Georgists

Article

Georgists is a recurring concept in the Astral Codex Ten archive, appearing 4 times across 4 issues between December 09, 2021 and June 18, 2025. The archive places it in contexts such as “Georgists assert that if we sufficiently tax land in this manner”; “For Georgists, land is the key to understanding the whole economy”; “which accrue rental income and are considered ‘Economic Land’ by Georgists”. It most often appears alongside Astral Codex Ten, Australia, Lars Doucet.

Metadata

Category: Concepts
Mention count: 4
Issue count: 4
First seen: December 09, 2021
Last seen: June 18, 2025

Appears In

- Astral Codex Ten (3 shared issues)
- Australia (3 shared issues)
- Lars Doucet (3 shared issues)
- United States (3 shared issues)
- Albouy (2 shared issues)
- Alexandra Elbakyan (2 shared issues)
- ATCOR (2 shared issues)
- Britain (2 shared issues)
- California (2 shared issues)
- China (2 shared issues)
- Count Bla (2 shared issues)
- COVID (2 shared issues)

External Links

Source Context

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

Does Georgism Work? Part 1: Is Land Really A Big Deal?

December 09, 2021 · Original source

In real life you can't accurately assess land value separately from improvements, so even if LVT would work in theory, it doesn't work in practice Today we'll start with point 1, and subsequent articles posted in the next two days will address points 2 and 3. I'll probably write further articles on the subject, but I make no presumptions about whether I'll have worn out my welcome on Astral Codex Ten by then. If you haven't read the Book Review yet, I've posted a brief recap of the relevant concepts below. Otherwise, feel free to skip directly to the subsequent section. 0. A Brief Recap Georgism is a school of political economy that is really upset about, among other things, the Rent Being Too Damn High. It seeks to liberate labor and capital alike from those who gatekeep access to scarce "non-produced assets," such as land and natural resources, while still affirming the virtues of hard work and free enterprise. George uses the term "Land" to mean not just regular land, but everything that is external to human beings and the things they produce–nature itself, really. Georgism's chief insight is to move economic thinking from a two-factor model (Labor and Capital) to a three-factor model (Land, Labor, and Capital). It's chief (but not only) policy prescription is the Land Value Tax (LVT), which taxes real estate at as close to 100% of its "land rent" as possible (the amount of rent due to the land alone apart from "improvements" such as buildings). In actual practice, most Georgists seem to think 85% is a reasonable figure to target. Let's carefully unpack what those terms means. "Land value" refers to the full market value of a property, excluding all of its improvements, such as buildings. This is the portion of a property's value arising solely from its location and natural attributes (agricultural fertility, endowment of stuff like water, minerals, etc.). "Land rent" (AKA "ground rent") refers to the recurring rental income a property is capable of generating from the market because of its land value. It is Land Rent which Land Value Tax is intended to capture. You can think of it as a Location or Site Value Tax if that's more helpful. It's not a tax on the full market purchase price of a property, nor is it a fixed amount of tax per acre of land, but rather a tax proportional to the market value of the land alone (or better yet, the land rent). When assessed correctly, as LVT approaches 100% the market selling price of the land itself will approach zero. Don't let the "100%" confuse you, either. If a piece of land costs $10,000 to buy, and is leased for $500/year, then an LVT that captures 100% of the land rent is $500/year, which works out to a 5% annual tax of the land value. LVT should not be confused with a property tax. Property taxes consider land plus improvements (typically buildings). An LVT considers land value alone. Georgists assert that if we sufficiently tax land in this manner, we'll not only end the housing crisis but also fix a bunch of misaligned incentives that cause poverty to persist alongside economic progress, while raising a bunch of revenue that can lower or even eliminate other less efficient taxes, such as sales and income taxes. This is because virtually all economists agree that LVT has zero "deadweight loss"–a fancy word for a drag on the economy that makes certain activities no longer profitable. Other taxes with no deadweight loss include Pigouvian taxes on bad things, like congestion and pollution. But won't landlords just raise the rent to make up for the LVT, passing the burden of the tax on to the tenants? Georgists say no, because land is special in that it is scarce and nobody can make any more of it. Indeed, LVT is a rare form of taxation that actually boosts the economy, because it discourages rent-seeking and speculation. Some Georgists even go so far as to say that LVT can raise enough revenue to replace all other less efficient taxes, becoming the so-called "Single Tax," but this is not a universally held position among modern Georgists. To be clear, proponents of the "Single Tax" believe that LVT is sufficient for all public purposes and that no other taxes (such as income tax, capital taxes, and tariffs) are necessary for revenue generation, although they still might support carbon taxes or "sin taxes" on things they want to discourage. Georgism doesn't begin and end with the LVT, however, and the movement isn't solely concerned with real estate and tax revenue. Henry George was an early proponent of what we now call "Universal Basic Income," or as he called it, the "Citizen's Dividend" (funded by LVT, naturally). But even if you threw every penny of LVT revenue into the sea, the anti-sprawl effects of the policy are appealing enough by themselves to earn the endorsement of YIMBY's and urbanists like Strong Towns. If you take Georgism to its natural conclusions, you might start to question government-enforced monopolies over other kinds of "Land," such as electromagnetic spectrum, water and mineral rights, and orbital real estate for satellites, not to mention the deadweight loss created by intellectual property gatekeepers over, say, research papers. And if you have my day job as an analyst for the video games industry, one day you'll find yourself applying the observed 30-year history of housing crises in MMO's to virtual real estate sales in leading blockchain games. Some people come to Georgism because of their aversion to income and capital taxes, some want to use LVT to fund generous social programs, some are motivated by the beneficial environmental effects, and some just think the Rent is Too Damn High. No matter where you come from on the political compass, there's probably a way to mix up a club soda and Georgism that's right for you. 1. Is Land Really a Big Deal? Paul Krugman speaks for many mainstream economists when he admits that Georgist analysis is sound, but he insists that it's a moot point because land just isn't important anymore in the modern economy: Believe it or not, urban economics models actually do suggest that Georgist taxation would be the right approach at least to finance city growth. But I would just say: I don't think you can raise nearly enough money to run a modern welfare state by taxing land. It's just not a big enough thing. By George, if land just isn't a big deal, then LVT can't raise much money, the problems of speculative landownership are vastly overstated, and you can stop reading this article. The main tension between Georgists on the one hand, and Marxists and Neoclassicals on the other, is that the latter two significantly downplay land, centering the whole discussion instead on labor and capital. For Georgists, land is the key to understanding the whole economy. Krugman's main complaint is that LVT can't raise enough money, which is a response to the "Single Tax" movement in particular. In George's time, it was popular to advocate for a 100% Land Value Tax and the elimination of all other taxes. Keep in mind that in George's time, there was no federal income tax, and state and federal spending was much lower, so whether LVT could raise enough money wasn't nearly as controversial as it is today. But even if it turns out that a modern-day "Single Tax" isn't enough to cover the federal budget, Krugman misses the point. The purpose of LVT is not just to raise revenue, but to end speculation, rent-seeking, unaffordable housing, and wasteful, environmentally damaging sprawl. LVT is worth doing for those good effects alone. The revenue it generates doesn't need to fund literally every penny of government spending to still be a win, which is why Georgist economist Terrence Dwyer calls LVT "better than neutral." Liberal Krugman and conservative Milton Friedman both seem to agree that LVT has no deadweight loss, which means LVT, unlike income and capital taxes, doesn't create a drag on productivity. This means that if we can raise enough money from LVT, we can reduce at least some inefficient taxes, such as those on labor, while keeping government spending the same. Not only could this be popular politically, it would also boost the economy. Those are the claims Georgists make, at least. Let's see if they're true. Here are a few testable hypotheses that capture different aspects of land being a "really big deal": Most of the value of urban real estate is land

Inline links: the Book Review, political economy, Pigouvian, YIMBY's, Strong Towns, research papers, housing crises in MMO's, leading blockchain games, Paul Krugman, Milton Friedman, deadweight loss

The bullish values in blue all come from estimates by various Georgists cited in Smith's book and are naively back-extrapolated by me just to set an upper bound.

Okay, so let's look at Smith's method. Instead of doing a whole new study, he singles out Albouy as having the best methodology and makes some adjustments. You see, Albouy estimated the value of urban land alone, leaving out federal lands, agricultural lands, and things like water rights and natural resources, which accrue rental income and are considered "Economic Land" by Georgists.

Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?

December 11, 2021 · Original source

Georgists assert we're consistently undervaluing land basically everywhere

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

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

Your Book Review: The Laws of Trading

July 21, 2023 · Original source

Our customers’ business viability “Many trades that look different on the surface can in fact be the same trade in disguise, and trades whose edge appears to derive from one risk are actually bets on another risk.” It might make sense to hedge some of that risk – simply having friends that work at other companies and in other industries so that all of my social capital isn’t in one basket is a start4. My only gripe here is that I would have liked to see Lebron call out ergodicity more explicitly. Blowing up your account might be fine as a trader – if you have a decent prior track record, you can probably just get a job at a different firm – but in life other losses are less reversible. As far as we know, this is the only universe we have access to. It doesn’t matter if your bet was positive EV and you won in 51% or 75% or even 99% of universes. You should place a high premium on staying alive and having enough bankroll to play the next round of the game. This is more important outside of finance than in the world of trading. 4: Liquidity Put on a risk using the most liquid instrument for that risk. Liquidity isn’t something I think about in daily life. But I probably should. A personal example: I gave up the liquidity of a month-to-month gym contract in New York City in February 2020. I paid one year upfront for a 10% discount. Oops. Lebron also reminds us that the 30-Year Mortgage is an Intrinsically Toxic Product, a concept that will resonate with all of the Georgists here. “The usual path to homeownership exposes people to a financial decision that would, it seems clear, be ridiculed if it were taken by any self-respecting public company.” Among other issues: “The home is bought and sold through an opaque cartel of brokers whose interests are demonstrably not aligned with those of their customers”

Inline links: 4, 30-Year Mortgage is an Intrinsically Toxic Product

ACX Grants 1-3 Year Updates

June 18, 2025 · Original source

Minnesota and Virginia also have legislation to enable cities to implement land value taxes. We are monitoring these efforts. There are a few other cities we are operating in. We have helped another organization prepare for a meeting in Tennessee by doing impact analysis of land value taxes in the city. We have presented to city officials in the City of South Bend who have expressed support for land value taxes. Finally, we are in conversation with a State Senator in Colorado who is a champion of land value taxes. Meanwhile, we have soft launched and developed the OpenAVMKit, which uses a unified schema to do assessment accuracy reports and automated valuation methods for any property tax data given. Valuation of land is the key binding constraint to successful implementation of land value taxes. We plan to be the leaders in this space with strong benchmarking capabilities and a repo that can enable the open-source community to make the best automated valuation methods. Along with these efforts, we have expanded the movement. We have posted to the Progress and Poverty Substack growing the subscriber base to around 5,000 subscribers. We have spoken to over 25 local advocates interested in working on land value taxes in their local communities. Yet, there is a long way to go. We need to start earning income through technical assistance contracts as our grant funding expires. We need to continue pushing for a state to implement, and we need to be prepared to tell the success story for when they do. 65: EN’s Work On Bacteriophage Therapy Our project is aimed at pioneering phage therapy in Nigeria, where limited resources/infrastructure have historically held back research in this field. Starting from the ground up, we are establishing the foundational systems needed to support a robust phage research ecosystem. So far, we’ve isolated 34 bacteriophages targeting Pseudomonas aeruginosa, an essential step toward building a comprehensive phage bank. This began with collecting a wide range of clinical Pseudomonas isolates, which we are now characterizing alongside the phages through genome sequencing and phenotypic assays including studies on phage stability across pH, temperature, and salinity ranges. Our long-term goal is to develop a phage-based hydrogel for treating diabetic wounds. On the regulatory front, we have secured approval from the Attorney General to register our nonprofit organization, the Centre for Phage Biology and Therapeutics. Additionally, we’re expanding into vaccine development; following a research stay in Prof. Roderick's lab at the University of Waterloo, we have initiated the design of a phage-based universal Salmonella vaccine aimed at covering all major serotypes—an urgent need underscored by Africa’s reliance on external vaccine sources during the COVID-19 pandemic. I have signed an MTA agreement with Roderick to use his phage-based vaccine platform patents to enable us to design vaccines against any common disease affecting us. This is only the beginning, but we are proud to be laying the scientific and institutional groundwork for homegrown phage innovation in Africa. Emergent Ventures funded EN before we did and deserves a lot of credit here also. 66: Create An Artificial Kidney For an implantable artificial kidney, the first essential component is a hemofilter designed to emulate the glomerulus. Critical requirements for this hemofilter include high permeability (to maximize flow for a given area), selectivity (specifically, the retention of albumin), and robust blood compatibility (ensuring sustained function over time). Our initial strategy focused on using negative surface charge to reduce fouling. I began by testing polyelectrolyte (PE) coatings on 24nm pore membranes featuring a negative terminal charge, similar to the glomerular barrier. These initial static tests, assessing platelet adsorption in whole blood, yielded positive outcomes for some polyelectrolytes, indicating potentially desirable blood compatibility. However, static test setups are not truly representative of dynamic in-vitro conditions and don't provide data on key parameters like permeability, fouling progression, or changes in membrane selectivity. To address these limitations, I designed and built a blood filtration setup. This system sustains human whole blood in circulation for 20 minutes, allowing us to analyze all the aforementioned parameters, as well as platelet activation markers. This has resulted in a fairly high-throughput system for evaluating any surface coating. I'm pleased to report this setup has been accepted for presentation at this year's European Society for Artificial Organs (ESAIO) conference. I am also currently working on a full manuscript, as I believe this system offers a viable way to partially replace animal experiments in our early-stage research, requiring only 1.2ml of human blood per run. Working with a PhD student (hired to support both this research and work on membrane substrates), we have continued testing these PE coatings, alongside PEG coatings, on our membranes. Here, we're finding that optimization of the coating layer is crucial. With the current PE coatings, we observe a permeability drop of about an order of magnitude compared to the base membrane, making them unsuitable for an implantable device in their present form. This is likely due to the specific nature of the initial PE layer, which we can modify. We also suspect there may be ingress of PE into the pores, meaning we're not achieving just a surface coating (our goal), but rather a very thick coating, which would explain the flux loss. Optimizing the coating process to control penetration depth is now a primary focus of my ongoing work. I am currently aiming for a flux of 20ul/min (as this is cap introduced by the protein gel layer anyway) but for it to be at this 'steady state' permeability without drop in permeability. I am also imaging the membranes after contact with SEM to see if there is indeed any platelet adsorption etc. Tugrul has the dubious honor of maybe being "the only person to climb a 4000m peak with severe kidney failure". To raise money and awareness for his artificial kidney project, he is running Climb Against Time, where he will climb 41 mountains over 4000m (13000 ft) this summer. He is looking for donors and climbing partners. 67: Add Tardigrade Genes To Human Cells The goal of this one was to make hybrid cells that are more resilient for research and certain medical applications. They report: The grant was to synthesize vectors for the expression of humanized tardigrade proteins that can be targeted to different areas of the cell. All the vectors were designed, generated, and transposed into human cells. The proteins all localize successfully (e.g. they match the designed target), with one exception (we are still working on validating it). We've done some stress testing with the trangenic cells, but haven't reached firm conclusions yet. We've further generated some multigene designs but have not yet transposed them into cells, but should shortly. We're hoping to submit a manuscript on the first round later this year. 68: Teach Forecasting To EU Policy-Makers The original project didn't work out, but our grantee (who still prefers to remain anonymous) is now working with an EU think tank pursuing the same agenda, and has been teaching forecasting workshops to policy-makers for the past two months. 69: Platform For Single-Cell Imaging They ended up unable to accept this grant and returned the money. 70: Open Source Polygenic Predictor For EA/IQ They have an update here. They think they have a predictor that can explain 12% of variance in intelligence, and they’re working on validating it and creating an easy-to-use website. 71: Improve Flu Vaccines The grant mainly funded agent based modelling to demonstrate the benefit of pre-existing immunity to pandemic influenza if and when a future pandemic occurs (academic publication will result). The original proposal was to attempt to influence the WHO influenza strain selection process. After attending WHO meetings and a global influenza conference, I believe this is not feasible. Stakeholder feedback was the potential short term negative effect on vaccine hesitancy is believed to outweigh the less tangible future benefit. Given the conservative nature of decision makers, pandemic vaccines are likely to remain research only. There are still green shoots of research into pandemic preparedness/prevention that I am continuing to work on. I'm working under the "Australians for Pandemic Prevention" brand of Good Ancestors, another group that ACX funded in 2024. 72: Scenario Analysis For Developing World Agricultural Programs In addition to the research and analysis funded by the grant, I’ve learned to code with LLMs and have built an MVP of the project. The app is being considered for further development by staff at a large international organization. 73: Further C’s Political Career C’s political career is going well, but he continues to think it wouldn’t be strategic to give more information publicly at this time. Lessons Learned I'm most impressed with our lobbying/advocacy organizations. In particular, Good Ancestors has gotten the Australian government to sign onto an international AI safety declaration, partner with various x-risk-related organizations, and (possibly) extend charity tax deductions to some EA causes that previously didn't have it - I think this on its own goes a substantial way to paying back the cost of all ACX Grants. Coalition to Modify NOTA has a kidney donation bill in front of Congress that the (very illiquid) prediction markets give a 45% chance of passing; if it works, it could save thousands of lives. The Georgists are partly responsible for bills making land value taxes slightly easier to implement in a handful of states. Good Science Project seems to have significantly improved science. Are lobbying organizations a better bet than other types of nonprofit (within the constraints of ACX Grants)? I'm not sure. It could just be that lobbyists are (naturally) better at playing themselves up and sounding successful than (for example) scientists, or that politicians are good at people-pleasing and make people feel heard and encouraged in a way that might not change overall policy later. Also, I recently talked to some grantmakers who funded a lobbying organization that superficially seems excellent, but they expressed concern it was net negative (!) by taking away oxygen and spotlight from potentially more effective orgs. So I am encouraged but wary. Animal welfare organizations were another standout success. Again, I don't know how to think about this - while I think our grantees were exceptional, there's also an issue where the scale of animal welfare challenges is so great, and work on them so neglected, that lots of organizations can save a million chickens here, or a million fish there, without particularly making a splash. On the one hand, this is exactly what effective altruism should be doing - exploring grants that are very high in linear utility even if they don't feel satisfying. On the other, they're unsatisfying - and also hard to assess retroactively. How many chickens should a good animal welfare grant save? Any realistic number will both be overwhelmingly large in absolute terms and far too small in relative terms. I'm most ambivalent about our science grants. Many of them say they are successful and can point to published papers which explain the science they did. But it's hard to judge whether anything useful has changed based on the science getting done. I know it's important to fund basic research and not just last-mile technology startups, but it's hard for a mini-grants program like this one to evaluate these kinds of abstract interventions. One disappointing result was that grants to legibly-credentialled people operating in high-status ways usually did better than betting on small scrappy startups (whether companies or nonprofits). For example, Innovate Animal Ag was in many ways overdetermined as a grantee - former Yale grad and Google engineer founder, profiled in NYT, already funded by Open Philanthropy - and they in fact did amazing work. On the other hand, there were a lot of promising ACX community members with interesting ideas who were going to turn them into startups any day now, but who ended up kind of floundering (although this also describes Manifold, one of our standout successes). One thing I still don't understand is that Innovate Animal Ag seemed to genuinely need more funding despite being legibly great and high status - does this screen off a theoretical objection that they don't provide ACX Grants with as much counterfactual impact? Am I really just mad that it would be boring to give too many grants to obviously-good things that even moron could spot as promising? Someone (I think it might be Paul Graham) once said that they were always surprised how quickly destined-to-be-successful startup founders responded to emails - sometimes within a single-digit number of minutes regardless of time of day. I used to think of this as mysterious - some sort of psychological trait? Working with these grants has made me think of it as just a straightforward fact of life: some people operate an order of magnitude faster than others. The Manifold team created something like five different novel institutions in the amount of time it's taken some other grantees to figure out a business plan; I particularly remember one time when I needed something, sent out a request to talk about it with two or three different teams, and the Manifold team had fully created the thing and were pestering me to launch a trial version before some of the other people had even gotten back to me. I take no pleasure in reporting this - I sometimes take a week or two to answer emails, and all of the predictions about my personality that this implies would be correct - but it's increasingly something that I look for and respect. A lot of the most successful grants succeeded quickly, or at least were quick to get on a promising track. Since everything takes ten times longer than people expect, only someone who moves ten times faster than people expect can get things done in a reasonable amount of time. In almost every case where I thought to myself “this is a cool idea, but I don’t know how it’s going to really pay off, as opposed to reaching a cool intermediate accomplishment and then stagnating”, this was a correct criticism, and I should have taken it more seriously. But I can’t rule out that these were good in vague and hard-to-measure ways that I should take more seriously. This one is really self-serving, but in general when people were good communicators (or even bloggers) and wowed me with the writing-composition of their application, they turned out to be a good bet. And when people were hard to understand and annoying to communicate with, even if their ideas seemed good, they were less likely to pan out. Overall Thoughts The total cost of ACX Grants, both rounds, was about $3 million. Do these outcomes represent a successful use of that amount of money? Very naively, startups originating from ACX Grants have about $50 million in value1. If ACX Grants is equivalent to a pre-seed funder, and pre-seed funders usually get ~5%, then if we were VCs we would have a portfolio worth $2.5 million. About 1/5 of ACX Grants were attempting to be market-valued startups, so if we assume the charitable portion did about as well as the startup portion, then the charity portion is “worth” $10 million. There’s some reason to expect this is too high, since much of the startup value came from one successful outlier. But there’s another reason to expect this is too low, since we were aiming at charity rather than market cap, and any actual market cap that our grantees got was an unexpected side effect. I’m treating this as a sanity check rather than as a real number. It’s harder to produce Inside View estimates, because so many of the projects either produce vague deliverables (eg a white paper that might guide future action) or intermediate results only (eg getting a government to pass AI safety regulations is good, but can’t be considered an end result unless those regulations prevent the AI apocalypse). Because we tend towards incubating charities and funding research (rather than last-mile causes like buying bednets), achieved measurable deliverables are thin on the ground. But here are things that ACX grantees have already accomplished: Improved the living/slaughter conditions of 30 million fish.

Inline links: Progress and Poverty Substack, Climb Against Time, here, 1

Codebuff, an AI coding startup I probably can’t take full credit for all of this just from giving them $20K in seed funding, but I continue to appreciate everything they do for this community and the world. 35: Further S’s Political Career This person didn’t win their election, but has since pivoted to AI safety and works in a well-regarded AI policy think tank. 36: Seeds Of Science, A Journal Of Non-Traditional Research No update received, but this was a public journal and it is easy to follow their work, see their website and Substack. They published two dozen articles of widely varying quality through 2023 and 2024, then closed in 2025. A remnant of the original vision survives as a science blogging aggregator. This was about my median expectation for this grant, but it was very inexpensive and I decided to take a chance on it anyway. 37: Good Science Project, Working To Improve Federal Science Funding No update received, but they have a public Substack discussing their progress. Their proposals for NIH reform have influenced Congress and made government agencies pay more attention to scientific integrity. 38: Advising Developing Countries On How To Grow Their Economies With our initial ACX grant, we piloted the Growth Teams model in Rwanda, helping the government jumpstart the export-oriented call center (BPO) industry. Since 2022, that effort has contributed to the creation of 2,000 formal jobs and the emergence of some of the country’s largest private employers. We’ve since expanded to Tanzania, Malawi, and the Indian states of Goa and Meghalaya. To refocus the global development discourse on broad-based economic growth, we co-organized the Growth Summit with the Center for Global Development and the Charter Cities Institute, and have published articles in leading outlets including Stanford Social Innovation Review, ProMarket, and the Global Prosperity Institute. Our work has attracted support from Open Philanthropy, Schmidt Futures, and Mulago Foundation, and our advisors now include economists Lant Pritchett, Stefan Dercon, and Kunal Sen. 39: Help Luca De Leo Get Started In AI Safety Research No update received, but Luca now runs the AI safety group at the University of Buenos Aires, Argentina. 40: Typist For Saharon Shelah This was another ACXG+ Grant, funded by an anonymous outside funder and not listed in the original announcement. Saharon is a prolific and influential Israeli mathematician, but many of his discoveries are hand-written in an unpublishable format. This grant funded a typist to help make his results suitable for publication. According to this page, they have made over fifty new papers and preprints available. Second Cohort: One Year Updates 41: Lead-Acid Battery Recycling In Nigeria The Nigeria field research was a major success. We spent most of September doing field research in multiple major cities in Nigeria, and got a good sense of the used lead-acid battery supply chain. This field research served as the foundation for expanding our project, and has been very impactful in shaping our ongoing research. We published our findings from Nigeria, which were shared with Nigerian government regulators and global NGOs working on lead poisoning. The grant also gave us the on-the-ground experience we needed to both fully understand and credibly engage with groups, both in Nigeria and globally, on the ULAB issue. In the meantime, beyond continued research, we’ve also launched a dashboard (trade.leadbatteries.org) for analyzing global lead trade data. Right now, we’re: Launching two studies (one RCT, one environmental analysis) in Nigeria in collaboration with local universities to develop a more rigorous understanding of lead pollution due to low-standard ULAB recycling in Nigeria Collaborating with a non-profit incubator to launch an NGO focused on demand-side solutions Beginning a partnership with a West African environmental regulator to scale cheap air monitoring technology to quickly identify and reduce lead pollution from low-standard smelting If any of this sounds interesting to you, please sign up for our Substack (leadbatteries.substack.com) or send us an email at hugosmith@uchicago.edu! 42: Compensation For Kidney Donors The End Kidney Deaths Act (H.R. 2687 / EKDA) is a groundbreaking ten-year pilot program designed to save lives and reduce healthcare costs. It provides a refundable tax credit of $10,000 per year for five years, a total of $50,000, to living kidney donors who donate to a stranger, helping those who’ve waited the longest on the transplant list. Between 2010 and 2021, 100,000 Americans died while qualified and waiting for a kidney. The EKDA aims to change that trajectory. Within ten years of its passage, up to 100,000 Americans could receive a life-saving living donor kidney which typically lasts twice as long as a deceased donor kidney. This would not only save lives but also save taxpayers up to $37 billion. The legislation has been reintroduced in the House, and we have a committed Republican Senate lead. Now, we need a Democratic Senator to co-lead and help move this bipartisan effort forward. Time is short, and we are racing to pass the bill this Congressional session. 36 organizations already support the EKDA. Join the movement and help end preventable kidney deaths. Visit EndKidneyDeaths.org to help us get to the finish line. Elaine and her org have been working extremely hard on this; you can read a Vox article on their campaign here. If you want to sign up for her email list and get updates any time there is a representative you can contact or meeting you can join in, go here. 43: Genetic Hack To Prevent Suffering In the estimate of multiple team members, the ACX grant was “worth it” - it likely had a counterfactual net positive impact, even though we had to pivot from our initial fast-track plans for developing the precision anti-suffering therapy. We identify three primary streams of value: a) reducing uncertainty in the emerging field through early exploratory research, helping with the identification of dead ends and promising R&D trajectories; b) a wide range of downstream effects (beyond the “raising awareness” cliché), including talent mobilization and rekindled interest in suffering abolitionism as a distinct cause area; and c) certain developments that cannot yet be publicly disclosed. In December 2024, Marcin Kowrygo (Acting CEO & volunteering contributor), David Pearce (Director of Bioethics), Aatu Koskensilta (President), and a few other team members decided to leave The Far Out Initiative. They look forward to collaborating and applying their experience to advance the suffering abolitionist lineage in the spirit of open science, public good, and thoughtfully decentralized governance. Feel free to reach out to us at suffab at protonmail dot com to discuss collaboration opportunities! I wrote a post profiling the Far Out Initiative here. Unfortunately there were some internal disagreements, and the people ACX Grants was closest to left the organization. I plan to continue to monitor whatever they do next. 44: Advocate For Pandemic Response Team At FDA This team prefers has asked me not to discuss their progress publicly, but you can probably guess what their lives are like right now, and your guess would be correct. 45: Anti-Mosquito Drones We developed a cheap sonar that is able to detect, track and classify the ultrasonic echoes of mosquito wings at more than three meters. I believe it’s a world first! We also have control algorithms that take the sonar data and output control commands that both ram into mosquitoes and avoid the walls of a simulated environment. Our current work is on integrating both components on a real drone, and we expect to be able to kill mosquitoes by June. We’ve also made an internal impact study (napkin-sized) that shows we’ll be more cost-effective than ITNs in urban to periurban environments. So, we’re super excited with what comes next and can’t wait to share the videos of our first interceptions! More information [in the video below] and on our website, https://tornyol.com 46: Tarbell Fellowship For AI Journalism No update received, but they have a public website. I can’t find the Voices program in particular, but the overall fellowship completed their first class of seven fellows and is working on their second. 47: Germicidal UV Lamp Study The research has successfully demonstrated the ability of off the shelf ozone scrubbers to mitigate the ozone production of far-UVC lamps, is now available as a preprint (https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7). The paper has been submitted for publication and is currently undergoing peer review. Any ideas you have for potential funders we can approach to help execute our six-year plan to accelerate far-UVC would be appreciated https://blueprintbiosecurity.org/introducing-project-air/ 48: Technological Solutions To Animal Welfare Challenges Directly because of Innovate Animal Ag's work, the first U.S. egg producer publicly announced in the New York Times their adoption of in-ovo sexing technology, eliminating the need to cull day-old male chicks. The initial in-ovo sexing machine began operating in the U.S. at the end of 2024, with the first eggs from these hens expected on shelves in mid-2025. External evaluations estimate our work accelerated U.S. adoption of this technology by over seven years, meaning that once fully implemented, more than 2 billion chicks will have been spared. In addition to continuing to support the rollout of in-ovo sexing in the US and globally, we're now exploring other technologies and paths to impact. Current promising projects include developing humane slaughter methods for fish and advocating for USDA approval of a poultry vaccine against bird flu. They add: If you ever meet folks that are interested animal welfare and are partial to more technocratic and practical solutions, please continue to pass them our way, or connect them directly to me. 49: Assurance Contract Website www.Spartacus.app is an ACX grantee that created a platform to help solve coordination and collective action problems. It enables the creation of campaigns that build critical mass through conditional commitments, which only activate when a sufficient number of people join, converting risk and uncertainty into a higher probability of successful outcomes. They are currently facilitating several projects that leverage conditional commitments, including a dominant assurance contract interface for fashion pop-ups, accelerating a community business association's membership drive, and helping an AI safety organization organize petitions and events, among others. They have pivoted from an emphasis on high-stakes coordination problems requiring anonymity (because they occur too infrequently) to a broader range of more common use cases and have successfully run small-scale campaigns, but are still working toward product-market fit. Despite resource constraints and split time commitments that have impeded faster progress, they remain dedicated to the project's growth and success. You can follow its progress on X or Substack, or email Jordan directly here. 50: Cause Prioritization @ Center For Exploratory Altruism Research Moderately good progress on a salt reduction policy advocacy project we funded; informal commitments have been made by the Ministry of Health, and we're awaiting the publication of a formal administrative order. The official description sounds maximally generic, but this is an EA charity with a broad mandate whose current thesis is that dietary guidelines in developing countries can have outsized effects in saving lives. They’re making some progress on a salt reduction campaign in a developing country they prefer not to name publicly. 51: Mark Webb Studying Land Reform The purpose of this project was to identify specific farmland that could be acquired and transferred to the farmers already working the land. This has been difficult to achieve. I have been able to connect with other charities and landless farmers, and was able to interview a number of people about what their situation looks like, as well as what it would look like to them personally if they owned, rather than rented, their farmland. All this was immensely helpful in pushing this long-term project forward, even if I was unable to identify a specific plot of land that could be used to try the experiment. I intend to continue this project. If you have any insights or connections, I am interested. 52: More AI Advocacy In Australia Good Ancestors is focused on AI safety policy in Australia. Middle powers might be a useful path to influence as the US and China focus on racing, rather than safety. The ACX grant helped us give testimony about AI safety to the Australian Senate alongside Google, Microsoft and Facebook (We were the only nonprofit to give oral evidence to the inquiry. We also engaged government on other AI-related issues, including cybersecurity, biosecurity, consumer law and automated decision making (https://www.goodancestors.org.au/ai-safety). We’re currently working to inform voters about where parties stand on AI safety for the election, ahead of engaging on a likely Australian AI Act in 2025 (https://www.australiansforaisafety.com.au/). This is the same Australian lobbying organization we founded in Year 1, after a change in name and leadership. I continue to be excited about AI safety in middle-tier countries for a few reasons. First, these countries have some power in international organizations to set international standards. Second, companies will usually comply with any not-excessively-burdensome regulation set by any country with a significant market. Third, AI safety is underfunded by the standard of government programs, so Australia setting up a national AI Safety Institute would significantly expand the field. It’s kind of crazy that ACX Grants tier levels of money can have significant effects at this scale, but GA continues to do a great job and we continue to be proud to support them. 53: Campus For African School Of Economics At Zanzibar Charter City The ACX grant helped launch the first research center at the African School of Economics-Zanzibar, which is a main anchor of the Fumba Town charter city project in Zanzibar. This research center is called the Africa Urban Lab (AUL), focused on rapid urbanization across Africa. The AUL launched its first Diploma program in Urban Development with 38 students in our first cohort (now graduated!), including mayors, and deputy mayor, a director of a national Ministry of urban development, and many others. We published our research framing papers for the AUL's research agenda. We raised funding to launch an Urban Expansion Program that's now selecting 15 African cities to support in implementing urban expansion planning on the urban periphery. We held two Public Talks by renowned cities scholars and practitioners. We received additional funding from Emergent Ventures and from the Templeton Foundation. And we've partnered with 8 universities across the region, and with one of these universities (Ardhi) we'll be working with them to update their urban planning and urban economics curriculum (amplifying AUL's impact beyond our own organization). A longer update from end of 2024 is here: https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights) 54: Online Training Program For Health Workers In Developing Countries To date, over 11,000 health workers in Nigeria have completed our course on basic, life-saving newborn care. ACX funding was catalytic for helping us secure government approvals and complete an evaluation of the impact of our training on health workers' clinical practices. The evaluation shows that birth attendants provide better birth care after taking the course. We fed the evaluation results into an updated model, which suggests the program is 24 times more cost-effective than direct cash transfers (a widely recognized benchmark for cost-effectiveness). The program is likely to become even more cost-effective as we scale up. https://healthlearn.org/blog/updated-impact-model 55: Smartphone Pupillometry To Diagnose Neurological Conditions We have continued to expand our work in the smartphone pupillometry space and the development of our application, PupilScreen (https://www.apertur.ai/). We have expanded our pilot/research program to include new sites across the United States (Missouri, New Jersey, Kentucky, USAC racing, PitFit driver performance training in Indiana) and the world (Nepal, Taiwan, South Africa). We continue to publish at the leading edge of the pupillometry literature as well looking at concussion (https://neuro.jmir.org/2024/1/e58398 and https://pubmed.ncbi.nlm.nih.gov/39682632/), cerebral vasospasm (https://pubmed.ncbi.nlm.nih.gov/39128501/), and stroke (https://pubmed.ncbi.nlm.nih.gov/39674431/ and https://pubmed.ncbi.nlm.nih.gov/39561861/). Currently, we are raising a $3 million seed round via a SAFE to fund the expansion of our work into the hands of healthcare workers and the general public. We will first focus on traumatic brain injury for clinical use and develop a neuro-monitoring wellness application utilizing our technology for the general public. They add: “We would welcome connections to anyone that you think might be interested in supporting our work further by investing in our $3M seed round of funding.” 56: Mike Saint-Antoine’s Biology Tutorial Videos Since getting the grant, I've continued to make Youtube tutorials as planned. One series that I'm especially proud of is about how to make a neural network in the Julia programming language completely from scratch, with no imports, up to the point of being able to solve MNIST (https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE). Also, a college student in Pakistan came across my videos and invited me to give a virtual Zoom-lecture to her department, so I ended up teaching a 6-hour "Python-for-Biologists" workshop to more than a hundred college students in Pakistan over Zoom. So that was pretty awesome. Also, lately I've been teaching some in-person classes too, mostly at Fractal University in NYC, and I also recently organized a day-long, in-person Beginner Python class for people in my local area (Philly suburbs) who wanted to learn some basic programming. I'm having a lot of fun with this project, and am grateful to Scott and the grant funders for their generosity! 57: Conceptual Boundaries Workshop On AI Safety The workshop was completed successfully; you can read a writeup here. 58: Apart Research To Incubate AI Safety Scientists No update received, but they have a public website, and you can see their impact metrics here. They seem to be in urgent need of more funding. 59: Primer On How To Achieve Political Change No update received and I can’t find anything about this. 60: Research IVF Clinic Success Rates We've built a predictive model that estimates the odds of having a child at different IVF clinics across the country while controlling for factors like patient age and infertility differences that can falsely make some clinics look better than others. We found that an average patient can increase their odds of having a kid by 43% just by going to a top 10% clinic. Patients unlucky enough to go to a bottom 10% clinic will reduce their odds of having a kid by 40%. Next month, we're adding several more clinics, 2023 data, additional procedural controls, and donor/gestational carrier models, which should push our accuracy beyond state-of-the-art models in this space and better isolate clinic impact on patient outcomes. We've launched ivf.clinic, a website where patients can access personalized IVF reports and browse our clinic rankings (though we're still squashing some bugs). Currently, we're expanding our research to include comprehensive insurance coverage and pricing data across clinics nationwide. If anyone has insights on automating the collection of IVF clinic pricing information, I'd love to hear from you at scelarek@gmail.com. 61: Replicate Study On Brain Wave Synchronization For Speeding Learning We have acquired and configured the OpenBCI UltraCortex Mark IV 8-channel EEG headset and a clinical-grade Biosemi 32-channel EEG system. We’ve implemented the required components for the experimental pipeline (computing alpha from EEG, flashing bright white light, presenting stimulus images). We are currently putting them together into a single system that we’ll use to collect the data from several participants. We are aiming to gather data on several participants in late June / early July and complete the pilot of the replication in July 2025. If you’d like to be a participant in the study, [they might announce a link once they have it]. 62: Advocate Repeal Of Interstate Runaway Compact No update received and I can’t find anything about this. 63: Animal Welfare (Especially Fish) In Turkiye Future For Fish asks companies to sign up to FFF's fish welfare commitment, which requires producers to certify their facilities and enforce specific standards for stocking density and harvest. Luckyfish, İlknak, Divan (35 restaurants, 17 hotels) and NG Hotels (5 hotels) have signed and published FFF's fish welfare commitment with İlknak publishing the commitment on their website. Kılıç published its first sustainability report detailing fish welfare policies, including enforcing a maximum stocking density of 10 kg/m³ and confirmation of electrical stunning practices. Longer version with some caveats: https://manifund.org/projects/improving-fish-w From the longer document, these commitments involve things like reducing overcrowding, or stunning fish before killing them. Over 30 million fish were affected just from their single largest commitment, and they say 100 fish are helped per dollar spent. 64: More Georgism Advocacy Lars and Will used the 2021 grant to co-found ValueBase. Will remained with the company, and Lars left to do advocacy work at the Center For Land Economics. Here’s their summary of how things are going: [Our] organization transitioned leadership with Greg Miller, a former Program Analyst at the US Department of Housing and Urban Development, and Lars Doucet, author of Land is A Big Deal and Co-Founder of Valuebase, working full time and Joe Caissie stepping aside. This transition happened naturally as the next career transition for each respective person. Since then, progress has been made on pushing forward legislation. Maryland had two bills introduced to give Baltimore and counties the ability to enact split-rate taxes. One of the bills passed the state senate and would allow Baltimore to enact land value taxes within one mile of rail corridors–this contains 50% of Baltimore’s land value. However, the legislative session ended. We expect the bill to revive next session. The Center for Land Economics has been actively working to help efforts to get this bill passed the line. At the same time, we have uncovered systematic undervaluing of vacant land in assessments. We are writing a report on the assessment issues in Maryland with actionable steps to resolve them.

Inline links: Codebuff, website, Substack, survives, a public Substack, in Rwanda, Growth Summit, Stanford Social Innovation Review, ProMarket, Global Prosperity Institute, Saharon, this page, eadbatteries.substack.com, here, here, a post profiling the Far Out Initiative here, https://tornyol.com, a public website, https://chemrxiv.org/engage/chemrxiv/article-details/67e4cde76dde43c9084d88b7, https://blueprintbiosecurity.org/introducing-project-air/, our way, connect them directly to me, www.Spartacus.app, X, Substack, here, https://www.goodancestors.org.au/ai-safety, https://www.australiansforaisafety.com.au/, https://www.aul.city/blog/reflecting-on-africa-urban-lab-s-inaugural-year-2024-highlights, https://healthlearn.org/blog/updated-impact-model, https://www.youtube.com/playlist?list=PLWVKUEZ25V97tNULapu07DhWv6_W4NfpE, here, public website, here, in urgent need, https://manifund.org/projects/improving-fish-w

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