a series of accelerating movements, which would crescendo at the end of time in approximately 2029. Six years to go. San Francisco doesn’t feel like the sort of place willing to wait another six years. The doomed summoning-city at the end of time seethes with palpable impatience. Too much Ethiopian methylxanthine, that’s my diagnosis. It feels eerie and unreal in the darkness, like everything is underwater, and I remember Poe:
It is a bright, clear morning in San Francisco, and I almost believe it.
The two densest US cities, ie the cities with the greatest housing supply per square kilometer, are New York City and San Francisco. These are also the 1st and 3rd most expensive cities in the US.
The picture on the left is Manhattan Island, NY. The picture on the right is Conanicut Island, RI. Both islands are about the same size, the same climate, the same distance from the mainland. Both are near good natural harbors. In 1600, some early European explorer would have considered them basically interchangeable. Still, the cost of housing in Manhattan is about $2000/sqft, and the cost of housing in Conanicut is about $500/sqft. Why? God didn’t create these two islands with different land value; something must have happened to make one 4x as expensive as the other. The obvious answer is “the Dutch chose to build their colonial capital on Manhattan, more and more people moved in, it became ever denser and more urban in a virtuous cycle, now it is very dense and urban, and, in the current regulatory regime, dense urban areas have higher housing prices than empty rural ones.” If back in 1624 the Dutch had decided to build their capital on Conanicut, maybe today it would be a city of 10 million people, and Manhattan would be an empty rural area. In that case, I would expect Conanicut to have 4x the house price of Manhattan. If I were a Native American living on Manhattan, and I was committed to keeping housing prices there low, I would ask the Dutch to build their capital on Conanicut instead. In fact, whenever a European came to my island seeking to build houses, I would try to fight them off. If I somehow succeeded at this for four hundred years, and Manhattan remained an empty rural area, then I would expect Manhattan prices to be much lower than they are now. So in response to all of your comments that I don’t understand basic causal inference, I answer that history provides quasi-experiments, and no, I’m pretty sure that Manhattan has high prices because lots of people moved there, rather than because of some other factor. Or, rather, both density and desirability feed into the other, but the density step is a crucial input. 2. Comments About Jobs And Amenities (And Not Density Per Se) Producing Desirability But Martin Blank writes: NYC/SF are expensive because there are MANY good jobs there and people WANT to live there. Not because of the density of housing. You could build 500,000 homes in the middle of your empty field in North Dakota, and it wouldn't do much for the demand there. You aren't going to create Manhattan by magicking 3.5 million housing units of similar quality into the Red Lake Indian reservation in Northern Minnesota. I originally found the various comments saying this annoying. Yes, there are many good jobs in NYC. You can be a barista at Starbucks, you can be an actor on Broadway, you can be a train conductor for the MTA. But why is it easier to be a barista in NYC than in North Dakota? Surely because there are millions of people in New York, those people drink a lot of coffee, and so they need a lot of baristas. Likewise, they watch a lot of plays, and ride a lot of trains, so they need actors and train conductors. If all the residents moved to North Dakota, there would be lots of demand for baristas, actors, and train conductors in North Dakota, and none in NYC. But some people gave versions of this argument that I found harder to dismiss. JSwiffer writes: The key fact your missing is if you wave a magic wand and 10x San Francisco you wouldn't 10x all jobs. You would 10x the # of waiters, and garbage men but you wouldn't 10x the # of 500k/yr Google site reliability engineers. And it's the latter not the former that are driving up prices. Other commenters analogized this to factory or coal mining towns. Here’s how I ended up thinking about this: suppose someone strikes oil in an uninhabited part of North Dakota, enough to produce 1,000 good oilman jobs. 1,000 oilmen move to the area and start a town. Because there are no NIMBYs, they build 1,000 houses. Each oilman creates demand for a certain amount of waiters (to serve them food), doctors (to treat their illnesses), teachers (to teach their children), etc. How many waiters, doctors, teachers, etc move to the town? Assume for the sake of argument that all jobs earn the same salary, $50,000. In that case, it has to be fewer than 1,000. Each oilman earns $50,000, and some of that gets spent on taxes and out-of-town goods. So he has less than $50,000 to spent on in-town goods and services, so (in this hypothetical) creates less than one other job. Each waiter needs doctors to treat their illnesses and teachers to teach their children, so each service employee creates some number of additional service employee jobs. 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?)
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?)
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?)
Across the country, NIMBYs and status-quo defenders exploit procedural rules to block new development, giving us a world where it takes longer to get approval for a single new building in San Francisco than it did to build the entire Empire State Building, where so-called “environmental review” is weaponized to block even obviously green initiatives like solar panels, and where new public works projects are completed years late and billions over budget—or, like California’s incredible shrinking high-speed rail, may never be completed at all.
Some AI companies are trying to give their AIs exactly our current values. This is obviously bad if you don’t like the values of the 2023 San Francisco professional managerial class. But even if you do like those values, it risks permanently shutting off the capacity for moral progress. Is there any other solution?
Arguing about gender is like taking OxyContin. There can be good reasons to do it. But most people don’t do it for the good reasons. And even if you start doing it for good reasons, you might get addicted and ruin your life. Walk through San Francisco if you want to see people who ruined their lives with opioids; browse Substack to get a visceral appreciation of the dangers of arguing about gender.
SAN FRANCISCO, CALIFORNIA, USA Contact: Jill & Daniel Contact Info: jill[dot]dma[at]gmail[dot]com Time: Saturday, September 16th, 10:00 AM Location: The giant wooden bench overlooking the city right outside Cafe Josephine, by the Randall Museum in Corona Heights Park. We'll bring an ACX sign. Coordinates: https://plus.codes/849VQH76+PWW Notes: Kids and dogs are very welcome. Great bathrooms, café, and children's museum on premises. Also tree shade and stunning view of the city.
Arguing about gender is like taking OxyContin. There can be good reasons to do it. But most people don’t do it for the good reasons. And even if you start doing it for good reasons, you might get addicted and ruin your life. Walk through San Francisco if you want to see people who ruined their lives with opioids; browse Substack to get a visceral appreciation of the dangers of arguing about gender.
The specific elites include the Collison brothers, Reid Hoffman, Nat Friedman, Marc Andreessen, and others, led by the mysterious Jan Sramek. The specific land is farmland in Solano County, about an hour’s drive northeast of San Francisco. The specific utopian city is going to look like this:
1: New meetups have been registered in Canterbury, UK and Tallinn, Estonia. Meetups scheduled for the coming week include Los Angeles, Ottawa, Milano, Lisbon, Moscow, Edinburgh, Montreal, Waterloo, San Francisco, Atlanta, Ann Arbor, Rio de Janeiro, Cape Town, Auckland, Berlin, Denver, Istanbul, and many more! As always, check the meetups list for details.
The car commute from Montezuma Hills in roughly the geographic weighted center of the land purchases to San Francisco looks like roughly 3 hours and ten minutes round trip on the average working day […]
A couple would definitely need two cars. And over three hours of commuting per day to San Francisco would mean it's not that much more desirable of a location than existing downscale exurbs like Stockton, which could depress the quality of residents who'd be attracted by it.
There's a train track between Sacramento and San Francisco that's about 10 or 20 miles away, but most railroads in the U.S., other than specifically commuter railways in places like NYC and Chicago, prioritize freight over passengers, so schedules for passenger trains are often fictional, with passenger trains being sidetracked to let freight roar by. (America, by the way, has very efficient freight trains in return for having terrible inter-city passenger rail.)
HOW LONG TO PAUSE. The biggest disadvantage of pausing for a long time is that it gives bad actors (eg China)1 a chance to catch up. Suppose the West is right on the verge of creating dangerous AI, and China is two years away. It seems like the right length of pause is 1.9999 years, so that we get the benefit of maximum extra alignment research and social prep time, but the West still beats China. Obviously the problem with the Surgical Pause is that we might not know when we’re on the verge of dangerous AI, and we might not know how much of a lead “the good guys” have. Surgical Pause proponents suggest being very conservative with both free variables. This is less of a well-thought-out plan and more saying “come on guys, let’s at least try to be strategic here”. At the limit, it suggests we probably shouldn’t pause for six months, starting right now. Since this involves leading labs burning their lead time for safety, in theory it could be done unilaterally by the single leading lab, without international, governmental, or even inter-lab coordination. But you could buy more time if you got those things too. Some leading labs have promised to do this when the time is right - for example OpenAI and (a previous iteration of) DeepMind - with varying levels of believability. AnonResearcherAtMajorAILab discussed some of the strategy here in Aim For Conditional AI Pauses, and this Less Wrong post is also very good. Regulatory Pause: If one benefit of the Simple Pause is to use the time to prepare for AI socially and politically, maybe we should just pause until we’ve completed social and political preparations. David Manheim suggests a monitoring agency like the FDA. It would “fast-track” small AIs and trivial re-applications of existing AIs, but carefully monitor new “frontier models” for signs of danger. Regulators might look for dangerous capabilities by asking AIs to hack computers or spread copies of themselves, or test whether they’ve been programmed against bias/misinformation/etc. We could pause only until we’ve set up the regulatory agency, and take hostile actions (like restrict chip exports) only to other countries that don’t cooperate with our regulators or set up domestic regulators of their own. Many people in tech are regulation-skeptical libertarians, but proponents point out that regulation fails in a predictable direction: it usually does successfully prevent bad things, it just also prevents good things too. Since the creation of the Nuclear Regulatory Commission in 1975, there has never been a major nuclear accident in the US. And sure, this is because the NRC prevented any nuclear plants from being built in the United States at all from 1975 to 2023 (one was finally built in July). Still, they technically achieved their mandate. Likewise, most medications in the US are safe and relatively effective, at the cost of an FDA approval process being so expensive that we only get a tiny trickle of new medications each year and hundreds of thousands of people die from unnecessary delays. But medications are safe and effective. Or: San Francisco housing regulators almost never approve new housing, so housing costs millions of dollars and thousands of San Franciscans are homeless - but certainly there’s no epidemic of bad houses getting approved and then ruining someone’s view or something. If we extrapolate this track record to AI, AI regulators will be overcautious, progress will slow by orders of magnitude or stop completely - but AIs will be safe. This is a depressing prospect if you think the problems from advanced AI would be limited to more spam or something. But if you worry about AI destroying the world, maybe you should accept a San-Francisco-housing-level of impediment and frustration. A regulatory pause could be better than a total stop if you think it will be more stable (lots of industries stay heavily regulated forever, and only a few libertarians complain), or if you think maybe the regulator will occasionally let a tiny amount of safe AI progress happen. But it could be worse than a total stop if you expect continued progress will eventually produce unsafe AIs regardless of regulation. You might expect this if you’re worried about deceptive alignment, eg superintelligent AIs that deliberately trick regulators into thinking they’re safe. Or you might think AIs will eventually be so powerful that they can endanger humanity from a walled-off test environment even before official approval. The classic Bostrom/Yudkowsky model of alignment implies both of these things. David Manheim and Thomas Larsen set out their preferred versions of this strategy in What’s In A Pause? and Policy Ideas For Mitigating AI Risk. Total Stop: If you expect AIs to exhibit deceptive alignment capable of fooling regulators, or to be so dangerous that even testing them on a regulator’s computer could be apocalyptic, maybe the only option is a total stop. It’s tough to imagine a total stop that works for more than a few years. You have at least three problems: NON-PARTICIPANTS. As with any pause proposal, unfriendly countries (eg China) can keep working on AI. You can refuse to export chips to them, which will slow them down a little, but their own chips will eventually be up to the task. You will either need a diplomatic miracle, or willingness to resort to less diplomatic forms of coercion. This doesn’t have to be immediate war: Israel has come up with “creative” ways to slow Iran’s nuclear program, and countries trying to frustrate China’s chip industry could do the same. But great powers playing these kinds of games against each other risks wider conflict.
For every word like "trust" or "worried", assume I mean "...enough to outweigh other considerations" Along with this overall arc, the debate included a few other points: Holly Elmore argued in The Case For AI Advocacy To The Public that pro-pause activists should be more willing to take their case to the public. EA has a long history of trying to work with companies and regulators, and has been less confident in its ability to execute protests, ads, and campaigns. But in most Western countries, the public hates AI and wants to stop it. If you also want to stop it, the democratic system provides fertile soil. Holly is putting her money where her mouth is and leading anti-AI protests at the Meta office in San Francisco; the first one was last month, but there might be more later. Source: AI Policy Institute and YouGov, h/t Holly Matthew Barnett said in The Possibility Of An Indefinite AI Pause that it might be hard to control the length of a pause once started, and might drag on longer than people who expected a well-planned surgical pause might like. He points to supposedly temporary moratoria that later became permanent (eg aboveground nuclear test ban, various bans on genetic engineering) and regulatory agencies that became so strict they caused the subject of their regulation to essentially cease to happen (eg nuclear plant construction for several decades). Such an indefinite pause would either collapse in a disastrous actualization of compute overhang, or require increasingly draconian international pressure to sustain. He thinks of this as a strong argument against most forms of pause, although he is willing to consider a “licensing” system that looks sort of like regulation. Quintin Pope said in AI Is Centralizing By Default, Let’s Not Make It Worse that the biggest threat from AI is centralizing power, either to dictators or corporations. AIs are potentially more loyal flunkies than humans, and let people convert power (including political power and money) into intelligence more efficiently than the usual methods. His interest is mostly in limiting the damage, putting him skew to most of the other people in this debate. He would support regulation that makes it easier for small labs to catch up to big ones, or that limits the power-centralizing uses of AI, but oppose regulation focused on centralizing AI power into a few big, supposedly-safer corporations. Percent of population in each country saying AI has more benefits than drawbacks. Pope uses this table to suggest AI regulation would be decentralizing, since the furthest-ahead countries are the most eager to regulate. Source: Ipsos; h/t Quintin II. For a “debate”, this lacked much inter-participant engagement. Most people posted their manifesto and went home. The exception was the comments section of Nora’s post, AI Pause Will Likely Backfire. As usual, a lot of the discussion was just clarifying what everyone was fighting about, but there were also a few real fights: Gerald Monroe thought that the history of nuclear weapons suggested pauses like this were impossible (because many countries did build nuclear weapons). David Manheim thought it suggested pauses like this could work (because there were some successful arms limitation treaties, and less nuclear proliferation than would have happened without international cooperation). Manheim also brought up the successful bans on ozone-destroying CFCs and on human cloning.
Holly Elmore and PauseAI are holding pro-pause protests October 21 in eight cities around the world, including San Francisco.
This isn’t controlling for selection bias - but neither was my uncle’s anecdotal observation. So although it does make me slightly nervous, I’m not going to treat it as actionable evidence. Still, my girlfriend ending up begging me not to donate, and I caved. But we broke up in 2019. The next few years were bumpy, but by 2022 my life was in a more stable place and I started thinking about kidneys again. By then I was married. I discussed the risks with my wife and she decided to let me go ahead. So in early November 2022, for the second time, I sent a form to the University of California San Francisco Medical Center saying I wanted to donate a kidney. IV. Something else happened that month. On November 11, FTX fell apart and was revealed as a giant scam. Suddenly everyone hated effective altruists. Publications that had been feting us a few months before pivoted to saying they knew we were evil all along. I practiced rehearsing the words “I have never donated to charity, and if I did, I certainly wouldn’t care whether it was effective or not”. But during the flurry of intakes, screenings, and evaluations that UCSF gave me that month, the doctors asked “so what made you want to donate?” And I hadn’t rehearsed an answer to this one, so I blurted out “Have you heard of effective altruism?” I expected the worst. But the usual response was “Oh! Those people! Great, no further explanation needed.” When everyone else abandoned us, the organ banks still thought of us as those nice people who were always giving them free kidneys. We were giving them a lot of free kidneys. When I talked to my family and non-EA friends about wanting to donate, the usual reaction was “You want to what?!” and then trying to convince me this was unfair to my wife or my potential future children or whatever. When I talked to my EA friends, the reaction was at least “Cool!”. But pretty often it was “Oh yeah, I donated two years ago, want to see my scar?” Most people don’t do interesting things unless they’re in a community where those things have been normalized. I was blessed with a community where this was so normal that I could read a Vox article about it and not vomit it back out. This is surprising, because kidney donation is only medium effective, as far as altruisms go4. The average donation buys the recipient about 5 - 7 extra years of life (beyond the counterfactual of dialysis). It also improves quality of life from about 70% of the healthy average to about 90%. Non-directed kidney donations can also help the organ bank solve allocation problems around matching donors and recipients of different blood types. Most sources say that an average donated kidney creates a “chain” of about five other donations, but most of these other donations would have happened anyway; the value over counterfactual is about 0.5 to 1 extra transplant completed before the intended recipient dies from waiting too long. So in total, a donation produces about 10 - 20 extra quality-adjusted life years. This is great - my grandfather died of kidney disease, and 10 - 20 more years with him would have meant a lot. But it only costs about $5,000 - $10,000 to produce this many QALYs through bog-standard effective altruist interventions, like buying mosquito nets for malarial regions in Africa. In a Philosophy 101 Thought Experiment sense, if you’re going to miss a lot of work recovering from your surgery, you might as well skip the surgery, do the work, and donate the extra money to Against Malaria Foundation instead5. Obviously this kind of thing is why everyone hates effective altruists. People got so mad at some British EAs who used donor money to “buy a castle”. I read the Brits’ arguments: they’d been running lots of conferences with policy-makers, researchers, etc; those conferences have gone really well and produced some of the systemic change everyone keeps wanting. But conference venues kept ripping them off, having a nice venue of their own would be cheaper in the long run, and after looking at many options, the “castle” was the cheapest. Their math checked out, and I believe them when they say this was the most effective use for that money. For their work, they got a million sneering thinkpieces on how “EA just takes people’s money to buy castles, then sit in them wearing crowns and waving scepters and laughing at poor people”. I respect the British organizers’ willingness to sacrifice their reputation on the altar of doing what was actually good instead of just good-looking. I worry that people use suffering as a heuristic for goodness. Mother Teresa becomes a hero because living with lepers in the Calcutta slums sounds horrible - so anyone who does it must be really charitable (regardless of whether or not the lepers get helped). Owning a castle is the opposite of suffering - it sounds great - therefore it is fake charity (no matter how much good you do with the castle). This heuristic isn’t terrible. If you’re suffering for your charity, then it must seem important to you, and you’re obviously not doing it for personal gain. If you do charity in a way that benefits you (like gets you a castle), then the personal gain aspect starts looking suspicious. The problem is the people who elevate it from a suspicion to an automatic condemnation. It seems like such a natural thing to do. And it encourages people to be masochists, sacrificing themselves pointlessly in photogenic ways, instead of thinking about what will actually help others. But getting back to the point: kidney donation has an unusually high ratio of photogenic suffering to altruistic gains. So why do EAs keep doing it? I can’t speak for anyone else, but I’ll speak for myself. It starts with wanting, just once, do a good thing that will make people like you more instead of less. It would be morally fraught to do this with money, since any money you spent on improving your self-image would be denied to the people in malarial regions of Africa who need it the most. But it’s not like there’s anything else you can do with that spare kidney. Still, it’s not just about that. All of this calculating and funging takes a psychic toll. Your brain uses the same emotional heuristics as everyone else’s. No matter how contrarian you pretend to be, deep down it’s hard to make your emotions track what you know is right and not what the rest of the world is telling you. The last Guardian opinion columnist who must be defeated is the Guardian opinion columnist inside your own heart. You want to do just one good thing that you’ll feel unreservedly good about, and where you know somebody’s going to be directly happy at the end of it in a way that doesn’t depend on a giant rickety tower of assumptions. Dylan Matthews wrote: As I’m no doubt the first person to notice, being an adult is hard. You are consistently faced with choices — about your career, about your friendships, about your romantic life, about your family — that have deep moral consequences, and even when you try the best you can, you’re going to get a lot of those choices wrong. And you more often than not won’t know if you got them wrong or right. Maybe you should’ve picked another job, where you could do more good. Maybe you should’ve gone to grad school. Maybe you shouldn’t have moved to a new city. So I was selfishly, deeply gratified to have made at least one choice in my life that I know beyond a shadow of a doubt was the right one. …and it really resonated. Everything else I try to do, there’s a little voice inside of me which says “Maybe the haters are right, maybe you’re stupid, maybe you’re just doing the easy things. Maybe you’re no good after all, maybe you’ll never be able to figure any of this out. Maybe you should just give up.” The Talmud is very clear: that voice is called the evil inclination, and it dwells in the left kidney. There is only one way to shut it off forever. I was ready. V. You might not be a masochist. But hospitals are sadists. They want to hear you beg. After I submitted the donation form, I was evaluated by a horde of indistinguishable women. They all had titles like “Transplant Coordinator”, “Financial Coordinator”, and “Patient Care Representative”. Several were social workers; one was a psychiatrist. They would see me through a buggy version of Zoom that caused various parts of their body to suddenly turn into the UCSF logo, and they all had questions like “Are you sure you want to do this?” and “Are you going to regret this later?” and “Is anyone pressuring you to do this?” and “Are you sure you want to do this?” After clearing that gauntlet came the tests. Blood tests - I think I must have given between 20 and 50 vials of blood throughout the screening process. Urine tests - both the normal kind where you pee in a cup, and a more involved kind where you have to store all your urine for 24 hours in a big jug, then take it to the lab. “Urinate into a jug” ought to be the easiest thing in the world, but some of the labs have overly complicated jugs that I, with my mere MD, couldn’t always get right - hence my experience accidentally pouring urine on myself in an Uber. Then came the big guns. Echocardiogram. MRI. One of my urine tests was slightly off, so I also got a nuclear kidney scan, where they injected radioactive liquid in me and monitored how long it took to come out the other end (I remember asking a friend “Can I use your bathroom? My urine might be slightly radioactive today, but it shouldn’t be enough to matter.”) Finally, five months after I originally applied, I got a phone call from the Transplant Coordinator. The test results were in, and . . . I had been rejected because I’d had mild childhood OCD. This was something I’d mentioned offhandedly during one of the psych evaluations. As a child, I used to touch objects in odd patterns that only made sense to me. I got diagnosed with OCD, put on SSRIs for a while, finally did therapy at age 15, hadn’t had any problems since. I still go back on SSRIs sometimes when I’m really stressed, and will grudgingly admit to the occasional odd-pattern-touching when no one’s looking. But it’s nothing anyone would know about if I didn’t tell them! It was mild even at age 15, and it’s been close-to-nonexistent for the past twenty years! Now I’m a successful psychiatrist who owns his own psychiatry practice and helps other people with the condition! I told them all this. They didn’t care. I asked them if there was anything I could do. They said maybe I could go to therapy for six months, then apply again. I asked them what kind of therapy was indicated for mild OCD that’s been in remission for twenty years. They sounded kind of surprised to learn there were different types of therapy and said whatever, just talk to someone or something. I asked them how frequent they thought the therapy needed to be. They sounded kind of surprised to learn that therapy could have different frequencies, and said, you know, therapy, the thing where you talk to someone. I asked them if they actually knew anything about OCD, psychotherapy, or mental health in general, or if they had just vaguely heard rumors that some people were bad and crazy and shouldn’t be allowed to make their own decisions, and that a ritual called “therapy” could absolve one of this impurity. They responded as politely as possible under the circumstances, but didn’t change their mind. I wasn’t going to waste an hour a week for six months, and spend thousands of dollars of my own extremely-not-reimbursed-by-UCSF money, to see a randomly-selected therapist for a condition I’d gotten over twenty years ago, just so I could apply again and get rejected a second time. This was one of the most infuriating and humiliating things that’s ever happened to me. We throw around a lot of terms like “stigma” and “paternalism”, and I’ve worked with patients who have dealt with all these issues (it’s UCSF in particular a surprising amount of the time!). But I was still surprised how much it hurt when it happened to me. Being denied the right to control your own body because of some meaningless diagnosis on a chart somewhere is surprisingly frustrating, even compared to things that should objectively be worse. I thought I was going to be able to do a good deed that I’d been fantasizing about for years, and some jerk administrator torpedoed my dreams because I had once, long ago, had mild mental health issues. So I gave up. I spent the next few weeks unleashing torrents of anti-UCSF abuse at anyone who would listen. This turned out to be very productive! When I was unleashing a torrent of anti-UCSF abuse to Josh Morrison of WaitlistZero, he asked if I’d tried other hospitals. I hadn’t. I’d assumed they were all in cahoots. But Josh said no, each hospital had their own evaluation process. Weill Cornell, a hospital in NYC, was one of the best transplant centers in the country, and had a reputation for fair and thoughtful pre-donor screening. Why didn’t I talk to them? NYC was far away, and I hate to travel, but I was just angry enough to accept. At this point I’d forgotten whatever good altruistic motivations I might have originally had and was fueled entirely by spite. Getting my kidney taken out somewhere else felt like it would be a sort of victory over UCSF. So I went for it. Cornell was lovely. They tried to do as much of the process as they could via Californian intermediaries, so that I only had to fly to New York twice. Their psychiatrist evaluated me, listened to me explain my weak history of OCD, then treated me like a reasonable adult who tells the truth and can handle his own medical decisions. They were concerned that I sometimes self-prescribed Lexapro to deal with anxiety. But we agreed on a compromise: I found another psychiatrist, let her give me the exact same prescription of Lexapro at a much higher cost to my insurance, and that resolved the problem. So in late September 2023 - ten months after I started the process - I finally got fully cleared to donate, surgery set for October 12. VI. I knew, in theory, that anaesthetics existed. Still, it’s weird. One moment you’re lying on a table in the OR, steeling yourself up for one of the big ordeals of your life. The next, you’re in a bed in the recovery room, feeling fine. The operation - this thing you’ve been thinking about and dreading for months - exists only as a lacuna in your memory. Not even some kind of fancy lacuna, where you remember the darkness closing in on you beforehand, or have to claw yourself back into consciousness afterwards. The most ordinary of lacunas, like a good night sleep. There was no pain, not at first. The painkillers and nerve blocks lasted about a day after the surgery. By the time they wore off, it was more of a dull ache. The hospital offered me Tylenol, and I wanted to protest - really? Tylenol? After major surgery? But the Tylenol worked. Some people will have small complications (I am a doctor, pretty jaded, and my definition of “small” may be different from yours). Dylan Matthews wrote about an issue where his scrotum briefly inflated like a balloon (probably this is one of the ones that doesn’t feel small when it’s happening to you). I missed out on that particular pleasure, but got others in exchange. I had an unusually hard time with the catheter - the nurse taking it out frowned and said the team that put it in had “gone too deep”, as if my urinary tract was the f@#king Mines of Moria - but that was fifteen seconds of intense pain. Then a week afterwards, just when I thought I’d recovered fully, I got bowled over by a UTI which knocked me out for a few days. But overall, I was surprised by the speed and ease of my recovery. A few hours after the surgery, I walked a few steps. After a day, I got the catheter out and could urinate normally again. After two days, I was eating “SmartGel”, a food substitute that has mysteriously failed to catch on outside of the immobilized-hospital-patient market. After three, I was out of the hospital. After four, I started easing myself back into (remote) work. After a week, I flew cross-country. . . . and then I got the UTI. If this section sounds schizophrenic, it’s because it’s a compromise between an original draft where I said nothing went wrong and it was amazing, and a later draft written after a haze of bladder pain. Just don’t develop complications, that’s my advice. Still, I recently heard from the surgeon that my recipient’s side of the surgery was a success, that my kidney was in them and going fine - and that put things back into perspective. To a first approximation, compared to the inherent gravity of taking an organ out of one person and putting it in a second person and saving their life - it was all easy and everything went well. When I look back on this in a decade, I’ll remember it as everything being easy and going well. Even now, with some lingering bladder pain, modern medicine still feels like a miracle. VII. In polls, 25 - 50% of Americans say they would donate a kidney to a stranger in need. This sentence fascinates me because of the hanging “would”. Would, if what? A natural reading is “would if someone needs it”. But there are 100,000 strangers on the waiting list for kidney transplants. Between 5,000 and 40,000 people die each year for lack of sufficient kidneys to transplant. Someone definitely needs it. Yet only about 200 people (0.0001%) donate kidneys to strangers per year. Why the gap between 25-50% and 0.0001%? Some of you will suspect respondents are lying to look good. But these are anonymous surveys. Lying to themselves to feel good, then? Maybe. But I think about myself at age 20, a young philosophy major studying utilitarianism. If someone had asked me a hypothetical about whether I would donate a kidney to a stranger in need, I probably would have said yes. Then I would have continued going about my business, never thinking of it as a thing real-life people could do. Part of this would have been logistics. I wouldn’t have known where to start. Do you need to have special contacts in the surgery industry? Seek out a would-be recipient on your own? Where would you find them? But more of it would have been psychological: it just wasn’t something that the people I knew did, and it would be weird and alienating for me to be the only one. This is going to be the preachy “and you should donate too!” section you were dreading all along, but I’m not going to make a lot of positive arguments. If 90% of the people who answer yes on those surveys are lying to feel good, then only 3 - 5% really want to donate. But bringing the donation rate from 0.0001% of people to 3 - 5% of people would solve the kidney shortage many times over. The point isn’t to drag anti-donation-extremists kicking and screaming to the operating table. The point is to reach the people who already want to do it, and make them feel comfortable starting the process. 20-year-old me was in that category. The process of making him feel comfortable involved fifteen years of meeting people who already done it. During residency, I met a fellow student doctor who had donated. Later, I got involved in effective altruism, and learned that movement leader Alexander Berger - a guy who can easily direct millions of dollars at whatever cause he wants - had donated his personal kidney as well. Some online friends. Some people I met at conferences. And Dylan Matthews, who I kept crossing paths with (most recently at the Manifest journalism panel). After enough of these people, it no longer felt like something that nobody does, and then I felt like I had psychological permission to do it. (obviously saints can do good things without needing psychological permission first, but not everyone has to be in that category, and I found it easier to get the psychological permission than to self-modify into a saint6.) So I’m mostly not going to argue besides saying: this is a thing I did, it’s a thing hundreds of other people do each year, getting started is as simple as filling out a form, and if it works for you, you should go for it7. When I woke up in the recovery room after surgery, I felt great. Amazing. Content, peaceful, proud of myself. Mostly this was because I was on enough opioids to supply a San Francisco homeless encampment for a month. But probably some of it was also the warm glow of having made a difference or something. That could be you! VIII. The ten of you who will listen to this and donate are great. That brings the kidney shortage down from 40,000 to 39,990/year. Everyone knows we need a systemic solution, and everyone knows what that solution will eventually have to be: financial compensation for kidney donors. But so far they haven’t been able to get together enough of a coalition to overcome the usual cabal of evil bioethicists who thwart every medical advance. My kidney donation “mentor”8 Ned Brooks is starting a new push - the Coalition To Modify NOTA - which proposes a $100,000 refundable tax credit - $10,000 per year for 10 years - for kidney donors. There would be a waiting period and you’d have to get evaluated first, so junkies couldn’t walk in off the street and get $100K to spend on fentanyl. No intermediate company would “profit” off the transaction, and rich people wouldn’t be able to pay directly to jump in line. It would be the same kidney donation system we have now, except the donors get $100,000 back after saving the government $1MM+. (the libertarian in me would normally prefer a free market, but “avoid taxes by selling your organs” also has a certain libertarian appeal) This came up often when I talked to other donors. They all had various motivations, but one of the things they cared about was being able to advocate for these kinds of systemic changes more effectively. I personally have been wanting to push this in an essay here for a while, but it seemed hypocritical to play up the desperate kidney shortage while I still had two kidneys. Now I can support NOTA modification whole-heartedly . . . full-throatedly? . . . it’s weird how many of these adverbs involve claims to still have all of your organs. This is also one of the answers to the question I asked in section IV: how do you balance acts of heroic altruism that everyone will love you for vs. acts of boring autistic altruism that will make everyone hate you, but which will accomplish more good in the end?) Coalition To Modify NOTA is full of previous living kidney donors, who are using the moral clout and recognition they’ve gotten to get attention and change the system in an unglamorous way. I find this an admirable way of squaring the circle: do the flashy heroic things to gain social capital, then spend the social capital on whatever’s ultimately most important. If you get one takeaway from this, let it be that those guys who bought the castle were good guys. Two takeaways, and it’s that plus modify NOTA. Three takeaways, and you should feel permission to (if you want) donate a kidney. You can sign up here.9 Feel free to email me at scott@slatestarcodex.com if you have questions about the process. 1Further perspective: I’m 38, which gives me a 2/million total chance of dying per day. So the likelihood that I would die during my kidney operation equals the likelihood that I would die during a randomly chosen two months of everyday life. 2Maybe, kind of. Our knowledge of how radiation causes cancer comes primarily from Hiroshima and Nagasaki; we can follow survivors who were one mile, two miles, etc, from the center of the blast, calculate how much radiation exposure they sustained, and see how much cancer they got years later. But by the time we’re dealing with CAT scan levels of radiation, cancer levels are so close to background that it’s hard to adjust for possible confounders. So the first scientists to study the problem just drew a line through their high-radiation data points and extended it to the low radiation levels - ie if 1 Sievert caused one thousand extra cancers, probably 1 milli-Sievert would cause one extra cancer. This is called the Linear Dose No Threshold (LDNT) model, and has become a subject of intense and acrimonious debate. Some people think that at some very small dose, radiation stops being bad for you at all. Other people think maybe at low enough doses radiation is good for you - see this claim that the atomic bomb “elongated lifespan” in survivors far enough away from the blast. If this were true, CTs probably wouldn’t increase cancer risk at all. I didn’t consider myself knowledgeable enough to take a firm position, and I noticed eminent scientists on both sides, so I am using the more cautious estimate here. 3I told them I had an aunt who died of radiation-induced cancer. It’s true, but I feel grubby for bringing her into this; I thought doctors would be more likely to listen to an emotional story than cold logic. 4EAs have been debating the exact effectiveness of kidney donations for a long time. You can find good skeptical arguments by Jeff Kaufman and Derek Shiller, and good arguments in favor by Alexander Berger and Tom Ash. 5Outside of Philosophy 101 thought experiments, there’s a nonprofit that will often reimburse you for lost wages from your donation. 6Self-modifying into a person who can act boldly without social permission is a more general solution and has many other advantages. But the long version involves living a full life of accumulating moral wisdom, and the short version starts with removing guardrails that are there for good reasons. 7But here are some practical points you might not already appreciate: You shouldn’t have to pay much money. If, like me, you need to travel (eg to New York), kidney related charities will reimburse your travel costs (in theory, I haven’t yet proven this, and a few costs were illegible and I decided not to submit them).
So in late September 2023 - ten months after I started the process - I finally got fully cleared to donate, surgery set for October 12. VI. I knew, in theory, that anaesthetics existed. Still, it’s weird. One moment you’re lying on a table in the OR, steeling yourself up for one of the big ordeals of your life. The next, you’re in a bed in the recovery room, feeling fine. The operation - this thing you’ve been thinking about and dreading for months - exists only as a lacuna in your memory. Not even some kind of fancy lacuna, where you remember the darkness closing in on you beforehand, or have to claw yourself back into consciousness afterwards. The most ordinary of lacunas, like a good night sleep. There was no pain, not at first. The painkillers and nerve blocks lasted about a day after the surgery. By the time they wore off, it was more of a dull ache. The hospital offered me Tylenol, and I wanted to protest - really? Tylenol? After major surgery? But the Tylenol worked. Some people will have small complications (I am a doctor, pretty jaded, and my definition of “small” may be different from yours). Dylan Matthews wrote about an issue where his scrotum briefly inflated like a balloon (probably this is one of the ones that doesn’t feel small when it’s happening to you). I missed out on that particular pleasure, but got others in exchange. I had an unusually hard time with the catheter - the nurse taking it out frowned and said the team that put it in had “gone too deep”, as if my urinary tract was the f@#king Mines of Moria - but that was fifteen seconds of intense pain. Then a week afterwards, just when I thought I’d recovered fully, I got bowled over by a UTI which knocked me out for a few days. But overall, I was surprised by the speed and ease of my recovery. A few hours after the surgery, I walked a few steps. After a day, I got the catheter out and could urinate normally again. After two days, I was eating “SmartGel”, a food substitute that has mysteriously failed to catch on outside of the immobilized-hospital-patient market. After three, I was out of the hospital. After four, I started easing myself back into (remote) work. After a week, I flew cross-country. . . . and then I got the UTI. If this section sounds schizophrenic, it’s because it’s a compromise between an original draft where I said nothing went wrong and it was amazing, and a later draft written after a haze of bladder pain. Just don’t develop complications, that’s my advice. Still, I recently heard from the surgeon that my recipient’s side of the surgery was a success, that my kidney was in them and going fine - and that put things back into perspective. To a first approximation, compared to the inherent gravity of taking an organ out of one person and putting it in a second person and saving their life - it was all easy and everything went well. When I look back on this in a decade, I’ll remember it as everything being easy and going well. Even now, with some lingering bladder pain, modern medicine still feels like a miracle. VII. In polls, 25 - 50% of Americans say they would donate a kidney to a stranger in need. This sentence fascinates me because of the hanging “would”. Would, if what? A natural reading is “would if someone needs it”. But there are 100,000 strangers on the waiting list for kidney transplants. Between 5,000 and 40,000 people die each year for lack of sufficient kidneys to transplant. Someone definitely needs it. Yet only about 200 people (0.0001%) donate kidneys to strangers per year. Why the gap between 25-50% and 0.0001%? Some of you will suspect respondents are lying to look good. But these are anonymous surveys. Lying to themselves to feel good, then? Maybe. But I think about myself at age 20, a young philosophy major studying utilitarianism. If someone had asked me a hypothetical about whether I would donate a kidney to a stranger in need, I probably would have said yes. Then I would have continued going about my business, never thinking of it as a thing real-life people could do. Part of this would have been logistics. I wouldn’t have known where to start. Do you need to have special contacts in the surgery industry? Seek out a would-be recipient on your own? Where would you find them? But more of it would have been psychological: it just wasn’t something that the people I knew did, and it would be weird and alienating for me to be the only one. This is going to be the preachy “and you should donate too!” section you were dreading all along, but I’m not going to make a lot of positive arguments. If 90% of the people who answer yes on those surveys are lying to feel good, then only 3 - 5% really want to donate. But bringing the donation rate from 0.0001% of people to 3 - 5% of people would solve the kidney shortage many times over. The point isn’t to drag anti-donation-extremists kicking and screaming to the operating table. The point is to reach the people who already want to do it, and make them feel comfortable starting the process. 20-year-old me was in that category. The process of making him feel comfortable involved fifteen years of meeting people who already done it. During residency, I met a fellow student doctor who had donated. Later, I got involved in effective altruism, and learned that movement leader Alexander Berger - a guy who can easily direct millions of dollars at whatever cause he wants - had donated his personal kidney as well. Some online friends. Some people I met at conferences. And Dylan Matthews, who I kept crossing paths with (most recently at the Manifest journalism panel). After enough of these people, it no longer felt like something that nobody does, and then I felt like I had psychological permission to do it. (obviously saints can do good things without needing psychological permission first, but not everyone has to be in that category, and I found it easier to get the psychological permission than to self-modify into a saint6.) So I’m mostly not going to argue besides saying: this is a thing I did, it’s a thing hundreds of other people do each year, getting started is as simple as filling out a form, and if it works for you, you should go for it7. When I woke up in the recovery room after surgery, I felt great. Amazing. Content, peaceful, proud of myself. Mostly this was because I was on enough opioids to supply a San Francisco homeless encampment for a month. But probably some of it was also the warm glow of having made a difference or something. That could be you! VIII. The ten of you who will listen to this and donate are great. That brings the kidney shortage down from 40,000 to 39,990/year. Everyone knows we need a systemic solution, and everyone knows what that solution will eventually have to be: financial compensation for kidney donors. But so far they haven’t been able to get together enough of a coalition to overcome the usual cabal of evil bioethicists who thwart every medical advance. My kidney donation “mentor”8 Ned Brooks is starting a new push - the Coalition To Modify NOTA - which proposes a $100,000 refundable tax credit - $10,000 per year for 10 years - for kidney donors. There would be a waiting period and you’d have to get evaluated first, so junkies couldn’t walk in off the street and get $100K to spend on fentanyl. No intermediate company would “profit” off the transaction, and rich people wouldn’t be able to pay directly to jump in line. It would be the same kidney donation system we have now, except the donors get $100,000 back after saving the government $1MM+. (the libertarian in me would normally prefer a free market, but “avoid taxes by selling your organs” also has a certain libertarian appeal) This came up often when I talked to other donors. They all had various motivations, but one of the things they cared about was being able to advocate for these kinds of systemic changes more effectively. I personally have been wanting to push this in an essay here for a while, but it seemed hypocritical to play up the desperate kidney shortage while I still had two kidneys. Now I can support NOTA modification whole-heartedly . . . full-throatedly? . . . it’s weird how many of these adverbs involve claims to still have all of your organs. This is also one of the answers to the question I asked in section IV: how do you balance acts of heroic altruism that everyone will love you for vs. acts of boring autistic altruism that will make everyone hate you, but which will accomplish more good in the end?) Coalition To Modify NOTA is full of previous living kidney donors, who are using the moral clout and recognition they’ve gotten to get attention and change the system in an unglamorous way. I find this an admirable way of squaring the circle: do the flashy heroic things to gain social capital, then spend the social capital on whatever’s ultimately most important. If you get one takeaway from this, let it be that those guys who bought the castle were good guys. Two takeaways, and it’s that plus modify NOTA. Three takeaways, and you should feel permission to (if you want) donate a kidney. You can sign up here.9 Feel free to email me at scott@slatestarcodex.com if you have questions about the process. 1Further perspective: I’m 38, which gives me a 2/million total chance of dying per day. So the likelihood that I would die during my kidney operation equals the likelihood that I would die during a randomly chosen two months of everyday life. 2Maybe, kind of. Our knowledge of how radiation causes cancer comes primarily from Hiroshima and Nagasaki; we can follow survivors who were one mile, two miles, etc, from the center of the blast, calculate how much radiation exposure they sustained, and see how much cancer they got years later. But by the time we’re dealing with CAT scan levels of radiation, cancer levels are so close to background that it’s hard to adjust for possible confounders. So the first scientists to study the problem just drew a line through their high-radiation data points and extended it to the low radiation levels - ie if 1 Sievert caused one thousand extra cancers, probably 1 milli-Sievert would cause one extra cancer. This is called the Linear Dose No Threshold (LDNT) model, and has become a subject of intense and acrimonious debate. Some people think that at some very small dose, radiation stops being bad for you at all. Other people think maybe at low enough doses radiation is good for you - see this claim that the atomic bomb “elongated lifespan” in survivors far enough away from the blast. If this were true, CTs probably wouldn’t increase cancer risk at all. I didn’t consider myself knowledgeable enough to take a firm position, and I noticed eminent scientists on both sides, so I am using the more cautious estimate here. 3I told them I had an aunt who died of radiation-induced cancer. It’s true, but I feel grubby for bringing her into this; I thought doctors would be more likely to listen to an emotional story than cold logic. 4EAs have been debating the exact effectiveness of kidney donations for a long time. You can find good skeptical arguments by Jeff Kaufman and Derek Shiller, and good arguments in favor by Alexander Berger and Tom Ash. 5Outside of Philosophy 101 thought experiments, there’s a nonprofit that will often reimburse you for lost wages from your donation. 6Self-modifying into a person who can act boldly without social permission is a more general solution and has many other advantages. But the long version involves living a full life of accumulating moral wisdom, and the short version starts with removing guardrails that are there for good reasons. 7But here are some practical points you might not already appreciate: You shouldn’t have to pay much money. If, like me, you need to travel (eg to New York), kidney related charities will reimburse your travel costs (in theory, I haven’t yet proven this, and a few costs were illegible and I decided not to submit them).
Seen at San Francisco Airport. The best part was that I saw this the week that the American Psychiatric Association had its national conference in SF, so hundreds of Freudian psychoanalysts must have passed through here.
I bet all the other districts of San Francisco were so mad that this one thought of doing this first.
In February 2023 I found myself in the waiting room of a San Francisco fertility clinic, holding a cup of my own semen.
xi summons his cabinet and announces that it is time to strike america. he proposes they nuke san francisco.
SAN FRANCISCO, CALIFORNIA, USA Contact: Jill and Daniel Contact Info: jill[dot]dma[at]gmail[dot]com Time: Saturday, April 20th, 11:00 AM Location: We'll be outside the cafe at the Randall Museum in Corona Heights (near the Castro) in San Francisco. The Randall Museum has a cafe, Cafe Josephine - we'll be sitting at a public park bench (by the overlook) just outside the cafe. Randall Museum is kid-friendly and has free admission, bathrooms, etc. We'll bring an ACX sign. Coordinates: https://plus.codes/849VQH76+XW Group Link: https://groups.google.com/g/bayarealesswrong Notes: We're bringing our kids (ages 1 and 3) - feel free to bring other small mammals. You can also get in touch with us at (415) 692-4814
18: Amidst a rapidly-expanding YIMBY movement, continuing homelessness and rent affordability crises, and growing consensus that building more houses has to be part of the solution, the San Francisco Board of Supervisors has voted to . . . make it harder to build houses in San Francisco, and overturned the pro-YIMBY mayor’s veto.
(since there’s been some confusion - I like suburbs and am against YIMBYs’ obvious lust for destroying them. But if you want the suburban life, you shouldn’t be sitting on space in America’s second densest and most dynamic city. San Francisco’s utility is as a giant spiritual prison that keeps the sort of people who enjoy living in San Francisco - the LinkedIn types, the school-renamers and statue-puller-downers, the e/accs, the r/fuckcars posters, the street-blocking-protest-havers, the people who want to ban everything except crime, the people who think there need to be five nightclubs per city block, Aaron Peskin, - from bothering everyone else. We don’t need to turn currently-habitable places into prisons, but it’s fine to say the prison is growing overcrowded and needs more cell blocks.)
What follows is an exquisite outsider’s view of nineteenth-century Californian society. Upon their arrival in San Francisco, the Japanese are shocked by the sight of horse-drawn carriages, wall-to-wall carpeting, and ice-filled champagne glasses. Fukuzawa is also amazed by the prices of groceries in California (plus ça change) and is even more astounded when a gentleman he meets says he does not know where George Washington’s descendants live (“I could not help feeling that the family of Washington should be regarded as apart from all other families”).
But Fukuzawa’s greatest joy comes from having his photograph taken. At the studio, he invites the photographer’s daughter to pose next to him, to which she readily agrees. After leaving San Francisco harbor, Fukuzawa shows his prize to his fellow crew members: “You all talk a lot about your affairs,” he jokes, “but how many of you have brought back a picture of yourselves with a young lady as a souvenir of San Francisco?” Fukuzawa basks in the crew’s “extreme envy of [his] relic.”
Cozy! Even at costs likely 10% of ours, the 1950s couldn’t really afford to keep these institutions around; states were spending about 10% of their total budget just to maintain buildings that looked like the picture above. That segues into the political problem - once there were other options (penicillin, antipsychotics, nursing homes), the public willingness to pay to maintain the institutions collapsed. On the other hand, when I calculate this out, it doesn’t seem so bad? The average cost of a psychiatric hospital bed is about $300K per year (sanity check: a California prison bed is $130K per year, and the psych hospital needs more medical personnel, so this seems plausible). There are about 8,000 homeless in San Francisco, but assume that most are ordinary people down on their luck, and we only need to institutionalize 2,000. That suggests a cost of $600 million/year using state-of-California numbers, but everything (eg real estate) is more expensive in SF, so round up to $1 billion/year. I don’t know if this counts the amortized cost of building the institution, but let’s assume it does. San Francisco currently spends about $1 billion/year on homelessness. These institutions would only cover the worst 25% of homeless people, so you’d need maybe another $500 million for the rest, but whatever, same order of magnitude. I think this is more affordable than I expected. The remaining problems are: Where is this? I don’t think there’s anywhere in SF city limits to put it. I suggest putting it in Marin, to piss off George Lucas’ neighbors. But I don’t know about the legalities of a city using an extraterritorial detention institution.
$1 billion/year in projected costs, translated into Californian, means $100 trillion quadrillion/year in actual costs. Of these, I think 3 is the biggest deal. If it’s as hard to commit someone to these institutions as it is to convict them of a crime, then these institutions don’t help much above how much the existence of prison also helps (eg not much). If you invent a new legal maneuver where it’s easier to commit someone than to convict them of a crime, then why do you even need the step where you build the institution? Just invent the legal maneuver and send more people to prison! I think that maybe the thought is that the institution seems more “humane” than prison, and so people will be more willing to allow low-friction legal maneuvers for confining people there. I think this is cope; not only won’t the institutions be more humane than prisons, but people won’t believe they are and won’t allow the low-friction legal maneuvers. Drethelin writes: What if we abolish the DEA and just let anyone buy anti-psychotics over the counter? This would be the FDA we’re abolishing, but otherwise yes, this is the sort of clever outside-the-box thinking that I appreciate from my commenters. Antipsychotics are very cheap (some well-regarded drugs like Abilify and Seroquel cost about ~$10 per month of pills). On the other hand, homeless people have very little money. So if you were going to do this, it would make sense for the government to give them away for free. These drugs have many potentially serious side effects. But it’s not clear how much homeless people’s 5-minute monthly visits with a bored Medicaid doctor does to avert these side effects, over having some kind of pharmacist or advocate or social worker in the free distribution center giving helpful advice. Like everything, I think this would only help around the edges - the fraction of homeless mentally ill people who drugs can help, who are willing to take the drugs, and who are prevented only by cost and bureaucracy. What percent is that? Low confidence guess 25%. DZ writes: I think you’re missing the goal of a short arrest (few days). Part of the problem is the homeless are in areas where society doesn’t want them to be. They’re near city downtowns where tourists spend time or near commercial districts or in otherwise nice parks. If you can arrest them for a few days and keep arresting them until they move somewhere else … the goal is to eventually force them to move to the more acceptable areas vs. least acceptable areas. This is obviously not ideal but in the mean time the city gets more tourism, more office rentals, etc. Europeans ruthlessly arrest homeless people who hang out in the touristy areas. SF doesn’t, yet. I commented that I was worried that “out of touristy areas” means “into residential areas”. And I feel worse making residents deal with this than tourists, and am less confident that the city cares enough about them to fight back. DZ responded: Agreed. People don’t want them in the residential areas or suburbs either and for good reason. But my guess is cities can identify certain areas where they would prefer the tents to set up. Something like industrial areas or run down parks. The key is that city officials should be able to use arrests as a strategy to move the tents/homeless concentrations without having to face a million lawsuits. I don’t know if there are really areas like this, but I welcome learning more from people who know cities better. SMK writes: This probably sounds draconian and cruel, too, but in fairness, all these discussions seem to assume that this person is in San Francisco and can never ever leave for some other, more affordable place. I get it -- it's tough leaving home, and maybe they'd be leaving friends. But they wouldn't be the only people leaving SF over rent prices, and they'd pretty clearly be among the most rational. So I dislike articles like this when they say things like "the average wait time for a homeless shelter bed is 826 days" or "cheap apartments in SF are $1000 / month." I have a friend who was homeless for around a year in another major American city, and he said it was always 100% feasible to get a shelter bed if he wanted one. Indeed, there were several options. On a different note, I also think that if one were going to go a "cruel and draconian" route, homeless shelters might be able to change policies to better support that and prevent some of the issues you highlight. If it takes 826 days to get a shelter bed, then zero of the typical people you mention who are briefly homeless are getting shelter beds. If all of the people who were homeless for longer were either leaving or in jail, then more of those people probably would get beds. Am I saying this is the policy I favor? No, I agree it's a hard problem and I'm not sure what the right answer is. But things like this need to be kept in mind, too. Again, I think it’s helpful to go to the specific policy level. What’s the policy here? Give homeless people brochures reminding them that other cities exist? I’m sure they know this. Give homeless cities free mandatory bus trips to those other cities? What prevents the other cities from giving them free mandatory bus trips back? Even if they don’t, what if the homeless prefer being homeless in San Francisco to having a better situation in a cheaper city? A bus from Phoenix to SF is only $60; even a homeless beggar might be able to scrounge up that much money if they’re motivated. Maybe some plan like making a deal with a big cheap city in Texas to take SF homeless in exchange for money, and as soon as the homeless get off the bus, they’re met by a Texan social worker who gives them a shelter bed and social services? Might help along the edges, but remember that only about half of homeless people want/will accept shelter beds (depending on how good the shelter beds are). Sergei writes: After checking a bit, let me point out the obvious. What works elsewhere is PATERNALISM. Once you are in the "clutches of psychiatry", they don't let you go. Upon release you are placed into some sort of housing, your appointments are monitored and a social worker will find you and drive you there. You will be given multiple chances to get a job and/or rehab. Your meds will be delivered to you if you cannot pick them up. They remind you to take them. There will be a social safety net so you are never in a situation where you end up on the street unless you really really try to. In retrospect, it makes sense: people who are not able to take care of themselves for a time because of a fixable mental infirmity are taken care of by the state, until they can. That's what we do with children already. I continue to want people to provide details. “They don't let you go" - okay, so the person is in a locked facility? Placed in "some kind of housing"? Does the housing have locks on the door, or can they leave? What if they do leave? "Multiple chances to get a job", oh, great, with whom? How are you enforcing that they take mentally ill people? What happens when the mentally ill people are less good workers than other people they could hire, or have some kind of crisis on the job, as even the best-treated person might once in a while? Maybe we can charitably fill in the details. Something like: Ban some combination of camping outside and being visibly mentally ill.
In San Francisco, homeless people already get an Access Pass, ie free public transportation on the municipal rail system. I don’t think there’s a similar program for the BART (intercity rail system), probably because other cities don’t want homeless people traveling there. There are definitely lots of homeless people on the BART; I don’t know how this works, but I think they make more begging than they lose from the fares.
9: Did you know: a century ago, insurances didn’t cover earthquake damage. After the 1906 San Francisco quake, “Word spread throughout the city that fires were covered by insurance and people started burning down their properties. Fires raged on for 3 days.”
Contact: Julius Contact Info: julius[dot]simonelli[at]gmail[dot]com Time: Saturday, September 21st, 01:00 PM Location: Bird Park Coordinates: https://plus.codes/8544PVQ8+PC2 Group Link: https://meetup.com/san-diego-rationalists/ SAN FRANCISCO, CALIFORNIA, USA Contact: Andrew Gaul Contact Info: gaul[a t]gaul[d ot]org Time: Sunday, October 20th, 02:00 PM Location: Dolores Park near the tennis courts Coordinates: https://plus.codes/849VQH6F+55 Group Link: https://groups.google.com/g/bayarealesswrong Notes: Please RSVP via email so I can coordinate tarps, drinks, and other picnic gear
3: “Comment” of the week is this subreddit post announcing the Bell Riots in San Francisco this Monday. In the Star Trek universe, one of the precipitants to Earth’s eventual utopian government was the anti-homelessness “Bell Riots” that took place in San Francisco on September 2, 2024. In their honor, some YIMBYs will be holding anti-homelessness riots protests in Golden Gate Park on that date.
“Maybe not at Thiel Capital. But go outside, and you’ll find that people are, in fact, having sex all the time. Tindr. Hinge. Grindr. Young people are going out and having casual sex every weekend. There are fourteen different BDSM sex clubs in San Francisco alone. If you look at the modal society throughout history, they’re forbidding their women from leaving the house, or holding them to such high modesty standards that showing a bare ankle would be a scandal. They’re locking people up for owning porn, or killing them for being gay, or calling them rakes or sluts for having sex outside of marriage. Meanwhile, in our society women go everywhere in skimpy skin-tight clothing, you can f@&k a different partner every week, you can be polyamorous or transgender. I read conservative writers saying that no society like ours can survive over the long term. But I’ve thought about it longer than they have, and I think no society like ours could ever come to exist at all. That’s because it’s not a real society. It’s somebody’s weird fetish free-use-adjacent fantasy.”
San Francisco
SAN FRANCISCO - Saturday, October 19, 2:30 - 4:30pm - Noasis, 22G Day Street (door code 4578) - Contact https://lu.ma/0ufpys63
…[going to] “A.I. salons” held in San Francisco. Last year, Mr. Wiener attended a series of those salons, where young researchers, entrepreneurs, activists and amateur philosophers discussed the future of artificial intelligence.
Nancy’s involvement was the biggest surprise. The boring explanation is that she represents San Francisco and has a lot of tech investor friends and donors, but you can get more conspiratorial. For example, Pelosi (current net worth $240 million) is known to get impossibly good investing returns, so much so that there are ETFs that try to replicate her stock picks; everyone assumes she must be doing some kind of scummy-but-legal insider trading. Her portfolio is currently weighted towards AI - did that influence her decision? Even more conspiratorial, insiders say Pelosi is waging a “shadow campaign” against Scott Wiener, the only San Francisco politician popular enough to challenge her daughter and hand-picked successor Christine Pelosi for SF’s House seat after she retires. Is she trying to deny Wiener a victory?
AUSTIN: Guide here BOSTON: Guide here CHICAGO: Guide here LOS ANGELES: Guide here NEW YORK CITY: Guide here OAKLAND/BERKELEY: Guide here PHILADELPHIA: Guide here SAN FRANCISCO: Guide here SEATTLE: Guide here
LOS ANGELES: Guide here. Los Angeles is almost party-line Democrat, but manages to deviate from consensus in a few places, including going against celebrity liberal district attorney George Gascon. Gascon was previously San Francisco DA (sandwiched between Kamala Harris and Chesa Boudin), attracted both furor and adulation for his anti-incarceration/soft-on-crime policies, quit, moved to Los Angeles to take care of his aging mother, and then became Los Angeles DA the next year! He is a fascinating character, and someone should write his biography, but apparently Los Angeles Rationality is tired of him. The group did however support increasing taxes to fund anti-homelessness programs, saying that LA's past anti-homelessness programs have a history of actually working.
SAN FRANCISCO: Guide here. I got confused because San Francisco has their own list of local propositions which are suspiciously similar to the statewide propositions (for example, both sets have an education bond first). That's not all that's confusing - California has a long history of propositions that invalidate past ballot propositions, but this is the first time I've ever seen them try this during the same election cycle. Proposition L raises taxes on ride-share companies, but Proposition M simplifies the tax system in various ways including cancelling Proposition L. If both pass, then whichever gets more votes wins. This creates a weird situation where several interest groups have strong opinions that both propositions should pass but that L should pass by more votes than M; how do you even make a voter guide for a situation like that? Maybe you should recommend stochastic voting - tell people to vote yes on L, then roll a die, and vote yes on M unless they get a 1? The San Francisco meetup group takes a simpler path and just recommends yes on both.
This wasn’t officially part of the conference, but at lunch somebody told me that the San Francisco legs of the BART - the Bay Area’s light rail, infamous for being noisy, dirty, and violent - had become comparatively safe and clean over the past few months, after the city installed fare gates that actually worked and couldn’t trivially be jumped over. Apparently the people ruining the BART for everyone weren’t even paying the fare. I always would have guessed there was a correlation between bad behavior and nonpayment, but am surprised at exactly how high the correlation has turned out to be (supposedly - I haven’t been on the BART myself recently to confirm).
(The rise in cocaine and methamphetamine overdoses over this same period most likely also reflects the spread of fentanyl. The data here record deaths by “non-mutually exclusive substance category” – that is, if a person is found dead with both fentanyl and methamphetamine in their system, both will be recorded. These combined overdoses from fentanyl+meth or fentanyl+cocaine are common in San Francisco.)
Prop 36 doesn’t set aside any funding for new mental health and substance treatment beds. This might make sense if California was full of empty treatment centers waiting for addicts to fill them. But in fact, the state desperately lacks treatment beds. A 2022 report by the California Department of Health Care Services found that 70% of our counties "urgently need" residential addiction treatment services. 40% – that’s 23 counties – have none at all. Even the larger and wealthier counties are struggling. San Francisco currently has 2,551 residential treatment beds, of which just 690 are for substance abuse treatment – but the city’s homeless population in need of treatment alone measures over 8,000. Worse still, due to post-COVID staffing shortages, the state is actually losing thousands of treatment beds.
(talking about state averages here is actually slightly misleading: shoplifting is down or static in California's small counties and a few of the larger ones, but very high in Los Angeles and the Bay Area. Shoplifting rates in San Francisco were 24% higher in 2022 compared to 2019, while San Mateo saw a rise of 53%. This is another indicator that Prop 47 – a statewide measure – isn’t the primary driver of the trend.)
1: Comment of the week is Graham on the Prop 36 post - he argues that the reason cops aren’t enforcing the existing misdemeanor penalty for shoplifting (up to six months in jail) is that by the time it gets through the DAs, this is reduced to “a stern talking to”, and it’s not worth cops’ time to arrest anyone who won’t be punished. Therefore, in order to get the six months in jail that’s already on the books, we apparently have to increase the law to three years in jail. I appreciate this perspective, but it only leaves me more confused - for example, didn’t San Francisco recall its soft-on-crime DA and replace him with a tough-on-crime DA who promised to throw the book at shoplifters? Don’t these charts from the San Francisco DA show that most arrests lead to charges, and the problem is almost entirely that most reports don’t lead to arrests? I still don’t feel like I understand the dynamics behind why our current laws can’t be used to arrest and imprison shoplifters.
A red sun dawns over San Francisco. Juxtaposed against clouds and sea, it forms a patriotic tableau: blood red, deathly white, and the blue of the void. As its first rays touch the city, the frantic traffic slows to a crawl; even the birds cease to sing. It is Election Day in the United States.
London Breed is the Mayor of San Francisco. My understanding is that she is relatively moderate for a San Francisco politician - YIMBY, pro-sheltering-the-homeless, and less-than-infinitely-soft-on-crime. San Francisco is still terrible, but has gotten slightly better during her term, and my impression is that the small magnitude of the improvement is more because it’s too much for any one person to solve in a few years than because of any flaw in Mayor Breed’s policies. Still, voters are understandably upset and her chances are slim. A highly deranged Kalshi market favors Daniel Lurie, an extreme outsider who is the heir to the Levi Strauss fortune but otherwise has no experience with anything; his pitch is “Vote for me, I have never been even slightly involved in SF politics before”, and it seems to resonate.
Thankfully, San Francisco has ranked-choice voting, so voters will be able to freely choose their favorite among this diverse group of candidates. I am not a praying man, but I would like to request that Aaron Peskin, the worst NIMBY in San Francisco, not get elected.
Aside from all of this, the Romans were practicing sex-selective infanticide, reducing their female numbers still further, and making the Christians even more proportionally female-heavy. If the Christians, like many modern cults, were 65% female, and the Romans (as some sources attest) were about 40 - 45% female, this is a pretty profound difference. The Romans grumbled about marriage, but in the end most Roman men did want wives (if only to avoid government penalties). But 1.4 men per women - maybe even less among the upper classes - puts young men seeking wives in a difficult situation (for comparison, modern San Francisco is only 1.05 men per women, and dating is already hell). To any remotely heterosexual Roman men, the 65% female Christian community must have started looking pretty good. Meanwhile, the Christians had the opposite problem: too many women, not enough men. There’s an obvious solution, and it sounds like the pagans and Christians had also figured it out: From 1 Peter 3: Wives ... submit yourselves to your own husbands so that, if any of them do not believe the Word, they may be won over without words by the behavior of their wives, when they see the purity and reverence of your lives. History records many such intermarriages, almost always ending with the conversion of the pagan husband. If you are a Christian of English descent, you may owe your religion to Queen Bertha of Kent, who convinced her husband, one of the early Anglo-Saxon kings, to take her faith. But Ruxandro Teslo has a great post reviewing the work of historian Michele Salzman, who disagrees with all of this. Salzman has a database of 400 aristocratic Romans during the 4th century period of Christianity’s fastest growth. She finds few intermarriages, few examples of women converting their husbands, and equal (or slightly male-biased) conversion ratios. Granted, this is only a small sample from one period. But it makes us question how good our evidence really is. Doesn’t all this hinge on one passage from Paul which, technically, named more men than women, plus one inventory of tunics which was so female-biased that it couldn’t possibly have been representative of even a very woman-heavy church? Are we sure that we can make the leap from “Christianity promised women more rights” to “Therefore, women flocked to Christianity?” Wasn’t that the same argument that pundits used last week to predict a blue wave for Kamala? Didn’t white women actually go for Trump, 53-46? Salzman has one more concern, which is that women had so few rights in ancient Roman society that it’s hard to see how they could have converted at all. When unmarried, they were under the care of their father, who would hardly have let them go out visiting churches full of strange men. When married, they were under the care of their husband, who likewise. A typical Roman man wouldn’t have cared about his wife’s religious opinions, which is maybe why so many of our stories about intermarriages and conversions come from later periods like the Anglo-Saxons. I don’t know enough about history to referee this dispute, except that say that I think the answer could easily have been different for each of early Romans, late Romans, Hellenized-Jewish-Romans, pagan Romans, upper-class Romans, and lower-class Romans, plus all combinations thereof. Because Of The Testimony Of The Martyrs The martyrs are one of the most dramatic parts of the early Christian story. Men and women would endure seemingly-unbearable tortures, continuing to praise God the whole time, sometimes in spite of Roman officials who promised to let them go free if they would just make the tiniest concession to praising Jupiter. These martyrdoms impressed their contemporaries as much as they impress us, and were a major factor driving pagans to Christianity. The Christian Martyrs’ Last Prayer, by Jean-Leon Gerome (maybe slight nominative determinism?) Stark is writing in the 1990s, and martyrology c. 1995 does not exactly cover itself in glory. At the time of writing, the most popular theory among scholars (claims Stark) was that the martyrs were masochists. He considers this dumb and offensive theory a natural consequence of historians being reluctant to accept anything that sounds too miraculous or amazing, and there being few other hard-headed rational explanations of the martyrs’ behavior (for some reason, the obvious one - that they believed in God and Heaven - impresses neither Stark’s foils nor himself). He sets out to build an alternative theory: the martyrs were rationally seeking the approval of their community. Martyrdom not only occurred in public, often before a large audience, but it was often the culmination of a long period of preparation during which those faced with martyrdom were the object of intense, face-to-face adulation. Consider the case of Ignatius of Antioch … Ignatius was condemned to death as a Christian. But instead of being executed in Antioch, he was sent off to Rome in the custody of ten Roman soldiers. Thus began a long, leisurely journey during which local Christians came out to meet him all along the route, which passed through many of the more important sites of early Christianity in Asia Minor on its way to the West. At each stop Ignatius was allowed to preach to and meet with those who gathered, none of whom was in any apparent danger although their Christian identity was obvious. Moreover, his guards allowed Ignatius to write letters to many Christian congregations in cities bypassed along the way, such as Ephesus and Philadelphia … As William Schoedel remarked, “It is no doubt as a conquering hero that Ignatius thinks of himself as he looks back on part of his journey and says that the churches who received him dealt with him not as a ‘transient traveller,’ noting that ‘even churches that do not lie on my way according to the flesh went before me city by city.’” What Ignatius feared was not death in the arena, but that well-meaning Christians might gain him a pardon…He expected to be remembered through the ages, and compares himself to martyrs gone before him, including Paul, “in whose footsteps I wish to be found when I come to meet God.” It soon was clear to all Christians that extraordinary fame and honor attached to martyrdom. Nothing illustrates this better than the description of the martyrdom of Polycarp, contained in a letter sent by the church in Smyrna to the church in Philomelium. Polycarp was the bishop of Smyrna who was burned alive in about 156. After the execution his bones were retrieved by some of his followers - an act witnessed by Roman officials, who took no action against them. The letter spoke of “his sacred flesh” and described his bones as “being of more value than precious stones and more esteemed than gold.” The letter-writer reported that the Christians in Smyrna would gather at the burial place of Polycarp’s bones every year “to celebrate with great gladness and joy the birthday of his martyrdom.” The letter concluded, “The blessed Polycarp ... to whom be glory, honour, majesty, and a throne eternal, from generation to generation. Amen.” It also included the instruction: “On receiving this, send on the letter to the more distant brethren that they may glorify the Lord who makes choice of his own servants.” In fact, today we actually know the names of nearly all of the Christian martyrs because their contemporaries took pains that they should be remembered for their very great holiness. I don’t know, I’m not putting too much effort into writing up this section, because it doesn’t feel like as much of a mystery as some of the others. Maybe all of this was weird in 1996. But since then, we’ve seen plenty of suicide bombers willing to die for their faith. I accept that the Christian martyrs were more impressive - a slow death in the Colosseum takes more grit than the quick detonation of an explosive vest, and dying for peace is more impressive than dying in war - but it hardly seems like as much of a leap. Honestly, Stark’s “social approval” theory seems only slightly less objectifying than the masochism theory. Some people just have a tendency towards self-sacrifice. I know many effective altruists who, for example, deliberately let themselves be infected with malaria to help speed vaccine research. If someone told them a way that they could help the neediest people in the world by feeding themselves to lions, the lions would no doubt eat well. Because They Survived The Plagues However bad you imagine daily life in ancient Rome, it was worse. Historians estimate that ancient Rome had a population density of 300 people per acre. That’s almost ten times denser than modern New York City, two thousand years before anyone invented the skyscraper3. How did they do it? By cramming people together in unbearable filth and misery: Most people lived in tiny cubicles in multistoried tenements…”there was only one private house for every 26 blocks of apartments”. Within these tenements, the crowding was extreme - the tenants rarely had more than one room in which “entire families were herded together”. Thus, as Stambaugh tells us, privacy was “a hard thing to find”. Not only were people terribly crowded within these buildings, the streets were so narrow that if people leaned out their window they could chat with someone living across the street without having to raise their voices… To make matters worse, Greco-Roman tenements lacked both furnaces and fireplaces. Cooking was done over wood or charcoal braziers, which were also the only source of heat; since tenements lacked chimneys, the rooms were always smoky in winter. Because windows could be “closed” only by “hanging cloths or skins blown by rain”, the tenements were sufficiently drafty to prevent frequent asphyxiation. But the drafts increased the danger of rapidly spreading fires, and “dread of fire was an obsession among rich and poor alike.” Packer4 (1967) doubted that people could actually spend much time in quarters so cramped and squalid. Thus he concluded that the typical residents of Greco-Roman cities spent their lives mainly in public places and that the average “domicile must have served only as a place to sleep and store possessions.” These tenements had no plumbing. Waste was eliminated by pouring it onto the street, often to the detriment of people walking underneath. Water was brought home from public wells; if you were out, you either walked back to the well or made do. The total public baths capacity of Rome was about 30,000; the total population of Rome was about a million; in practice, the upper classes used the “public” baths and the average citizen had never bathed in their life. Soap had been invented a century or two earlier but was limited to a small pool of early adopters. The cities buzzed with flies, mosquitos, and other insects. It would be eighteen hundred years before anyone invented germ theory. Tenements were six stories high and frequently collapsed, killing everyone inside. Fires consumed the city on a regular basis, giving rise to colorful legends like Nero fiddling while Rome burnt. Police were limited, and it was understood that you would be robbed immediately if you set foot outside at nighttime. This kind of smart, walkable, mixed-use urbanism is illegal to build in most American cities. How did people survive? Mostly they didn’t. Cities were destroyed regularly - multiple times within a single human lifetime! - then rebuilt and replenished with rural population. Stark focuses on Antioch, a Syrian city which was a center of early Christianity. During “six hundred years of intermittent Roman rule”, he finds: It was conquered 11 times
He proposes some alternatives, of which the most controversial is the Presidio - a federally-owned historic fortress in the middle of San Francisco which is now mostly used as a park. But the government technically has the rights to the land, and if they wanted to, they could zone the whole thing for six-story apartments and add 120,000 people to SF. I don’t know which part of this is most likely to make San Franciscans explode with rage - the loss of their (admittedly beautiful) historic park, the Donald Trump involvement, or the prospect of actually building things in their city. It would be a blowup for the ages.
California Forever, the ambitious planned city an hour north of San Francisco, is on hold.
2: Google DeepMind’s alignment team has new job postings, for research scientist and research engineer, available “in London, Zurich, New York, Mountain View or San Francisco”. Applications open until 2/28. They’ve also released their own free short video course on AI safety.
Contact: Julius Contact Info: julius[period]simonelli[a t]gmail[period]com Time: Saturday, May 10th, 1:00 PM Location: Wisdom Park Coordinates: https://plus.codes/8544VRXM+55 Group Link: https://www.meetup.com/san-diego-rationalists/ SAN FRANCISCO Contact: Nate, Andrew, and Austin Contact Info: acx-everywhere-sf-spring-2025[ at]googlegroups[period]com Time: Saturday, April 5th, 2:00 PM Location: 1680 Mission St (Mox) Coordinates: https://plus.codes/849VQHCJ+82 Notes: Kids welcome!
If some Aboriginal anthropologist came to America and tried to list our sex taboos, what would they find? No sex between close family members up to second cousin, that’s obvious. No sex with bosses, subordinates, or clients. A strict taboo on even mentioning sex at work, enforced by a caste of powerful witches called “HR ladies”. Acknowledging even the slightest attraction to anyone under 18 makes you a monster, but people who are just slightly older than 18 - even by one day - are called “barely legal” and feature especially prominently in sexual imagery. No sex across “age gaps”, where some people define “gap” as “half your age plus seven” and other people refuse to define it but reserve the right to judge transgressors anyway. Men can’t have sex with other men, unless they’re part of a special caste called “gay people” who must move to New York, speak in a weird voice, and cultivate an interest in musical theater. Married people can’t have sex with anyone besides their spouse, unless they’re part of a special caste called “poly people” who must move to San Francisco, dye their hair, and get jobs in tech. At some point maybe our hypothetical anthropologist starts longing for the simplicity of someone telling him which 12.5% of the population are his classificatory wives.
It is eerily silent in San Francisco tonight. Since Mayor Lurie's crackdown, the usual drug hawkers, catcallers, and street beggars are nowhere to be seen. Still, your luck can’t last forever, and just before you reach your destination a man with bloodshot eyes lurches towards you. You recognize him and sigh. "Go away!" you shout.
Mark Zuckerberg, heavily bruised and covered in glitter, stands in front of the smoking ruins of your wheelchair and laughs maniacally. He is holding a small silver box he has extracted from one of the fragments. “I got it!” he shouts. “I got the last GPU in San Francisco!”
I think UBI isn't enough here, you also need to do something about status competition. I could probably afford most necessities I consume on half my current income (revealed preference: I invest a lot of that income. Albeit obviously having a lot invested income is valuable for deferred consumption), but I am also a social primate attempting to maintain status for myself and my offspring among a bunch of other social primates similarly competing. "Literally not starving" is basically a solved problem, and "having sufficient good and services to live a superior life in terms of objective rather than relative consumption to a supermajority of historical humans" is also basically solved. But, for example, living in San Francisco requires a San Francisco income, and living well / keeping up in status competition requires more of that.
Contact: Julius Contact Info: julius[period]simonelli[a t]gmail[period]com Time: Saturday, October 4th, 11:00 AM Location: Wisdom Park Coordinates: https://plus.codes/8544VRXM+65 Group Link: https://www.meetup.com/san-diego-rationalists/ SAN FRANCISCO Contact: Andrew Gaul Contact Info: gaul[a t]gaul[period]org Time: Saturday, October 4th, 1:00 PM Location: Mox, 1680 Mission St, San Francisco, CA Coordinates: https://plus.codes/849VQHCJ+82 Group Link: https://www.lesswrong.com/groups/qQkgmEeEreY6gjd7o Notes: Please RSVP on LessWrong so we can bring coffee and snacks
Contact: Andrew Gaul Contact Info: gaul[a t]gaul[period]org Time: Saturday, October 4th, 1:00 PM Location: Mox, 1680 Mission St, San Francisco, CA Coordinates: https://plus.codes/849VQHCJ+82 Group Link: https://www.lesswrong.com/groups/qQkgmEeEreY6gjd7o Notes: Please RSVP on LessWrong so we can bring coffee and snacks
61: Miles Brundage’s palindrome about San Francisco (X):
He was a Navy man, a radar technician, just 20 years old when he shipped out of San Francisco. As he tells it, the entire crew were very nervous, seeing as they were being sent off to invade Japan. But just as the ship sailed past the Bay Bridge, “the captain came out on the bridge and looked down on us. ‘Japan just surrendered!’ he shouts. And suddenly all propriety leaves us, and we all say, ‘well then, for Christ’s sake, turn around!’”
“That’s every date in San Francisco. But when you curtfish, sometimes she comps your meal from her expense account. It’s a strict Pareto improvement!”
1: Meetups this week include Ankara, Bangalore, Dallas, DC, Delhi, Denver, Hyderabad, Istanbul, LA, Raleigh-Durham, San Diego, San Francisco, Zagreb; see the meetup post for more information.. And late additions Aachen, Lviv, and Malaga have been added to the list for October.
Thomas Briggs, $5K, for the Center for Educational Progress. CEP was founded by Jack Despain Zhou, who you may know better by his blogging pseudonym TracingWoodgrains; he is currently on leave as he pursues his legal training, but will return next year. The Center advocates effective pedagogy, especially ability tracking, ie letting faster and slower students each move at their own pace. In practice, this seems to mean a lot of legal briefs telling San Francisco why they shouldn’t ban algebra in middle schools. We support their work and are happy to fill their suspiciously-low funding request.
Dating Men In The Bay Area, by Alex King. Alex is an engineer from San Francisco. She’ll be experimenting with more essays on her new blog, King of Daydreams. When she’s not igniting turmoil in the ACX comments section, she can be found mentoring young engineers, hosting community events, and failing to find a boyfriend. She pinky-promises she is not Aella.
See here for a more complete history of the island, and here for a Charter Cities Institute podcast on the topic. California Maybe Actually Pretty Soon Now California Forever, the project to build a new city in unoccupied land an hour from San Francisco, has overcome a first round of political headwinds. In 2023, a stealth mode company announced it had quietly bought up a city-sized tract of land in Solano County, and would be placing an initiative on the county ballot to let them build a futuristic planned community there. Enough local NIMBYs protested that the company and county jointly withdrew the initiative in favor of seeking some other agreement. In 2025, they announced their new strategy: they would partner with nearby Suisun City. Suisun would annex their land and permit development there, avoiding a county-wide referendum (they might also make a deal with another nearby city, Rio Vista). The new plan is moving forward: earlier this month, California Forever submitted their annexation paperwork, which was deemed complete by the city. The remaining steps are: Suisun City Council must approve their environmental impact report (may cause delays and added expense, but unlikely to block the project outright)
The proposal also moots two additional megaprojects: the Solano Shipyard, where the new city touches the upper tributaries of the San Francisco Bay. American shipbuilding has long been something of an embarrassment, the Trump administration is working on it, and the new city would be strategically placed to benefit if the federal government could remove some of the barriers that make US naval manufacturing unprofitable. And the Solano Foundry would be the “the largest [advanced manufacturing] park in the US”. Many of Silicon Valley entrepreneurs’ manufacturing startups set up shop in Southern California - for example, Elon Musk’s original base for SpaceX and the Boring Company was in Hawthorne, near LA - just because the Bay has so few good industrial locations. The Foundry aims to change that, and aims for 40,000 new manufacturing jobs. Finally, something nobody else will care about but which is close to my heart - Jan is pursuing a partnership with Monumental Labs, a group working on “AI-enabled robotic stone carving factories”. The question of why modern architecture is so dull and unornamented compared to its classical counterpart is complicated, but three commonly-proposed reasons are: Ornament costs too much
Last year, I wrote that it would be very hard to decrease the number of mentally ill homeless people in San Francisco. Commenters argued that no, it would be easy, just build more jails and mental hospitals.
A year later, San Francisco feels safer. Visible homelessness is way down. But there wasn’t enough time to build many more jails or mental hospitals. So what happened? Were we all wrong?
Every city accuses every other city of shipping homeless people across their borders, but this probably doesn’t explain most of what’s going on in San Francisco in particular.
4: FRAME is a “fully funded creative fellowship for video creators, storytellers, and communicators who want to shape how the world understands AI Safety and risk concepts”. If you create (or hope to create) AI safety related videos, they want to fly you to San Francisco and teach you how to do it better. Apply by December 20.
In such a situation, greatness is pathetically easy. A random woman gave Jesus a washcloth to wipe his face on the way to his crucifixion. She is now known as St. Veronica, patroness of laundry workers, and one out of every 2,500 girls in America is named in her honor. She has an annual feast day, approximately one million beautiful Renaissance paintings, a chapel in Jerusalem, and lesser churches all around the world (including one here in San Francisco). The richest olive merchant in Jerusalem that year is long forgotten, but she endures.
Every few months, some group of bright nerds in San Francisco has the same idea: we’ll use our intelligence to hack ourselves to become hot and hard-working and charismatic and persuasive, then reap the benefits of all those things! This is such a seductive idea, there’s no reason whatsoever that it shouldn’t work, and every yoga studio and therapist’s office in the Bay Area has a little shed in the back where they keep the skulls of the last ten thousand bright nerds who tried this. I can’t explain why it so invariably goes wrong. The best I can do is tell a story where, when you’re trying to do this, you’re selecting for either techniques that can change you, or techniques that can compellingly make you think you’ve been changed. The latter are much more common than the former. And the most successful parasites are always those which can alter their host environment to be more amenable to themselves, and if you’re a parasite taking the form of a bad idea, that means hijacking your host’s rationality. So you’re really selecting for things that are compelling, seductive, and damage your ability to tell good ideas from bad ones. This is a just-so story that I have no evidence for - but seriously, go to someone who has the words “human potential” on their business card and ask them if you can see the skull shed.
Adams started out by stressing that he was politically independent. He didn’t support Trump, he was just the outside hypnosis expert pointing out what Trump was doing. IT’S JUST A THOUGHT EXPERIMENT, IF YOU TAKE IT SERIOUSLY, YOU FAIL. Indeed, “this person is a charismatic manipulator hacking the minds of irrational sheep” is hardly a pro-Trump take. And he lived in Pleasanton, California - a member in good standing of the San Francisco metropolitan area - and nice Pleasantonians simply did not become Trump supporters in 2016.
Partly this is because we’re too similar for me to hate him without hating myself. You’re a bald guy with glasses named Scott A who lives in the San Francisco Bay Area. You think you’re pretty clever, but the world has a way of reminding you of your limitations. You try to work a normal job. You do a little funny writing on the side. People like the funny writing more than you expected. Hardly believing your luck, you quit to do the funny writing full time. You explore themes about the irrationality of the world. You have some crazy ideas you’re not entirely willing to stand behind, and present them as fiction or speculation or April Fools jokes. You always wonder whether your purpose in life is really just funny writing - not because people don’t love the stuff you write, not even because you don’t get fan mail saying you somehow mysteriously changed people’s lives, but just because it seems less serious than being a titan of industry or something. You try some other things. They don’t go terribly, but they don’t go great either. You decide to stick with what you’re good at. You write a book about the Lurianic kabbalah. You get really into whale puns.
34: List of every time someone said “I am a…” or “As a…” at a San Francisco governmental meeting (h/t Riley Walz)
62: Tyler Cowen podcast on San Francisco, blogging, and effective altruism. I watched this one because someone said it mentioned me, and was impressed by Tyler’s podcasting skills. The host tries to bait him into boring object-level positions on various controversies and hot takes, and Tyler always gives a classy response that neither takes the bait nor avoids the question, but ends up illuminating the subject in some kind of interesting way. I think I could do this too - if I had ten minutes to craft the perfect paragraph. Tyler does it on the fly!
The 1960-2023 data come from FBI Data Explorer via Vital City; the 2024 and 2025 data come directly from the FBI website, with 2025 annualized via incomplete Jan - Oct data. This one may or may not be an all-time low, but it’s pretty good. These data are counterintuitive. Are they wrong? Could This Be An Artifact Of Reporting Bias? People could be so inured to crime that they stop reporting it to the police. Or the police could be so overwhelmed that they stop accepting the reports. Since most crime statistics are based on police reports, this would look like crime going down. There’s some evidence of this happening in specific situations, like shoplifting in San Francisco. Could it be the whole effect? No, for three reasons. The National Crime Victimization Survey is a government-run survey of a 240,000 person nationally representative sample. They find random people and ask whether they were the victims of crimes in the past year. This obviously doesn’t work for murder, but they keep statistics on rape, assault, larceny, and burglary. Their numbers mostly mirror those reported by police and used in the usual statistics about crime rates. But here there’s no extra step of needing to trust the police enough to make a report: the surveyors ask the victims directly. Although there could be biases in this methodology too, it would be an extraordinary coincidence if they exactly matched the proposed reporting bias to police. Also, you can use NCVS and police reports to calculate reporting rates directly. Overall, they seem to have increased over time - did you know that the 9-1-1 emergency hotline wasn’t available in most areas until the 1970s? This is especially true for aggravated assault (which will become important later). (source: Baumer and Lauritsen) There’s one caveat - FBI statistics show that crime had a small local peak in 2020/2021, then fell in 2023 - 2025. The most recent NCVS survey, in 2024, shows a smaller fall, leaving us still above 2019 lows. There’s some debate over whether the FBI vs. NCVS numbers are better for the 2022 - 2025 period, but they don’t change the overall trajectory or the fact that we’re at least close to record lows. Murder is almost always reported to and investigated by police; there’s a person who should be alive but isn’t, and people inevitably notice and care about this. Therefore, reported murder rates should be accurate. But murder has decreased at about the same rate as every other crime. Therefore, we should believe that other crimes have gone down too (for the objection that murder statistics are unusually untrustworthy because of improving medical care, see below). And car theft is consistently reported to the police, because insurances require a police report before they will compensate the lost car. So even if the victim doesn’t trust the police to do a good job investigating, they report it anyway. But car theft rates have declined at similar rates to other crimes. This is further evidence that the decline can’t be explained by poor reporting. Could This Be An Artifact Of Improving Medical Care? Good medical care can help victims survive, transforming murders into attempted murders or aggravated assaults (after this: “AM/AA”). If the same gunshot is only half as likely to kill someone today as it would have been in 1960, then a seemingly-equivalent murder rate would correspond to twice as many people getting shot. Could this explain the apparent decline in murders? The argument would go something like: murder is the only crime that we’re completely sure gets reported consistently. But the murder rate is artificially depressed by improving medical care. Therefore, maybe the seemingly-low murder rate is because of the medical care, the seemingly-low rates of other crimes are because of reporting bias, and actually crime is up. We’ve already seen that several parts of this can’t be true: other crimes like car theft are reported consistently, and among the inconsistently reported ones, reports are more often increasing than decreasing. But the part about murder also fails on its own terms. The source for the claim that improving medical care lowers murder rates is Harris et al, which analyzed crime from 1960 - 1999 and concluded that “the principal explanation of the downward trend in lethality involves parallel developments in medical technology”. They found that aggravated assaults rose faster than murders during this time; AAs increased by 5x, while murders “merely” doubled. Under the reasonable assumption that these crimes have similar generators, they suggested that the cause was improved medical care saving the lives of those who would have otherwise died, converting potential murders into AAs. If murders rose at the same rate as AAs, then the true murder rate could be up to 3x higher than reported. Source: FBI UCR But more recent research, especially Eckberg (2014), challenges this story. Eckberg argued the AA vs. murder divergence was caused by two things: first, better reporting of aggravated assault (as discussed above), and second, police being more likely to classify borderline causes as aggravated assault rather than regular assault. He turned to the National Crime Victimization Survey, which escapes reporting bias and police classification flexibility. In these data, AAs and murder rose at about the same rate. He concluded that (my emphasis): Their lethality trend is not compatible with the previous finding [of declining lethality] across 1973 through 1999, remaining stable rather than falling. After 1999, both Uniform Crime Reports (UCR)-and NCVS-based measures indicate increases in lethality. How is this possible, since medical technology has certainly improved? It seems that gun injuries are getting worse over time. Livingstone et al studied changing characteristics of gunshot victims between 2000 and 2011. They found that the proportion of patients with 3+ wounds almost doubled (13% → 22%) during that period (p < 0.0001). Manley et al did a similar study looking at 1996 - 2016 and found a similar result, saying that “wounding in multiple body regions suggests more effective weaponry, including increased magazine size”. A letter by top trauma doctors to the American Journal of Public Health describes: …increases in gunshot injuries per patient, gunshot injuries to critical regions (head, spine, chest), and gunshot injuries to multiple regions. Injury Severity Scores were also higher over similar intervals correlating with lower probability of survival. Despite which …patients surviving evaluation in the emergency department had no significant increase in mortality. Major strides in trauma care have occurred over the last two decades, and nationwide organizational changes have expanded the delivery of these improvements. Sakran et al, studying the 2007 - 2014 period, have an especially vivid portrayal of this pattern: Likelihood of dying before hospitalization - primarily dependent on injury severity - went up. Likelihood of dying in the hospital went down, probably because trauma care improved (although this could also be because more of the sickest patients died before entering the hospital). Cook et al studied gunshot lethality during a slightly different period - 2003 - 2012 - and also found that it stayed the same overall. There are three plausible explanations for gun injuries getting worse over time: Improved weapons technology (e.g. switch to semi-automatics)
Graffiti: There are no good data for graffiti. Most of the discussion focuses on New York, where everyone agrees the long-term trend is down since 1970. The Graffiti In New York City Wikipedia page has a “decline of New York graffiti subculture” section, which explains that in the 1980s, when “broken window” policing became popular, the police cracked down on graffiti and this worked somewhat. The only numbers are here, and they describe a decrease of 13% in calls to the graffiti hotline between 2011 and 2016. But the more recent picture, and the story in other cities, is less sanguine; in the past few years, graffiti is “a bigger problem than ever” in Los Angeles and has “gotten worse” in San Francisco. Plausibly this is the same pattern as crime, which was declining for decades until COVID and the Black Lives Matter protests caused it to rebound in 2020. A contrary data point is Britain, where graffiti reports almost doubled between 2013 - 2017; I don’t know enough about the British context to have an opinion.
Even if we worry about the increase over the 2005 low, it seems to be only about 33%, over fifteen years, which should be hard to notice. Strange! (the FBI runs a different shoplifting reporting program, NIBRS. This does show a large increase since 2018, but is considered less reliable because new cities keep joining and so year-to-year reports aren’t comparable.) Maybe the problem is limited to a few big cities? What about San Francisco in particular? At least in these data, it’s - if anything - less. Okay, so could stores be failing to report to police? Some stores say they’re doing this, and there was an embarrassing incident - it might be the 2021 spike on the graph above - where two stores briefly changed their reporting policy and nearly doubled the total report number. We need an equivalent of the NCVS - reports coming from the victims themselves. Our best bet is the National Retail Survey, from a retail organization which asks stores what percent of their inventory they believe they lose to various causes, including shoplifting. Only about a 20% increase during the 2004 - 2022 period. The NRS is sponsored by a retail trade industry group which really wants to find shoplifting so they can lobby for better anti-shoplifting measures. In 2024 they were so embarrassed by their failure to do so that they stopped the survey entirely and sold the survey brand to an anti-shoplifting security tech company (no bias there!). The company replaced it with a survey of vibes among store owners, and dutifully reported that the vibes about shoplifting had never been worse and you needed to buy their product right away. Now what? The survey doesn’t disaggregate by city, so maybe national shoplifting is stable, but San Francisco really is worse, and just isn’t reporting it to the police? Might this be because there are fewer stores (everyone is buying through Amazon) and therefore even if all existing stores are crammed with shoplifters all the time, it shows up as less shoplifting? This isn’t trivially true - the number of stores has declined less than I would expect, maybe not at all - but there’s been a shift in types of stores (from big box to local). If these types have different shoplifting or reporting patterns, that might matter. Otherwise, we’re in the awkward position where everyone (including stores) reports higher shoplifting numbers, but two datasets both disagree. Homelessness and Tent Encampments: Here’s a graph of homelessness, courtesy of Claude: I’ve confirmed the post 2009 trend; I haven’t fully double-checked the others but they match my impressions. This looks like a similar pattern to crime, although here the likely explanation for the COVID bump is the pandemic-associated rise in house prices. Good measures of tent encampments over long periods are hard to find. San Francisco has this one: …but it starts in 2019, peaks during the pandemic, and then declines. This can’t really show whether 2019 was already higher than some previous year. Here is an interesting graph of Seattle homeless sweeps, ie number of times the police acted against encampments: …but it doesn’t tell us whether encampments are increasing, or the police are taking them more seriously. It does rule out a story where encampments are increasing because the police are no longer taking action - aside from the pandemic, police are taking more action than ever, at least as measured here. People With Loud Boom Boxes In Public Places: All I have to say about this one is that it’s terrible and I hate it. Overall, it’s surprisingly hard to find data confirming that disorder has increased: Littering seems to be down
Only about a 20% increase during the 2004 - 2022 period. The NRS is sponsored by a retail trade industry group which really wants to find shoplifting so they can lobby for better anti-shoplifting measures. In 2024 they were so embarrassed by their failure to do so that they stopped the survey entirely and sold the survey brand to an anti-shoplifting security tech company (no bias there!). The company replaced it with a survey of vibes among store owners, and dutifully reported that the vibes about shoplifting had never been worse and you needed to buy their product right away. Now what? The survey doesn’t disaggregate by city, so maybe national shoplifting is stable, but San Francisco really is worse, and just isn’t reporting it to the police? Might this be because there are fewer stores (everyone is buying through Amazon) and therefore even if all existing stores are crammed with shoplifters all the time, it shows up as less shoplifting? This isn’t trivially true - the number of stores has declined less than I would expect, maybe not at all - but there’s been a shift in types of stores (from big box to local). If these types have different shoplifting or reporting patterns, that might matter. Otherwise, we’re in the awkward position where everyone (including stores) reports higher shoplifting numbers, but two datasets both disagree. Homelessness and Tent Encampments: Here’s a graph of homelessness, courtesy of Claude: I’ve confirmed the post 2009 trend; I haven’t fully double-checked the others but they match my impressions. This looks like a similar pattern to crime, although here the likely explanation for the COVID bump is the pandemic-associated rise in house prices. Good measures of tent encampments over long periods are hard to find. San Francisco has this one: …but it starts in 2019, peaks during the pandemic, and then declines. This can’t really show whether 2019 was already higher than some previous year. Here is an interesting graph of Seattle homeless sweeps, ie number of times the police acted against encampments: …but it doesn’t tell us whether encampments are increasing, or the police are taking them more seriously. It does rule out a story where encampments are increasing because the police are no longer taking action - aside from the pandemic, police are taking more action than ever, at least as measured here. People With Loud Boom Boxes In Public Places: All I have to say about this one is that it’s terrible and I hate it. Overall, it’s surprisingly hard to find data confirming that disorder has increased: Littering seems to be down
1: Mox asks me to advertise their 2026 fundraiser. They’re a rationalist/EA coworking space in San Francisco that hosts ACX meetups, ACX grants infrastructure, AI safety work, and more. And while I’m advertising them, they also offer deals on personal and organizational office space.
Contact: Julius Contact Info: julius[.]simonelli[@]gmail[.]com Time: Saturday, May 2nd, 11:00 AM Location: Wisdom Park Coordinates: https://plus.codes/8544VRXM+56 Group Link: https://www.meetup.com/san-diego-rationalists/ SAN FRANCISCO Contact: Nate Contact Info: natesternberg[at]gmail[dot]com Time: Saturday, May 02, 01:00 PM Location: Mox (first floor), a co-working space in SoMa Coordinates: https://plus.codes/849VQHCJ+82 Group Link: https://www.lesswrong.com/events/jjEeHyzxhZndrewjz/san-francisco-acx-meetups-everywhere-spring-2026 Additional Notes: Please RSVP on LessWrong so I can email you a door code ahead of time.
1: Late additions to spring meetup list: San Francisco, Belo Horizonte, Birmingham, Columbus, Hobart, Hyderabad, Madrid.