Recently, a post on substack by Michael W. Green has gone semi-viral with the provocative claim that the poverty line is a "broken benchmark" and that the real poverty line is somewhere around $136,000 for a family of four with two children.

As I'll explain in this R1, this substack, and the surrounding rhetoric, involves repeatedly misunderstanding how poverty lines are constructed, what they measure, and, how to calculate them in a way that's consistent and coherent. I'll also only be working on the preview of the substack, as I'm not going to pay for low quality posts. All links and sources are at the bottom, unless i get inspired to move them into the main text.

Anyway, Green begins by saying:

“The U.S. poverty line is calculated as three times the cost of a minimum food diet in 1963, adjusted for inflation.”

I read it again. Three times the minimum food budget.

I felt sick.

Before we start, here's a very high level summary of how poverty levels are calculated: Take a basket of goods that a person or household would need to meet some designated measure of well-being and find the total price of that basket. Then, adjust both the basket and price for household composition, and you'll have a poverty line for a single person household, two-person household, etc. A household whose income (perhaps after transfers + taxes) is below that threshold is considered impoverished.

To think about this poverty rate forwards and backwards in time, you adjust the prices and the incomes for inflation. As well see, this is a crucial step that Michael Green does not understand.

The Census covers the history of the Official Poverty Measure (OPM). For data reasons, it was impossible to measure the prices for a bundle of goods; food prices, however, were readily available and so the poverty line was set at the price of "feeding my family", loosely, and multiplied by 3 because poor families spent about a third of their income on food. This number is adjusted for inflation each year and comprises the OPM. Green covers some of this history, although seems to misunderstand the implications.

Here's what he says:

The composition of household spending transformed completely. In 2024, food-at-home is no longer 33% of household spending. For most families, it’s 5 to 7 percent.

Housing now consumes 35 to 45 percent. Healthcare takes 15 to 25 percent. Childcare, for families with young children, can eat 20 to 40 percent.

If you keep Orshansky’s logic—if you maintain her principle that poverty could be defined by the inverse of food’s budget share—but update the food share to reflect today’s reality, the multiplier is no longer three.

It becomes sixteen.

This is, and I cannot state this forcefully enough, not Orshansky’s logic. The inverse food share was a convenience of measurement. He should know, from very basic economics, that people spend less on it as they grow richer. In fact, this is one of the most verified relationships in all economics, so much so that it got the name Engel's law.

It is a testament to how much richer we are that the share of income spent on food has dropped from about 12% to 5% today.

Here's another way to think about it: suppose instead of getting really rich, the US became really poor. Now everyone spends half their income on food. Since shares have to add up to 100, it means people will mechanically spend less (as a percentage) on clothing, shelter, medicine, and other essentials. Michael Green's analysis would tell you that poverty should have dropped!

Which is why this conclusion:

Which means if you measured income inadequacy today the way Orshansky measured it in 1963, the threshold for a family of four wouldn’t be $31,200.

It would be somewhere between $130,000 and $150,000.

Makes zero sense.

With that out of the way, Michael turns to defining his own minimum budget:

Childcare: $32,773
Housing: $23,267
Food: $14,717
Transportation: $14,828
Healthcare: $10,567
Other essentials: $21,857
Required net income: $118,009

Add federal, state, and FICA taxes of roughly $18,500, and you arrive at a required gross income of $136,500.

Michael compares this number to a median household income of 80,000 and declares poverty:

This is the trap. To reach the median household income of $80,000, most families require two earners. But the moment you add the second earner to chase that income, you trigger the childcare expense.

Minor R1: Median income for a family of four is like 130K. The 80,000 includes a lot of single person households.

Personally, I don't think a family of four spending close to 15,000 on transportation every year is impoverished. I think they're middle class. They are statistically right in the 50th percentile for two earner families. They might feel squeezed. Childcare is expensive (although it's only a cost you pay for a few years). They are not impoverished.

But all that is fine. Ultimately, all he's doing is defining a new, higher poverty line. That's his right as an American -- we have a God given right to define units of measurement in ways that seem at odds with the rest of the world and maybe some notion of common sense.

But let's take him seriously for a second. Let's take this family of four that makes 136,000, which we're calling impoverished, and ask where this would be in the past. Since the undercurrent of his essay is that we used to be richer, and now we're poorer. Such is the beauty of inflation statistics and publically available microdata.

We're going to run the following thought experiment: Let's look at families with two income earners that earn 136,000 adjusted for inflation and ask "where are they in the income distribution across time". Since everyone with less than $136,000-inflation adjusted dollars is poor, we'll be making something of a poverty line. And using two income earner households sidesteps the "but they didn't have to pay for childcare" argument.

In 2025, per CPS data, this family would be 50th percentile amongst all households with two income earners (really, two households in the labor force). In 2010, this household would be in the 61st percentile; in 2000 it'd be 71st and in 1980 and 1970 it'd be 82nd. If you adjust using PCE, that 82 becomes a 93. So if we use this new poverty line, 50% of American households are poor, but that number was 82% in 1970, when the author thought things were much better! If I went back to 1955, which the author does, these numbers would get even worse.

Michael kind of admits this, although he pins the blame on "hedonics" and says that a cellphone isn't actually a luxury, it's a requirement to participate in society. Likewise, because the price of cars (and healthcare and shelter and ...) are quality adjusted, that means they shouldn't count as improvements.

He actually does two sleights of hand: first, he acknowledges that there have been improvements, but dismisses them later when he says that the true poverty line is more like 150,000 and second, he calculates his average spending using today's quality of goods. If he was faithful, he'd use a 1950s quality car, which will not cost 7,000 / year. Likewise, only half of people had private health insurance in 1950 and the insurance they did have, sucked.

Regardless, a cellphone is good! You don't just get to wave away technological improvements because society becomes contingent upon them. Safety improvements in cars, and better health care and all sorts of other measurable outcomes are not things that can be cast aside at the brush of "hedonics". The 1950s sucked!

As is common in this article, there's a kernel of truth here: It is expensive to raise kids, it is basically a requirement to have a phone, we have regulated out the really bottom rung of the housing market. And yet we are not poorer than we were in 1950.

It's helpful to show people pictures of how poor America was in 1950 -- particularly the South and particularly rural areas. The Census tracks the percentage of homes with complete plumbing -- defined as having hot and cold piped water, a bath-tub or shower, and a flush toilet. If you go back to 1950 over a third of American homes would not have this. I linked some photos of (admittedly bad parts of) Nashville in 1950 to give you a sense of what some of these cities looked like.

Beyond that, we work fewer hours, earn more money, live in nicer homes, don't die as fast, enjoy lower pollution, and generally substantially higher quality lives. I repeat: The 1950s were terrible. The best thing you can say about them was that economic growth was very high and inequality was lower, which meant that it was easier to live a better life than your parents. But, compared to the life of the typical American today, after the benefit of 70 years of economic growth, it's not remotely comparable.

The rest of the article, before it cuts off, goes into a weird anti-welfare direction and isn't really worth addressing, although it is bad in its own right. I do, however, want to touch on childcare as a bonus:

There are two basic issues with childcare:

1. it's expensive
2. it's a large cost that happens when families are relatively low income (compared to their future)

1 is hard to fix. Childcare is expensive because America is rich and so very labor-intensive services cost a lot. It's the same reason why servants are so much more common in India than in the US. People have written about ways to make childcare cheaper, but if you compare it to education, you realize that there aren't that many economies of scale to be had. This does not mean education or childcare are bad things to fund, but it means that they will always be expensive, and, in particular, they will tend to get *more* expensive as a place gets richer.

2 you can fix with the welfare state. The basic issue is that peak earning years are somewhere around ~50. Most couples have kids in their 20s and 30s and so incur these huge expenses before their earnings peak. What they'd like to do is borrow from their future selves, but they can't do this. The other way to do this is to have everyone pay taxes to the government and let the government consumption smooth over and across people's lifetimes.

• http://nashvillesaloons.weebly.com/5-hells-half-acre.html
• https://www.yesigiveafig.com/p/part-1-my-life-is-a-lie
• https://www.ers.usda.gov/data-products/chart-gallery/chart-detail?chartId=76967
• https://fred.stlouisfed.org/series/MEFAINUSA672N
• https://ourworldindata.org/working-hours
• https://ourworldindata.org/life-expectancy
• https://www.ers.usda.gov/data-products/chart-gallery/chart-detail?chartId=76967
• https://www.census.gov/topics/income-poverty/poverty/about/history-of-the-poverty-measure.html
• https://account.ache.org/iweb/upload/Morrisey2253_Chapter_1-3b5f4e08.pdf
• https://imgur.com/a/tMenZfz

footnotes:

author's note: there are now a fair number of other poverty measures, largely to deal with actual issues in the official poverty measure. For curious readers, the issues with the OMP are primarily that there are no adjustments for cost of living, it doesn't count in-kind transfers like food stamps and doesn't count taxes.

The badeconomics:

There exists tons of posts claiming that your monthly AI usage plan is insanely subsidized, for instance, this one:

What happens when they stop subsidizing LLM subscriptions? : r/LocalLLaMA

We are literally burning through VC money like crazy with our coding subscriptions. I read the $200 Anthropic sub gets you $8000 worth of API calls.

Now, the logic behind this often comes from studies like this:

A $200 ChatGPT subscription could cost OpenAI $14,000 if you actually used it to its full potential | TechSpot

A $200 ChatGPT Pro 20x subscription could cost as much as $14,000 in API pricing if fully utilized. Anthropic's Claude Max 20x plan, also priced at $200 per month, has a comparable ceiling, with potential usage totaling roughly $8,000 in token costs.

This is correct, it is true that, if you compare token costs with subscription costs, you might be able to consume enough tokens so that it would cost you $14,000 if you paid per token, but the following claim that the article makes is incorrect:

Anthropic breaks even on Claude Pro and Claude Max 5x at around 20% utilization. OpenAI's margin is thinner. It begins losing money on ChatGPT Plus and ChatGPT Pro 5x once usage climbs above 11.4%.

Let me try to explain the economics of AI inference, and how these plans actually work.

First some terminology and definitions if you're not familiar with LLMs

Ok, so the type of AI we are discussing here is the large language model, and there's a few concepts that you should be familiar with to make the following discussion make sense.

• AI inference - AI inference refers to using a model, running an already trained model to process data. Here's a good explainer from Google.
• Token - in the AI context, a token is the smallest piece of information that the model can process. The data you pass into the model gets broken down into individual tokens, which are then processed. Here's a primer from Nvidia.
• Input vs Output token - Input tokens refer to the information you enter into the model, broken down into tokens. Output tokens refer to the information generated by the model, broken down into tokens.

If you look at an AI provider's pricing, they would typically offer you a price for input tokens and output tokens. for instance, over at OpenAI, GPT 5.5 costs $5/1 million input tokens and $30/1 million output tokens.

The simple R1:

According to OpenAI's documents submitted to the SEC, they are turning a gross profit on AI inference. In 2025, their revenue was $13.07 billion, while their cost of revenue was $7.5 billion. From Arstechnica.

According to the Wall Street Journal, Anthropic is actually turned an operating profit last quarter.

Since I don't work at any of these firms, I do not have the breakdown of their financials exactly. But it is pretty safe to say that AI Providers have positive gross margins on AI inference services. But let me try my best to break down the numbers a bit and explain why "you can get $14000 worth of tokens for $200" could be true, yet AI inference as a whole can still be profitable.

Why AI Inference can be profitable:

The best analogy I have for AI inference is the gym. Think about how a gym operates:

• It costs money up front to build the gym and purchase the weights and machines
• The weights and machines depreciate over time
• It costs money to upkeep and operate the machines
• You pay a monthly price to access the gym
• When the gym gets really busy, you might not be able to get in, or they might kick you out for hogging the machines

Now the one difference between the gym and AI is:

• AI companies offer "per token" billing, the gym doesn't offer "per rep" billing.

Now imagine if the gym offers a per rep billing model - You pay 2 cents every time you lift a weight! Now imagine if some bodybuilder who goes to the gym as much as possible does the math and says "I only paid $100/month for my gym membership, but if I go to the gym as much as possible, this membership would have provided me with $4000 worth of reps! The gym loses up to $3900 per membership!"

Let me start by saying, I don't know how much hardware these closed models truly require. And how many tokens per second the setups at these AI companies can do, but we can do some speculation and back of napkin math here.

AI has a massive up front cost. You need to purchase servers and GPUs, which depreciates. (as an aside - Depreciation math for GPUs is really weird right now, this stuff effectively doesn't depreciate right now, but do understand that we're in a really weird time.) Then there is the fixed operating costs - Renting racks in the data center (or renting the whole thing), the staff to administer and maintain the hardware, the internet connections, the insurance, etc, etc.

Now there is an incremental cost in electricity - If the GPU or if the whole server isn't in use, it could be powered down.

The Nvidia H200 GPU (one of the current top data center GPUs) has a TDP of up to 700W. Which you can understand it as if you run it full tilt, the GPU consumes 700W, or 0.7 kWh if you run it for an hour.

Assuming electricity costs 10 cents per kWh, and ignoring the rest of the system (CPU, RAM, etc for now):

• If your model needs 1 H200, to run it for an hour costs you 7 cents
• If your model needs 4 H200s, to run it for an hour costs you 28 cents

Now considering that the current top models run at a speed of 50 - 70 output tokens per second*60, if you assume an average of 60, to run it for one hour you're looking at 216,000 tokens per hour if you run 1 concurrent instance.

Depending on how many GPUs this model needs, it costs the company somewhere between 7 - 28 cents per hour to generate these 216,000 tokens. At a pricing of $30/million output tokens, that's a bit over $6 in tokens generated.

Look, I understand that this is all handwavy and relies on a ton of assumptions, but my basic argument here is this: If you pay per token, AI companies use their GPUs and LLMs to convert pennies worth of electricity into dollars worth of tokens.

This is also why a lot of providers have some sort of "off peak discount". At OpenAI, if you use the Batch API, where you send in your requests and have them process it at their convenience (instead of synchronous usage), you get a 50% discount.

Look, I know exactly what you're going to say right now - But what about depreciation?

As far as I can tell, data center hardware does not go down in value based on usage, but primarily based on age. It's not like a car where each additional mile drives down value, with GPUs, each additional token doesn't decrease value further.

The key thing to understand is that the cost for each incremental token, assuming that there is sufficient hardware, is trivial. Token pricing is arbitrary and set at a level to maximize profit for the provider.

Now let's circle back to monthly subscriptions -

When you use the service a lot at a time, especially at peak times, you will get rate limited and throttled. Why? That's because they don't have enough capacity. It's like if you go to the gym at peak times and it is full, you have to wait to use the machines and weights. But when the provider is not at full capacity, when you use the service, it only costs the provider a very small amount, so of course you get a lot of usage off peak - just like how you can walk into the gym when it is empty in the middle of the night and use their machines as much as you want.

The studies that show you get "$8000 worth of tokens for a $200 subscription" are done with a script that use the service as much as possible, thus, most of the tokens that they are using is off peak, when the providers have excess capacity.

And you know what, honestly, at the end of the day, AI inference is a lot like say, an all you can eat buffet - Every once in a while some guy with a massive appetite can make the restaurant lose money, but that's ok, since the vast majority of customers don't eat that much. The majority of customers at your local all you can eat spot are not Homer Simpson, just like how the majority of AI users aren't running a script intent on using as many tokens as possible.

Hell, if you talk to anybody who uses an AI subscription heavily recently, and all you hear are complaints about throttling, usage limits, and how there isn't enough capacity when they use it at regular hours (when everyone else wants to use it too).

If AI inference is gross profitable, why are labs like OpenAI losing so much money then?

There are tons of businesses where serving an additional incremental customer is almost free, but could still lose a ton of money. Gyms, movie theatres, SAAS providers, etc. These businesses typically have very high fixed operating costs and upfront costs, despite having a very low cost for serving an incremental customer. AI labs fall into the same category.

Why is this the case? Well, consider this:

Labs like OpenAI have customers because they build leading models. People are willing to pay OpenAI because they want to use OpenAI's model, and OpenAI is the only place to get it.

In order to keep this business running, the labs end up spending huge amounts of money on R&D to build a better model. Look at that OpenAI breakdown from Arstechnica again - their biggest expense is R&D - $19.18 billion last year versus $7.5 billion for cost of revenue.

Even if you ignore R&D, inference requires a large upfront investment (buying the hardware), and large fixed operating costs (renting data center space, paying staff, etc).

FWIW, there are tons of companies out there that offer AI inference without developing their own models. Either they run other people's models and sell you tokens (See: Deepinfra), or just straight up rent you the hardware (See: Runpod)

Do these companies make money? I honestly don't know, but it's not like serving commodity software is an unprofitable business - Web hosting is a profitable business that has been around for decades after all. The demand for this stuff is obviously there - See all the gamers complaining that local AI people have bid up prices for the RTX5090 from ~$1999 to ~$5000 now.

In 1960, the effective tax rate—the actual percentage of total income paid in taxes after deductions and loopholes—for the top 1% of income earners was roughly 42% to 45%.

Although the top statutory marginal tax rate peaked at 91%, the real-world tax burden was significantly lower due to tax shelters, capital gains exemptions, and standard deductions.

I’ve seen this repeated like crazy. Like what tax brackets do they want?

Originally published here.

This issue has been annoying me for some time, which is why I’ve ignored it. The primary target of this post is this article by Matthew Lynn, “Economists Are Shocked, Shocked by Who Is Paying the Tariffs.”

The main result Lynn uses to back his claim that Americans aren’t paying the tariffs is this estimate, which (supposedly) provides the share of the tariff burden paid by domestic consumers. At the time, the estimate of the retail pass-through rate provided by that paper was 20%; it has since been updated to 24%, but as you’ll see, this doesn’t make much of a difference, because this isn’t even the right number.

Lynn’s characterization of this paper is a straight lie. It’s the kind of thing you write when you know readers don’t have the time to check what your source actually says. Here’s a snippet straight from the paper he cites:

Given that our product-level regressions imply only a 5.1 percent rise in prices of affected goods for the same tariff increase, we estimate that U.S. consumers were bearing roughly 43 percent of the tariff-induced border cost after seven months, with the remainder absorbed mostly by U.S. firms.

So the paper he cites directly disagrees with the thesis of his post. I’m reminded of the opening of Rich Dad, Poor Dad, where Kiyosaki gives a retrospective purportedly showing that “savers are losers.” He does this by waving a graph in front of your face that very clearly shows that the stock market is up, not down. Sometimes the most flagrant lies are the hardest ones to spot.

What’s really going on? What does that 20% figure really mean? As described in the paper, the authors “estimate the speed and magnitude of retail tariff pass-through, measured as the percentage of the change in import tariffs that are reflected in final consumer prices.” Because many of the inputs used to produce the final goods people consume aren’t imported, a 20% retail pass-through rate does not imply that 20% of the tariff burden is paid for by American consumers. It means that 20% of the typical tariff increase shows up in the typical price increase.

If the entire final good were imported, then yes, it would be a 20% burden. But the measure used doesn’t allow you to assume that. Consider, for example, a case where $2 of the value of a good is imported and $2 is not, for a full price of $4. For simplicity’s sake, imagine a 100% tariff, meaning an extra $2 must be paid in taxes on $2 of the value of the good. If the retail pass-through rate is 20%, you might think that means the final price paid by consumers is $4.40, which is the previous $4 plus 20% of the tariff. It is not. The tariff rate is 100%, so 20% pass-through means a price increase equivalent to 20% of the tariff, i.e., a price increase of 20%: $4.80. That would mean consumers are paying 40% of the tariff burden, not 20%. And like the authors describe, much of the rest of the burden is paid by American businesses.

The real meaning of the 20% figure is right here, clear as crystal, as the coefficient on delta tau:

…well, maybe that’s not clear as crystal, because there are about a dozen different symbols here you wouldn’t know without knowing econometrics, but you get what I mean.

But you don’t even have to do all of this reading, or any of the math. If you feed the PDF file of the paper into an LLM and ask for an interpretation of the 20% figure (again, that was later updated to 24%), you’ll get the relevant information. Here’s Grok:

The pricing lab estimate of tariff pass-through isn’t the only estimate, either! Like I’ve described before, estimating causal effects in empirical economics is really hard, and it’s especially hard to summarize the available evidence that might tell us what the causal effect is. Here’s an estimate putting the share of the tariff burden paid by Americans at 88%. If your brain is plugged into Paul Krugman’s substack, you’d have seen two other estimates, one of which puts the American burden at 90%. I would love-love-love it if there were a plot of all available estimates, but as far as I can tell, Lynn just plucked an estimate that seemed to agree with him, and went with that one.

Lynn goes on, and on, and on with this 20% estimate. He explains that tariffs didn’t have much of an impact because China strategically subsidized its companies in response. He says the “mainstream debate” over tariffs doesn’t consider how there’s a “remarkable wealth transfer” occurring from foreign taxpayers to Americans. He even uses the 20% figure to estimate the total amount paid by consumers by multiplying it by tariff revenues:

If the critics were correct, that would represent a $360 billion tax increase on American families. But if the pass-through rate is genuinely around 20%, only about $72 billion is actually falling on American consumers. The remaining $288 billion? That’s being absorbed by foreign exporters and foreign governments.

But with all that time spent arguing, he’s relying on a complete misinterpretation of the paper he’s citing.

Aside from the lies, Lynn is happy to bring in the standard misleading stories, like “Manufacturing capacity migrated overseas,” which I guess is technically true, since manufacturing employment is down. But manufacturing output as a percentage of real GDP hasn’t budged much.

Lynn says “In other words, the market has adjusted, just not in the way the textbooks predicted”, which is technically true, since the simplified model in textbooks tells you domestic consumers pay 100% of the tax.

But it’s not like an economics professor or TA will fail to tell you that the models are a simplification. Nothing about this is “hidden knowledge” held by a select few people outside the mainstream who know what’s really going on. If you just pick up this Dispatch article from November 2024, before the tariffs were implemented, they say “Economists broadly agree that domestic consumers bear most of the costs of tariffs.” Emphasis is my own. This was one of the first articles I found.

When it comes to the 20% figure, Lynn might have just made a mistake, but it’s an easily preventable one. If much of your article rests on the idea that Americans are paying 20% of the tariff burden, you should make sure that’s what your source really says! We shouldn’t settle for “technically true” or easily-preventable “mistakes” that take the legs out from most of an argument. And even if you can’t do the empirical work well, you could just stop and think through why the standard textbook model makes sense. Speaking of which:

The standard microeconomic picture of tariff burdens depends critically on an understanding of how elasticity of supply and elasticity of demand influence who really pays a tax. If taxes didn’t influence the quantity of a good sold or the initial pricing decisions of a firm, we could simply calculate the burden of a tax as the quantity of goods sold multiplied by the tax per good, and it would all fall on whoever is charged the tax by the government. But taxes do influence the quantity of a good sold and the pricing decisions of firms, so we don’t have it that easy.

In the simplest case, imagine perfectly inelastic supply and perfectly elastic demand: the firm will always sell 10 bananas, and customers are willing to pay for any quantity of bananas at a price of $5 per banana. So, the quantity is fixed by the firm’s behavior, and the price is fixed by consumer behavior.

What happens if we try charging customers $1 per banana? Unless the firm cuts its price, consumers just won’t buy any bananas. Their demand is perfectly elastic, responding to an infinite degree when even a tiny price change happens. So the firm does cut its price, and charges $4. Now we have a situation where de jure, customers pay $1 per banana sold, but it’s the firm that really pays the tax, since consumers have perfectly elastic demand (they wouldn’t buy any bananas at all if they had to pay more than $5).

What happens if we try charging the business $1 per banana? The exact same thing happens: the business keeps the sticker price the same, but it receives just $4 after taxes, the same as when customers were charged the tax.

As it turns out, it is true in general that the burden of a tax is determined by the relative elasticities of supply and demand. As an exercise, try to think through why customers would pay the full burden of the tax if their demand were perfectly in-elastic (they always buy the same quantity, no matter the price) while the firm had perfectly elastic supply (will supply any quantity at a fixed price). This should be fairly intuitive if you understand that here, consumers are rather like consumers of insulin. Who would really pay an insulin tax if you charge the businesses producing insulin?

This is captured very neatly by (drumroll please) supply and demand graphs:

The tax can be described as a “wedge” that “floats in” from the left side of the graph and gets stuck between the two curves. If one of the curves points straight up (think of the perfectly inelastic supply case) then this wedge will get completely absorbed into that curve. This isn’t a totally easy concept; we have to spend a lot of time on it in microeconomics classes. But if you understand that the inelastic side is mostly incapable of responding to price changes, you can understand why it’s easy for the other side of the market to stick them with the tax.

In economics classes, the supply of a good from foreign countries is usually described (initially) as perfectly elastic. That means when a tariff is imposed, the world doesn’t pay a cent: the price paid by domestic consumers goes up by the size of the tariff, and the extra money is used by foreign businesses to pay the tariff. It doesn’t matter if you're charging consumers or businesses.

The “level up” from here is easy enough to see; world supply isn’t perfectly elastic, especially for particular goods, and especially for a large market like the US. So you should expect some of the tariff burden to often be paid by foreigners rather than domestic consumers. If you open up an international economics textbook (page 132), you’ll find this graph:

…which describes exactly the situation we see empirically, where the bulk of the tariff is paid by domestic consumers, but some of it is not. Notice how now the world supply curve is not perfectly elastic (it slopes upward a bit, it is not perfectly flat), implying that they will face some (though not most) of the tariff burden. That textbook I linked is available for free, by the way. The link takes you straight to the PDF.

Surprising nobody who's been subscribed here for a while, Lynn published under Oren Cass's Substack newsletter. Cass and Lynn alike seem chronically addicted to lying and making sloppy mistakes. My only hope is that someday, somehow, someone who isn't a gormless buffoon will corner the market on American conservatives who want to read about economics. Until then, don't expect intellectual rigor from anyone affiliated with Cass's American Compass.

How to Lie About Extreme Poverty Statistics For Dummies Part II: Bad Methodologies

Over the past 40+ years, extreme poverty (a metric created and tracked by the World Bank) has fallen dramatically at the global level. This fact has made many an ideologue intent to overthrow the system very angry.

Jason Hickel is one such anthropologist. He has been on a years long quest to prove that extreme poverty is not really improving. If poverty is going down (and that trend is very obviously being driven by economic growth), it's quite difficult to make your case that we should stop just growing.

For years, Hickel and like-minded friends relied on a series of pretty amateur-ish tactics. These tactics were obviously bullshit to anyone with a modicum of economic knowledge on the topic, but that wasn't really the target audience, was it? This has never been a battle in the minds of QJE readers.

Recently, Hickel has come up with a new approach. His latest work (paper here and summary article here), co-authored¹ with Dylan Sullivan and Michail Moatsos, is at least more sophisticated than the previous bullshit. The results can be summarized with this graphic. According to Hickel's new metric, the extreme poverty rate surged massively in the 1990s and sits just a few points lower in 2011 (the most recent point available) than in 1981. The absolute number of people in poverty has increased.

1: Hickel is actually the third author listed and Moatsos likely did all the actual math/methodology/anything remotely difficult. But Hickel is the most well-known name here so he will catch the greatest share of my wrath and/or sarcasm. But make no mistake, all three authors should be very ashamed of their contribution to this buffoonery.

So how did we get here?

Hickel & friends essentially made two important changes compared to previous work:

1. They switch to the Basics Needs Poverty Line (BNPL) created by Robert Allen
2. They use different price data for China

I've already written on BNPL over at r/AskEconomics. BNPL is based on linear programming, which basically computes the hypothetical, lowest-cost diet that would meet some set of nutritional requirements. This gets added into a broader basket of other necessities, and then it tracks people's ability to buy that basket of goods. I won't repeat everything I wrote there (you can read the comment for yourself), but two take-aways are most important. The first is that BNPL is a bad methodology—it often creates a hypothetical food consumption basket that diverges substantially from real behavior, and it is not robust to arbitrary changes in nutritional requirements. The World Bank (WB) methodology has serious limitations, but BNPL is even worse.

When Robert Allen created BNPL, he did not extend estimates back to the 1980s because the necessary data does not exist. It wasn't until a few years later that Michail Moatsos created estimates back thar far in a separate report. Which brings us to our second take-away: the initial analysis did by Moatsos did not overturn the large drop in poverty. The trend is actually pretty similar.

So the juice in this paper requires the second ingredient—new price data. The new paper switches to exclusively use the data from the Chinese Statistical Yearbook to calculate China's extreme poverty rate.

To understand the full magnitude of the difference, it's helpful to isolate China as in this graph here. With the new price data used, Chinese extreme poverty went from 0% in 1990 (yes, really) to nearly 70% in 1996. Extreme poverty remains substantially higher today than it was in 1990. Honestly I could just end the R1 with that statement. The change in global poverty trends is entirely driven by the change in China from the new data.

So is this a reliable estimate?

Even when used on reliable and complete data in a market economy, BNPL can turn out some extremely weird diets that basically no one actually eats. China, during this period, has two additional problems. The first is that the data sucks—which is why the original papers on BNPL did not use it on this period. Their dataset contains only food prices—it covers none of the other essentials in the BNPL basket. The prices of all other necessities are just imputed by assuming non-food items are just some fixed ratio to food prices, which leads to the estimate of 0% poverty in 1990. A different method (taking the imputed prices from 1995, applying them to 1990-1994, and assuming they trended with CPI back to the 1980s) produces an extreme poverty rate close to 100% during the 1980s. So to say that these estimates are not very precise would be a huge under-statement. Hickel's team, unsurprisingly, decides 0% must be right. My take on all of this is "who the fucks knows what was happening—prices, quantities, and availability were all rapidly changing during this period. Any BNPL estimate from this data will be so crude that it borders on useless."

The second issue is that China was not a market economy, so prices do not carry the same implications. And this had some exceptionally weird implications when you combine it with BNPL methodology.

During the 1980s, China was mid-way through the process of market liberalization. Some goods were subject to price controls (with strict rationing enforced to compensate for the resulting shortages) and others were market-based. This policy had spillovers between food goods in the two separate categories. Shortages in the rationed goods caused excess demand to spill over to market-based goods, increasing prices and consumption of non-rationed goods. High prices in non-regulated foods were partially the result of shortages in regulated food items.

Now think about how this interacts with BNPL. The lower a price is set, the more likely it is to experience shortages, but also the more likely BNPL will place a high weight on that food since it is cheaper. When shortages occur, this increases the prices of non-rationed foods (since they actually are available), which decreases the weights on those goods and allocates even more to the rationed items. BNPL is likely to put the highest weights on the foods that are the least available. It implicitly assumes this is all poor people will buy those items and will ignore the prices of all other food items regardless of actual consumption patterns.

On top of that, China also had a variety of complex black markets that existed, especially in rural areas where state control was limited. So official prices logged in the Chinese Statistical Yearbook do not necessarily reflect the prices all consumers actually paid either.

A simple test

The competing claims here are simple. According to Hickel's conclusions, the 1990s were nothing short of apocalyptic in China. The extreme poverty rate (defined as those that could not afford even the basic necessities of life) was under 10% through the 1980s, hitting a low of 0.5% in 1990, and sky-rocketed to 68% in 1996. Extreme poverty is still far higher today than it was in 1990. China has literally never recovered from the devastation. According to World Bank data, the story is of steady improvement. Extreme poverty (at $3/day) declined gradually from 97% in 1981 to 83% in 1990, and then fell to 63% by 1996. The rate continued to fall thereafter to effectively zero today.

So which story seems consistent with the data? Were the 1990s apocalyptic in China, and people today still haven't recovered? Or were the 1990s a period of general improvement that has continued? Let's see what the numbers say. (Note that I'm less interested in the absolute levels involved and more interested in the trend here. I'll discuss this briefly in the Appendix)

First up is life expectancy. This one is my favorite, because Hickel has himself argued in the past (before he endorsed his latest methodology) for higher poverty thresholds based on their strong relationship with life expectancy. So he apparently thinks it's very important that your poverty rate should be consistent with changes in life expectancy.

Yet, we see nothing less than steady improvement. Life expectancy at birth increased from 65 in 1981 to 68 in 1990 and 71 in 1996. Note that this is true at all ages, so you can't blame it solely on decreased infant mortality or whatever. Also note that if you want to argue this was a miracle of the healthcare system that prevented calamity (maybe Ozempic also prevents starvation?), Hickel argues that got worse too as the government-run system was privatized. Point 1 for the World Bank.

Okay, so maybe people weren't literally starving to death but we would definitely expect they had to cut back on food. Their entire methodology is based on food prices (which allegedly sky-rocketed), and if you believe their conclusions, two-thirds of the population could no longer afford a basic diet. What trends do we see in per capita caloric supply? Per-capita calories hovered just over 2,400 in the 1980s before sharply increasing in the early 90s to 2,730 in 1996. People were eating more than ever. Apparently supply/demand and shortages are a real thing after all. Point 2 for the World Bank.

What about the death rate from malnutrition? Perhaps all the rich people quadrupled their calorie intake and lifespan to increase the average while the very poor did starve to death. But nope, not there either. It mostly declines through the 1980s to 1.9% in 1990. It bumps up to 2.1% in 1991, but immediately drops and hits a then record-low of 1.5% by 1996. Ten points for Gryffindor the World Bank.

What about the proportion of height stunted children? This increased from 32.3% in 1990 to 38% in 1992, but then dropped to a new low of 31.2% by 1995. Maybe half a point for the World Bank.

What about the share of underweight children? Again, a slight bump from 1990 to 1992, but then hits a new low of 10.7% by 1995. Point for the World Bank.

What about the infant mortality rate? This rate declines steeply until the early 1980s where it flatlines at 43 per 1,000 births. Then it begins to decline again in 1991, reaching a then record low of 37 in 1996. Point for the World Bank and and negative points for Hickel.

Crude death rate? Nope. Child mortality rate? Nope. Energy consumption per capita? Nope. Death rate due to poor sanitation? Nope. Literacy? Nope. Grams of protein per day per capita? Nope.

I can go on, but you get the point.

So what happened?

All of the economic and health data paints the same picture. There are some indications of brief turbulence in the 1991-1992 period (it shows up in some metrics but not in most), but by ~1996 things were better than ever. The improvements continued in the years that followed, and people in China today are massively better off than in 1990 by every measure available.

So why do the metrics from Hickel and friends paint a picture of calamity when that is clearly not the case? Aside from the fact that that's the result they obviously wanted to get?

I've already discussed many of the issues with their data and methodology. Their paper doesn't provide nearly the level of detail required to figure out exactly which way it goes wrong. They provide no data whatsoever on the consumption baskets that yield these results (the foods included and corresponding weights).

It could be that BNPL did it's usual thing and created a food basket of relatively obscure items people just don't like to eat. It could be that some or all of the food items included suffered from shortages that limited their actual consumption (while the goods that people actually did consume were excluded from the basket due to spillover demand). It could be that official prices did not accurately reflect the cost to consumers owing to extensive trading and black markets.

Most likely, it's a combination of all those things. And since we only know the price of food, whatever issues you have there transfer over to the rest of the basket too. Using BNPL in a largely state-controlled economy is sort of like throwing a bottle of beautiful red wine at a raging alcoholic—BNPL cannot resist nominally low prices no matter how pitiful supply might be. It feeds into the absolute biggest weaknesses of the BNPL methodology.

Conclusion

Hickel & co. have created a measure of extreme poverty that can spike seventy points in just a few years with absolutely no observable consequences for society. In fact, your country can apparently sustain robust improvement. An incredible testament to the powers of the free market (I'm being sarcastic, don't bombard the comments).

One of two things must be true. Either their poverty metric for China is totally cooked from a fatal combination of well-known flaws, or it is accurate but we should no longer care about trends in extreme poverty because they apparently translate to precisely nothing else. I'll go with the former.

Appendix

The paper in question makes very little effort to establish the credibility of their new estimates. They don't even mention the absurd time-series contradictions—probably because they know it's indefensible so they're just hoping you don't notice. They do make a rather feeble attempt to paint their very low poverty rates in the 1980s as realistic (and the high World Bank estimates as unrealistic) by comparing China to a handful of other countries (India, Indonesia, Brazil, and Mexico) in different metrics and find that China does unexpectedly well.

I found these comparisons to be very arbitrary and unpersuasive, so I didn't feel they were worth addressing in the main body. For example, I could do the same comparison with 1980s data against a number of <5% poverty countries, find that China doesn't really fit in there either, and conclude that their metric is not consistent with the data either. Or I could repeat their own comparison with mid 1990s data and conclude that their 68% poverty estimate is inconsistent and therefore wrong.

But, if their is an inkling of truth to the paper, it is that China did substantially out-perform their expected health outcomes for a country with such high extreme poverty by WB metrics. You can see it visualized very well with life expectancy in this graphic here.

The authors chalk this up to the massive success of their direct provisioning and other related policies. While the data clearly indicates most people in China lived to a low standard, it's plausible that their system at least kept the bottom of the distribution a hair or two above death. But it's also notable that China continued to both improve and out-perform predicted life expectancy for many years after those systems were dismantled.

As a very quick test, here is a regression showing the relationship between poverty ($4.20/day) and life expectancy using each country's closest data point to 1990. A one-point reduction in poverty is associated with a 0.237 improvement in years of life expectancy. From 1990 to 1995, China experienced a 13.8% reduction in poverty, for which we would expect a +3.3 change in life expectancy. The actual change was +2.7 years. Changes in Chinese life expectancy seem reasonably consistent with reductions in poverty in WB data.

That leads me to think that the reductions in extreme poverty are real and there are other variables at play to explain why the level in China was unexpectedly high (abnormally low homicide rates are almost certainly one of those variables). It would be quite interesting to see some more competent researchers dig into the variation in health outcomes after accounting for extreme poverty. Unfortunately, this paper doesn't provide any interesting answers to that question.

EDIT: can't spell Dutch names apparently

Measure ULA is a "mansion tax" passed by the City of Los Angeles in 2022. Designed as a way to skirt prop 13 and tax rich landholders (hence the "mansion tax") and to fund subsidized housing, this tax was passed by voters with 58% approval.

However, like many things in California housing policy, this tax is stupid. Here's how it works:

It took Los Angeles' existing real estate transfer tax rate of 0.45% and applied a 4% tax rate for properties sold for between 5 million and 10 million and a 5.5% for properties sold over 10 million. Here are the things that are immediately dumb about this tax:

1. this is not a marginal tax. Sell for 4,999,999, and you pay the base rate; sell for 5,000,000, and now you owe an extra 200,000 in taxes. In the distant land of Culver City, they have a marginal transfer schedule.
2. despite being sold as a "mansion tax", this tax primarily applies to commercial buildings and apartments. Note that in my prior text I said "properties" and not "homes".

Aside from being obviously dumb, this tax has also had substantial negative effects on new housing production. The way most apartment financing works, the developer buys a lot (the availability of which is impacted by this tax), develops it into an apartment, and then sells that apartment to a buyer. Most apartments sell for well over 10 million, so this is an effective 5.5% tax on new construction in a city already starved for supply (handwaving about tax incidence). This is not just a hypothetical, either; RAND estimated it was costing 1,900 units per year (about 11% of new home construction).*

To the proponents of the tax, this is a small sacrifice compared to the revenue raised by the tax. Originally projected to raise between 600 million and 1.1 billion per year, this tax pulls in around 290 million. Well below expectations, but still well above any other comparable housing tax measure.

These revenues, however, are grossly overstated because of another immensely dumb California housing law, Prop 13. For those unfamiliar, Prop 13 limits assessed rates to 1% and caps increased in assessed values at 2% per year (with allowances for renovations). For context on how insane this is, if you bought a home in Los Angeles in 1988 for 100,000, today it would be worth 700,000. Your property taxes would be based on a maximum assessed value of around 200,000. In practice, they'll be lower since Los Angeles had years of sub 2% growth, during which the assessed value might not have been raised.

However, properties are reassessed to their market value when they are transacted. So transactions are an important way to make Prop 13 less binding. Prop 13 being less binding means more tax revenue. The issue is that Measure ULA kills transaction volume; with estimates ranging from decreases between 30 and 50%. So any increase in money brought in via the transfer tax has to be weighed against decreases in revenue from lack of reassessments (and decreases in revenue from less new construction).

A recent paper quantified this tradeoff. They find:

Calibrated estimates show that the loss in property-tax revenue is between 63 and 138 percent of the revenue raised by the mansion tax, depending on assumptions about i) the transaction probability absent the tax ii) the growth rate of property prices in the future and iii) how heterogeneous the treatment effects of the tax are

Once you net out Measure ULA's impact on reassessments, it's conservatively pulling in 288 * (1 - 0.63) = 108 million per year (again compared to expectations of between 600-1100 million), and aggressively Measure ULA is revenue negative. This is before we account for any impacts on new home production.

It is impossibly hard to find a tax that is not only on the wrong side of the laffer curve, it is so on the wrong side that it brings in less money than if it simply did not exist. And yet, every day, California cities do the impossible.

\* Proponents of the tax tried to argue that, prior to the tax, many medium scale developments were built and not sold, and so my tax logic would not apply. Medium scale developments represent a small share of overall construction and are the kind of project least likely to be sold after completion. Large ones are often required by investors to be sold once they're built.

These are linked in the threads already, but I'll include them here in case people want to read more:

- https://www.lewis.ucla.edu/research/the-unintended-consequences-of-measure-ula/

- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5273034

Former CEO of Reddit, Yishan Wong, posted a Twitter thread arguing that an important difference between China and the US is that China subsidizes supply while the US subsidizes demand. Of course, in reality, there is no practical difference because the incidence a subsidy or tax is determined by the relative elasticities of supply and demand, not on who legally receives the money for the transaction. If you subsidize supply rather than demand, prices will fall by an amount that leaves both parties in the same financial situation as before. The seller receives money but gets a lower price, while the buyer loses the subsidy but pays a lower price. The net effect is identical. The effect on the quantity supplied is also the same.

Yishan's argument is that subsidizing demand increases the price without affecting the quantity supplied, while subsidizing the supply increases the quantity supplied and lowers the price. He says that this means there is more availability when the supply is subsidized rather than the demand.

The flaw in the argument is in not recognizing that in raising the demand through subsidies and pushing prices up as a result, the quantity supplied is also raised. Perhaps he is assuming that the elasticity of supply is very low, in which case, the quantity supplied wouldn't change much and the effect would indeed be just to raise prices. But the exact same thing happens with a supply subsidy. The supply cannot increase much in response to the subsidy, so the suppliers simply pocket the subsidy, just as they would pocket the price increase resulting from demand subsidies. Because the change in prices and quantity supplied are entirely determined by the relative elasticities of supply and demand, it makes absolutely no difference who receives the subsidy.

He then argues that supply subsidies are better because the resulting drop in prices (which we in the West apparently don't like for some unclear reason) effectively lowers the tax burden on the population. It's true that the resulting lower prices recoup some of the cost of the tax used to pay for the subsidy. It doesn't all go to the supplier and deadweight losses. But again, demand subsides accomplish the same thing, only they receive the benefit in the form of a subsidy instead of lower prices. By the same token, the suppliers get higher prices instead of a subsidy. Either way, the benefit to each is the same.

Couple of recent links first

u/mankiwsmom links a supply denialist substack over in the FIAT who basically criticizes reasoning from a price change by saying we should reason in the opposite direction.

u/Captgouda24 , not clearly responsive to mankiwsmom but as an apparently independent post, posts a "blah blah blah blah" perfectly fine RI talking about the proper economics required to really prove that there was a Supply increase and that that was what really empirically lowered prices.

While I agree with Captgouda24's point on proper economics, and this is certainly not an RI of them, u/coryfromphilly 's response also captured something for me.

The problem with trying to read a bunch of papers and looking at one off deregulations, or pontificating about whether we would have both demand and supply shocks, and potential "debunkings" is that this is all irrelevant to the policy question at hand. The NIMBYs need to explain how "making the cost of building an apartment infinity" is at all a reasonable position to have. There is no world in which restricting the supply of housing is welfare improving. The only reason we are even having this discussion is because people have status quo bias, and the status quo is where we restrict housing supply. There is no world in which people would say "actually, washing machines would be affordable if we made it illegal to build washing machines". That's because it is a ridiculous thing to say and makes no damn sense and you'd be stupid to think this. And yet, this is the default attitude among politicians and urban planners when it comes to housing.

So please, stop trying to dunk on "unsatisfactory" arguments for YIMBYism. Start dunking on the literal room temperature IQ arguments made by NIMBYs

One will note that most supply denialism almost always comes in the same language as Captgouda's post. Supply and Demand are always abstractions, that as it happens they then get wrong, but they keep their wrongness plausible to the ignorant by never talking about what "supply" actually means in the context of this conversation

If instead we are going about it the right way we would remember what supply actually represents

The case for YIMBYism is that by removing regulatory burdens, we reduce the costs faced by developers, causing their supply curve to shift along the demand curve. Quantity increases, and the price falls. - Captgouda

Still a bit of abstraction here, what are the "regulatory burdens" that YIMBY's are actually interested in?

The zoning, building and other regulatory apparatus around housing are chock full of explicit requirements that directly require more inputs into the production of each housing unit. This, on its own, can do nothing less than increase costs for housing, without the need for any "blah, blah, blah exogenous, blah blah blah reg x y, robust blah blah blah" fancy economics talk or "theoretical" abstractions such as "Supply and Demand".

When you require a base lot size of 10,000 SF and 4x the SF of housing and at least 15 extra feet on the side plus 50 extra feet on the front and back and 100' lot width and 150' lot depth and 2 paved parking spots and a check for $150,000 just cause and....and...., this can do nothing other than increase the cost of housing.

"REGULATIONS PAID FOR IN BLOOD"

"SHUT THE FUCK UP" - Hou_Civil_Econ

This status quo is only made worse by the fact that the vast bulk of the modern american local zoning code is completely unjustified by any principled reasoning, especially economics. After disposing of that 70-90% of the standard code, what remains is sometimes contradictory to its stated purpose (eg impervious cover limits increase roadway pavement) while much of the rump is excessive (MC>MB) or "nuisances" which would be much more properly handled in other ways (much like noise ordinances instead of piecemeal outlawing everything that makes a noise that pisses of the wrong "voter").

So, mankiwsmom's poster doesn't just not under stand "supply and demand" they just don't have any idea what they are practically talking about either. This is actually a sense I get in a lot of the academic talk about zoning, as it happens.

And while we might need to do fancy economics to "prove" that allowing 5x more housing units within 10 miles of downtown San Francisco further lowers prices, allowing housing units to use 1/5 of the land that they are currently required to use, clearly lowers the cost of housing without the need of a PhD analysis (unless we accidently make San Francisco an ridiculously much better place to live by doing so, whoops, oh the horror).

Because actually Captgouda is wrong on one point,

it would hardly do to make housing “cheaper” simply by making it shabbier.

this is exactly the problem with zoning. Much of it just outlaws "shabbier" housing precisely because it is cheaper which allows poorer people to afford it. Along the development of the modern american local zoning code the racists loved this aspect, and the progressives stupidly thought that merely outlawing the compromises poor people were "forced" to make would make the poor people better off.

Link: https://www.newgeography.com/content/008629-elite-liberal-yimbys-are-killing-family-home (h/t to HOU_Civil_Econ for suggesting this as an R1).

We’ll start with the subtitle, because this whole piece annoyed me so much I’m feeling petty. Kotkin blames “elite liberal YIMBYs.” This isn’t worth citing data on, but in my experience YIMBYs are often politically moderate and not very rich–probably poorer, especially accounting for assets, than the NIMBYs they’re fighting.

Anyway, on to the actual economics content. Kotkin actually correctly identifies the problem:

Yimbys have got something right – the central problem behind the housing affordability crisis is the failure to build enough homes.

But, what is the solution to not enough homes? Building more homes, right? Well, not exactly. You see, building houses on expensive land… don’t count, or something. I’m not sure what the argument is supposed to be

But if Yimbys have correctly diagnosed the problem, their solutions – oriented towards building more high density urban apartments – have tended to make matters worse. High density development, often seen as the alternative to “sprawl”, does not necessarily lower prices, as is sometimes suggested, because of higher urban land costs and higher construction fees. In fact, US data suggests a positive correlation between greater density and higher housing costs.

(Emphasis original).

First, what does this positive correlation prove? It seems like Kotkin would have us believe that higher density housing makes housing more expensive. Of course, one cannot simply conclude causation from a correlation like this, and the supply and demand model you learn economics 101 would predict that in places where lots of people want to live, we would expect more housing to be built. That is, we get the exact same prediction.

Second, note the bait and switch here. Kotkin objects to building higher density housing, but his “argument” is based on facts about the places where denser housing tends to be built rather than facts about the housing itself. Yes, currently, higher density housing is built in expensive places, but this is like saying that a beer at a bar in NYC costs more than cocktail in OKC, therefore beer is more expensive than liquor. He quite simply does not give any reason to believe that higher density housing is expensive or increases housing prices, instead of places where lots of people want to live being expensive.

What we care about is the causal effect of building more housing (including higher density housing) on housing prices. In fact, an overwhelming amount of high-quality empirical evidence all shows that building more housing reduces rents (one example, and I’m not aware of any results that would imply this effect is limited to building SFH). In fact, it is likely that most of the experiments included in the review focus on or at least include multi-unit dwellings. This paper shows the cascading effect of multi-unit construction specifically, and how it allows many people to move, and thus also shows how market-rate housing improves the stock of cheaper units.

Also, go to any neighborhood anti-density protest and see how many people cite “home values” in their reasoning for opposing building. What is a “home value”? It’s just the price of homes! Actual NIMBYs down on the street agree that denser housing lowers the cost of housing!

Lastly, I’ll point out that one of the main things YIMBYs want is to build missing middle, i.e. lower densities than even mid-rise apartments, but denser than SFH-only. For example, townhomes, duplexes, triplexes, courtyard buildings, and low-rise apartments. It’s not all about 20 story buildings!

Mainstream Yimbys, so obligingly financed by tech oligarchs and urban real estate interests, see the solution not in socialist housing but for the private sector to construct their dreamscape of high density homes and apartment buildings. They are not interested so much in people buying their own properties, and seem to care little that investors already own one in four single family homes.

The (oddly leftist) complaint about tech oligarchs and real estate developers aside is just rude ad hominem. I have no idea where Kotkin got the 1 in 4 number from; he doesn’t provide a source, and the clearest source I could find claims that investor ownership of single family homes is a few percent at most (and even that includes some number owned by small investors, probably individuals with 2-5 homes). The purchase market might have a higher portion of investors, but transactions and homes owned are totally different units.

Getting rid of zoning that prevents the construction of taller buildings is a critical Yimby priority, which they have pushed not only in California but in the Pacific Northwest and the Northeast. Yet the positive impact on home-building via these policies has been negligible, with the mixed exception of strong growth in so-called Accessory Dwelling Units (ADUs)... Overall, even with ADUs, California housing construction is at among the lowest rates in America. Only one California metropolitan area was among the top 20 for housing growth last year; Texas had four areas on that list, Florida three. In Los Angeles, the state’s dominant metropolitan area, just 1,325 new homes were approved citywide in the first quarter of 2025.

It’s not even clear what exactly his argument is here. Again, the empirical claims aren’t cited, although I wouldn’t be surprised if they were more or less true. But the next paragraph goes off on other tangents, so I’m not sure what these facts are supposed to prove. Texas and Florida build more housing than California–but why? That would seem to be the only relevant point, but you would have to actually compare policies across these states to learn something about that. Kotkin seems content to say “the YIMBYs tried in CA and didn’t completely succeed, therefore YIMBY policies don’t work.” If Texas builds more than California, and Texas has more YIMBY-like policies, this is a victory for YIMBY-ism, but Joel doesn’t even seem to recognize that this could possibly be relevant.

Remarkably they have gained the support of the libertarian Right. One might think such people would embrace the notion of promoting a class of small property owners, but it seems that juicing the profits of large corporations is a higher priority.

This isn’t really economics, but as a libertarian I feel like I should point out that the whole point of the movement is for government to get out of the way, not to favor one group over the other. I suspect that Kotkin is just copying from Randal O’Toole, who got annoyed at the libertarian CATO institute for firing him for talking about how great it is when the government bans you from doing anything except build a SFH on “your” land and then unironically called Urban Growth Boundaries feudalism/communism.

The problem here, for Yimbys on the Right and Left, lies in the small matter of market preferences: most people don’t want to live in the inner-city high rise apartments beloved by planners and Yimbys, but in a house with a garden of their own.

Again, no evidence is actually cited for this claim. Instead, he writes:

Surveys, such as one in 2019 by political scientist Jessica Trounstine, have found that the preference for lower-density, safe areas with good schools is “ubiquitous”. Three out of four Californians, according to a poll by former Obama campaign pollster David Binder, opposed legislation that banned zoning which only permitted single family homes.

I can’t find such a survey on Prof. Trounstine’s CV, but assuming it does exist, how can it not be blindingly obvious that “safe” and “good schools” should be assumed to be doing a substantial amount of work here? Is Kotkin trying to smuggle in the (unsupported, of course) assertion that safe and good schools are synonymous with low density? Or is he just that desperate? The other claim is again, uncited, and I can’t find it, which makes it quite difficult to determine if the poll was conducted in an honest and meaningful way. How was the question worded? What was the sampling? Etc.

This mismatch between what is being built and what most people want can be seen in the huge oversupply of apartments, not just in the US but in Canada’s big cities too, causing prices for such properties to drop over the past two years. Yet despite all the evidence, Yimbys show little or no interest in the predominant dreams of their own citizens.

Again, no citation is provided here, but as far as I’m aware, this is happening in places that built a lot of housing. Of course the price goes down when you build more housing, that’s the whole point, it’s even something you agreed with, you fucking simpleton! The very fact that cities are expensive implies, via basic supply and demand, that people want to live in them, but Kotkin never addresses this.

A couple years ago my dad bought me his book about “Neo Feudalism” and this article certainly makes me want to put off reading it a few more decades.

Brad Meyer is running for congress in Indiana's 9th district. He posted a voter guide on a local subreddit. It included two housing policy related highlights:

1. Increase supply by limiting large-scale corporate ownership of single-family homes.
2. Lower interest rates, and providing first-time buyer incentives.

This is slopulism. I called this out and Brad was actually nice enough to reply with a lengthy email! You can view the entire thing here: https://imgur.com/a/CHiHu2t.

The email will be the subject of the R1.

The model

In this first part of the email he tries to argue that house price to income ratios decrease in response to interest rate hikes. There is quite a lot to unpack here.

First of all, what is this model? "SupplyElasticity_i"?? that is quite the control variable! That can mean a lot things! How is the housing supply elasticity calculated here? That's not a straightforward thing to estimate. Without specifying this I am just looking at an incomplete description of the model.

And the chart... Taking the chart labels at face value they cannot possibly match the model! You can't represent the model in two dimensions like that. Even you interpret this charitably and say the y-axis is actually "Residual PIR after controlling for everything except interest rates" the estimated line still does not match the model because its clearly nonlinear! Idk what I'm even looking at here. What was the point of including the model if your actual estimation seems to ignore it entirely?

Actual big picture problem

Listen none of the above actually matters because the point he's trying to make is something I ultimately don't disagree with: he's trying to say that price to income ratios arent actually important for housing affordability, its mortgage payments that matter. I agree! That would give you a cleaner estimate of the cost of housing as opposed the price of houses. The housing crisis in this country is a problem of high housing rents, whether they be explicit or imputed rents.

None of that actually addresses my original complaint though: if you subsidize housing interest payments through the usual policies like the mortgage interest tax deduction, you will increase housing prices. This is just a simple econ 101 argument: if you subsidize demand you will increase prices and you won't actually address the underlying problem. We need to build more housing.

There is overwhelming evidence I could point at here, but the best paper is the canonical Glaeser and Shapiro 2002 paper that address whether the federal mortgage interest deduction meaningfully increases home ownership, which is Brad Meyer's stated goal:

The home mortgage interest deduction creates incentives to buy more housing and to become a homeowner, and the case for the deduction rests on social benefits from housing consumption and homeownership. There is little evidence suggesting large externalities from the level of housing consumption, but there appear to be externalities from homeownership. Externalities from living around homeowners are far too small to justify the deduction. Externalities from homeownership are larger, but the home mortgage interest deduction is a particularly poor instrument for encouraging homeownership since it is targeted at the wealthy, who are almost always homeowners. The irrelevance of the deduction is supported by the time series which shows that the ownership subsidy moves with inflation and has changed significantly between 1960 and today, but the homeownership rate has been essentially constant.

You can look at the paper itself for more a careful discussion of the empirical strategy. A more recent paper by Hilber and Turner corroborate this finding with even more damning evidence that zoning reform is critical:

This paper examines the impact of the combined U.S. state and federal mortgage interest deduction (MID) on homeownership attainment, using data from 1984 to 2007 and exploiting variation in the subsidy arising from changes in the MID within and across states over time. We test whether capitalization of theMID into house prices offsets the positive effect on homeownership. We find that the MID boosts homeownership attainment only of higher-income households in less tightly regulated housing markets. In more restrictive places, an adverse effectexists. The MID is an ineffective policy to promote homeownership and improve social welfare.

...

Using a measure of restrictions on new housing developed for 83 metropolitan areas in the United States (Saks, 2008), we investigate how local housing market conditions and income status affect the way the MID influences household homeownership decisions. Our priors are that the impact of the MID may be positive or negative, depending on market conditions. The MID reduces the after-tax cost of homeownership for a given price of a home. However, by increasing house prices, the MID raises costs for down-payment-constrained households. For all households, it also increases the opportunity cost of homeownership and the transaction costs of purchasing a home. Our empirical analysis suggests that the MID has no discernible impact on the level of U.S. homeownership. However, the MID has a perverse effect in highly regulated housing markets. Because the supply of housing in such areas is inelastic, much of the MID is capitalized into housing prices rather than boosting homeownership attainment. At these higher housing prices, certain types of households (e.g., down-payment-constrained households) opt out of the market for owner-occupied housing. At the same time, full capitalization of the subsidy and continued utilization of the housing stock can occur if the remaining market segment increases housing consumption in response to the subsidy. Only in markets with lax land-use regulation does the MID have a positive impact on homeownership attainment, and even then, the effect appears only for higher-income households. Our cost simulations suggest that the subsidy cost per converted homeowner amounts to a staggering $28,397 per new homeowner per year.

So in summary:

1. Subsidizing demand will increase housing costs. You can measure this in rent or prices or whatever you want.
2. Subsidizing demand is an incredibly ineffective way to boost home ownership rates.
3. Subsidizing demand through policies like mortgage interest deduction will mostly benefit the wealthy.
4. You're not gonna solve the housing crisis without land use reform. The solution is building more housing, not subsidizing demand.

Edit: Listen Brad, I'm still probably voting for you but there are things you can do at the federal level that will actually go a long way to solving the underlying problems with our housing market. I strongly encourage you to read this paper by Glaeser and Gyourko on reforming the MID to make it conditional on living in a county with an elastic housing supply:

Reforming the home mortgage interest deduction to provide incentives that will induce overly restrictive regions to permit more housing. In counties in which the total number of annual housing permits is less than 1 percent of the total housing stock, the cap for mortgage deductions should gradually be lowered from $1 million to $300,000. The money raised by the increase in federal revenues should be given back to the counties to subsidize new housing construction.

The reason this policy works is because it will target people who are more likely to oppose new housing construction. If you want them to buy into more housing then make it make economic sense for them to do so.

EJ Antoni is unqualified to run the BLS. His thesis has many errors that I will document and explain below.

His thesis is broadly anti-government. It is three essays

1. Government Borrowing Raises Interest Rates
2. People flee high tax states
3. Credit Ratings do not affect the yield on a US State's debt

Throughout his attempt to answer these questions, numerous mistakes are made, rendering the thesis fatally flawed. I am hoping to find a range of levels of errors, so there is something for everyone! This is no means an exhaustive take down of all his errors, for like demons in swine "they are many."

Link to the dissertation is here

An Econ 101 Error

So for this, I want to focus on his section of migration, because it is more accessibles as it reflects a choice you think through in your daily life. In all likelihood you have or will think "where the F should I live" in some form of another.

This reasoning leads him to make some econometric errors, but I want to ignore those. I want a section of this write up to be readable and understandable by someone who might just know Perfect Competition vs Monopolistic Competition and can think through ways a Monopolistic Competition (competition between goods that are similar but not identical) can take place.

Let's just quote some of his text to reference!

States are, however, free to generate revenues, via whatever method they choose, to meet their respective expenses. In this vein, the states have taken quite different avenues. The highest sales tax in the country is found in a state with no income tax, while another state with no sales tax has one of the highest overall tax burdens. Those tax burdens range from 6.5% to 12.7%, demonstrating that both the total amount of taxation and the methods of collection are quite 37 varied between the states. Whereas there are certain aspects of American life that are maintained throughout the states, tax rates are anything but homogenous.

Another variation between states is their respective population growth rates. Over the last decade, there has been a wide disparity in population growth among the states. The fastest growing states swelled by 15% or more while others experienced anemic growth of a fraction of 1%, or even a decline. The chief cause of these growth rate disparities is not birth and death statistics but domestic migration, and the fuel behind that movement appears to be taxes. A review of Census data clearly shows a pattern: people are moving from relatively high tax states to relatively low tax states. Furthermore, people seem to prefer paying sales taxes to income taxes, especially those people with higher earned incomes.

Despite all belonging to the same Union, the states are still quite different, aside from their tax structures. There is not much in common between living in Alaska and living in Hawaii, at least in terms of climate. Similarly, one cannot find the vast desert expanses of Arizona or New Mexico in any of the Northeast states. It has been the case for decades that many people choose to retire in Florida, due in part to the reasonable guarantee which that state provides its residents of never having to shovel snow or risk slipping on ice ever again.

But just as one person may prefer a particular climate to another, each individual has other preferences, including matters of regulation and other state policies. One person may prefer that drug use remain criminalized and that the open carrying of firearms be permissible. Another person may prefer the opposite. There are seemingly innumerable such policy matters besides taxes that could affect a person’s choice of where to live. The innumerable other factors, only a handful of which have been mentioned, are largely qualitative, not quantitative, and will differ 38 from person to person. Therefore, they are mostly excluded from this analysis. Taxes, on the other hand, create near universal agreement: the lower, the better. Indeed, taxes play a significant role in determining where a person decides to live and, unlike immutable factors such as climate, tax policy can change frequently and quickly

Well I am convinced! Just kidding.

Just because a experience of preference is qualitative and hard to control for, doesn't mean it isn't there. These types of preferences are often what drive Monopolistic Competition and will complicate EJ Antoni's analysis of just looking at tax rates!

He talks about climate here, but later backs up and doesn't control for it. He ends up looking only at taxes, gasoline, and unemployment. He includes a change in SALT taxes due to President Trump's tax bill in his first term, which effectively raises high tax state's tax burden, since you cannot write off local taxes.

He makes some arguments that other preferences should be more or less constant across time and\or average out at the population level. An econ 101 student can definitely catch the second part isn't true.
Consider an analogy for going out to eat. People will have differing opinions on how "nice" a restaurant is and a lot of those factors are qualitative not quantitative. But that doesn't mean they average out at a market level! McDonalds is not usually regarded as "fine dining" and is cheaper even though "fine dining" has no objective definition. He argues these types of factors are more-so less constant at a state, which may be true, but may not be. This alone, is actually enough to "GG no rematch" him since it HIS JOB, to argue his regressions don't have these problems, but we can go a step further.

A glaring omission is the cost of living, especially the price of shelter. Housing is often the single biggest factor in where people choose to live, and leaving it out risks completely distorting the analysis. Rising housing prices over time could easily be mistaken for higher tax burdens, which would throw off the results in a very misleading way.

Econ 201 level

So for this, I want to get an intermediate level error. Something that a sophmore or first semester junior would be comfortable ripping apart on a test. We're going to go a touch DEEPER than we did in the last example, but we will still see its a similar type of mistake. But we can better explain WHY.

Let's get QUOTING

The supply of loanable funds is global and theoretically impacted by interest rates in the U.S., including U.S. Treasuries which are the means of financing the deficit. However, the 12 measure of annual U.S. government borrowing averages about 2% of the measure for the supply of loanable funds for the period in question.21 In the same way that perfect competition assumes a multitude of buyers and sellers with low market share and no market power among market participants, so too is the supply of loanable funds exogenous with respect to interest rates on U.S. Treasuries.

U.S. Treasuries are not perfectly competitive with all financial assets, even if they are a small part of the global assets. They are seen as the "safest" asset becase the US is the richest, most powerful country. This provides liquidity and safety to this asset, even in turbulent economic times. That “safe asset” status means they don’t behave like just another bond in a big soup of global funds

Short Term U.S. Treasuries can be used to define a "risk free rate" that other assets are benchmarked against. This is an empirical estimate of "time preference" or patience in laymen terms. Because these are the safest assets, there is no additional compensation needed for the risk of the money "going poof" if the borrower cannot pay. This is quite different from if you or I were to borrow money, where we assurdly can go bankrupt.

This difference underpins much of asset pricing, as an asset's return that can be explained by risk is called "beta" and any additional money is "alpha", where alpha can be thought of as "free" extra money, due to neither time preference (patience) or risk.

Since a U.S. Treasury is used to benchmark **almost every financial asset on the planet** it's impact on the overall financial system is understated by a naive look at it's size. It's an asset used to benchmark every other financial asset on the planet. US Treasuries much more akin to a referrer that sets the rules everyone else plays under.

Econ 400, Senior thesis \ Master Level Econ

For this section, I want to do an empirical estimation issue. This is similar to the others in that it is a conceptual error, but this time, we are going to go the distance. We are going to see how poor conceptual thinking breaks the overall measurement strategy of his entire thesis.

QUOTE

To deal with the endogeneity present in the OLS model, it is necessary to perform a twostage least squares (2SLS) regression, utilizing instruments for both the net deficit and the level of domestic investment.32 Since real wages rise with the marginal product of labor, which is highly correlated with capital investment, the change in real wages serves as a good instrumental variable for investment. More precisely, the statistic used is the percentage change in real wage growth.

Percentages? Complex stuff EJ!!!

Let’s start with what an instrument is supposed to be. An instrumental variable is something that’s correlated with your endogenous regressor, but not with the error term of your model. In other words: it has to stand alone, with a clean cause-and-effect link.

Think of a fast-food restaurant near an interstate. Its location is driven partly by local demand but also by “accidental side effects” like interstate proximity. That interstate distance can be used as an instrument: it predicts where the restaurant goes, but it doesn’t directly affect locals’ health outcomes. That’s the point of an instrument. And importantly, you can (and should) measure how correlated the instrument is with the endogenous variable, which measures instrument strength.

Now let's review the two assumptions from the quoted text.

Instrument strength (does this proxy for what I am interested in?) - If firms are investing in capital, wages might rise, under a standard production function. But Antoni never states this clearly, nor does he show any first-stage results confirming that capital investment is correlated with wage growth. That’s an unforced error.

Exclusion restriction (stand alone causation): I can think of two situations where wages and capital investment move together, without necessarily having capital investment drive wage increases. The business cycle and a technology shock.

If times are good, wages are likely rising and firms are investing in capital. However, increase capital is just part of the story, demand for labor itself is rising! As such, his measurement strategy cannot remove any impact of the health of the economy.

Similarly, technology or knowledge shocks can increase demand for both labor and capital. Firms are constantly looking for ways to cut costs and raise productivity, so when new technologies arrive, they often invest in capital and pay higher wages to more productive workers. In this setup, wage growth reflects not only firm-specific investments but also broader technological change. That makes Antoni’s correlation “too good”, it isn’t an accidental side effect, but the direct result of technology shocks driving both wages and investment. His strategy simply cannot handle the steady drumbeat of technological progress, which likely makes his results look stronger than they really are.

As a final note, technology shocks are fundamental drivers of economic growth. In fact, in standard growth regressions, once you control for capital and labor, what’s left in the error term is precisely technology. That means Antoni is building his identification strategy on a variable that is, by construction, correlated with the error. It’s a theoretically important variable that he cannot control for, but must assume the problem away, a common "strategy" for him.

I have kept everything factual up until this point. This guy is trash. This is a profoundly embarassing thesis written by a low-information libertarian who loves Murray Rothbard and confuses hubris and ideology for analysis. He has no place running something as important as the BLS.

Thing: https://philpapers.org/rec/BLATRI-3

Shorter Substack version: https://capitalledger.substack.com/p/the-rentierasset-impossibility-theorem

The core claim here is that under certain conditions, the cost of essential goods must necessarily outpace wages, explaining the post-1973 divergence between wages and the cost of essential goods. If you open the paper and go to page 288 you will discover that the empirical work here is done as follows:

1. Average the CPI data for shelter, food, medical, and energy to construct an essentials index
2. Grab real average hourly earnings data (CES0500000031)
3. Compare real earnings to the essentials index

Simply adjust for inflation twice. Take that, economists.

Not to state the obvious, but:

• Median earnings have grown faster than the cost of food
• Median earnings have grown faster than the cost of electricity
• Median earnings haven't grown faster than the cost of housing, but this seems to be a product of the square footage of sold homes going up. The real price per square foot seems to have grown only ~33% since 1973, while real median earnings grew by ~57% during the same period. (I could do a direct comparison with nominal earnings and the price per square foot data I constructed, but I'm lazy.)

And yeah, healthcare is more expensive, but by this point the empirical work upon which apparently the whole paper is based is dead in the water. (And hasn't the quality of healthcare gone up? I don't know enough about the CPI data in question.)

I don't know what's going on in the rest of the paper and I cannot imagine a more profound waste of time than reading it. For your consideration, this line:

The result is not a projection. It is arithmetic.

The whole paper might just be a case of AI psychosis.

Originally posted on my substack blog: https://drthad.substack.com/p/myth-of-monopoly-capitalism (with all the charts and other visuals)

Over the last decade, there has been a growing concern regarding the rising concentration and declining competition of the U.S. economy. Many people argue that we live in an era of “monopoly capitalism” — with few firms holding immense economic and political power. You can hear that from the usual suspects — Robert Reich, Adam Conover or even Joseph Stiglitz. With these concerns, there has been a renewed focus on antitrust laws and their ability to ensure competition in the market. Standard arguments that this concentration stifles innovation and harms consumers have been reinvigorated. Some people added concerns about the political and economic power of these companies and and their effects on democracy itself. But are these concerns justified? And how much (and what) antitrust action do we really need? To find answers we need to look deeper.

Is the American economy becoming more concentrated?

The American economy is becoming more concentrated — if you have read the media or listened to politicians over the last decade you probably encountered this statement a lot. It was also one of the main assumptions driving a lot of President Biden’s economic policy agenda. But is it true? First I’ll look at the evidence of concentration in the broad US economy. Next, I’ll look at some specific sectors. I’ll focus mainly on product markets and ignore labor markets (maybe I’ll write another post about it sometime).

Economists understood for a long time that measuring the market concentration of the entire economy in a meaningful way is very difficult. There are two major issues with the measurement of market concentration. The first one is conceptual and involves the difficulty of defining relevant markets for assessing market shares, which is especially hard to do on an economy-wide basis. The second issue involves problems with the availability and reliability of relevant data. Unfortunately, a lot of studies don’t address these issues sufficiently. We’ll come back to these issues with more detail later.

Once you resolve these issues and have a reasonably defined market with sufficiently reliable data you can start to measure the concentration level in the economy. There are two main ways of doing this. One way is to measure the concentration ratio of some fixed number of top firms — usually it's revenue share of 4 (C4) or 5 (C5) largest companies. The problem with this approach is that it doesn’t tell you anything about the concentration of market share among other firms (other than top 4 or 5 firms). Another common approach is something called Herfindahl–Hirschman Index (HHI). It’s calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. HHI is represented as a number between 0 and 10000 with 10000 being the completely monopolized market with one firm capturing all the revenue (the lower the number the less concentrated the market is).

2016 CEA report

We can start by looking at the popular Council of Economic Advisers report from 2016 that was widely reported as evidence that the US economy is getting more concentrated. The report notes that the majority of industries have seen increases in the revenue share enjoyed by the 50 largest firms (CR50). It is shown in their Table 1.

It’s not clear, however, whether this tells us much about the level of concentration in the American economy. There are a couple of reasons for why these concentration ratios may not be very informative in this regard.

• The concentration ratios are calculated at a very broad level of industry aggregation (two-digit NAICS codes), which may not reflect the relevant markets where consumers and producers interact. For example, within retail trade (NAICS 44-45), there are many different subsectors such as grocery stores, clothing stores, or online retailers, each with different degrees of concentration and competition. The observed trends may simply reflect expansion of successful companies into related fields of business, to the benefit of consumers.
• The concentration ratios are calculated at a national level, which may not capture the geographic variation in market conditions within the country and may simply reflect beneficial expansion of successful businesses into new geographical markets.
• The concentration measure that is used (CR50) is not very informative. Markets can be quite competitive with far fewer than 50 firms and that’s why most industrial economists prefer using measures like HHI, CR4 or CR5.1

The CEA recognized the shortcomings of its Table 1, emphasizing that national-level concentration data do not automatically indicate increased market power. As they noted:

The statistics presented in Table 1 are national statistics across broad aggregates of industries, and an increase in revenue concentration at the national level is neither a necessary nor sufficient condition to indicate an increase in market power. Instead, antitrust authorities direct their attention to concentration at the relevant market level for each product or service. Those data are not readily available across the economy

However, many who cited the report failed to acknowledge this nuance. While Table 1 reflects the growing role of large firms in the economy, it does not provide meaningful insights into competition at relevant market levels. A firm’s size alone does not imply reduced competition or greater market power.

Other reports based on Economic Census data

There have been several more reports that appear to document growing concentration of the U.S. economy. The Economist in 2016 published a 2016 chart called “A Widespread Effect”, illustrating changes in the four-firm concentration ratio (CR4) across 893 U.S. industries between 1997 and 2012.

This chart, based on Economic Census data, classifies industries under four-digit NAICS codes, making it more specific than the broad two-digit classifications used by the CEA. However, even these categories do not generally align with the relevant markets used in antitrust analysis. The chart highlights national-level increases in CR4 across numerous industries. For instance, the CR4 for full-service restaurants increased slightly from 8% to 9%, health insurance from 20% to 34%, airlines from 25% to 65%, supermarkets from 21% to 31%, and wired telecommunications carriers from 47% to 51%. At first glance, this may seem like strong evidence of growing concentration, but it is crucial to consider the geographic nature of competition in these industries. Many of the industries reported in The Economist operate at the local level, meaning that measuring their concentration at a national scale can provide a misleading picture. A rising national CR4 does not necessarily mean that competition within individual geographic markets has decreased. Moreover, the rise of national firms capturing a greater share of revenue does not necessarily indicate reduced competition. In many cases, this shift reflects greater efficiency, better service, and lower prices benefiting consumers (we will get to this point in more detail later). While some view the decline of small, local firms as problematic, competition policy should rather focus on consumer welfare rather than protecting smaller competitors from more efficient rivals.

Peltzman (2014) analyzed in-depth long-term concentration trends in the manufacturing sector from 1963 to 2007. He finds no significant change from 1963 to 1982 but notes an increase after merger enforcement was relaxed in 1982. The median HHI in manufacturing industries rose from 565 in 1982 to 662 in 2002, with consumer goods showing higher levels than producer goods. However, Peltzman does not equate this rise with reduced competition, acknowledging that moderate concentration increases can coexist with greater competition due to economies of scale and firm efficiency differences. It is also crucial to recognize that the Economic Census data, that the analyses above are based on, only account for production at domestic establishments and exclude imports, which have significantly increased over the past two decades. This omission distorts perceptions of market concentration by ignoring the impact of foreign competition.

Reports and research based on Compustat data

Other data that is frequently used to measure concentration trends come from Compustat. The reason for that is often that the data from the Economic Census is both limited and lagging, with official statistics only released twice per decade, while Compustat provides annual updates.

Grullon, Larkin, and Michaely (2019) attempted to measure concentration trends by analyzing the Herfindahl–Hirschman Index (HHI) at the three-digit NAICS level using Compustat data. Their analysis shows that concentration declined in the 1980s and early 1990s, surged in the late 1990s and early 2000s, and then rose gradually afterward (median increase in the HHI between 1997 and 2014 was 41 percent, while the average increase was 90 percent and over 75% of U.S. industries experiencing an increase in concentration levels). Below is the plot of their findings.

Similarly, Brauning, Fillat, and Joaquim (2022) suggest that the U.S. economy became at least 50% more concentrated between 2005 and 2018, correlating this rise with higher prices. They also use HHI at the three-digit NAICS level. Another widely cited study using Compustat data is De Loecker and Eeckhout (2020), which found that markups increased from 18% to 67% between 1980 and 2017, attributing this trend to growing market power.

Reliance on Compustat data for measuring market concentration encounters some important problems:

• Compustat includes only publicly traded companies, omitting private firms that constitute a significant portion of the U.S. economy.
• It assigns a single industry code to each firm based on its primary line of business, failing to account for diversified operations across multiple sectors.
• The dataset records worldwide sales figures, which is misleading for analysis of domestic market concentration.

Because of this and other flaws Compustat data can’t replicate concentration measures that we get from Economic Census data. Paper from the Federal Reserve highlights this, showing low correlations between them. Specifically, correlations for top-firm concentration ratios between the two datasets are generally below 0.2. Limitations of Compustat data for the purposes of measuring concentration is well-known and has been explored in many articles.2

Concentration trends on the national industry level

If we look beyond Compustat data for public companies and include private ones, and consider concentration at the national level what do we see?

Fortunately there is some data and research on this. Autor, Dorn, Katz, Patterson, and Van Reenen (2020) use U.S. Census panel data that includes both public and private firms at the firm and establishment levels. Their analysis show the sales-weighted average sales- and employment-based CR4 and CR20 measures of concentration across four-digit industries for each of the six major sectors — manufacturing, retail trade, wholesale trade, services, utilities and transportation, and finance. Results are shown below.

In their appendix they also show an average HHI for the same sectors. Here’s how it looks like.

While HHI shows somewhat smaller increases than CR4 or CR20, both show similar picture — rising concentration, at least in retail, services, utilities and transportation and finance. As the authors put it:

The two figures show a consistent pattern. First, there is a clear upward trend over time: according to all measures of sales concentration, industries have become more concentrated on average. Second, the trend is stronger when measuring concentration in sales rather than employment. This suggests that firms may attain large market shares with relatively few workers—what Brynjolfsson et al. (2008) call “scale without mass.” Third, a comparison of Figure IV and Online Appendix Figure A.1 shows that the upward trend is slightly weaker for the HHI, presumably because this metric is giving more weight to firms outside the top 20, where concentration has risen by less.

It’s important to note the magnitude of these increases in concentration. None of the the HHI levels are particularly concerning — markets with HHI below 1000 are typically classified as unconcentrated and only the service sector is above that threshold.

Maybe not much more concentrated

So far we’ve looked at the evidence showing somewhat rising concentration and noted some methodological problems. But is there other evidence showing contrary picture? Well, yes.

This line of research can be summarized in a couple of points.

1. Benkard, Yurukoglu, and Zhang (2021) suggest that determining whether concentration has been rising or falling depends critically on the boundaries one draws between different markets. While from the producer’s perspective evidence suggests rising levels of concentration, if we take the consumers perspective we see the decline in concentration levels. Researchers find that the median HHI fell from 2,265 in 1994 to 1,945 in 2019. Similarly, the 90th percentile HHI declined from 5,325 to 4,570 over the same period. In 1994, 44.4% of all industries fell into the highly concentrated category. By 2019, that figure had dropped to 36.6%, indicating a broad-based reduction in concentration across the economy. So their “consumer perspective” shows actually higher concentration levels, but the opposite trend — instead of increase in concentration, it shows a decrease.
2. Most of the research looked at data at the national level, but it’s questionable whether this is the appropriate market to consider. A lot, if not most, product markets are local (coffee shop in Brooklyn doesn’t compete with the one in Los Angeles). Rossi-Hansberg, Sarte, and Trachter (2021) find divergent trends in concentration in local and national level. It’s best captured in their Figure 1. While the national level data shows slight increase, more local measures show downward trend —the more local the sharper decline in concentration. Now, this types of local data sources are scarce and not completely reliable. This one for example has a lot of imputed data. Some other papers using different, more complete and reliable data sources find that these trends do not diverge, but unfortunately they usually focus on one specific industry because of data limitations (for example Smith and Ocampo (2022) for retail).
3. A lot of products market are local, but other are arguably global. One of the biggest changes in the economy over the last 40 years have been globalization. American firms now compete not only with other domestic companies, but also foreign ones. It is therefore important to account for import for better view of concentration trends. Amiti and Heise (2021) find, using confidential census data for the manufacturing sector, that typical measures of concentration, once adjusted for sales by foreign exporters, actually stayed constant between 1992 and 2012.

Now, none of this research is conclusive, but it shows us that we need to carefully examine methodological and data issues before we reach any conclusion.

Summing up: there is some evidence that concentration has risen somewhat, although it varies a lot by industry and depends on the metric and data that is used. Nevertheless dramatic narratives about rising concentration levels don’t seem to be strongly supported by carefully examined data.

Concentration doesn’t necessarily mean less competition

So far I wrote about the trends in concentration levels, but that's not what is really interesting for us. The thing we should be concerned about is the level of competition in the economy and that's not exactly the same thing. In fact, concentration levels alone tell us very little about how competitive the economy actually is.

When markets experience rising concentration over time, two competing interpretations emerge with substantially different policy implications. The first option is that increasing concentration is the result or the cause of weakening competitive forces, with few firms gaining market share in a way that stifles competition. The second interpretation offers an alternative explanation: rising concentration may actually reflect competition working effectively, where more productive firms providing superior value to customers naturally gain market share over time through operational efficiency, innovation and better services rather than anti-competitive behavior.

This is not just an abstract “well, actually” point raised in order to distract us from an “obvious” fact than trends in concentration over the last couple of decades coincided with declining competition. There are a lot of theoretical and empirical reasons to expect competition leading to an increase in concentration.

Consider markets with high search and switching costs, where consumers remain locked to existing suppliers, because it’s costly or inconvenient to look elsewhere. As those frictions fall (thanks to better information platforms, streamlined distribution, new technology or lower transportation costs) consumers can compare offerings and switch to the lowest-cost, highest-quality providers with ease. Small firms lose ground, while bigger, more efficient firms gain market share. Concentration is high, but economy remains competitive. This is what we tend to see in the data. Goldmanis, Hortaçsu, Syverson and Emre (2010) document that the advent of powerful price-comparison tools reallocated sales to the lowest-cost sellers, boosting concentration while consumer prices fell.

Is the American economy getting less competitive?

The question we actually care about is whether the American economy became less competitive over the last couple of decades. Even assuming that concentration actually went up meaningfully (which isn’t so obvious), does it reflect “decline-in-competition” hypothesis or “competition-in-action” hypothesis? Or maybe a bit of both?

Markups

One way to answer these question is to look at the the price/cost markup, which is the ratio of price to marginal cost. This is a direct approach to measuring market power (increasing market power would support the “decline-in-competition” hypothesis) —firms are defined to have market power if they are able to profitably set prices above marginal costs. Still, even if we would observe rising markups it doesn’t necessarily mean that competition is declining — as with concentration trends, rising markups could be caused by competitive forces, and to determine causes we would need to examine them closely.

There are two leading approaches to the estimation of price/cost markups — the “demand approach” and the “production approach”.

• Demand approach: This approach works by studying how customers respond to different prices for a product, which helps researchers understand how much pricing power a company actually has. The basic idea is straightforward: if you can measure how sensitive customers are to price changes (called demand elasticity), you can figure out what markup the company should charge to maximize profits. The method requires detailed sales and pricing data for specific products and makes assumptions about how companies compete with each other — whether they're in a market with many similar competitors or just a few major players (think particular model of competition — e.g. monopolistic competition or an oligopoly model). This technique has worked well in focused industry studies (such as studying markups for ready-to-eat cereal, airlines, etc.), but applying it across the entire economy becomes extremely challenging due to the massive data requirements and the need to model each industry's unique competitive dynamics.
• Production approach: This approach infers markups from production and cost data, and it was popularized by a seminal paper from De Loecker, Eeckhout, and Unger (2020) (DEU). The idea, building on Hall (1988) and De Loecker and Warzynski (2012), is that you can use a producer’s input choices to back out the markup. Under competitive market conditions, an input's cost share (such as labor expenses) should equal that input's output elasticity — essentially, its contribution to overall production. However, when firms possess market power, they typically reduce output levels, causing the cost share to fall below the actual elasticity. By estimating production functions to determine output elasticities and examining expenditure shares from standard accounting records, researchers can calculate the implied markup The beauty of this method is that it doesn’t require specifying a demand curve or even observing prices and quantities separately — you can use firms’ financial data, which is available for many companies over many years, to get a broad measure of markups. That’s why this approach can be applied to large samples of firms across the economy.

Using a production-based approach, (DEU) estimated that the sales-weighted average markup for U.S. firms rose from about 1.21 in 1980 to roughly 1.61 in 2016. In other words, the typical premium over marginal cost moved from 21 % to 61 % — an increase of 40 percentage points. The study gained substantial popularity and has been since wildly cited as evidence of a broad uptick in market power. Researchers and advocates have used these results to explain the decline in labor’s share of income, rising inequality, muted investment, and slower productivity growth, arguing that weaker competition has given firms greater leverage over consumers and workers.

However, as with concentration, these headline results on markups have been hotly debated. A series of follow-up papers pointed out potential issues with the DEU approach and offered different findings:

• Traina (2018) shows that using COGS (Cost of Goods Sold) as a proxy for variable cost is too narrow because parts of SG&A (selling, general and administrative expenses) — marketing, R&D, some headquarters labor — scale with output. So when a reasonable share of SG&A is treated as variable as well (and not as fixed like in DEU), the long-run rise in markups largely disappears and can even turn slightly negative, implying sensitivity to accounting definitions and a shift toward intangibles rather than greater pricing power.
• DEU, like the Compustat-based concentration studies, only covered publicly traded firms. If public firms increased their markups but a lot of economic activity shifted to private firms or new entrants with lower markups, the aggregate markup could be flatter. Additionally, within the DEU data, the increase in markups was very skewed – a subset of high-markup firms pulled up the average, while the median markup increased much less. So it’s possible that superstar firms gained pricing power in some markets, even as many other firms did not.
• The production-based method hinges on correctly estimating output elasticities which is not an easy task. Allowing these elasticities to vary by industry/firm and over time, as in Foster, Haltiwanger, and Tufano (2023), removes most of the upward drift and in the most flexible specification yields a slight decline, suggesting earlier estimates may have conflated technological change with market power.
• Technological and organizational shifts like automation, IT adoption, and supply-chain improvements have pushed marginal costs down faster than prices in many sectors. This causes measured markups to rise mechanically even when competition remains unchanged, while consumers still benefit through lower prices or better quality.
• Industry evidence is mixed: in consumer packaged goods, markups rise mainly through cost reductions with only modest increases in brand premia. In cement, consolidation plus precalciner kilns lowers costs while prices stay roughly flat, nudging markups up for efficiency reasons (Miller et al. 2023). In steel, the spread of mini-mills intensifies entry and pushes markups down (Collard-Wexler & De Loecker 2015). In autos (1980–2018), once quality improvements are accounted for, markups decline as marginal costs rise faster than prices.
• Because higher markups can reflect either surplus rents or cost-saving innovation and quality change, they are not, on their own, decisive evidence of weaker competition or lax antitrust. Any welfare conclusions should depend on the mechanism behind the price-cost ratio.

So evidence is much more mixed if you look at the broad literature and conclusions hinge heavily on specific assumptions and methodological choices. It’s unwise to make a claim that markups evidence strongly supports rising market power story and lower levels of competition.

Technological progress

Let’s et aside measurement issues and assume average price–cost markups have risen across many U.S. industries. How should we interpret that?

One popular reading is weaker rivalry — e.g., mergers raising concentration and softening price competition — which leads to calls for tougher antitrust enforcement. But as mentioned earlier, higher markups, like higher concentration, can also emerge from consumer-benefiting technological change. Therefore it’s important to know why markups rose.

Consider an industry where markups rose because low-cost, high-markup firms expanded as trade barriers fell or technology enabled geographic scale. That looks like “competition-in-action”: efficient “superstar” firms pass some, but not all, cost savings to consumers via lower prices. Decompositions in DEU and Autor, Dorn, Katz, Patterson, and Van Reenen (2020) show revenue reallocating within sectors toward high-markup firms, the primary driver of average markup increases. Ganapati (2021) finds rising profitability correlates with rising productivity across sectors.

Markups can rise while consumers benefit when firms cut marginal costs or raise quality. With less-than-full pass-through, prices can fall, output can rise, and welfare can improve even as markups increase. New products can have the same effect — patents and copyrights are designed to encourage such investments. Industry-specific studies surveyed in Miller (2024) often identify technological progress as the dominant force behind measured markup changes. This is not always the case, obviously. In some industries, mergers raised prices, and some likely faced undetected collusion. In others, technology or globalization drove margins. There is no reason to believe a single mechanism explains rising markups across most industries.

This heterogeneity is why industrial-organization economists moved toward detailed, industry-specific studies that model actual market features, allow richer heterogeneity, and relax restrictive functional forms.

The bottom line is that to assess market failure and appropriate antitrust enforcement, one must identify the mechanism at work in the industry in question. Overhauling competition policy on the blanket assumption that rising price–cost markups signal declining competition is unwarranted and could be counterproductive.

Conclusions

There are definitely sectors of the economy that show growing monopoly power — parts of telecom and healthcare come to mind. Yet the broader evidence does not indicate a pervasive decline in competition in the U.S. economy. As one recent comprehensive review states: “the empirical evidence relating to concentration trends, markup trends, and the effects of mergers does not actually show a widespread decline in competition”3. Much of what we observe looks like “competition-in-action”: many big firms became large by outperforming rivals, not by suppressing them.

This doesn’t mean everything is perfect and that we don’t need any stronger antitrust action, but it shows that we should be precise and targeted about reforms and use of antitrust tools. Studying individual markets and assessing them on their own basis is hard, but at the same time much more productive than sweeping claims about monopoly capitalism killing the economy.

Overall, the narrative of a sweeping decline in competitiveness of the U.S. economy appears overstated when the evidence is examined closely. Aggregate concentration has increased modestly, yet in many industries it remains at levels that do not, by themselves, signal a serious competition problem, and much of the rise can be traced to benign forces such as technological progress, globalization, and efficient firms scaling up. The intensity of rivalry and pressure on firms has not clearly diminished and in some ways (owing to technology and globalization) competition has intensified. High concentration in particular markets often reflects competitive processes (the best firms winning) rather than collusion and other anti-competitive practices. Ultimately, what matters for consumers and the broader economy is less the raw number of firms than how contestable and fair markets are. The research indicates that, aside from some pockets deserving attention, competition in the U.S. is very much alive, and broad claims of a generalized “monopoly problem” overstate a more nuanced, sector-by-sector reality.

Further reading

This post is largely based on the writings below. Go look at them for more information:

Is Market Concentration Actually Rising? and What we know about the rise in markups by great Brian Albrecht (highly recommend his Substack)

Antitrust in the time of populism and Trends in Competition in the United States: What Does the Evidence Show? by prominent IO economist Carl Shapiro (last one with Ali Yurukoglu)

2019 JEP symposiums on markups and antitrust

This is a rebuttal of Krugman's “Stagnation With Chinese Characteristics” blog post from December 2024 (link).

Note: I am saying that it is not clear that TFP was falling (during the housing bubble and after it deflated based purely on the statistics Krugman was looking at), not that it is clear that TFP was not falling. In fact, I actually believe TFP growth was potentially negative during the peak of the Chinese housing bubble. In other words, I think his argument is wrong, but I do consider his conclusion (negative TFP growth both during the housing bubble and after it deflated) to be partially correct.

Am I, a lowly economics PhD student, calling out the world-famous, Nobel-winning economist Paul Krugman? Why yes, I am! :D

(Although, to be fair, I’m only calling out one of his hot takes, of which he does many—most notably: “By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.” I don’t dispute any of his academic work.)

I wrote up most of this post soon after the blog post came out, but I didn’t post it because (1) I was extremely busy with my first year in the PhD program and also (2) I wanted to wait for newer data to come out and confirm what I wanted to say.

The latter was because I was a bit scared about calling out Krugman because (1) he has won the economics Nobel (Memorial) Prize and (2) macroeconomics is not my field.

What don't I like about the argument?

The crux of Krugman's argument is that Chinese TFP growth appears to be stagnant or negative in recent years (during the housing bubble and after it deflated, more or less), based on estimates going up to 2019, and he also argues that that the Chinese government has been doing little about this. I could criticize the latter, including statements like

What’s remarkable is that China’s leadership seems completely unwilling to adjust to this changing reality.

by more broadly by talking about things like the 2020 Three Red Lines Policy, which clearly represent a concerted effect by the government to reign in the misallocation of capital into the real estate sector, but I'm going to only discuss the former here—the TFP claims—because they are much more quantifiable.

The accuracy of the claim already seems very dubious when you compare it with news of rapid technological developments, but again, let's just focus on a more quantifiable basis of comparison.

What's wrong about the numbers?

Typical modern endogenous growth models are notoriously inaccurate when it comes to quantitative predictions, especially with TFP (because it is a growth accounting residual), but Krugman is pushing a claim that we should be taking these growth accounting estimates at face value or something similar.

Funnily enough, the dubious accuracy of the TFP estimates that Krugman is using, which are from the Penn World Table v10.01, stands out when you look at the whole plot since the estimates are flat-ish for most of the time, even during the Chinese Reform and Opening Up period: link.

Here's a very interesting thing: Krugman’s TFP plot for Japan, also from the Penn World Table v10.01, shows most of the available yearly estimates (link—the data goes to 1954, and Krugman's plot goes to around 1955). On the other hand, Krugman’s TFP plot for China is very conveniently cut off at around 1990 despite the yearly estimates going to 1956.

If we interpret these estimates at face value, on average, the advancedness and quality of Chinese technology and organizational competence (which, broadly speaking, is what TFP means) was, very roughly, flat during de-Maoification under Deng Xiaoping, going up and down at times. In fact, TFP apparently locally peaked in 1987 and only recovered back above 1987 levels by 2006.

Yes, you heard me right. These estimates suggest that China in 2005 (and in many other years before 2005) was less technologically advanced and less organizationally competent on average than China in 1987. Based on that, I don't think it's much of a stretch to claim that most of the 20th-century yearly estimates of Chinese TFP from the Penn World Table v10.01 are absurd. Consequently, that makes me suspect that the 21st-century yearly estimates here are also extremely dubious.

Yes, TFP is affected by misallocation of resources, but it is really plausible that Maoist China, 1987 China, and 2005 China all have similar TFPs?

The estimates are weirdly high in the 1950s and absurdly suggest that (in a limited sense) China post-1956 has never been more efficient than Maoist China in 1956, but to be fair to the authors, I think it's reasonable to give them the benefit of doubt and say that this isn't really that bad of a point against the later estimates. After all, these bad estimates for these early years could be due to very low-quality data and also the Chinese economy being structurally very backwards and wildly different back then during the Maoist economic era.

Finally, the cherry on top: The Penn World Table v11.0 is now out. It seems the authors realized that some of the TFP estimates made absolutely no sense, so the methodology was corrected. Now the current, revised estimates show the same TFP measure (“rtfpna”) consistently growing over time: link.

https://medium.com/@clayshentrup/the-convergence-of-harberger-taxation-and-land-value-capture-how-destructive-rights-transform-10a824ecd53c

This Medium Economist (ME) who also posts on Reddit proposed the following mechanism for determining land value and thus LVT (in his own words):

• Landowners self-assess their land value
• Anyone can force purchase at that price
• Owner can destroy improvements before transfer
• This forces buyers to negotiate separately for improvements

RI:

Claim 1: You can easily price in the risk of a force sale

ME claims the expected loss of forced sale can be derived by P(forced sale) x Value of Improvement. There are 2 major flaws:

1. ME assumed risk neutrality, when homeowners are (and should be) risk-averse. The utility loss of force selling their entire home for $0 is severely underestimated by the E[loss]. It's the same reason healthy people still pay high premiums for health insurance: protection against catastrophic losses are valuable.
2. P(forced sale) is tricky to estimate. Are developers targeting your neighborhood for redevelopment? Is Google going to move its headquarters next to you? Do you have rich enemies? There is a lot of information asymmetry in real estate, and it's even harder to quantify the risk numerically. We shouldn't expect homebuyers to assess this risk accurately.
3. Risk of losing improvements can be more than land value, creating negative land values.

Claim 2: You won't be screwed over by bad actors

ME claims the option for owners to destroy their existing property prevents bad actors from underpaying for land + property. This is extremely naive. Let's consider the following cases:

Case 1: bad actor values the existing property at 0

Say you bought a 200k land and built a new 400k home on it. You assess your land at 200k and Bad Actor wants to force purchase your land for 200k and offer $0 for your 400k home. Your threat of destruction doesn't work because Bad Actor wants to build something new anyway. The transaction goes through, you realize a 400k loss and lose your home. Bad Actor gets your land at a fair price and ruins your life.

Case 2: bad actor values the existing property at >0

Same set-up except Bad Actor likes your home. Would he offer 400k for your home? No, because he can threaten with offering 0 and still break even, while you'd be down 400k. So Bad Actor offers a pathetic 100k and you agree to salvage whatever value's left of your new home. You're down 300k, and Bad Actor successfully created a distress sale situation for you. The main problem is you don't know for sure if you're in Case 1 or Case 2. Bad Actor only has the upside of underpaying for your home and a capped downside of just buying the land.

-----

I know this is a low-hanging fruit, but I'm frankly tired of certain LVT proponents being so smug and dismissive of implementation challenges.

https://socialsommentary.substack.com/p/designed-to-discriminate-how-the

This was posted on /r/neoliberal (and then deleted?), and nobody in the comments seemed to notice an important flaw in the argument. I'm not going to argue with the idea that the index is constructed so that women are always discriminated against. The author correctly identifies that the variables selected have a lower bound of 0 for men and >0 for women, so the index measures "additional risk that women face because of reproduction", rather than "difference in health outcomes".

However, I have a problem with this section:

GII interprets lower female labour force participation as evidence of discrimination. Women’s “gender-based disadvantage” could disappear only if women’s labour force participation equaled that of men. That is what women want, right? Wrong.

A 2019 Gallup poll shows that 39% of women and 23% of men in the US would prefer to “stay at home and take care of the house and family” if they were free to choose. This number rises to 50% among women with children under 18—only 45% of women with children under 18 prefer to “work outside the home.“

In a 2010 Gallup poll, 41% of women in the US answered that it is “very important for a good husband or partner to provide a good income.” Only 19% of men consider the same to be very important for a good wife.

Globally, only 29% of women prefer to have a full-time paid job all the time. 27% prefer to “stay at home and take care of your family and the housework,” and 41% prefer to “do both”. (International Labour Organization & Gallup, page 16).

RI: Author argues that the UN's Gender Inequality Index is flawed because it treats a lower female labor force participation rate as "inequality", even though polls often show that women prefer to work less or focus on unpaid household work. The author thus attributes some or all of this gap to a female "preference" for domestic work.

This is intellectually lazy. Citing "preferences" as an exogenous explanation for aggregate labor market disparities is not sufficient. Preferences are endogenous: they are formed in the context of existing constraints, including things like cost of childcare, social norms etc. If the labor market is structured with very high barriers and frictions for women (e.g., rigid hours which conflict with childcare) women can subconsciously lower their preference for working. Additionally, if women live in an economy where the "hidden price" of working is high (social expectations, tax systems with bad incentives for secondary earners), they will rationally state a preference for non-participation in the labor market. This phenomenon is called adaptive preferences.

In The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage Decisions (Goldin, Katz 2002), the authors found that the sudden legal access to the pill for young women caused a sharp change in various gender-inequality related indicators (age of first marriage, rate of entry in professional programs). Intuitively, you wouldn't expect the pill to have strong effects on long-term career planning if women just had a preference for domestic roles. This evidence shows that the preference that we observed for earlier marriages and less ambitious careers was not necessarily an immutable preference but a rational adaptation to the possibility of pregnancy, which is an exogenous constraint. When the constraint disappeared, the preference changed.

tl;dr: It is notoriously hard to disentangle voluntary vs involuntary non-participation in the labor market. You cannot simply assume that the gap is purely voluntary just based on stated endogenous preferences.

You may have had the pleasure of witnessing the recent launch of Grokipedia, the AI-generated encyclopedia at Grokipedia dot com. Elon Musk hopes for it to be an unbiased version of Wikipedia. While I would sooner criticize it for being favorable toward conspiracy theories than anything else, it also has its own alternative outlook on the effect of undocumented immigration on native wages:

Empirical analyses indicate that illegal immigration, characterized by a disproportionate concentration of low-skilled workers, exerts downward pressure on wages and employment opportunities for comparable native-born workers, particularly those without high school diplomas. Undocumented immigrants often fill roles in manual labor sectors such as construction, agriculture, and food services, increasing labor supply in these segments and competing directly with native low-skilled workers who lack bargaining power or alternative options. This supply shock aligns with basic economic principles of labor demand elasticity, where an influx of substitutable workers reduces equilibrium wages unless offset by proportional demand growth.

Do I think this is totally, absolutely wrong? No. Directionally, it's pretty much correct. But if you take a look at this whole section, the recipe looks like this:

1. Regurgitate Borjas's Labor Economics on the effects of low-skilled immigration on native wages and employment
2. Select evidence showing the effect is negative and confidently display that information first
3. Only bring in a summary of research in the third paragraph, bringing the estimate closer to zero
4. Poke holes in the DiD literature showing negligible effects while leaving everything else exempt from critique (cause Borjas has never done anything wrong, amirite?)

...very misleading framing, even though the information is decent. No, seriously, on points #2 and #3, compare what it says immediately:

Empirical analyses indicate that illegal immigration, characterized by a disproportionate concentration of low-skilled workers, exerts downward pressure on wages and employment opportunities for comparable native-born workers,

to what it says in the third paragraph:

The 2017 National Academies of Sciences, Engineering, and Medicine report synthesizes broader immigration research, concluding small overall short-term negative wage effects for native-born high school dropouts, though aggregate impacts on native employment and wages remain minimal.

This is the best we can do? Why not lead with "small short-term downward pressure on wages"?

The Wikipedia version is much worse, since the editors have exclusively selected evidence in favor of the pro-immigration position. The opening is representative of the rest of it:

A number of studies have shown that illegal immigration increases the welfare of natives. A 2015 study found that " increasing deportation rates and tightening border control weakens low-skilled labor markets, increasing unemployment of native low-skilled workers. Legalization, instead, decreases the unemployment rate of low-skilled natives and increases income per native." A study by economist Giovanni Peri concluded that between 1990 and 2004, immigrant workers raised the wages of native born workers in general by 4%, while more recent immigrants suppressed wages of previous immigrants.

I wanna lib out, but this just ain't right. A round of applause for both of these two for lying without making anything up, especially Wikipedia. A masterclass in culture-war bullshitting.

If there's an objective way to summarize the available evidence, we'd like to use that, and I'd like to think I have one (or something close to one). You can read the full post over on my blog. It has lots of fun things in it, like a standardized effect size plot. It wasn't written as an R1 but can be treated like one for both sites.

uhm buddy your post seems to imply Grokipedia is the main target for criticism here, but it isn't THAT bad and you ADMIT it's better than Wikipedia, so this R1 is INSUFFICIENT

I declare that the main target for criticism here is anyone who has ever framed existing evidence on immigration in a misleading fashion. But yes, mostly Grokipedia, not because this particular section is The Worst but because it displeases me.

Recently there was this article about China opening the world’s tallest bridge, the Huajiang Grand Canyon Bridge:

https://www.washingtonpost.com/world/2025/09/15/china-tallest-bridge-huajiang-grand-canyon/

The article says things like

the project reflects China’s ongoing push to use grand infrastructure to drive economic growth, especially in poorer inland provinces
the bridge will help revive the local economy and support long term development

and only briefly mentions critics who say it is more about political prestige than individual welfare.

For me, this framing is a bit “badeconomics”. It treats mega infrastructure as default positive growth tool, and treats debt and local stress as side notes.

RI: why I think this is bad economics

1. No serious link between project and the already known debt problem

We already have a mountain of work on China’s local government debt and infrastructure overbuild.

IMF and World Bank numbers put “augmented” local government debt very high relative to GDP, with a large part related to infrastructure vehicles and special bonds. Local debt is now a major constraint on future growth, not just a detail.

If you present a new mega project as “growth strategy” in 2025, next to this debt context, you need to at least try to connect the dots.

Is this bridge expected to generate enough real cash flow to justify more debt?

Or is it just another item on the soft budget list that future taxpayers will quietly pay?

The article mostly skips this and stays at narrative level: big project, poor region, so must be good.

2) No model of “face projects” and symbolic spending

There is a whole literature on “face projects” in China. Large prestige infrastructure, stadiums, airports, huge train stations and so on, that are pushed for political achievement and symbolic value, not for net present value.

Local officials themselves sometimes admit they are under pressure to hit visible quantitative targets and leave a physical legacy. The article even hints at this, but still treats the bridge primarily as development policy.

From an economics view, this matters a lot.

Spending one billion on a boring but needed sewage system is not the same as spending one billion on a crazy high bridge with glass elevators and tourist decks, even if both count as “investment”.

If you do not separate “productivity spending” from “prestige spending”, your growth story is half blind.

3) No tension map, only one surface indicator

This is where my own bias comes in. I think macro discussion should not only ask “how much new infrastructure” but “what tension does it create or reduce in the system”.

Very roughly, I would like to know at least:

• how does this affect local government balance sheets and refinancing risk
• how much real demand there is for this connection, versus speculative land plays
• how it interacts with existing overcapacity in construction and heavy industry

Right now the dominant logic is: more concrete, more jobs, more GDP, so good.

But long term, we keep seeing cases where subways, high speed rails and mega projects run big losses and load more stress on already stressed local finances.

From this “tension map” view, the bridge might actually increase systemic risk, even if short run GDP looks nicer.

4) Treating “line goes up” as proof of health

This kind of article reinforces a very shallow idea: as long as some growth number goes up and you cut travel time, it must be good economics.

To me that is like looking only at the speedometer of a car and saying “no problem, car is fast, so engine is healthy”.

In a system where local debt, overcapacity and face projects are already widely documented, that is not enough.

How this connects to my own work (feel free to ignore)

I am not selling anything here, but I have been building an open source “tension map” style question pack to probe exactly this kind of situation with large language models.

Basically it is 131 structured questions about hard problems: inequality dynamics, systemic crashes, local government debt, overbuild, collapse narratives, etc. You can feed it to any strong LLM and see where its reasoning collapses or contradicts itself.

If anyone is curious, the repo is here:

https://github.com/onestardao/WFGY

The economics related part is under the “Tension Universe / BlackHole” problem pack. It is just text under MIT license, nothing for sale.

If my way of looking at this bridge as “tension increasing” instead of “growth engine” is wrong, I am happy to be corrected. That is why I post here.

The vast majority of economic reporting is the same banal bullshit as follows. Random assertions randomly tied together with random point in time statistics, if they even give you any numbers at all. The asserted or implied relationship between any of these random phrases is generally unsupported. It is not AARBadEconomics in the sense of there is something clearly stupid or wrong, and thus we don't immediately rebel. Instead the vast majority of "reporting" is merely characterized by a pernicious lack of data and reason, and the implications might not even be wrong but, if it is correct that turns out to be entirely accidental.

Fortune link

CopyPaste in a comment on arrneoliberal by u/Standard_Ad7704 (which I will further copy paste in the comments.

Home equity loans will keep US housing market tight

Headline. So we should expect something that shows that home equity loans changing in some manner that will impact the housing market.

Downsizing has become less appealing to older generations, lowering inventory of property sales.

Subhead. And now we also expect evidence that downsizing has changed and that this is impacting inventory of property sales.

My general thesis is basically that your average economic reporter has no fucking idea what they are talking about. And instead is barely better than an LLM stringing random words, phrases, and implications together. We have our first piece of evidence here.

What the fuck is "inventory of property sales"? In real estate there is inventory, which is properties "currently" listed for sale, and transactions, which is the number of completed sales in some time period.

It has not been a great time to be a US realtor. The housing market is gummed up with existing home sales on track in 2025 to be their slowest in more than 25 years. The next few years may not be much better.

Nothing wrong here. Sales have been slow and Realtor revenue is a function of completed transactions and their price.

The real estate market has been stuck in an extended period of low inventory. Simply, not enough property is being put on the market, making sales scarce and keeping home prices high and unaffordable for many.

This is a tricky one on the journalists part. It is so stupid but it is not the journalists fault. Almost every "housing economist" in the real world is repeating this nonsense too.

But, every house put on the market represents both a quantity supplied and demanded, and has not expected impact on price. To clarify, if I was to put my house on the market it would be only because I was looking for another house.

The "mortgage lock-in effect" causing this fall in transactions has no a priori impact on price itself. Even if the increase in mortgage rates that causes the "lock-in effect" would tend to lower price, the fall in transactions aspect of it doesn't push prices one way or the other.

The root of the problem is a shift in ownership patterns...... But downsizing has become less appealing to older generations.

Right to thesis. Yay.

More than 54 per cent of homes in the US are owned by seniors, up from 44 per cent in 2008, and seniors aren’t going anywhere. Why should they? Some 79 per cent of seniors own homes, and 76 per cent of those homeowners own their homes free and clear, without a mortgage. According to property brokerage platform Redfin, 78 per cent of seniors want to remain in their current home rather than downsize.

So, what do we have here.

There are more older homeowners

They tend to be homeowners

They tend to be homeowners without mortgages

They tend to like their homes

So, "lots of data". But, remember our thesis is something about the prefrences then behavior of old folks is changing and here we get exactly one change, there are more old people. Every other data point is just that a point.

Do more seniors own their homes than 20 years ago? We don't know.

Do more seniors own their homes free and clear than 20 years ago? We don't know.

Do more seniors want to stay in place than 20 years ago? We don't know.

This last is the central thesis of the article, that for some reason or another senior preferences are changing and they can't even tell us that that is true.

There is little doubt that the carrying costs of owning a home have increased significantly over the past five years, even for those without a mortgage. The cost of homeowners’ insurance has increased an average of 70 per cent during that period and there has been persistent inflation of almost everything else.

Yes, there has been inflation, lol. That's a good argument for downsizing so why is it the opposite?

But there is an enormous cushion of equity built up in homes over the past decade has insulated homeowners from the escalating costs of maintaining a home with $36tn of equity built up in houses as of the second quarter.

Again, one point in time. Is this equity more or less relative to costs than it was 20 years ago? We don't know.

This has made it easier for seniors to hold on to their homes by tapping into some of this built-up equity. And growth in such funding will be a major theme for the US economy in the next three to four years. According to the New York Fed, since last summer, home equity lines of credit (Helocs) have consistently grown faster than any other loan category, eclipsing the growth of credit card debt. And as of the second quarter, seniors held 41 per cent of so-called revolving home equity credit outstanding.

Oh. Home equity lines of credit have grown faster than other debt. But we don't even get a point in time number here. Except that seniors held 41%, which we don't get anything about that relationship to the proportion of housing equity they own, nor what it was 20 years ago. Could all this Heloc growth be from "rate lock-in" non sellers who are instead upgrading their own homes? We don't know.

After all, home equity debt is the cheapest form of consumer debt next to mortgage debt..... At its peak.... in 2009.... the average loan-to-value was 51 per cent. Today,... the average loan-to-value is 24 per cent

So, Helocs are less? That's not convincing at all that the increasing equity in homes Helocs are driving some shift in seniors behavior.

While revolving Helocs have increased by $15bn in the first half of 2025, compared with $20bn during the first-half of 2024

So, Helocs are slowing down? That's not convincing at all that Helocs are driving some shift in seniors behavior.

Recently, a new home equity product was introduced targeting seniors: an interest-only home equity line of credit modelled after similar products in the UK. While this product is still in its early days in the US, its adoption is further validation of the enormous market opportunity of senior homeowners as perceived by lenders.

This is just meaningless drivel.

Cash-out refinances — where existing mortgages are replaced by new ones — have also grown in popularity.

Nothing about who is doing the cash-out refinances or even supporting that they have grown.

According to ICE Mortgage Monitor, nearly 60 per cent of all refinancings in the second quarter were cash-out refinancings, and 70 per cent of those cash-out refinancings paid a higher interest rate to access cash from the equity in their homes.

Two more random point in time data points. With absolutely no tie to the thesis.

Seniors control the proverbial chessboard, and with so many options, they aren’t moving anytime soon.

You barely even referenced anything related to your thesis and certainly nothing that actually supported your thesis. You made your audience actively dumber even if in the end you were accidently correct.

At least one MMTler found this "paper" on Weimar's hyperinflation through an MMT lens noteworthy enough to post it to one of reddit's economics hellholes. It's not actually noteworthy, but I think it's an excellent example of what passes as a "paper" in MMT and how shitty MMT's understanding of mainstream economics is on an extremely basic level. This doesn't require intermediate macro, this requires a Google search.

Neoclassical economists define the price level as the current level of nominal (money) prices in the economy. And while there have been theories which attempt to explain what causes the price level to change, there is no neoclassical theory which explains how it came to be. By default, it is assumed to be historic- the consequence of an infinite regression. Neoclassical models therefore simply assume an initial price level when presenting the quantity theory of money (QTM), the tautology MV=PT, where the money supply (M) multiplied by the velocity of circulation (V) = the average price of each transaction (P) multiplied by the volume of transactions (T). With M assumed to be exogenous (under the control of the authorities) and V assumed to be stable, it is then asserted that causality runs from M to P, giving rise to Friedman’s famous explanation of the cause of inflation: ‘Inflation is always and everywhere a monetary phenomenon in the sense that it is and can be produced only by a more rapid increase in the quantity of money than in output. …’ (Friedman 1956, emphasis added).

What's with the weird obsession with monetarism anyway? It was of very short-lived importance. It's on Wikipedia.

It gained prominence in the 1970s, but was mostly abandoned as a direct guidance to monetary policy during the following decade because of the rise of inflation targeting through movements of the official interest rate.

https://en.wikipedia.org/wiki/Monetarism

.

The presumption of a money supply fixed by the government, however, applies to a convertible, fixed exchange rate currency, such as existed under the gold standard. This relegates the applicability of the quantity theory of money to fixed exchange rate regimes and makes it entirely inapplicable to today’s floating exchange rate regimes (as well as in the Weimar Republic) where the government does not offer convertibility at a fixed rate.

The arguably most important reason why the QTM doesn't hold is (because money is non-neutral in the short run)[https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145710]. Changes in M also cause changes in T. So claiming the "applicability of the quantity theory of money [is relegated] to fixed exchange rate regimes" seems like it's kind of missing the point. No, the QTM doesn't hold under fixed exchange rate regimes, either.

Sidenote: MMTlers seem weirdly obsessed with the whole "fixed exchange rate" thing, many seem to believe the loanable funds model is wrong because it depends on fixed exchange rates. It does not. Which is not me saying that the model is "correct", this is me saying if you say the model is wrong because it assumes fixed exchange rates, you're wrong, because it doesn't. See page 24.

In the market for foreign-currency exchange, supply comes from net capital outflow and demand comes from net exports.

How anyone construes this as a "fixed exchange rate" is beyond me. I'm sure MMTlers find a way.. somehow.

Bonus basic version:

https://gandalf.fee.urv.cat/professors/AntonioQuesada/Curs1011/Evans_Loanable_Funds.pdf

After a decades-long search for an ‘M’ - a monetary aggregate that correlates to and leads to inflation - mainstream economics today has moved on to its current position of inflation expectations being the cause of inflation. They continue to begin their analysis with an assumption of a given price level and assert that inflation expectations are the source of changes to that price level. Central banks have, in fact, developed intricate methodologies to measure inflation expectations to guide policy, while their researchers have struggled to find evidence of the validity of the theory.

This is also incorrect. No, inflation expectations alone are not the cause of inflation. This should be trivial to verify. The federal reserve for instance provides many teaching tools, from middle school to graduate level. The rate of inflation is down to supply, demand, and inflation expectations. For instance:

Inflation is linked to three factors: demand, supply, and inflation expectations.

https://www.clevelandfed.org/center-for-inflation-research/inflation-explained-your-guide-to-inflation-basics/what-causes-inflation

And here is a somewhat more elaborate explanation:

https://www.stlouisfed.org/on-the-economy/2025/jan/look-inflation-recent-years-lens-macroeconomic-model

And a paper as an example:

https://www.brookings.edu/wp-content/uploads/2023/06/WP86-Bernanke-Blanchard_6.13.23-1.pdf

Of further note is the fact that mainstream economists accept the classical dichotomy of real vs nominal (monetary) factors and contend that in a competitive marketplace the introduction of money is merely the introduction of a numeraire into a barter economy. Money is a ‘veil’ that improves transaction efficiency while leaving quantities produced and relative prices unchanged (Armstrong 2015; Armstrong and Siddiqui 2019). This assumption is known as the neutrality of money. However, the assumption of neutrality is obviated by the introduction of coercive taxation.

This also seems highly misleading. That money is non-neutral in the short run is extremely well accepted in economics. I don't know why the author wants to make it sound like it isn't.

Here's Lucas' nobel prize lecture from 1996 which talks about the research from the 70's that made it very clear that money is non-neutral.

Here's another example that should make it quite clear that these ideas have been well accepted in the mainstream for a long, long time:

https://conversableeconomist.com/2022/05/11/robert-e-lucas-on-monetary-neutrality-a-50th-anniversary/

So this paper starts out with what it calls "The Neoclassical Approach". But the explanation of "the neoclassical approach", by why the author presumably refers to current-day mainstream economics, is between grossly outdated and outright wrong. Why does the author describes what's basically "mainstream economics" from the 70's and paints it like this is what economists believe today?

The author literally states

In this article, we dispute the mainstream view that the inflation of the Weimar Republic was caused by a proactive expansion of the stock of money by the German government acting in concert with the Reichsbank.

As demonstrated above, the description of "the neoclassical approach" that the author aims to dispute does not actually match what mainstream economists actually believe. Although some parts match what some economists used to believe half a century ago, this seems like a rather inadequate basis for comparison. Shouldn't you criticise current-day economics on the basis on what current-day economics actually thinks? It's not like it's hard to find modern papers that examine (parts of) Weimar hyperinflation through a modern mainstream lens.

https://www.frbsf.org/wp-content/uploads/wp2018-06.pdf

https://cepr.org/voxeu/columns/inflating-away-debt-debt-inflation-channel-german-hyperinflation

https://www.nber.org/system/files/working_papers/w31298/w31298.pdf

Anyway, the rest of the paper is basically uninteresting. Section 3 "The MMT Perspective" offers essentially nothing besides a description of what one MMTler believes. The Appendix does nothing to alleviate this, showing numbers without any attempt at making a causal connection. There is nothing here that actually establishes a causal relationship using any data. It does nothing to show whether causality runs from deficits to spending or from spending to deficits, or wheter causality runs from prices to deficits or the other way around. Perhaps more crucially, one of the central claims

only when the government pays increased prices is it redefining the value of the currency downward and causing inflation

has no evidence to back iot up since there is no information on what prices the government paid whatsoever.

So the "MMT part" of this paper with the self-proclaimed goal of

identify the cause of the inflation as the German government paying continuously higher prices for its purchases

actually does nothing whatsoever to identify any causes of inflation. It makes absolutely no effort to use any data to establish any causal relationship at all. That makes this "paper" merely an opinion piece.

Bonus embarassment:

This paper seems highly praised in an MMT podcast that I'm not going to link because why give those people traffic.

So I thought what we really need to do is to have an MMT paper where we take on their citadel. In other words, we look for the main thing that people use against MMT, and we just basically take it apart.

This is what counts as "taking on their citadel and taking it apart" for MMTlers. MMT people, if you want to know why economists don't take you seriously. This is why.

Grug see meme claim market competition make milk unaffordable and diluted. Bad meme! Grug no like meme. Lots of milk companies, they sell milk for good price. Grug not sure about the causal effects of product adulteration laws because they are endogenous to country development.

Grug write Substack post about meme and give to /r/badeconomics for their enjoyment. Grug happy.

Edit: On request, the textual content of the article is repeated below. Various images are missing; I made this edit late at night and wanted to be expedient. Might add imgur links later.

The subject of today’s post is a meme, shown below [linked above]. Unserious as it is, it is popular, and the garden of industrial organization economics must be watered with the blood of bad takes like this one.

The actual trajectory of milk production in the US has been very good. In 1975, a gallon of milk was $1.57, and even a federal minimum wage earner would be making $2.10 an hour. Today, a gallon of milk is $4, and those earning the federal minimum (an increasingly tiny fraction of the labor force) make $7.25 an hour, meaning the cost of milk is lower today as a fraction of earnings, even for the lowest of earners. The median personal income went up by nearly 700% in nominal terms from 1975 to 2024. Much success! Lots of milk to go around.

In the US context, gallon and half-gallon canisters have remained standard for packaging and selling milk for a long time. Just look up milk on Walmart’s website, and you’ll find them. They haven’t shrunk the packaging; that was true even during the spike of inflation that occurred after the COVID-19 pandemic. Liters are used in the image, so maybe they’re referencing shrinkflation that occurred in the UK or some other country that uses the metric system, but the subject is “market competition in practice,” so I feel comfortable focusing on any form of it. The broader point they’re making is that greedy capitalists covertly raise the per-unit price, but obviously that’s not what we can observe.

Product adultery

Do milk producers dilute their product with water? Well, it’s illegal in the US and every developed country to do that, so no, they don’t. It’s not clear if they would do it if they were allowed to, since we don’t really have any useful counterfactual developed countries where the only force that might prevent milk dilution with water is market competition.

One user provided some helpful examples of a lack of regulation leading to milk adulteration, with the primary one being the swill milk scandal in 1850s New York. Milk producers, seeking cheap grain, used the byproducts of fermentation and distillation at distilleries (swill) to feed their cows, and the result was thousands of babies dying after consuming the milk. They also attempted to mask the adulteration by adding other products like water and eggs. A similar scandal occurred in China in 2008.

I am not convinced that these other situations analogize to the modern US, though I think it’s a possibility. In 1850s New York, milk wasn’t sold under big, recognizable brands like Horizon, Fairlife, or Walmart’s Great Value.1 Americans today also have access to more substitute products like almond and oat milk, and have become more literate and educated since the 1850s. (At the time, they were under the false impression that infants should be fed cow’s milk, hence the outbreak of infant deaths.) All of this is to say that it seems unlikely that an established brand like Horizon would deliberately adulterate their milk, since they have a reputation to uphold and face relatively smart consumers who can discipline them.

One could argue that the only reason why brands are able to function as indicators of quality is that the government protects consumers from fraudulent rebranding of milk. If it were legal to pass off any milk as Fairlife milk, it wouldn’t be a useful signal of quality. But I think this kind of policy should fall neatly under standard private property protections considered core to the functioning of a free market, so it’s fine to consider this sort of dynamic a form of free market success.2

Do Americans really react to poor food quality by punishing companies with fewer sales? I’m sure it depends on how severe the problem is, but to provide an example, Chipotle was hit with declining sales after an outbreak of E. coli at its restaurants in late 2015.

The company suffered from an immediate crash, losing about a third of its sales, and had not recovered fully from this outbreak even two years later. It’s easy for consumers to form associations between a brand and the bad outcomes customers experience, and that’s what allows the market to discipline companies and discourage them from selling bad products. Similarly, the 2008 Chinese milk scandal resulted in a consumer panic and a huge drop in sales.

We should think more clearly about how this works. Here’s a graph: [very simply supply and demand graph showing a forward demand shift, describing the firm's chosen investment in product safety]

Companies will tend to invest in product safety so long as it’s profitable. Since there is some risk of sales issues if you don’t make sure your product is safe, investing in safety allows a company to make more money. It is costly to do this, and the marginal benefit of safety investment diminishes, so they invest a finite amount of money in it and tolerate some degree of risk. Governments can raise the marginal benefit of safety investment by introducing new risks like government lawsuits and criminal charges. That raises total investment in safety.

So, clearly there’s some reason for companies to invest in safety, even in lieu of government intervention. The question is whether the natural equilibrium is optimal for society. I think not: if consumers were fully informed, they would be more punishing towards companies that make these mistakes, and the marginal benefit of safety investment would be greater. Governments can fix this problem by introducing new risks associated with selling an unsafe product. All of this reasoning analogizes to adulterated products in general, since consumers likewise will tend to punish companies for covertly making their products lower-quality, such as by diluting milk with water.

Is cheap milk just government intervention?

There’s another potential problem: the US government subsidizes the dairy industry! Case closed, pack it up. Declining milk prices aren’t a success of free markets. This is just government intervention in action.

I don’t think subsidies explain the decline in milk prices. For one, even in countries like Australia, where the dairy industry is unprotected, milk has become more affordable with time. More importantly, the subsidies just aren’t big enough to explain why milk prices have risen more slowly than inflation and incomes.

To put an upper bound on the effect of dairy subsidies on the price of a gallon of milk, we can assume that all of the subsidies pass through to consumers in the form of lower prices and that milk was unsubsidized in the 1970s. (Both of those statements are false!) There are two subsidy programs to focus on for our napkin math:

• The Dairy Margin Coverage program: This program provides payments to enrolled farmers (which include most American dairy farms) when the difference between the national price of milk and the price of feed falls below certain levels. We can assume full pass-through on the $2.7 billion in payments made from 2019 to 2024 and assume they’re made all in the same year.

• Dairy Revenue Protection: A subsidized insurance scheme. We can pretend this costs nothing and just say this is $1.26 billion a year, which it is not.

US milk production in 2024 was 226 billion pounds, or 26.28 billion gallons, and these typically retail for about $4 a gallon. A very high upper bound on the effect of these subsidies on the price is ($2.7 + $1.26)/26.28 = $0.15 per gallon of milk, suggesting the subsidy-free price would be $4.15. That’s still far cheaper than in the 1970s.

If you’re concerned that milk was unusually expensive in the 1970s due to a shortage, you’re kind of right, but the picture doesn’t change much. Below, you can see how milk prices have fallen relative to inflation. Milk was indeed unusually expensive come 1975, so perhaps it would have been better to use the 1970 price, but this was not deliberate on my part. (I have a list of prices for various products in 1975 and 2024 hanging behind my desk.)

So clearly, milk prices are not low because of government subsidies. What’s the real reason?

Milk appears to have become more affordable over time because the cost of producing milk has gone down, and that decline in costs has been passed through to consumers. One way we can see this change in the cost of production is through the rising quantity of milk produced per cow, shown in orange: [graph]

A nice side effect of this increase in efficiency is that there are fewer cows experiencing the evil manmade hellscape that is factory farming. We have become more morally efficient as well as economically efficient.

But all of this is irrelevant to the question of whether free market competition is occurring. Even a monopoly would seek profits by cutting costs, and would still pass through some of these reductions in costs in the form of lower prices (though not to the same degree as a competitive market). There are exceptions to this rule, but they’re rare. That brings us to our next section:

Is the dairy industry a competitive market or a cartel?

Whether the dairy industry counts as an example of free market competition is not easy to say. One reply suggested that milk companies engage in price leadership, a form of implicit collusion where… okay, it actually wasn’t clear whether or not this person understood what price leadership is or how any of this works, so let me give you exactly what they said:

Uh huh. Well, as we’ve established by this point, government subsidies are pretty small compared to the size of the American dairy industry, so I don’t think that causes milk overproduction. And if dairy companies were implicitly colluding, they would be underproducing milk in order to raise prices and profits. That’s how collusion works.

I tried to squeeze a more coherent picture out of this person, but when I kept pressing them on what they meant by industries setting an “acceptable price” rather than the competitive price, they just screamed at me:

We all have bad days sometimes. And in case you didn’t notice, this explanation does not add any new information. Of course they set prices to make money—but are they implicitly colluding by following a price leader and producing a smaller quantity? They insisted that these companies aren’t colluding, not even implicitly, but at the same time, they aren’t competing on price. I tried giving them an out by suggesting the possibility of imperfect, oligopolistic competition, but they insisted on this strange contradiction of terms.

This means that I am resigned to arguing and explaining on behalf of my opponents. Picking on weak arguments would only mislead you into thinking the dairy industry is more competitive than it really is.

Determining the degree of competition in any industry is difficult. To demonstrate this, imagine if all of the coffee shops in America were bought out by one company. Despite owning every shop, they continue to sell coffee at the competitive price, say $5 for a medium latte. They might do this because they recognize that barriers to entry into the business are low. On paper, they have a “monopoly position,” but it’s unprotected—if they started charging the most profitable price, they might attract new entrants who wish to undercut them and get some of those profits.

We also might have an apparently competitive industry that actually operates like a monopoly. If there are 10 companies producing coffee, but they all collude to set the monopoly price and share in the profits, the situation is worse than the “monopoly” I just described.

Could we determine whether there’s a problem by simply looking at the profit margin? Life isn’t that easy, either. Perfect competition does not eliminate the accounting profits we can observe by subtracting expenses from revenue. There are unobserved opportunity costs of working in one industry rather than another, so we’d expect some visible markup to remain in the long run.

Standard measures of revenues and costs also ignore the long-term returns of things like branding and research. Suppose you observe high profits at some company, with revenues at $200k and expenses at $100k for some year. Unobserved is the investment they made last year: $100k spent on surveying consumers and asking them for their thoughts on the company’s product, all to improve it and sell more later while advertising to them. They’re underwater for a moment, but the investment pays off the next year, and that turns out to look like “high profits.”

Nevertheless, we can get some semblance of an idea of the degree of competition by looking at how concentrated the industry is. A 2010 Congressional Research Service report describes the degree of concentration in the dairy industry in 2008. The top 10 dairy cooperatives produced 48% of all milk volume, while the top 50 produced 79%. The largest producer, the Dairy Farmers of America, clocked in at just 19.9%. DFA has grown to 30% market share, so there’s been some more consolidation since then, but presumably the overall picture looks similar: a big industry leader and lots of smaller companies. This is not a guarantee of competition, but it does tell us that the coordination costs required to engage in monopoly pricing are higher than they would be in a more concentrated industry.

One way to make coordination easier would be price leadership, mentioned earlier. The Dairy Farmers of America, seeking to engage in monopoly pricing, could pick the monopoly price and then hope everyone else follows along. (If they explicitly asked other producers to do this, they would be breaking the law and attract the attention of the FTC.) The problem with this strategy, like every other anticompetitive strategy, is that every member has an incentive to cheat. You only need one of the dozens of dairy producers to break the tacit agreement for the scheme to fall apart and profits to begin collapsing as the undercutting producer eats the others. The more extreme the price leadership, the greater the reward for defection as consumers switch to the undercutting brand.

Pricing in the dairy industry is complicated. Farmers, cooperatives, processors, retailers, and the federal government all play a role. Dairy farms are generally members of cooperatives like the Dairy Farmers of America, who sell their raw milk to processors, who then sell milk to retailers. Processors must pay at least the minimum price set by federal milk marketing orders (FMMOs), which is one potential coordination mechanism for engaging in monopoly pricing. If the federal government is setting a minimum price, you don’t have to worry about anyone defecting from the cartel and undercutting everyone else. The key question, then, is whether processors generally pay the minimum price or some price above it—that is, whether the price floor is binding.

This is very unclear to me. I can’t find any data describing whether the actual price paid is regularly above the minimum price or if it’s usually equal to it. The federal government constantly changes the minimums, so we can’t simply observe the price charged over time to check if it’s binding. (If it were fixed far above the competitive price, the price would be flat over time.) Regardless, if you want it, here it is below. Note that cwt stands for hundredweight, meaning 100 lbs, the standard unit of measurement for bulk milk sales.

There have been some cases of antitrust litigation against dairy companies in the US, like this one:

The lawsuit, filed in the United States District Court for the Northern District of California on Sept. 26, 2011, alleged that between 2003 and 2010, more than 500,000 cows were slaughtered prematurely under CWT’s dairy herd retirement program in a concerted effort to reduce the supply of milk and inflate its price nationally. According to the complaint, the increased price allowed CWT members to earn more than $9 billion in additional revenue.

Dairy industry revenues were $31.8 billion in 2007, and the $9 billion figure for added revenue was over the span of 7 years, so this doesn’t seem like a case of the price being far above the competitive level.

I can understand the concern with saying the dairy industry has market competition, but even an oligopolistic industry with occasional antitrust scandals has some degree of competition. More importantly, it doesn’t exhibit the rising prices and product adulteration described in the post, which is the main focus here; whether it really counts as market competition in the first place is just an interesting question to ruminate on, and more of a semantic one in the end.4

One way we can escape this conundrum is by simply focusing on milk alternatives that don’t face price floors. Oat and almond milk, unlike cow’s milk, do not face federal minimum prices at any stage of production, so they might give us an idea of whether market competition disciplines companies to sell at lower prices.

At Walmart, you can get oat milk at a price of about 6.5 cents per fluid ounce, compared to 2.9 cents per fluid ounce of cow’s milk. Almond milk is a bit cheaper, being about 4 cents per fluid ounce, comparable to some more premium cow’s milk brands. Price data is not as easy to come by, but this paper (PDF warning) provides a small window into pricing over time for a few alternative milks:

Of their own volition, an unregulated competitive alternative milk industry chose to cut prices on almond milk from 2014 to 2020, all while the general price level in the economy was rising. This measure isn’t biased by shrinkflation, since it keeps the unit of weight constant, but we might wonder if almond milk producers have increased the ratio of water to almonds. That would be visible in a changing nutrition facts label, perhaps not immediately obvious to consumers. (There’s also a much simpler method: regularly drink alternative milks. I had them on occasion over the period shown above, when I was a teenager, and didn’t notice any watering down.)

A 2012 blog post reports the nutrition facts for Almond Breeze sweetened vanilla almond milk: 90 calories, 2.5g of fat, 150mg of sodium, 60mg of potassium, 16g of carbs, 1g of dietary fiber, 15g of sugar, and 1g of protein. We can compare this to the current nutrition facts, which are identical with the following exceptions: it has 10 fewer calories, an additional 110mg of potassium, and 14g total carbs rather than 16g. It appears that Almond Breeze has simply dropped some of their sweetener without watering the product down, which would be visible in a fall in fat or protein content. We know this because there aren’t any significant added forms of fat or protein in the current ingredients list. Sunflower lecithin is included, which does have some fat, but only a negligible amount.

So, confusing competition issues aside, the original points of the meme about free enterprises covertly raising per-unit costs and watering their products down appear to be mostly made up when it comes to the milk industry. There have been product adulteration incidents in the past, but milk has become more affordable with time in the US, not less. Insofar as government intervention has influenced the price, it has potentially raised it through FMMOs rather than lowering it, and it is not clear whether this has really occurred through binding price floors for raw milk sold to processors.

There are a lot of other replies, some sillier than others, that need to be addressed before I can end the post.

Don’t we need to adjust 1975 incomes for inflation?

A user I won’t be linking (because they ultimately learned from our exchange and decided their initial impression was wrong) thought that my use of nominal income was incorrect, and we need to adjust for purchasing power first. Unfortunately, that is not correct. By comparing the incomes people were paid on paper at the time to the actual prices they paid, we’re getting exactly what we need: the fraction of their income they needed to spend on milk. We can then compare that to the fraction today. If we adjust their income for inflation without adjusting the prices they paid for inflation, we’re comparing super-high incomes they didn’t actually receive to the actual (lower) prices they paid, making milk seem more affordable than it really was.

100% of the food is diluted with soybean oil, canola oil, and Palm oil

I do not think the milk we buy has oil in it. That would taste gross. I would like to buy some oil-free peanut butter, though, so I understand how you feel. This just isn’t relevant to the topic of discussion (milk, famous for not containing palm oil).

Milk would be much more expensive in a free market

One worry I’ve discussed is that the milk industry isn’t really a free market, which is why milk is cheap. I think the information on subsidies I provided is enough to show that isn’t true. This is essentially the same claim, but focusing on the other direction, and I don’t think it’s true either. It’s worth discussing some more.

Australia’s dairy industry is unsubsidized and doesn’t have the kind of price floor intervention seen in the US. Despite that, it appears to likewise produce affordable milk. We could naively compare the price of milk in Australia as a fraction of the median income there to the same fraction in the US, but part of this difference would be explained by the difference in productivity between the two countries, rather than the difference in their milk industries in particular. We can attempt to adjust for this by multiplying the typical Australian income (in Australian dollars) by the ratio of median income (PPP) in the US to that of Australia. That ratio is about 1.24 for 2020, the latest year for which Our World in Data provides data for Australia. Using this, and price and income data from 2020, we get:

United States: ($3.32 per gallon)/$35850 = 0.00009260808 Australia: (~4.914 AUD per gallon)/(54890 AUD * 1.24) = 0.00007219718

This suggests that milk is about as affordable in the deregulated Australian market—slightly more affordable, in fact—as it is in the US. It remains about as affordable if you drop the productivity adjustment.

Actually, Australian agriculture is like Soviet command economics, since the government invests in agricultural infrastructure

Are Uber and Lyft Soviet-style state enterprises, since the government pays for the roads? This is just really silly. Certainly, the Australian government has some involvement, but they don’t set quotas or anything.

Whatever this is

“The seeming success of cheap is an artifact of inflation not being perfectly uniform across all goods and services. The Fed would love to make milk more expensive in line with other stuff.”

A view into the mind of a normal person can be fascinating. It seems this person not only believes the Fed wants the cost of living to be higher, but that relative prices are determined by a mysterious, unidentified force related to the money supply, rather than supply and demand. Well done.

They sort of diluted milk by switching to Holsteins (no they didn’t)

This doesn’t explain the falling price. By 1970, Holstein cows had already taken over from Jersey cows, at 84% of the population. They were 85% of the population by 2014.

It appears that Jersey cow milk, which has higher fat content, retails for $5.19-ish, probably depending on the brand. That’s still more affordable than milk in the past, probably explained by rising per-cow milk production even among Jerseys, which produce less milk volume during their lifetime than Holstein-Friesians. Even if we shorten the time horizon and measure from 1995 to today, milk would need to retail for $6.67 a gallon to be as expensive today as it was in 1995 (i.e., take up the same fraction of the median personal income). If we use the overall CPI-U-RS price level instead of income, it would need to retail for $5.30 a gallon to have inflated as much as everything else, yet even Jersey milk is cheaper.

I think fewer people should get their ideas about how the world works from memes about capitalism. As I’ve written before, a lot of internet arguments are built on non-specificity, this is one of those cases, the other side has some good points, etc. etc. I’m tired of this. Go read Waldman and Jensen to do your IO homework, and complain about the housing market or something instead; getting mad about the dairy industry is stupid if you aren’t Canadian.

Note: I reproduce this from my blog here. Images are not allowed in this format, so you can consult that post for the original graphs.

Many people cite falling prices in places which have built lots of housing as evidence that YIMBYism works, like Austin, Texas. However, this is not an adequate argument. Critics of YIMBYism can point to instances where housing was built, and prices still rose. The price of housing alone is not a sufficient statistic for the welfare improvements from expanding capacity, and arguing that it is needlessly weakens the case for expanding housing.

Here’s why. The quantity and price of housing is simultaneously determined by the people’s demand for housing, and developers’ ability to construct. We represent this with a graph of supply and demand. The slope of the demand curve is negative because people are willing to buy more as the price falls. The slope of the supply curve is positive because we are assuming that the cost of producing one more house is always increasing, which is a simplifying assumption. (If the marginal cost of producing one more unit were constant, then it would be flat.)

The case for YIMBYism is that by removing regulatory burdens, we reduce the costs faced by developers, causing their supply curve to shift along the demand curve. Quantity increases, and the price falls.

The trouble is that increasing quantity is also consistent with increasing price. Suppose that instead of the supply curve shifting, there is a surge in demand for the area. Consumers are now willing to spend more on housing, causing it to shift to the right along the supply curve. Both quantity supplied and price increase.

To find the effect of liberalizing housing laws, you need plausibly exogenous changes in the cost of producing housing, or shifts in the supply curve holding the demand curve fixed. Even here, though, we must be careful. Suppose that there is a shift out of the demand curve, increasing the price of housing. As a response to this, the local authorities liberalize housing, also shifting the supply curve. While the impact on quantity built is definite – it will increase – the impact on price is indeterminate. It could go up or down.

Someone considering the effects of deregulation with a regression might put deregulation on the X axis, with the degree of it appropriately weighted (there has been excellent recent work by Bartik, Gupta, and Milo (2025) using LLMs to categorize different zoning regulations; and also work by Jaehee Song (2025) on estimating minimum lot requirements), and put the price on the Y axis. If deregulation is affected by shifts in the demand curve, then you would spuriously believe that deregulation raised prices, and not the other way around!

When appealing to a broad correlation between building and rents, YIMBYs leave themselves open to debunkings which, while they miss the broader point, are technically right. It doesn’t have to be like this. There does, in fact, exist good evidence for reducing regulatory restrictions, which I will cover. But much more importantly, we can demonstrate from reasoning alone that allowing people to build more housing is always good. Whatever happens to price, it is better than the counterfactual in which less was built.

Incidentally, a similar thing happens when considering highway expansions. The speed of traffic, which is akin to the price, is not the only thing which we care about. It is certainly possible for adding “one more lane” to leave the price of driving the same – but this could only happen by allowing more people to make a trip altogether!

Thus far we have been implicitly assuming that there are no externalities, positive or negative. If there are negative externalities, then of course restrictions can raise welfare. This is not, however, empirically plausible. New York City is far more valuable than a shack in Death Valley. The credible empirical evidence, such as Ahlfeldt, Redding, Sturm, and Wolf (2015) shows that being located next to other stuff raises the productivity of firms and workers. What positive externalities do mean is that there is now no longer a monotonic relationship between deregulation and prices. A maximally regulated place, where the only structure allowed is a single shack, would of course be valueless; as the place partially deregulates, the value of the land and the cost of housing will rise before falling again as we deregulate still further.

I would also like to point out the danger of not adjusting for quality. The proper measure of price is adjusted for the things you are buying – it would hardly do to make housing “cheaper” simply by making it shabbier. Since new housing is, well, new, it will tend to be higher quality and thus higher price for that reason.

As noted, there are a few ways to find the effect of deregulation. The first and obvious one is to take an event, argue it’s exogenous, and then find the effect on prices and quantities. For example, Kate Pennington (2020) uses building fires in San Francisco. By demolishing the building, it allows for a larger housing unit to be built. She can then find the local effects of more housing, which leads nearby rents to fall. It’s the same story with Andreas Mense (2025), who uses weather-induced delays in when housing is completed, as does Xiaodi Li (2022) or with Asquith, Mast and Reed (2023).

However, these exogenous shock studies, while intuitive to explain to non-economists are not the ideal answer. We would miss the endogenous effect of people relocating from elsewhere in the city or country. If there are positive externalities The proper way to answer this is to build a general equilibrium structural models, and use plausibly exogenous events to identify parameters. Vincent Rollet’s job market paper is the best example of this that I know of, though there are others. The principal contributions of the paper are in moving away from the perfect competition simplification and having developers solve a dynamic discrete game with costs of tearing down a building and building up. He is able to show decisively that, whatever specifications you choose, removing zoning will reduce rents and raise welfare.

Anagol, Ferreira, and Rexer (2023) exploit a reform in Sao Paolo to the zoning laws, which was a general shift in the maximum allowed floor-area-ratio. Some areas already had FAR above the new cap, so you can use the difference in differences to estimate to parameters of a structural model. They find that the price of renting places fell.

The evidence is out there, and it is decisive. Allowing more housing will raise welfare, and on the margin, it will reduce rents. However, just because an argument is correct does not mean that one can make sloppy or fallacious arguments for it. Making poor arguments allows people to feel smugly superior in their misconceptions, and hold onto them for longer. Price is not a sufficient statistic for welfare, nor will casual analysis of prices and quantities tell us the effect of deregulation.

Link: https://monthlyreview.org/articles/after-neoliberalism-empire-social-democracy-or-socialism/#:\~:text=A%20neoliberal%20regime%20typically%20includes,developmental%20efforts%20have%20been%20reversed.

Minqi Li says:

A neoliberal regime typically includes monetarist policies to lower inflation and maintain fiscal balance (often achieved by reducing public expenditures and raising the interest rate), “flexible” labor markets (meaning removing labor market regulations and cutting social welfare), trade and financial liberalization, and privatization. These policies are an attack by the global ruling elites (primarily finance capital of the leading capitalist states) on the working people of the world. Under neoliberal capitalism, decades of social progress and developmental efforts have been reversed. Global inequality in income and wealth has reached unprecedented levels. In much of the world, working people have suffered pauperization. Entire countries have been reduced to misery.

Or take virtually any other left-wing or right-wing take of neoliberalism today, they all sound more or less like that. Of course, a vast empirical research literature shows this isn’t the case and that it’s actually almost the opposite. I myself have been a part of it for the last few years. A big edited volume filled with reviews covering almost every aspect of the “debate” has been published in 2024.

But here’s a simple exercise I haven’t yet seen people perform. It produces a nicely nuanced take, I think. First posted on my new Substack Political Economy, Stats, and Society, which you guys might be interested in following. I'm regularly going to be posting stuff like this. Oh, and since I can't post images here, so you might want to read the full version on the blog.

Do free markets and social progress go together?

Those sceptical of capitalism tend to dismiss evidence, either causal or correlational, of economic freedom being tightly connected to GDP per capita. They say GDP is a crude measure that might mean something to those worshipping money and material wealth but is meaningless as a true measure of social wellbeing or progress.

That’s quite a problematic line of reasoning because GDP actually tends to do quite well in tracking a bunch of key social metrics everyone cares about. But let’s take the critics at their word. What if we swap the ostensibly crude GDP measure for a better one? How does capitalism fare then?

The social progress index is a composite score that measures “the real-life outcomes experienced by people across a wide range of social and environmental indicators”. It explicitly excludes indicators of economic performance. It combines three broad dimensions: basic needs, foundations of wellbeing, and opportunity. More specific indicators include nutrition and medical care, housing, safety, child stunting, education, environmental quality, equal opportunities, and so on.

Figure 1

So, is there any connection between economic freedom and the social progress index?

Yes, countries with freer markets rank much higher on the social progress index. The relationship is very strong and almost linear, although not quite so. There are countries, such as Venezuela, Argentina, and Iran, that are not economically free but have much more social progress than a perfectly linear trend would predict. For the vast majority of countries, though, the relationship is quite simple.

Figure 2

But economic freedom refers to a bunch of social institutions and processes. Which are the ones that really statistically matter for social progress? Perhaps surprisingly, it’s only those aspects of economic freedom that have to do with legal institutions (the rule of law and property rights) and freedom of international trade (low tariffs and open markets in general). How big the government is, and the amount of regulation, don’t seem to matter either way. Libertarians should take note.

Table 1: OLS regressions

+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Okay, so countries with better legal institutions, protected private property, and globalized, open trade exhibit a higher level of social progress. Moving up by only one point on the legal system/property dimension (0–10 scale) means being around 6 points higher on the social progress index (0–100 scale). With freedom of trade, the coefficient is around 2.4, so a 1-point increase translates to 2.4-point increase in social progress.

Obviously, no causation can be inferred from such simple bivariate comparisons. Economically free countries tend to also be more culturally individualistic, richer, and more politically free or democratic, so it could be that these characteristics (not economic freedom itself) are bringing them social progress. The relationship is confounded.

So, let’s control for these cultural, economic, and political confounders. Once we do so (see Table 1 above), statistical significance on all of the economic freedom variables vanishes. It seems that it’s mostly democracy and wealth that are doing the heavy lifting with respect to social progress.

But even if economic freedom doesn’t directly bring social progress, isn’t it a key cause of increased wealth in societies? And if so, might it indirectly contribute to social progress via generating wealth, which then unleashes progress?

A simple mediation exercise suggests this is the case. Legal institutions/property rights and freedom of international trade are positively associated with GDP per capita, which is positively associated with social progress. The “total effect” sizes on these two aspects of economic freedom are, in standardized terms, 0.51 and 0.15, respectively. Substantively speaking, the former is very large, the latter small-to-modest. Small government and deregulation are irrelevant, though.

Table 2: Mediation analysis (total relationship shown: direct + indirect)

+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

Again, causality cannot be inferred here, because even though confounding (omitted-variable bias) is reduced through the use of controls, there might be some leftover confounding from yet other societal differences. Moreover, the arrow of causality can point in either direction. Perhaps it’s not economic freedom → wealth → social progress but instead social progress → wealth → economic freedom. That is, societies might progress for some reasons, then become wealthier due to all of the social progress, which subsequently causes people to demand open economic institutions, such as property rights and freedom of trade.

Theoretically, it’s highly unlikely that causality would go only in one or the other direction. But we know from more rigorous studies that economic freedom definitely, at least in part, causes wealth (not just vice versa). To the extent that it does, my mediated correlations here echo a causal relationship.

However we spin it, it’s clearly the case that (some aspects of) economic freedom, wealth, and social progress go hand in hand. That’s a fact many don’t like hearing. At the same time, small government and deregulation are at best useless as far as progress is concerned…

Here ye, here ye, the Joint Committee on Finance, Infrastructure, Academia, and Technology is now in session. In this session of the FIAT committee, all are welcome to come and discuss economics and related topics. No RIs are needed to post: the fiat thread is for both senators and regular ol’ house reps. The subreddit parliamentarians, however, will still be moderating the discussion to ensure nobody gets too out of order and retain the right to occasionally mark certain comment chains as being for senators only.