Home price targeting

Jan 29 JDN 2459973

One of the largest divides in opinion between economists and the general population concerns the question of rent control. While the general public mostly supports rent control (and often votes for it in referenda), economists almost universally oppose it. It’s hard to get a consensus among economists on almost anything, and yet here we have one; but people don’t seem to care.

Why? I think it’s because high rents are a genuine and serious problem, which economists have invested remarkably little effort in trying to solve. Housing prices are one of the chief drivers of long-term inflation, and with most people spending over a third of their income on housing, even relatively small increases in housing prices can cause a lot of suffering.

One thing we do know is that rent control does not work as a long-term solution. Maybe in response to some short-term shock it would make sense. Maybe you do it for awhile as you wait for better long-term solutions to take effect. But simply putting an arbitrary cap on prices will create shortages in the long run—and it is not a coincidence that cities with strict rent control have the worst housing shortages and the greatest rates of homelessness. Rent control doesn’t even do a good job of helping the people who need it most.

Price ceilings in general are just… not a good idea. If people are selling something at a price that you think is too high and you just insist that they aren’t allowed to, they don’t generally sell at a lower price—they just don’t sell at all. There are a few exceptions; in a very monopolistic market, a well-targeted price ceiling might actually work. And short-run housing supply is inelastic enough that rent control isn’t the worst kind of price ceiling. But as a general strategy, price ceilings just aren’t an effective way of making things cheaper.

This is why we so rarely use them as a policy intervention. When the Federal Reserve wants to achieve a certain interest rate on bonds, do they simply demand that people buy the bonds at that price? No. They adjust the supply of bonds in the market until the market price goes to what they want it to be.

Prices aren’t set in a vacuum by the fiat of evil corporations. They are an equilibrium outcome of a market system. There are things you can do to intervene and shift that equilibrium, but if you just outlaw certain prices, it will result in a new equilibrium—it won’t simply be the same amount sold at the new price you wanted.

Maybe some graphs would help explain this. In each graph, the red line is the demand and the blue line is the supply.

Here is what the market looks like before intervention: The price is $6. We’ll say that’s too high; people can’t afford it.


Now suppose we impose a price ceiling at $4 (the green line). You aren’t allowed to charge more than $4. What will happen? Companies will charge $4. But they will also produce and sell a smaller quantity than before.

Far better would be to increase the supply of the good, shifting to a new supply curve (the purple line). Then you would reduce the price and increase the amount of the good available.


This is precisely what we do with government bonds when we want to raise interest rates. (A greater supply of bonds makes their prices lower, which makes their yields higher.) And when we want to lower interest rates, we do the opposite.

Of course, with bonds, it’s easy to control the supply; it’s all just numbers in a network. Increasing the supply of housing is a much greater undertaking; you actually need to build new housing. But ultimately, the only way to ensure that housing is available and affordable for everyone is in fact to build more housing.

There are various ways we might accomplish that; one of the simplest would be to simply relax zoning restrictions that make it difficult to build high-density housing in cities. Those are bad laws anyway; they only benefit a small number of people a little bit while harming a large number of people a lot. (The problem is that the people they benefit are the local homeowners who show up to city council meetings.)

But we could do much more. I propose that we really use interest-rate targeting as our model and introduce home price targeting. I want the federal government to exercise eminent domain and order the construction of new high-density housing in any city that has rents above a certain threshold—if you like, the same threshold you were thinking of setting the rent control at.

Is this an extreme solution? Perhaps. But housing affordability is an extreme problem. And I keep hearing from the left wing that economists aren’t willing to consider “radical enough” solutions to housing (by which they always seem to mean the tried-and-failed strategy of rent control). So here’s a radical solution for you. If cities refuse to build enough housing for their people, make them do it. Buy up and bulldoze their “lovely” “historic” suburban neighborhoods that are ludicrous wastes of land (and also environmentally damaging), and replace them with high-rise apartments. (Get rid of the golf courses while you’re at it.)

This would be expensive, of course; we have to pay to build all those new apartments. But hardly so expensive as living in a society where people can’t afford to live where they want.

In fact, estimates suggest that we are losing over one trillion dollars per year in unrealized productivity because people can’t afford to live in the highest-rent cities. Average income per worker in the US has been reduced by nearly $7000 per year because of high housing prices. So that’s the budget you should be comparing against. Keeping things as they are is like taxing our whole population about 9%. (And it’s probably regressive, so more than that for poor people.)

Would this destroy the “charm” of the city? I dunno, maybe a little. But if the only thing your city had going for it was some old houses that are clearly not an efficient use of space, that’s pretty sad. And it is quite possible to build a city at high density and have it still be beautiful and a major draw for tourists; Paris is a lot denser than far-less-picturesque Houston. (Though I’ll admit, Houston is far more affordable than Paris. It’s not just about density.) And is the “charm” of your city really worth making it so unaffordable that people can’t move there without risking becoming homeless?

There are a lot of details to be worked out: How serious must things get before the federal government steps in? (Wherever we draw the line, San Francisco is surely well past it.) It takes a long time to build houses and let prices adjust, so how do we account for that time-lag? Where does the money come from, actually? Debt? Taxes? But these could all be resolved.

Of course, it’s a pipe dream; we’re never going to implement this policy, because homeowners dread the idea of their home values going down (even though it would actually make their property taxes cheaper!). I’d even be willing to consider some kind of program that would let people refinance underwater mortgages to write off the lost equity, if that’s what it takes to actually build enough housing.

Because there is really only one thing that’s ever going to solve the (global!) housing crises:

Build more homes.

What is it with EA and AI?

Jan 1 JDN 2459946

Surprisingly, most Effective Altruism (EA) leaders don’t seem to think that poverty alleviation should be our top priority. Most of them seem especially concerned about long-term existential risk, such as artificial intelligence (AI) safety and biosecurity. I’m not going to say that these things aren’t important—they certainly are important—but here are a few reasons I’m skeptical that they are really the most important the way that so many EA leaders seem to think.

1. We don’t actually know how to make much progress at them, and there’s only so much we can learn by investing heavily in basic research on them. Whereas, with poverty, the easy, obvious answer turns out empirically to be extremely effective: Give them money.

2. While it’s easy to multiply out huge numbers of potential future people in your calculations of existential risk (and this is precisely what people do when arguing that AI safety should be a top priority), this clearly isn’t actually a good way to make real-world decisions. We simply don’t know enough about the distant future of humanity to be able to make any kind of good judgments about what will or won’t increase their odds of survival. You’re basically just making up numbers. You’re taking tiny probabilities of things you know nothing about and multiplying them by ludicrously huge payoffs; it’s basically the secular rationalist equivalent of Pascal’s Wager.

2. AI and biosecurity are high-tech, futuristic topics, which seem targeted to appeal to the sensibilities of a movement that is still very dominated by intelligent, nerdy, mildly autistic, rich young White men. (Note that I say this as someone who very much fits this stereotype. I’m queer, not extremely rich and not entirely White, but otherwise, yes.) Somehow I suspect that if we asked a lot of poor Black women how important it is to slightly improve our understanding of AI versus giving money to feed children in Africa, we might get a different answer.

3. Poverty eradication is often characterized as a “short term” project, contrasted with AI safety as a “long term” project. This is (ironically) very short-sighted. Eradication of poverty isn’t just about feeding children today. It’s about making a world where those children grow up to be leaders and entrepreneurs and researchers themselves. The positive externalities of economic development are staggering. It is really not much of an exaggeration to say that fascism is a consequence of poverty and unemployment.

4. Currently the main thing that most Effective Altruism organizations say they need most is “talent”; how many millions of person-hours of talent are we leaving on the table by letting children starve or die of malaria?

5. Above all, existential risk can’t really be what’s motivating people here. The obvious solutions to AI safety and biosecurity are not being pursued, because they don’t fit with the vision that intelligent, nerdy, young White men have of how things should be. Namely: Ban them. If you truly believe that the most important thing to do right now is reduce the existential risk of AI and biotechnology, you should support a worldwide ban on research in artificial intelligence and biotechnology. You should want people to take all necessary action to attack and destroy institutions—especially for-profit corporations—that engage in this kind of research, because you believe that they are threatening to destroy the entire world and this is the most important thing, more important than saving people from starvation and disease. I think this is really the knock-down argument; when people say they think that AI safety is the most important thing but they don’t want Google and Facebook to be immediately shut down, they are either confused or lying. Honestly I think maybe Google and Facebook should be immediately shut down for AI safety reasons (as well as privacy and antitrust reasons!), and I don’t think AI safety is yet the most important thing.

Why aren’t people doing that? Because they aren’t actually trying to reduce existential risk. They just think AI and biotechnology are really interesting, fascinating topics and they want to do research on them. And I agree with that, actually—but then they need stop telling people that they’re fighting to save the world, because they obviously aren’t. If the danger were anything like what they say it is, we should be halting all research on these topics immediately, except perhaps for a very select few people who are entrusted with keeping these forbidden secrets and trying to find ways to protect us from them. This may sound radical and extreme, but it is not unprecedented: This is how we handle nuclear weapons, which are universally recognized as a global existential risk. If AI is really as dangerous as nukes, we should be regulating it like nukes. I think that in principle it could be that dangerous, and may be that dangerous someday—but it isn’t yet. And if we don’t want it to get that dangerous, we don’t need more AI researchers, we need more regulations that stop people from doing harmful AI research! If you are doing AI research and it isn’t directly involved specifically in AI safety, you aren’t saving the world—you’re one of the people dragging us closer to the cliff! Anything that could make AI smarter but doesn’t also make it safer is dangerous. And this is clearly true of the vast majority of AI research, and frankly to me seems to also be true of the vast majority of research at AI safety institutes like the Machine Intelligence Research Institute.

Seriously, look through MIRI’s research agenda: It’s mostly incredibly abstract and seems completely beside the point when it comes to preventing AI from taking control of weapons or governments. It’s all about formalizing Bayesian induction. Thanks to you, Skynet can have a formally computable approximation to logical induction! Truly we are saved. Only two of their papers, on “Corrigibility” and “AI Ethics”, actually struck me as at all relevant to making AI safer. The rest is largely abstract mathematics that is almost literally navel-gazing—it’s all about self-reference. Eliezer Yudkowsky finds self-reference fascinating and has somehow convinced an entire community that it’s the most important thing in the world. (I actually find some of it fascinating too, especially the paper on “Functional Decision Theory”, which I think gets at some deep insights into things like why we have emotions. But I don’t see how it’s going to save the world from AI.)

Don’t get me wrong: AI also has enormous potential benefits, and this is a reason we may not want to ban it. But if you really believe that there is a 10% chance that AI will wipe out humanity by 2100, then get out your pitchforks and your EMP generators, because it’s time for the Butlerian Jihad. A 10% chance of destroying all humanity is an utterly unacceptable risk for any conceivable benefit. Better that we consign ourselves to living as we did in the Neolithic than risk something like that. (And a globally-enforced ban on AI isn’t even that; it’s more like “We must live as we did in the 1950s.” How would we survive!?) If you don’t want AI banned, maybe ask yourself whether you really believe the risk is that high—or are human brains just really bad at dealing with small probabilities?

I think what’s really happening here is that we have a bunch of guys (and yes, the EA and especially AI EA-AI community is overwhelmingly male) who are really good at math and want to save the world, and have thus convinced themselves that being really good at math is how you save the world. But it isn’t. The world is much messier than that. In fact, there may not be much that most of us can do to contribute to saving the world; our best options may in fact be to donate money, vote well, and advocate for good causes.

Let me speak Bayesian for a moment: The prior probability that you—yes, you, out of all the billions of people in the world—are uniquely positioned to save it by being so smart is extremely small. It’s far more likely that the world will be saved—or doomed—by people who have power. If you are not the head of state of a large country or the CEO of a major multinational corporation, I’m sorry; you probably just aren’t in a position to save the world from AI.

But you can give some money to GiveWell, so maybe do that instead?

Charity shouldn’t end at home

It so happens that this week’s post will go live on Christmas Day. I always try to do some kind of holiday-themed post around this time of year, because not only Christmas, but a dozen other holidays from various religions all fall around this time of year. The winter solstice seems to be a very popular time for holidays, and has been since antiquity: The Romans were celebrating Saturnalia 2000 years ago. Most of our ‘Christmas’ traditions are actually derived from Yuletide.

These holidays certainly mean many different things to different people, but charity and generosity are themes that are very common across a lot of them. Gift-giving has been part of the season since at least Saturnalia and remains as vital as ever today. Most of those gifts are given to our friends and loved ones, but a substantial fraction of people also give to strangers in the form of charitable donations: November and December have the highest rates of donation to charity in the US and the UK, with about 35-40% of people donating during this season. (Of course this is complicated by the fact that December 31 is often the day with the most donations, probably from people trying to finish out their tax year with a larger deduction.)

My goal today is to make you one of those donors. There is a common saying, often attributed to the Bible but not actually present in it: “Charity begins at home”.

Perhaps this is so. There’s certainly something questionable about the Effective Altruism strategy of “earning to give” if it involves abusing and exploiting the people around you in order to make more money that you then donate to worthy causes. Certainly we should be kind and compassionate to those around us, and it makes sense for us to prioritize those close to us over strangers we have never met. But while charity may begin at home, it must not end at home.

There are so many global problems that could benefit from additional donations. While global poverty has been rapidly declining in the early 21st century, this is largely because of the efforts of donors and nonprofit organizations. Official Development Assitance has been roughly constant since the 1970s at 0.3% of GNI among First World countries—well below international targets set decades ago. Total development aid is around $160 billion per year, while private donations from the United States alone are over $480 billion. Moreover, 9% of the world’s population still lives in extreme poverty, and this rate has actually slightly increased the last few years due to COVID.

There are plenty of other worthy causes you could give to aside from poverty eradication, from issues that have been with us since the dawn of human civilization (the Humane Society International for domestic animal welfare, the World Wildlife Federation for wildlife conservation) to exotic fat-tail sci-fi risks that are only emerging in our own lifetimes (the Machine Intelligence Research Institute for AI safety, the International Federation of Biosafety Associations for biosecurity, the Union of Concerned Scientists for climate change and nuclear safety). You could fight poverty directly through organizations like UNICEF or GiveDirectly, fight neglected diseases through the Schistomoniasis Control Initiative or the Against Malaria Foundation, or entrust an organization like GiveWell to optimize your donations for you, sending them where they think they are needed most. You could give to political causes supporting civil liberties (the American Civil Liberties Union) or protecting the rights of people of color (the North American Association of Colored People) or LGBT people (the Human Rights Campaign).

I could spent a lot of time and effort trying to figure out the optimal way to divide up your donations and give them to causes such as this—and then convincing you that it’s really the right one. (And there is even a time and place for that, because seemingly-small differences can matter a lot in this.) But instead I think I’m just going to ask you to pick something. Give something to an international charity with a good track record.

I think we worry far too much about what is the best way to give—especially people in the Effective Altruism community, of which I’m sort of a marginal member—when the biggest thing the world really needs right now is just more people giving more. It’s true, there are lots of worthless or even counter-productive charities out there: Please, please do not give to the Salvation Army. (And think twice before donating to your own church; if you want to support your own community, okay, go ahead. But if you want to make the world better, there are much better places to put your money.)

But above all, give something. Or if you already give, give more. Most people don’t give at all, and most people who give don’t give enough.

Inequality-adjusted GDP and median income

Dec 11 JDN 2459925

There are many problems with GDP as a measure of a nation’s prosperity. For one, GDP ignores natural resources and ecological degradation; so a tree is only counted in GDP once it is cut down. For another, it doesn’t value unpaid work, so caring for a child only increases GDP if you are a paid nanny rather than the child’s parents.

But one of the most obvious problems is the use of an average to evaluate overall prosperity, without considering the level of inequality.

Consider two countries. In Alphania, everyone has an income of about $50,000. In Betavia, 99% of people have an income of $1,000 and 1% have an income of $10 million. What is the per-capita GDP of each country? Alphania’s is $50,000 of course; but Betavia’s is $100,990. Does it really make sense to say that Betavia is a more prosperous country? Maybe it has more wealth overall, but its huge inequality means that it is really not at a high level of development. It honestly sounds like an awful place to live.

A much more sensible measure would be something like median income: How much does a typical person have? In Alphania this is still $50,000; but in Betavia it is only $1,000.

Yet even this leaves out most of the actual distribution; by definition a median is only determined by what is the 50th percentile. We could vary all other incomes a great deal without changing the median.

A better measure would be some sort of inequality-adjusted per-capita GDP, which rescales GDP based on the level of inequality in a country. But we would need a good way of making that adjustment.

I contend that the most sensible way would be to adopt some kind of model of marginal utility of income, and then figure out what income would correspond to the overall average level of utility.

In other words, average over the level of happiness that people in a country get from their income, and then figure out what level of income would correspond to that level of happiness. If we magically gave everyone the same amount of money, how much would they need to get in order for the average happiness in the country to remain the same?

This is clearly going to be less than the average level of income, because marginal utility of income is decreasing; a dollar is not worth as much in real terms to a rich person as it is to a poor person. So if we could somehow redistribute all income evenly while keeping the average the same, that would actually increase overall happiness (though, for many reasons, we can’t simply do that).

For example, suppose that utility of income is logarithmic: U = ln(I).

This means that the marginal utility of an additional dollar is inversely proportional to how many dollars you already have: U'(I) = 1/I.

It also means that a 1% gain or loss in your income feels about the same regardless of how much income you have: ln((1+r)Y) = ln(Y) + ln(1+r). This seems like a quite reasonable, maybe even a bit conservative, assumption; I suspect that losing 1% of your income actually hurts more when you are poor than when you are rich.

Then the inequality adjusted GDP Y is a value such that ln(Y) is equal to the overall average level of utility: E[U] = ln(Y), so Y = exp(E[U]).

This sounds like a very difficult thing to calculate. But fortunately, the distribution of actual income seems to quite closely follow a log-normal distribution. This means that when we take the logarithm of income to get utility, we just get back a very nice, convenient normal distribution!

In fact, it turns out that for a log-normal distribution, the following holds: exp(E[ln(Y)]) = median(Y)

The income which corresponds to the average utility turns out to simply be the median income! We went looking for a better measure than median income, and ended up finding out that median income was the right measure all along.

This wouldn’t hold for most other distributions; and since real-world economies don’t perfectly follow a log-normal distribution, a more precise estimate would need to be adjusted accordingly. But the approximation is quite good for most countries we have good data on, so even for the ones we don’t, median income is likely a very good estimate.

The ranking of countries by median income isn’t radically different from the ranking by per-capita GDP; rich countries are still rich and poor countries are still poor. But it is different enough to matter.

Luxembourg is in 1st place on both lists. Scandinavian countries and the US are in the top 10 in both cases. So it’s fair to say that #ScandinaviaIsBetter for real, and the US really is so rich that our higher inequality doesn’t make our median income lower than the rest of the First World.

But some countries are quite different. Ireland looks quite good in per-capita GDP, but quite bad in median income. This is because a lot of the GDP in Ireland is actually profits by corporations that are only nominally headquartered in Ireland and don’t actually employ very many people there.

The comparison between the US, the UK, and Canada seems particularly instructive. If you look at per-capita GDP PPP, the US looks much richer at $75,000 compared to Canada’s $57,800 (a difference of 29% or 26 log points). But if you look at median personal income, they are nearly equal: $19,300 in the US and $18,600 in Canada (3.7% or 3.7 log points).

On the other hand, in per-capita GDP PPP, the UK looks close to Canada at $55,800 (3.6% or 3.6 lp); but in median income it is dramatically worse, at only $14,800 (26% or 23 lp). So Canada and the UK have similar overall levels of wealth, but life for a typical Canadian is much better than life for a typical Briton because of the higher inequality in Britain. And the US has more wealth than Canada, but it doesn’t meaningfully improve the lifestyle of a typical American relative to a typical Canadian.

Housing prices are out of control

Oct 2 JDN 2459855

This is a topic I could have done for quite awhile now, and will surely address again in the future; it’s a slow-burn crisis that has covered most of the world for a generation.

In most of the world’s cities, housing prices are now the highest they have ever been, even adjusted for inflation. The pandemic made this worse, but it was already bad.

This is of course very important, because housing is usually the largest expenditure for most families.

Changes in housing prices are directly felt in people’s lifestyles, especially when they are renting. Homeownership rates vary a lot between countries, so the impact of this is quite different in different places.

There’s also an important redistributive effect: When housing prices go up, people who own homes get richer, while people who rent homes get poorer. Since people who own homes tend to be richer to begin with (and landlordsare typically richest of all), rising housing prices directly increase wealth inequality.

The median price of a house in the US, even adjusted for inflation, is nearly twice what it was in 1993.

This wasn’t a slow and steady climb; housing prices moved with inflation for most of the 1980s and 1990s, and then surged upward just before the 2008 crash. Then they plummeted for a few years, before reversing course and surging even higher than they were at their 2007 peak:




This is not a uniquely American problem. The UK shows almost the same pattern:


But it’s also not the same pattern everywhere. In China, housing prices have been rising steadily, and didn’t crash in 2008:


In France, housing prices have been relatively stable, and are no higher now than they were in the 1990s:


Meanwhile, in Japan, housing prices surged in the 1970s, 1980s, and 1990s, ending up four times what they had been in the 1960s; then they suddenly leveled off and haven’t changed since:


It’s also worse in some cities than others. In San Francisco, housing now costs three times what it did in the 1990s, even adjusting for inflation:


Meanwhile, in Detroit, housing is only about 25% more expensive now than it was in the 1990s:


This variation tells me that policy matters. This isn’t some inevitable result of population growth or technological change. Those could still be important factors, but they can’t explain the strong varation between countries or even between cities within the same country. (Yes, San Francisco has seen more population growth than Detroit—but not that much more.)

Part of the problem, I think, is that most policymakers don’t actually want housing to be more affordable. They might say they do, they might occasionally feel some sympathy for people who get evicted or live on the streets; but in general, they want housing prices to be higher, because that gives them more property tax revenue. The wealthy benefit from rising housing prices, while the poor are harmed. Since the interests of the wealthy are wildly overrepresented in policy, policy is made to increase housing prices, not decrease them. This is likely especially true in housing, because even the upper-middle class mostly benefits from rising housing prices. It’s only the poor and lower-middle class who are typically harmed.

This is why I don’t really want to get into suggesting policies that could fix this. We know what would fix this: Build more housing. Lots of it. Everywhere. Increase supply, and the price will go down. And we should keep doing it until housing is not just back where it was, but cheaper—much cheaper. Buying a house shouldn’t be a luxury afforded only to the upper-middle class; it should be something everyone does several times in their life and doesn’t have to worry too much about. Buying a house should be like buying a car; not cheap, exactly, but you don’t have to be rich to do it. Because everyone needs housing. So everyone should have housing.

But that isn’t going to happen, because the people who make the decisions about this don’t want it to happen.

So the real question becomes: What do we do about that?

The injustice of talent

Sep 4 JDN 2459827

Consider the following two principles of distributive justice.

A: People deserve to be rewarded in proportion to what they accomplish.

B: People deserve to be rewarded in proportion to the effort they put in.

Both principles sound pretty reasonable, don’t they? They both seem like sensible notions of fairness, and I think most people would broadly agree with both them.

This is a problem, because they are mutually contradictory. We cannot possibly follow them both.

For, as much as our society would like to pretend otherwise—and I think this contradiction is precisely why our society would like to pretend otherwise—what you accomplish is not simply a function of the effort you put in.

Don’t get me wrong; it is partly a function of the effort you put in. Hard work does contribute to success. But it is neither sufficient, nor strictly necessary.

Rather, success is a function of three factors: Effort, Environment, and Talent.

Effort is the work you yourself put in, and basically everyone agrees you deserve to be rewarded for that.

Environment includes all the outside factors that affect you—including both natural and social environment. Inheritance, illness, and just plain luck are all in here, and there is general, if not universal, agreement that society should make at least some efforts to minimize inequality created by such causes.

And then, there is talent. Talent includes whatever capacities you innately have. It could be strictly genetic, or it could be acquired in childhood or even in the womb. But by the time you are an adult and responsible for your own life, these factors are largely fixed and immutable. This includes things like intelligence, disability, even height. The trillion-dollar question is: How much should we reward talent?

For talent clearly does matter. I will never swim like Michael Phelps, run like Usain Bolt, or shoot hoops like Steph Curry. It doesn’t matter how much effort I put in, how many hours I spend training—I will never reach their level of capability. Never. It’s impossible. I could certainly improve from my current condition; perhaps it would even be good for me to do so. But there are certain hard fundamental constraints imposed by biology that give them more potential in these skills than I will ever have.

Conversely, there are likely things I can do that they will never be able to do, though this is less obvious. Could Michael Phelps never be as good a programmer or as skilled a mathematician as I am? He certainly isn’t now. Maybe, with enough time, enough training, he could be; I honestly don’t know. But I can tell you this: I’m sure it would be harder for him than it was for me. He couldn’t breeze through college-level courses in differential equations and quantum mechanics the way I did. There is something I have that he doesn’t, and I’m pretty sure I was born with it. Call it spatial working memory, or mathematical intuition, or just plain IQ. Whatever it is, math comes easy to me in not so different a way from how swimming comes easy to Michael Phelps. I have talent for math; he has talent for swimming.

Moreover, these are not small differences. It’s not like we all come with basically the same capabilities with a little bit of variation that can be easily washed out by effort. We’d like to believe that—we have all sorts of cultural tropes that try to inculcate that belief in us—but it’s obviously not true. The vast majority of quantum physicists are people born with high IQ. The vast majority of pro athletes are people born with physical prowess. The vast majority of movie stars are people born with pretty faces. For many types of jobs, the determining factor seems to be talent.

This isn’t too surprising, actually—even if effort matters a lot, we would still expect talent to show up as the determining factor much of the time.

Let’s go back to that contest function model I used to analyze the job market awhile back (the one that suggests we spend way too much time and money in the hiring process). This time let’s focus on the perspective of the employees themselves.

Each employee has a level of talent, h. Employee X has talent hx and exerts effort x, producing output of a quality that is the product of these: hx x. Similarly, employee Z has talent hz and exerts effort z, producing output hz z.

Then, there’s a certain amount of luck that factors in. The most successful output isn’t necessarily the best, or maybe what should have been the best wasn’t because some random circumstance prevailed. But we’ll say that the probability an individual succeeds is proportional to the quality of their output.

So the probability that employee X succeeds is: hx x / ( hx x + hz z)

I’ll skip the algebra this time (if you’re interested you can look back at that previous post), but to make a long story short, in Nash equilibrium the two employees will exert exactly the same amount of effort.

Then, which one succeeds will be entirely determined by talent; because x = z, the probability that X succeeds is hx / ( hx + hz).

It’s not that effort doesn’t matter—it absolutely does matter, and in fact in this model, with zero effort you get zero output (which isn’t necessarily the case in real life). It’s that in equilibrium, everyone is exerting the same amount of effort; so what determines who wins is innate talent. And I gotta say, that sounds an awful lot like how professional sports works. It’s less clear whether it applies to quantum physicists.

But maybe we don’t really exert the same amount of effort! This is true. Indeed, it seems like actually effort is easier for people with higher talent—that the same hour spent running on a track is easier for Usain Bolt than for me, and the same hour studying calculus is easier for me than it would be for Usain Bolt. So in the end our equilibrium effort isn’t the same—but rather than compensating, this effect only serves to exaggerate the difference in innate talent between us.

It’s simple enough to generalize the model to allow for such a thing. For instance, I could say that the cost of producing a unit of effort is inversely proportional to your talent; then instead of hx / ( hx + hz ), in equilibrium the probability of X succeeding would become hx2 / ( hx2 + hz2). The equilibrium effort would also be different, with x > z if hx > hz.

Once we acknowledge that talent is genuinely important, we face an ethical problem. Do we want to reward people for their accomplishment (A), or for their effort (B)? There are good cases to be made for each.

Rewarding for accomplishment, which we might call meritocracy,will tend to, well, maximize accomplishment. We’ll get the best basketball players playing basketball, the best surgeons doing surgery. Moreover, accomplishment is often quite easy to measure, even when effort isn’t.

Rewarding for effort, which we might call egalitarianism, will give people the most control over their lives, and might well feel the most fair. Those who succeed will be precisely those who work hard, even if they do things they are objectively bad at. Even people who are born with very little talent will still be able to make a living by working hard. And it will ensure that people do work hard, which meritocracy can actually fail at: If you are extremely talented, you don’t really need to work hard because you just automatically succeed.

Capitalism, as an economic system, is very good at rewarding accomplishment. I think part of what makes socialism appealing to so many people is that it tries to reward effort instead. (Is it very good at that? Not so clear.)

The more extreme differences are actually in terms of disability. There’s a certain baseline level of activities that most people are capable of, which we think of as “normal”: most people can talk; most people can run, if not necessarily very fast; most people can throw a ball, if not pitch a proper curveball. But some people can’t throw. Some people can’t run. Some people can’t even talk. It’s not that they are bad at it; it’s that they are literally not capable of it. No amount of effort could have made Stephen Hawking into a baseball player—not even a bad one.

It’s these cases when I think egalitarianism becomes most appealing: It just seems deeply unfair that people with severe disabilities should have to suffer in poverty. Even if they really can’t do much productive work on their own, it just seems wrong not to help them, at least enough that they can get by. But capitalism by itself absolutely would not do that—if you aren’t making a profit for the company, they’re not going to keep you employed. So we need some kind of social safety net to help such people. And it turns out that such people are quite numerous, and our current system is really not adequate to help them.

But meritocracy has its pull as well. Especially when the job is really important—like surgery, not so much basketball—we really want the highest quality work. It’s not so important whether the neurosurgeon who removes your tumor worked really hard at it or found it a breeze; what we care about is getting that tumor out.

Where does this leave us?

I think we have no choice but to compromise, on both principles. We will reward both effort and accomplishment, to greater or lesser degree—perhaps varying based on circumstances. We will never be able to entirely reward accomplishment or entirely reward effort.

This is more or less what we already do in practice, so why worry about it? Well, because we don’t like to admit that it’s what we do in practice, and a lot of problems seem to stem from that.

We have people acting like billionaires are such brilliant, hard-working people just because they’re rich—because our society rewards effort, right? So they couldn’t be so successful if they didn’t work so hard, right? Right?

Conversely, we have people who denigrate the poor as lazy and stupid just because they are poor. Because it couldn’t possibly be that their circumstances were worse than yours? Or hey, even if they are genuinely less talented than you—do less talented people deserve to be homeless and starving?

We tell kids from a young age, “You can be whatever you want to be”, and “Work hard and you’ll succeed”; and these things simply aren’t true. There are limitations on what you can achieve through effort—limitations imposed by your environment, and limitations imposed by your innate talents.

I’m not saying we should crush children’s dreams; I’m saying we should help them to build more realistic dreams, dreams that can actually be achieved in the real world. And then, when they grow up, they either will actually succeed, or when they don’t, at least they won’t hate themselves for failing to live up to what you told them they’d be able to do.

If you were wondering why Millennials are so depressed, that’s clearly a big part of it: We were told we could be and do whatever we wanted if we worked hard enough, and then that didn’t happen; and we had so internalized what we were told that we thought it had to be our fault that we failed. We didn’t try hard enough. We weren’t good enough. I have spent years feeling this way—on some level I do still feel this way—and it was not because adults tried to crush my dreams when I was a child, but on the contrary because they didn’t do anything to temper them. They never told me that life is hard, and people fail, and that I would probably fail at my most ambitious goals—and it wouldn’t be my fault, and it would still turn out okay.

That’s really it, I think: They never told me that it’s okay not to be wildly successful. They never told me that I’d still be good enough even if I never had any great world-class accomplishments. Instead, they kept feeding me the lie that I would have great world-class accomplishments; and then, when I didn’t, I felt like a failure and I hated myself. I think my own experience may be particularly extreme in this regard, but I know a lot of other people in my generation who had similar experiences, especially those who were also considered “gifted” as children. And we are all now suffering from depression, anxiety, and Impostor Syndrome.

All because nobody wanted to admit that talent, effort, and success are not the same thing.

Working from home is the new normal—sort of

Aug 28 JDN 2459820

Among people with jobs that can be done remotely, a large majority did in fact switch to doing their jobs remotely: By the end of 2020, over 70% of Americans with jobs that could be done remotely were working from home—and most of them said they didn’t want to go back.

This is actually what a lot of employers expected to happen—just not quite like this. In 2014, a third of employers predicted that the majority of their workforce would be working remotely by 2020; given the timeframe there, it required a major shock to make that happen so fast, and yet a major shock was what we had.

Working from home has carried its own challenges, but overall productivity seems to be higher working remotely (that meeting really could have been an email!). This may actually explain why output per work hour actually rose rapidly in 2020 and fell in 2022.

The COVID pandemic now isn’t so much over as becoming permanent; COVID is now being treated as an endemic infection like influenza that we don’t expect to be able to eradicate in the foreseeable future.

And likewise, remote work seems to be here to stay—sort of.

First of all, we don’t seem to be giving up office work entirely. As of the first quarter 2022, almost as many firms have partially remote work as have fully remote work, and this seems to be trending upward. A lot of firms seem to be transitioning into a “hybrid” model where employees show up to work two or three days a week. This seems to be preferred by large majorities of both workers and firms.

There is a significant downside of this: It means that the hope that remote working might finally ease the upward pressure on housing prices in major cities is largely a false one. If we were transitioning to a fully remote system, then people could live wherever they want (or can afford) and there would be no reason to move to overpriced city centers. But if you have to show up to work even one day a week, that means you need to live close enough to the office to manage that commute.

Likewise, if workers never came to the office, you could sell the office building and convert it into more housing. But if they show up even once in awhile, you need a physical place for them to go. Some firms may shrink their office space (indeed, many have—and unlike this New York Times journalist, I have a really hard time feeling bad for landlords of office buildings); but they aren’t giving it up entirely. It’s possible that firms could start trading off—you get the building on Mondays, we get it on Tuesdays—but so far this seems to be rare, and it does raise a lot of legitimate logistical and security concerns. So our global problem of office buildings that are empty, wasted space most of the time is going to get worse, not better. Manhattan will still empty out every night; it just won’t fill up as much during the day. This is honestly a major drain on our entire civilization—building and maintaining all those structures that are only used at most 1/3 of 5/7 of the time, and soon, less—and we really should stop ignoring it. No wonder our real estate is so expensive, when half of it is only used 20% of the time!

Moreover, not everyone gets to work remotely. Your job must be something that can be done remotely—something that involves dealing with information, not physical objects. That includes a wide and ever-growing range of jobs, from artists and authors to engineers and software developers—but it doesn’t include everyone. It basically means what we call “white-collar” work.

Indeed, it is largely limited to the upper-middle class. The rich never really worked anyway, though sometimes they pretend to, convincing themselves that managing a stock portfolio (that would actually grow faster if they let it sit) constitutes “work”. And the working class? By and large, they didn’t get the chance to work remotely. While 73% of workers with salaries above $200,000 worked remotely in 2020, only 12% of workers with salaries under $25,000 did, and there is a smooth trend where, across the board, the more money you make, the more likely you have been able to work remotely.

This will only intensify the divide between white-collar and blue-collar workers. They already think we don’t do “real work”; now we don’t even go to work. And while blue-collar workers are constantly complaining about contempt from white-collar elites, I think the shoe is really on the other foot. I have met very few white-collar workers who express contempt for blue-collar workers—and I have met very few blue-collar workers who don’t express anger and resentment toward white-collar workers. I keep hearing blue-collar people say that we think that they are worthless and incompetent, when they are literally the only ones ever saying that. I can’t stop saying things that I never said.

The rich and powerful may look down on them, but they look down on everyone. (Maybe they look down on blue-collar workers more? I’m not even sure about that.) I think politicians sometimes express contempt for blue-collar workers, but I don’t think this reflects what most white-collar workers feel.

And the highly-educated may express some vague sense of pity or disappointment in people who didn’t get college degrees, and sometimes even anger (especially when they do things like vote for Donald Trump), but the really vitriolic hatred is clearly in the opposite direction (indeed, I have no better explanation for how otherwise-sane people could vote for Donald Trump). And I certainly wouldn’t say that everyone needs a college degree (though I became tempted to, when so many people without college degrees voted for Donald Trump).

This really isn’t us treating them with contempt: This is them having a really severe inferiority complex. And as information technology (that white-collar work created) gives us—but not them—the privilege of staying home, that is only going to get worse.

It’s not their fault: Our culture of meritocracy puts a little bit of inferiority complex in all of us. It tells us that success and failure are our own doing, and so billionaires deserve to have everything and the poor deserve to have nothing. And blue-collar workers have absolutely internalized these attitudes: Most of them believe that poor people choose to stay on welfare forever rather than get jobs (when welfare has time limits and work requirements, so this is simply not an option—and you would know this from the Wikipedia page on TANF).

I think that what they experience as “contempt by white-collar elites” is really the pain of living in an illusory meritocracy. They were told—and they came to believe—that working hard would bring success, and they have worked very hard, and watched other people be much more successful. They assume that the rich and powerful are white-collar workers, when really they are non-workers; they are people the world was handed to on a silver platter. (What, you think George W. Bush earned his admission to Yale?)

And thus, we can shout until we are blue in the face that plumbers, bricklayers and welders are the backbone of civilization—and they are, and I absolutely mean that; our civilization would, in an almost literal sense, collapse without them—but it won’t make any difference. They’ll still feel the pain of living in a society that gave them very little and tells them that people get what they deserve.

I don’t know what to say to such people, though. When your political attitudes are based on beliefs that are objectively false, that you could know are objectively false if you simply bothered to look them up… what exactly am I supposed to say to you? How can we have a useful political conversation when half the country doesn’t even believe in fact-checking?

Honestly I wish someone had explained to them that even the most ideal meritocratic capitalism wouldn’t reward hard work. Work is a cost, not a benefit, and the whole point of technological advancement is to allow us to accomplish more with less work. The ideal capitalism would reward talent—you would succeed by accomplishing things, regardless of how much effort you put into them. People would be rich mainly because they are brilliant, not because they are hard-working. The closest thing we have to ideal capitalism right now is probably professional sports. And no amount of effort could ever possibly make me into Steph Curry.

If that isn’t the world we want to live in, so be it; let’s do something else. I did nothing to earn either my high IQ or my chronic migraines, so it really does feel unfair that the former increases my income while the latter decreases it. But the labor theory of value has always been wrong; taking more sweat or more hours to do the same thing is worse, not better. The dignity of labor consists in its accomplishment, not its effort. Sisyphus is not happy, because his work is pointless.

Honestly at this point I think our best bet is just to replace all blue-collar work with automation, thus rendering it all moot. And then maybe we can all work remotely, just pushing code patches to the robots that do everything. (And no doubt this will prove my “contempt”: I want to replace you! No, I want to replace the grueling work that you have been forced to do to make a living. I want you—the human being—to be able to do something more fun with your life, even if that’s just watching television and hanging out with friends.)

If I had a trillion dollars…

May 29 JDN 2459729

(To the tune of “If I had a million dollars” by Barenaked Ladies; by the way, he does now)

[Inspired by the book How to Spend a Trillion Dollars]

If I had a trillion dollars… if I had a trillion dollars!

I’d buy everyone a house—and yes, I mean, every homeless American.

[500,000 homeless households * $300,000 median home price = $150 billion]

If I had a trillion dollars… if I had a trillion dollars!

I’d give to the extreme poor—and then there would be no extreme poor!

[Global poverty gap: $160 billion]

If I had a trillion dollars… if I had a trillion dollars!

I’d send people to Mars—hey, maybe we’d find some alien life!

[Estimated cost of manned Mars mission: $100 billion]

If I had a trillion dollars… if I had a trillion dollars!

I’d build us a Moon base—haven’t you always wanted a Moon base?

[Estimated cost of a permanent Lunar base: $35 billion. NASA is bad at forecasting cost, so let’s allow cost overruns to take us to $100 billion.]

If I had a trillion dollars… if I had a trillion dollars!

I’d build a new particle accelerator—let’s finally figure out dark matter!

[Cost of planned new accelerator at CERN: $24 billion. Let’s do 4 times bigger and make it $100 billion.]

If I had a trillion dollars… if I had a trillion dollars!

I’d save the Amazon—pay all the ranchers to do something else!

[Brazil, where 90% of Amazon cattle ranching is, produces about 10 million tons of beef per year, which at an average price of $5000 per ton is $50 billion. So I could pay all the farmers two years of revenue to protect the Amazon instead of destroying it for $100 billion.]

If I had a trillion dollars…

We wouldn’t have to drive anymore!

If I had a trillion dollars…

We’d build high-speed rail—it won’t cost more!

[Cost of proposed high-speed rail system: $240 billion]

If I had a trillion dollars… if I had trillion dollars!

Hey wait, I could get it from a carbon tax!

[Even a moderate carbon tax could raise $1 trillion in 10 years.]

If I had a trillion dollars… I’d save the world….

All of the above really could be done for under $1 trillion. (Some of them would need to be repeated, so we could call it $1 trillion per year.)

I, of course, do not, and will almost certainly never have, anything approaching $1 trillion.

But here’s the thing: There are people who do.

Elon Musk and Jeff Bezos together have a staggering $350 billion. That’s two people with enough money to end world hunger. And don’t give me that old excuse that it’s not in cash: UNICEF gladly accepts donations in stock. They could, right now, give their stocks to UNICEF and thereby end world hunger. They are choosing not to do that. In fact, the goodwill generated by giving, say, half their stocks to UNICEF might actually result in enough people buying into their companies that their stock prices would rise enough to make up the difference—thus costing them literally nothing.

The total net wealth of all the world’s billionaires is a mind-boggling $12.7 trillion. That’s more than half a year of US GDP. Held by just over 2600 people—a small town.

The US government spends $4 trillion in a normal year—and $5 trillion the last couple of years due to the pandemic. Nearly $1 trillion of that is military spending, which could be cut in half and still be the highest in the world. After seeing how pathetic Russia’s army actually is in battle (they paint Zs on their tanks because apparently their IFF system is useless!), are we really still scared of them? Do we really need eleven carrier battle groups?

Yes, the total cost of mitigating climate change is probably in the tens of trillions—but the cost of not mitigating climate change could be over $100 trillion. And it’s not as if the world can’t come up with tens of trillions; we already do. World GDP is now over $100 trillion per year; just 2% of that for 10 years is $20 trillion.

Do these sound like good ideas to you? Would you want to do them? I think most people would want most of them. So now the question becomes: Why aren’t we doing them?

Scalability and inequality

May 15 JDN 2459715

Why are some molecules (e.g. DNA) billions of times larger than others (e.g. H2O), but all atoms are within a much narrower range of sizes (only a few hundred)?

Why are some animals (e.g. elephants) millions of times as heavy as other (e.g. mice), but their cells are basically the same size?

Why does capital income vary so much more (factors of thousands or millions) than wages (factors of tens or hundreds)?

These three questions turn out to have much the same answer: Scalability.

Atoms are not very scalable: Adding another proton to a nucleus causes interactions with all the other protons, which makes the whole atom unstable after a hundred protons or so. But molecules, particularly organic polymers such as DNA, are tremendously scalable: You can add another piece to one end without affecting anything else in the molecule, and keep on doing that more or less forever.

Cells are not very scalable: Even with the aid of active transport mechanisms and complex cellular machinery, a cell’s functionality is still very much limited by its surface area. But animals are tremendously scalable: The same exponential growth that got you from a zygote to a mouse only needs to continue a couple years longer and it’ll get you all the way to an elephant. (A baby elephant, anyway; an adult will require a dozen or so years—remarkably comparable to humans, in fact.)

Labor income is not very scalable: There are only so many hours in a day, and the more hours you work the less productive you’ll be in each additional hour. But capital income is perfectly scalable: We can add another digit to that brokerage account with nothing more than a few milliseconds of electronic pulses, and keep doing that basically forever (due to the way integer storage works, above 2^63 it would require special coding, but it can be done; and seeing as that’s over 9 quintillion, it’s not likely to be a problem any time soon—though I am vaguely tempted to write a short story about an interplanetary corporation that gets thrown into turmoil by an integer overflow error).

This isn’t just an effect of our accounting either. Capital is scalable in a way that labor is not. When your contribution to production is owning a factory, there’s really nothing to stop you from owning another factory, and then another, and another. But when your contribution is working at a factory, you can only work so hard for so many hours.

When a phenomenon is highly scalable, it can take on a wide range of outcomes—as we see in molecules, animals, and capital income. When it’s not, it will only take on a narrow range of outcomes—as we see in atoms, cells, and labor income.

Exponential growth is also part of the story here: Animals certainly grow exponentially, and so can capital when invested; even some polymers function that way (e.g. under polymerase chain reaction). But I think the scalability is actually more important: Growing rapidly isn’t so useful if you’re going to immediately be blocked by a scalability constraint. (This actually relates to the difference between r- and K- evolutionary strategies, and offers further insight into the differences between mice and elephants.) Conversely, even if you grow slowly, given enough time, you’ll reach whatever constraint you’re up against.

Indeed, we can even say something about the probability distribution we are likely to get from random processes that are scalable or non-scalable.

A non-scalable random process will generally converge toward the familiar normal distribution, a “bell curve”:

[Image from Wikipedia: By Inductiveload – self-made, Mathematica, Inkscape, Public Domain, https://commons.wikimedia.org/w/index.php?curid=3817954]

The normal distribution has most of its weight near the middle; most of the population ends up near there. This is clearly the case for labor income: Most people are middle class, while some are poor and a few are rich.

But a scalable random process will typically converge toward quite a different distribution, a Pareto distribution:

[Image from Wikipedia: By Danvildanvil – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=31096324]

A Pareto distribution has most of its weight near zero, but covers an extremely wide range. Indeed it is what we call fat tailed, meaning that really extreme events occur often enough to have a meaningful effect on the average. A Pareto distribution has most of the people at the bottom, but the ones at the top are really on top.

And indeed, that’s exactly how capital income works: Most people have little or no capital income (indeed only about half of Americans and only a third(!) of Brits own any stocks at all), while a handful of hectobillionaires make utterly ludicrous amounts of money literally in their sleep.

Indeed, it turns out that income in general is pretty close to distributed normally (or maybe lognormally) for most of the income range, and then becomes very much Pareto at the top—where nearly all the income is capital income.

This fundamental difference in scalability between capital and labor underlies much of what makes income inequality so difficult to fight. Capital is scalable, and begets more capital. Labor is non-scalable, and we only have to much to give.

It would require a radically different system of capital ownership to really eliminate this gap—and, well, that’s been tried, and so far, it hasn’t worked out so well. Our best option is probably to let people continue to own whatever amounts of capital, and then tax the proceeds in order to redistribute the resulting income. That certainly has its own downsides, but they seem to be a lot more manageable than either unfettered anarcho-capitalism or totalitarian communism.

Maybe we should forgive student debt after all.

May 8 JDN 2459708

President Biden has been promising some form of student debt relief since the start of his campaign, though so far all he has actually implemented is a series of no-interest deferments and some improvements to the existing forgiveness programs. (This is still significant—it has definitely helped a lot of people with cashflow during the pandemic.) Actual forgiveness for a large segment of the population remains elusive, and if it does happen, it’s unclear how extensive it will be in either intensity (amount forgiven) or scope (who is eligible).

I personally had been fine with this; while I have a substantial loan balance myself, I also have a PhD in economics, which—theoretically—should at some point entitle me to sufficient income to repay those loans.

Moreover, until recently I had been one of the few left-wing people I know to not be terribly enthusiastic about loan forgiveness. It struck me as a poor use of those government funds, because $1.75 trillion is an awful lot of money, and college graduates are a relatively privileged population. (And yes, it is valid to consider this a question of “spending”, because the US government is the least liquidity-constrained entity on Earth. In lieu of forgiving $1.75 trillion in debt, they could borrow $1.75 trillion in debt and use it to pay for whatever they want, and their ultimate budget balance would be basically the same in each case.)

But I say all this in the past tense because Krugman’s recent column has caused me to reconsider. He gives two strong reasons why debt forgiveness may actually be a good idea.

The first is that Congress is useless. Thanks to gerrymandering and the 40% or so of our population who keeps electing Republicans no matter how crazy they get, it’s all but impossible to pass useful legislation. The pandemic relief programs were the exception that proves the rule: Somehow those managed to get through, even though in any other context it’s clear that Congress would never have approved any kind of (non-military) program that spent that much money or helped that many poor people.

Student loans are the purview of the Department of Education, which is entirely under control of the Executive Branch, and therefore, ultimately, the President of the United States. So Biden could forgive student loans by executive order and there’s very little Congress could do to stop him. Even if that $1.75 trillion could be better spent, if it wasn’t going to be anyway, we may as well use it for this.

The second is that “college graduates” is too broad a category. Usually I’m on guard for this sort of thing, but in this case I faltered, and did not notice the fallacy of composition so many labor economists were making by lumping all college grads into the same economic category. Yes, some of us are doing well, but many are not. Within-group inequality matters.

A key insight here comes from carefully analyzing the college wage premium, which is the median income of college graduates, divided by the median income of high school graduates. This is an estimate of the overall value of a college education. It’s pretty large, as a matter of fact: It amounts to something like a doubling of your income, or about $1 million over one’s whole lifespan.

From about 1980-2000, wage inequality grew about as fast as today, and the college wage premium grew even faster. So it was plausible—if not necessarily correct—to believe that the wage inequality reflected the higher income and higher productivity of college grads. But since 2000, wage inequality has continued to grow, while the college wage premium has been utterly stagnant. Thus, higher inequality can no longer (if it ever could) be explained by the effects of college education.

Now some college graduates are definitely making a lot more money—such as those who went into finance. But it turns out that most are not. As Krugman points out, the 95th percentile of male college grads has seen a 25% increase in real (inflation-adjusted) income in the last 20 years, while the median male college grad has actually seen a slight decrease. (I’m not sure why Krugman restricted to males, so I’m curious how it looks if you include women. But probably not radically different?)

I still don’t think student loan forgiveness would be the best use of that (enormous sum of) money. But if it’s what’s politically feasible, it definitely could help a lot of people. And it would be easy enough to make it more progressive, by phasing out forgiveness for graduates with higher incomes.

And hey, it would certainly help me, so maybe I shouldn’t argue too strongly against it?