The “productivity paradox”

 

Dec 10, JDN 2458098

Take a look at this graph of manufacturing output per worker-hour:

Manufacturing_productivity

From 1988 to 2008, it was growing at a steady pace. In 2008 and 2009 it took a dip due to the Great Recession; no big surprise there. But then since 2012 it has been… completely flat. If we take this graph at face value, it would imply that manufacturing workers today can produce no more output than workers five years ago, and indeed only about 10% more than workers a decade ago. Whereas, a worker in 2008 was producing over 60% more than a worker in 1998, who was producing over 40% more than a worker in 1988.

Many economists call this the “productivity paradox”, and use it to argue that we don’t really need to worry about robots taking all our jobs any time soon. I think this view is mistaken.

The way we measure productivity is fundamentally wrongheaded, and is probably the sole cause of this “paradox”.

First of all, we use total hours scheduled to work, not total hours actually doing productive work. This is obviously much, much easier to measure, which is why we do it. But if you think for a moment about how the 40-hour workweek norm is going to clash with rapidly rising real productivity, it becomes apparent why this isn’t going to be a good measure.
When a worker finds a way to get done in 10 hours what used to take 40 hours, what does that worker’s boss do? Send them home after 10 hours because the job is done? Give them a bonus for their creativity? Hardly. That would be far too rational. They assign them more work, while paying them exactly the same. Recognizing this, what is such a worker to do? The obvious answer is to pretend to work the other 30 hours, while in fact doing something more pleasant than working.
And indeed, so-called “worker distraction” has been rapidly increasing. People are right to blame smartphones, I suppose, but not for the reasons they think. It’s not that smartphones are inherently distracting devices. It’s that smartphones are the cutting edge of a technological revolution that has made most of our work time unnecessary, so due to our fundamentally defective management norms they create overwhelming incentives to waste time at work to avoid getting drenched in extra tasks for no money.

That would probably be enough to explain the “paradox” by itself, but there is a deeper reason that in the long run is even stronger. It has to do with the way we measure “output”.

It might surprise you to learn that economists almost never consider output in terms of the actual number of cars produced, buildings constructed, songs written, or software packages developed. The standard measures of output are all in the form of so-called “real GDP”; that is, the dollar value of output produced.

They do adjust for indexes of inflation, but as I’ll show in a moment this still creates a fundamentally biased picture of the productivity dynamics.

Consider a world with only three industries: Housing, Food, and Music.

Productivity in Housing doesn’t change at all. Producing a house cost 10,000 worker-hours in 1950, and cost 10,000 worker-hours in 2000. Nominal price of houses has rapidly increased, from $10,000 in 1950 to $200,000 in 2000.

Productivity in Food rises moderately fast. Producing 1,000 meals cost 1,000 worker-hours in 1950, and cost 100 worker-hours in 2000. Nominal price of food has increased slowly, from $1,000 per 1,000 meals in 1950 to $5,000 per 1,000 meals in 2000.

Productivity in Music rises extremely fast. Producing 1,000 performances cost 10,000 worker-hours in 1950, and cost 1 worker-hour in 2000. Nominal price of music has collapsed, from $100,000 per 1,000 performances in 1950 to $1,000 per 1,000 performances in 2000.

This is of course an extremely stylized version of what has actually happened: Housing has gotten way more expensive, food has stayed about the same in price while farm employment has plummeted, and the rise of digital music has brought about a new Renaissance in actual music production and listening while revenue for the music industry has collapsed. There is a very nice Vox article on the “productivity paradox” showing a graph of how prices have changed in different industries.

How would productivity appear in the world I’ve just described, by standard measures? Well, to say that I actually need to say something about how consumers substitute across industries. But I think I’ll be forgiven in this case for saying that there is no substitution whatsoever; you can’t eat music or live in a burrito. There’s also a clear Maslow hierarchy here: They say that man cannot live by bread alone, but I think living by Led Zeppelin alone is even harder.

Consumers will therefore choose like this: Over 10 years, buy 1 house, 10,000 meals, and as many performances as you can afford after that. Further suppose that each person had $2,100 per year to spend in 1940-1950, and $50,000 per year to spend in 1990-2000. (This is approximately true for actual nominal US GDP per capita.)

1940-1950:
Total funds: $21,000

1 house = $10,000

10,000 meals = $10,000

Remaining funds: $1,000

Performances purchased: 10

1990-2000:

Total funds: $500,000

1 house = $200,000

10,000 meals = $50,000

Remaining funds: $250,000

Performances purchased: 250,000

(Do you really listen to this much music? 250,000 performances over 10 years is about 70 songs per day. If each song is 3 minutes, that’s only about 3.5 hours per day. If you listen to music while you work or watch a couple of movies with musical scores, yes, you really do listen to this much music! The unrealistic part is assuming that people in 1950 listen to so little, given that radio was already widespread. But if you think of music as standing in for all media, the general trend of being able to consume vastly more media in the digital age is clearly correct.)

Now consider how we would compute a price index for each time period. We would construct a basket of goods and determine the price of that basket in each time period, then adjust prices until that basket has a constant price.

Here, the basket would probably be what people bought in 1940-1950: 1 house, 10,000 meals, and 400 music performances.

In 1950, this basket cost $10,000+$10,000+$100 = $21,000.

In 2000, this basket cost $200,000+$50,000+$400 = $150,400.

This means that our inflation adjustment is $150,400/$21,000 = 7 to 1. This means that we would estimate the real per-capita GDP in 1950 at about $14,700. And indeed, that’s about the actual estimate of real per-capita GDP in 1950.

So, what would we say about productivity?

Sales of houses in 1950 were 1 per person, costing 10,000 worker hours.

Sales of food in 1950 were 10,000 per person, costing 10,000 worker hours.

Sales of music in 1950 were 400 per person, costing 4,000 worker hours.

Worker hours per person are therefore 24,000.

Sales of houses in 2000 were 1 per person, costing 10,000 worker hours.

Sales of food in 2000 were 10,000 per person, costing 1,000 worker hours.

Sales of music in 2000 were 250,000 per person, costing 25,000 worker hours.

Worker hours per person are therefore 36,000.

Therefore we would estimate that productivity rose from $14,700/24,000 = $0.61 per worker-hour to $50,000/36,000 = $1.40 per worker-hour. This is an annual growth rate of about 1.7%, which is again, pretty close to the actual estimate of productivity growth. For such a highly stylized model, my figures are doing remarkably well. (Honestly, better than I thought they would!)

But think about how much actual productivity rose, at least in the industries where it did.

We produce 10 times as much food per worker hour after 50 years, which is an annual growth rate of 4.7%, or three times the estimated growth rate.

We produce 10,000 times as much music per worker hour after 50 years, which is an annual growth rate of over 20%, or almost twelve times the estimated growth rate.

Moreover, should music producers be worried about losing their jobs to automation? Absolutely! People simply won’t be able to listen to much more music than they already are, so any continued increases in music productivity are going to make musicians lose jobs. And that was already allowing for music consumption to increase by a factor of over 600.

Of course, the real world has a lot more industries than this, and everything is a lot more complicated. We do actually substitute across some of those industries, unlike in this model.

But I hope I’ve gotten at least the basic point across that when things become drastically cheaper as technological progress often does, simply adjusting for inflation doesn’t do the job. One dollar of music today isn’t the same thing as one dollar of music a century ago, even if you inflation-adjust their dollars to match ours. We ought to be measuring in hours of music; an hour of music is much the same thing as an hour of music a century ago.

And likewise, that secretary/weather forecaster/news reporter/accountant/musician/filmmaker in your pocket that you call a “smartphone” really ought to be counted as more than just a simple inflation adjustment on its market price. The fact that it is mind-bogglingly cheaper to get these services than it used to be is the technological progress we care about; it’s not some statistical artifact to be removed by proper measurement.

Combine that with actually measuring the hours of real, productive work, and I think you’ll find that productivity is still rising quite rapidly, and that we should still be worried about what automation is going to do to our jobs.

What will we do without air travel?

August 6, JDN 2457972

Air travel is incredibly carbon-intensive. Just one round-trip trans-Atlantic flight produces about 1 ton of carbon emissions per passenger. To keep global warming below 2 K, personal carbon emissions will need to be reduced to less than 1.5 tons per person per year by 2050. This means that simply flying from New York to London and back twice in a year would be enough to exceed the total carbon emissions each person can afford if we are to prevent catastrophic global climate change.

Currently about 12% of US transportation-based carbon emissions are attributable to aircraft; that may not sound like a lot, but consider this. Of the almost 5 trillion passenger-miles traveled by Americans each year, only 600 billion are by air, while 60,000 are by public transit. That leaves 4.4 trillion passenger-miles traveled by car. About 60% of US transportation emissions are due to cars, while 88% of US transportation is by car. About 12% of US transportation emissions are due to airplanes, while 12% of US passenger-miles are traveled by airplane. This means that cars produce about 2/3 as much carbon per passenger-mile, even though we tend to fill up airplanes to the brim and most Americans drive alone most of the time.

Moreover, we know how to reduce emissions from cars. We can use hybrid vehicles, we can carpool more, or best of all we can switch to entirely electric vehicles charged off a grid that is driven by solar and nuclear power. It is theoretically possible to make personal emissions from car travel zero. (Though making car manufacturing truly carbon-neutral may not be feasible; electric cars actually produce somewhat more carbon in their production, though not enough to actually make them worse than conventional cars.)

We have basically no idea how to reduce emissions from air travel. Jet engines are already about as efficient as we know how to make them. There are some tweaks to taxi and takeoff procedure that would help a little bit (chiefly, towing the aircraft to the runway instead of taking them there on their own power; also, taking off from longer runways that require lower throttle to achieve takeoff speed). But there’s basically nothing we can do to reduce the carbon emissions of a cruising airliner at altitude. Even very optimistic estimates involving new high-tech alloys, wing-morphing technology, and dramatically improved turbofan engines only promise to reduce emissions by about 30%.

This is something that affects me quite directly; air travel is a major source of my personal carbon footprint, but also the best way I have to visit family back home.
Using the EPA’s handy carbon footprint calculator, I estimate that everything else I do in my entire life produces about 10 tons of carbon emissions per year. (This is actually pretty good, given the US average of 22 tons per person per year. It helps that I’m vegetarian, I drive a fuel-efficient car, and I live in Southern California.)

Using the ICAO’s even more handy carbon footprint calculator for air travel, I estimate that I produce about 0.2 tons for every round-trip economy-class transcontinental flight from California to Michigan. But that doesn’t account for the fact that higher-altitude emissions are more dangerous. If you adjust for this, the net effect is as if I had produced a full half-ton of carbon for each round-trip flight. Therefore, just four round-trip flights per year increases my total carbon footprint by 20%—and again, by itself exceeds what my carbon emissions need to be reduced to by the year 2050.

With this in mind, most ecologists agree that air travel as we know it is simply not sustainable.

The question then becomes: What do we do without it?

One option would be to simply take all the travel we currently do in airplanes, and stop it. For me this would mean no more trips from California to Michigan, except perhaps occasional long road trips for moving and staying for long periods.

This is unappealing, though it is also not as harmful as you might imagine; most of the world’s population has never flown in an airplane. Our estimates of exactly what proportion of people have flown are very poor, but our best guesses are that about 6% of the world’s population flies in any given year, and about 40% has ever flown in their entire life. Statistically, most of my readers are middle-class Americans, and we’re accustomed to flying; about 80% of Americans have flown on an airplane at least once, and about 1/3 of Americans fly at least once a year. But we’re weird (indeed, WEIRD, White, Educated, Industrialized, Rich, and Democratic); most people in the world fly on airplanes rarely, if ever.

Moreover, air travel has only been widely available to the general population, even in the US, for about the last 60 years. Passenger-miles on airplanes in the US have increased by a factor of 20 since just 1960, while car passenger-miles have only tripled and population has only doubled. Most of the human race through most of history has only dreamed of air travel, and managed to survive just fine without it.

It certainly would not mean needing to stop all long-distance travel, though long-distance travel would be substantially curtailed. It would no longer be possible to travel across the country for a one-week stay; you’d have to plan for four or five days of travel in each direction. Traveling from the US to Europe takes about a week by sea, each way. That means planning your trip much further in advance, and taking off a lot more time from work to do it.

Fortunately, trade is actually not that all that dependent on aircraft. The vast majority of shipping is done by sea vessel already, as container ships are simply far more efficient. Shipping by container ship produces only about 2% as much carbon per ton-kilometer as shipping by aircraft. “Slow-steaming”, the use of more ships at lower speeds to conserve fuel, is already widespread, and carbon taxes would further incentivize it. So we need not fear giving up globalized trade simply because we gave up airplanes.

But we can do better than that. We don’t need to give up the chance to travel across the country in a weekend. The answer is high-speed rail.

A typical airliner cruises at about 500 miles per hour. Can trains match that? Not quite, but close. Spain already has an existing commercial high-speed rail line, the AVE, which goes from Madrid to Barcelona at a cruising speed of 190 miles per hour. This is far from the limits of the technology. The fastest train ever built is the L0 series, a Japanese maglev which can maintain a top speed of 375 miles per hour.

This means that if we put our minds to it, we could build a rail line crossing the United States, say from Los Angeles to New York via Chicago, averaging at least 300 miles per hour. That’s a distance of 2800 miles by road (rail should be comparable); so the whole trip should take about 9 and a half hours. This is slower than a flight (unless you have a long layover), but could still make it there and back in the same weekend.

How much would such a rail system cost? Official estimates of the cost of maglev line are about $100 million per mile. This could probably be brought down by technological development and economies of scale, but let’s go with it for now. This means that my proposed LA-NY line would cost $280 billion.

That’s not a small amount of money, to be sure. It’s about the annual cost of ending world hunger forever. It’s almost half the US military budget. It’s about one-third of Obama’s stimulus plan in 2009. It’s about one-fourth Trump’s proposed infrastructure plan (that will probably never happen).

In other words, it’s a large project, but well within the capacity of a nation as wealthy as the United States.

Add in another 500 miles to upgrade the (already-successful) Acela corridor line on the East Coast, and another 800 miles to make the proposed California High-Speed Rail from LA to SF a maglev line, and you’ve increased the cost to $410 billion.
$410 billion is about 2 years of revenue for all US airlines. These lines could replace a large proportion of all US air traffic. So if the maglev system simply charged as much as a plane ticket and carried the same number of passengers, it would pay for itself in a few years. Realistically it would probably be a bit cheaper and carry fewer people, so the true payoff period might be more like 10 years. That is a perfectly reasonable payoff period for a major infrastructure project.

Compare this to our existing rail network, which is pitiful. There are Amtrak lines from California to Chicago; one is the Texas Eagle of 2700 miles, comparable to my proposed LA-NY maglev; the other is the California Zephyr of 2400 miles. Each of them completes one trip in about two and a half daysso a week-long trip is unviable and a weekend trip is mathematically impossible. Over 60 hours on each train, instead of the proposed 9.5 for the same distance. The operating speed is only about 55 miles per hour when we now have technology that could do 300. The Acela Express is our fastest train line with a top speed of 150 miles per hour and average end-to-end speed of 72 miles per hour; and (not coincidentally I think) it is by far the most profitable train line in the United States.

And best of all, the entire rail system could be carbon-neutral. Making the train itself run without carbon emissions is simple; you just run it off nuclear power plants and solar farms. The emissions from the construction and manufacturing would have to be offset, but most of them would be one-time emissions, precisely the sort of thing that it does make sense to offset with reforestation. Realistically some emissions would continue during the processes of repair and maintenance, but these would be far, far less than what the airplanes were producing—indeed, not much more than the emissions from a comparable length of interstate highway.

Let me emphasize, this is all existing technology. Unlike those optimistic forecasts about advanced new aircraft alloys and morphing wings, I’m not talking about inventing anything new here. This is something other countries have already built (albeit on a much smaller scale). I’m using official cost estimates. Nothing about this plan should be infeasible.

Why are we not doing this? We’re choosing not to. Our government has decided to spend on other things instead. Most Americans are quite complacent about climate change, though at least most Americans do believe in it now.

What about transcontinental travel? There we may have no choice but to give up our weekend visits. Sea vessels simply can’t be built as fast as airplanes. Even experimental high-speed Navy ships can’t far exceed 50 knots, which is about 57 miles per hour—highway speed, not airplane speed. A typical container vessel slow-steams at about 12 knots—14 miles per hour.

But how many people travel across the ocean anyway? As I’ve already established, Americans fly more than almost anyone else in the world; but of the 900 million passengers carried in flights in, through, or out of the US, only 200 million were international Some 64% of Americans have never left the United States—never even to Canada or Mexico! Even if we cut off all overseas commercial flights completely, we are affecting a remarkably small proportion of the world’s population.

And of course I wouldn’t actually suggest banning air travel. We should be taxing air travel, in proportion to its effect on global warming; and those funds ought to get us pretty far in paying for the up-front cost of the maglev network.

What can you do as an individual? Ay, there’s the rub. Not much, unfortunately. You can of course support candidates and political campaigns for high-speed rail. You can take fewer flights yourself. But until this infrastructure is built, those of us who live far from our ancestral home will face the stark tradeoff between increasing our carbon footprint and never getting to see our families.

Games as economic simulations—and education tools

Mar 5, JDN 2457818 [Sun]

Moore’s Law is a truly astonishing phenomenon. Now as we are well into the 21st century (I’ve lived more of my life in the 21st century than the 20th now!) it may finally be slowing down a little bit, but it has had quite a run, and even this could be a temporary slowdown due to economic conditions or the lull before a new paradigm (quantum computing?) matures. Since at least 1975, the computing power of an individual processor has doubled approximately every year and a half; that means it has doubled over 25 times—or in other words that it has increased by a factor of over 30 million. I now have in my pocket a smartphone with several thousand times the processing speed of the guidance computer of the Saturn V that landed on the Moon.

This meteoric increase in computing power has had an enormous impact on the way science is done, including economics. Simple theoretical models that could be solved by hand are now being replaced by enormous simulation models that have to be processed by computers. It is now commonplace to devise models with systems of dozens of nonlinear equations that are literally impossible to solve analytically, and just solve them iteratively with computer software.

But one application of this technology that I believe is currently underutilized is video games.

As a culture, we still have the impression that video games are for children; even games like Dragon Age and Grand Theft Auto that are explicitly for adults (and really quite inappropriate for children!) are viewed as in some sense “childish”—that no serious adult would be involved with such frivolities. The same cultural critics who treat Shakespeare’s vagina jokes as the highest form of art are liable to dismiss the poignant critique of war in Call of Duty: Black Ops or the reflections on cultural diversity in Skyrim as mere puerility.

But video games are an art form with a fundamentally greater potential than any other. Now that graphics are almost photorealistic, there is really nothing you can do in a play or a film that you can’t do in a video game—and there is so, so much more that you can only do in a game.
In what other medium can we witness the spontaneous emergence and costly aftermath of a war? Yet EVE Online has this sort of event every year or so—just today there was a surprise attack involving hundreds of players that destroyed thousands of hours’—and dollars’—worth of starships, something that has more or less become an annual tradition. A few years ago there was a massive three-faction war that destroyed over $300,000 in ships and has now been commemorated as “the Bloodbath of B-R5RB”.
Indeed, the immersion and interactivity of games present an opportunity to do nothing less than experimental macroeconomics. For generations it has been impossible, or at least absurdly unethical, to ever experimentally manipulate an entire macroeconomy. But in a video game like EVE Online or Second Life, we can now do so easily, cheaply, and with little or no long-term harm to the participants—and we can literally control everything in the experiment. Forget the natural resource constraints and currency exchange rates—we can change the laws of physics if we want. (Indeed, EVE‘s whole trade network is built around FTL jump points, and in Second Life it’s a basic part of the interface that everyone can fly like Superman.)

This provides untold potential for economic research. With sufficient funding, we could build a game that would allow us to directly test hypotheses about the most fundamental questions of economics: How do governments emerge and maintain security? How is the rule of law sustained, and when can it be broken? What controls the value of money and the rate of inflation? What is the fundamental cause of unemployment, and how can it be corrected? What influences the rate of technological development? How can we maximize the rate of economic growth? What effect does redistribution of wealth have on employment and output? I envision a future where we can directly simulate these questions with thousands of eager participants, varying the subtlest of parameters and carrying out events over any timescale we like from seconds to centuries.

Nor is the potential of games in economics limited to research; it also has enormous untapped potential in education. I’ve already seen in my classes how tabletop-style games with poker chips can teach a concept better in a few minutes than hours of writing algebra derivations on the board; but custom-built video games could be made that would teach economics far better still, and to a much wider audience. In a well-designed game, people could really feel the effects of free trade or protectionism, not just on themselves as individuals but on entire nations that they control—watch their GDP numbers go down as they scramble to produce in autarky what they could have bought for half the price if not for the tariffs. They could see, in real time, how in the absence of environmental regulations and Pigovian taxes the actions of millions of individuals could despoil our planet for everyone.

Of course, games are fundamentally works of fiction, subject to the Fictional Evidence Fallacy and only as reliable as their authors make them. But so it is with all forms of art. I have no illusions about the fact that we will never get the majority of the population to regularly read peer-reviewed empirical papers. But perhaps if we are clever enough in the games we offer them to play, we can still convey some of the knowledge that those papers contain. We could also update and expand the games as new information comes in. Instead of complaining that our students are spending time playing games on their phones and tablets, we could actually make education into games that are as interesting and entertaining as the ones they would have been playing. We could work with the technology instead of against it. And in a world where more people have access to a smartphone than to a toilet, we could finally bring high-quality education to the underdeveloped world quickly and cheaply.

Rapid growth in computing power has given us a gift of great potential. But soon our capacity will widen even further. Even if Moore’s Law slows down, computing power will continue to increase for awhile yet. Soon enough, virtual reality will finally take off and we’ll have even greater depth of immersion available. The future is bright—if we can avoid this corporatist cyberpunk dystopia we seem to be hurtling toward, of course.

Sometimes people have to lose their jobs. This isn’t a bad thing.

Oct 8, JDN 2457670

Eleizer Yudkowsky (founder of the excellent blog forum Less Wrong) has a term he likes to use to distinguish his economic policy views from either liberal, conservative, or even libertarian: “econoliterate”, meaning the sort of economic policy ideas one comes up with when one actually knows a good deal about economics.

In general I think Yudkowsky overestimates this effect; I’ve known some very knowledgeable economists who disagree quite strongly over economic policy, and often following the conventional political lines of liberal versus conservative: Liberal economists want more progressive taxation and more Keynesian monetary and fiscal policy, while conservative economists want to reduce taxes on capital and remove regulations. Theoretically you can want all these things—as Miles Kimball does—but it’s rare. Conservative economists hate minimum wage, and lean on the theory that says it should be harmful to employment; liberal economists are ambivalent about minimum wage, and lean on the empirical data that shows it has almost no effect on employment. Which is more reliable? The empirical data, obviously—and until more economists start thinking that way, economics is never truly going to be a science as it should be.

But there are a few issues where Yudkowsky’s “econoliterate” concept really does seem to make sense, where there is one view held by most people, and another held by economists, regardless of who is liberal or conservative. One such example is free trade, which almost all economists believe in. A recent poll of prominent economists by the University of Chicago found literally zero who agreed with protectionist tariffs.

Another example is my topic for today: People losing their jobs.

Not unemployment, which both economists and almost everyone else agree is bad; but people losing their jobs. The general consensus among the public seems to be that people losing jobs is always bad, while economists generally consider it a sign of an economy that is run smoothly and efficiently.

To be clear, of course losing your job is bad for you; I don’t mean to imply that if you lose your job you shouldn’t be sad or frustrated or anxious about that, particularly not in our current system. Rather, I mean to say that policy which tries to keep people in their jobs is almost always a bad idea.

I think the problem is that most people don’t quite grasp that losing your job and not having a job are not the same thing. People not having jobs who want to have jobs—unemployment—is a bad thing. But losing your job doesn’t mean you have to stay unemployed; it could simply mean you get a new job. And indeed, that is what it should mean, if the economy is running properly.

Check out this graph, from FRED:

hires_separations

The red line shows hires—people getting jobs. The blue line shows separations—people losing jobs or leaving jobs. During a recession (the most recent two are shown on this graph), people don’t actually leave their jobs faster than usual; if anything, slightly less. Instead what happens is that hiring rates drop dramatically. When the economy is doing well (as it is right now, more or less), both hires and separations are at very high rates.

Why is this? Well, think about what a job is, really: It’s something that needs done, that no one wants to do for free, so someone pays someone else to do it. Once that thing gets done, what should happen? The job should end. It’s done. The purpose of the job was not to provide for your standard of living; it was to achieve the task at hand. Once it doesn’t need done, why keep doing it?

We tend to lose sight of this, for a couple of reasons. First, we don’t have a basic income, and our social welfare system is very minimal; so a job usually is the only way people have to provide for their standard of living, and they come to think of this as the purpose of the job. Second, many jobs don’t really “get done” in any clear sense; individual tasks are completed, but new ones always arise. After every email sent is another received; after every patient treated is another who falls ill.

But even that is really only true in the short run. In the long run, almost all jobs do actually get done, in the sense that no one has to do them anymore. The job of cleaning up after horses is done (with rare exceptions). The job of manufacturing vacuum tubes for computers is done. Indeed, the job of being a computer—that used to be a profession, young women toiling away with slide rules—is very much done. There are no court jesters anymore, no town criers, and very few artisans (and even then, they’re really more like hobbyists). There are more writers now than ever, and occasional stenographers, but there are no scribes—no one powerful but illiterate pays others just to write things down, because no one powerful is illiterate (and even few who are not powerful, and fewer all the time).

When a job “gets done” in this long-run sense, we usually say that it is obsolete, and again think of this as somehow a bad thing, like we are somehow losing the ability to do something. No, we are gaining the ability to do something better. Jobs don’t become obsolete because we can’t do them anymore; they become obsolete because we don’t need to do them anymore. Instead of computers being a profession that toils with slide rules, they are thinking machines that fit in our pockets; and there are plenty of jobs now for software engineers, web developers, network administrators, hardware designers, and so on as a result.

Soon, there will be no coal miners, and very few oil drillers—or at least I hope so, for the sake of our planet’s climate. There will be far fewer auto workers (robots have already done most of that already), but far more construction workers who install rail lines. There will be more nuclear engineers, more photovoltaic researchers, even more miners and roofers, because we need to mine uranium and install solar panels on rooftops.

Yet even by saying that I am falling into the trap: I am making it sound like the benefit of new technology is that it opens up more new jobs. Typically it does do that, but that isn’t what it’s for. The purpose of technology is to get things done.

Remember my parable of the dishwasher. The goal of our economy is not to make people work; it is to provide people with goods and services. If we could invent a machine today that would do the job of everyone in the world and thereby put us all out of work, most people think that would be terrible—but in fact it would be wonderful.

Or at least it could be, if we did it right. See, the problem right now is that while poor people think that the purpose of a job is to provide for their needs, rich people think that the purpose of poor people is to do jobs. If there are no jobs to be done, why bother with them? At that point, they’re just in the way! (Think I’m exaggerating? Why else would anyone put a work requirement on TANF and SNAP? To do that, you must literally think that poor people do not deserve to eat or have homes if they aren’t, right now, working for an employer. You can couch that in cold economic jargon as “maximizing work incentives”, but that’s what you’re doing—you’re threatening people with starvation if they can’t or won’t find jobs.)

What would happen if we tried to stop people from losing their jobs? Typically, inefficiency. When you aren’t allowed to lay people off when they are no longer doing useful work, we end up in a situation where a large segment of the population is being paid but isn’t doing useful work—and unlike the situation with a basic income, those people would lose their income, at least temporarily, if they quit and tried to do something more useful. There is still considerable uncertainty within the empirical literature on just how much “employment protection” (laws that make it hard to lay people off) actually creates inefficiency and reduces productivity and employment, so it could be that this effect is small—but even so, likewise it does not seem to have the desired effect of reducing unemployment either. It may be like minimum wage, where the effect just isn’t all that large. But it’s probably not saving people from being unemployed; it may simply be shifting the distribution of unemployment so that people with protected jobs are almost never unemployed and people without it are unemployed much more frequently. (This doesn’t have to be based in law, either; while it is made by custom rather than law, it’s quite clear that tenure for university professors makes tenured professors vastly more secure, but at the cost of making employment tenuous and underpaid for adjuncts.)

There are other policies we could make that are better than employment protection, active labor market policies like those in Denmark that would make it easier to find a good job. Yet even then, we’re assuming that everyone needs jobs–and increasingly, that just isn’t true.

So, when we invent a new technology that replaces workers, workers are laid off from their jobs—and that is as it should be. What happens next is what we do wrong, and it’s not even anybody in particular; this is something our whole society does wrong: All those displaced workers get nothing. The extra profit from the more efficient production goes entirely to the shareholders of the corporation—and those shareholders are almost entirely members of the top 0.01%. So the poor get poorer and the rich get richer.

The real problem here is not that people lose their jobs; it’s that capital ownership is distributed so unequally. And boy, is it ever! Here are some graphs I made of the distribution of net wealth in the US, using from the US Census.

Here are the quintiles of the population as a whole:

net_wealth_us

And here are the medians by race:

net_wealth_race

Medians by age:

net_wealth_age

Medians by education:

net_wealth_education

And, perhaps most instructively, here are the quintiles of people who own their homes versus renting (The rent is too damn high!)

net_wealth_rent

All that is just within the US, and already they are ranging from the mean net wealth of the lowest quintile of people under 35 (-$45,000, yes negative—student loans) to the mean net wealth of the highest quintile of people with graduate degrees ($3.8 million). All but the top quintile of renters are poorer than all but the bottom quintile of homeowners. And the median Black or Hispanic person has less than one-tenth the wealth of the median White or Asian person.

If we look worldwide, wealth inequality is even starker. Based on UN University figures, 40% of world wealth is owned by the top 1%; 70% by the top 5%; and 80% by the top 10%. There is less total wealth in the bottom 80% than in the 80-90% decile alone. According to Oxfam, the richest 85 individuals own as much net wealth as the poorest 3.7 billion. They are the 0.000,001%.

If we had an equal distribution of capital ownership, people would be happy when their jobs became obsolete, because it would free them up to do other things (either new jobs, or simply leisure time), while not decreasing their income—because they would be the shareholders receiving those extra profits from higher efficiency. People would be excited to hear about new technologies that might displace their work, especially if those technologies would displace the tedious and difficult parts and leave the creative and fun parts. Losing your job could be the best thing that ever happened to you.

The business cycle would still be a problem; we have good reason not to let recessions happen. But stopping the churn of hiring and firing wouldn’t actually make our society better off; it would keep people in jobs where they don’t belong and prevent us from using our time and labor for its best use.

Perhaps the reason most people don’t even think of this solution is precisely because of the extreme inequality of capital distribution—and the fact that it has more or less always been this way since the dawn of civilization. It doesn’t seem to even occur to most people that capital income is a thing that exists, because they are so far removed from actually having any amount of capital sufficient to generate meaningful income. Perhaps when a robot takes their job, on some level they imagine that the robot is getting paid, when of course it’s the shareholders of the corporations that made the robot and the corporations that are using the robot in place of workers. Or perhaps they imagine that those shareholders actually did so much hard work they deserve to get paid that money for all the hours they spent.

Because pay is for work, isn’t it? The reason you get money is because you’ve earned it by your hard work?

No. This is a lie, told to you by the rich and powerful in order to control you. They know full well that income doesn’t just come from wages—most of their income doesn’t come from wages! Yet this is even built into our language; we say “net worth” and “earnings” rather than “net wealth” and “income”. (Parade magazine has a regular segment called “What People Earn”; it should be called “What People Receive”.) Money is not your just reward for your hard work—at least, not always.

The reason you get money is that this is a useful means of allocating resources in our society. (Remember, money was created by governments for the purpose of facilitating economic transactions. It is not something that occurs in nature.) Wages are one way to do that, but they are far from the only way; they are not even the only way currently in use. As technology advances, we should expect a larger proportion of our income to go to capital—but what we’ve been doing wrong is setting it up so that only a handful of people actually own any capital.

Fix that, and maybe people will finally be able to see that losing your job isn’t such a bad thing; it could even be satisfying, the fulfillment of finally getting something done.

What is the processing power of the human brain?

JDN 2457485

Futurists have been predicting that AI will “surpass humans” any day now for something like 50 years. Eventually they’ll be right, but it will be more or less purely by chance, since they’ve been making the same prediction longer than I’ve been alive. (Similarity, whenever someone projects the date at which immortality will be invented, it always seems to coincide with just slightly before the end of the author’s projected life expectancy.) Any technology that is “20 years away” will be so indefinitely.

There are a lot of reasons why this prediction keeps failing so miserably. One is an apparent failure to grasp the limitations of exponential growth. I actually think the most important is that a lot of AI fans don’t seem to understand how human cognition actually works—that it is primarily social cognition, where most of the processing has already been done and given to us as cached results, some of them derived centuries before we were born. We are smart enough to run a civilization with airplanes and the Internet not because any individual human is so much smarter than any other animal, but because all humans together are—and other animals haven’t quite figured out how to unite their cognition in the same way. We’re about 3 times smarter than any other animal as individuals—and several billion times smarter when we put our heads together.

A third reason is that even if you have sufficient computing power, that is surprisingly unimportant; what you really need are good heuristics to make use of your computing power efficiently. Any nontrivial problem is too complex to brute-force by any conceivable computer, so simply increasing computing power without improving your heuristics will get you nowhere. Conversely, if you have really good heuristics like the human brain does, you don’t even need all that much computing power. A chess grandmaster was once asked how many moves ahead he can see on the board, and he replied: “I only see one move ahead. The right one.” In cognitive science terms, people asked him how much computing power he was using, expecting him to say something far beyond normal human capacity, and he replied that he was using hardly any—it was all baked into the heuristics he had learned from years of training and practice.

Making an AI capable of human thought—a true artificial person—will require a level of computing power we can already reach (as long as we use huge supercomputers), but that is like having the right material. To really create the being we will need to embed the proper heuristics. We are trying to make David, and we have finally mined enough marble—now all we need is Michelangelo.

But another reason why so many futurists have failed in their projections is that they have wildly underestimated the computing power of the human brain. Reading 1980s cyberpunk is hilarious in hindsight; Neuromancer actually quite accurately projected the number of megabytes that would flow through the Internet at any given moment, but somehow thought that a few hundred megaflops would be enough to copy human consciousness. The processing power of the human brain is actually on the order of a few petaflops. So, you know, Gibson was only off by a factor of a few million.

We can now match petaflops—the world’s fastest supercomputer is actually about 30 petaflops. Of course, it cost half a month of China’s GDP to build, and requires 24 megawatts to run and cool, which is about the output of a mid-sized solar power station. The human brain consumes only about 400 kcal per day, which is about 20 watts—roughly the consumption of a typical CFL lightbulb. Even if you count the rest of the human body as necessary to run the human brain (which I guess is sort of true), we’re still clocking in at about 100 watts—so even though supercomputers can now process at the same speed, our brains are almost a million times as energy-efficient.

How do I know it’s a few petaflops?

Earlier this year a study was published showing that a conservative lower bound for the total capacity of human memory is about 4 bits per synapse, where previously some scientists thought that each synapse might carry only 1 bit (I’ve always suspected it was more like 10 myself).

So then we need to figure out how many synapses we have… which turns out to be really difficult actually. They are in a constant state of flux, growing, shrinking, and moving all the time; and when we die they fade away almost immediately (reason #3 I’m skeptical of cryonics). We know that we have about 100 billion neurons, and each one can have anywhere between 100 and 15,000 synapses with other neurons. The average seems to be something like 5,000 (but highly skewed in a power-law distribution), so that’s about 500 trillion synapses. If each one is carrying 4 bits to be as conservative as possible, that’s a total storage capacity of about 2 quadrillion bits, which is about 0.2 petabytes.

Of course, that’s assuming that our brains store information the same way as a computer—every bit flipped independently, each bit stored forever. Not even close. Human memory is constantly compressing and decompressing data, using a compression scheme that’s lossy enough that we not only forget things, we can systematically misremember and even be implanted with false memories. That may seem like a bad thing, and in a sense it is; but if the compression scheme is that lossy, it must be because it’s also that efficient—that our brains are compressing away the vast majority of the data to make room for more. Our best lossy compression algorithms for video are about 100:1; but the human brain is clearly much better than that. Our core data format for long-term memory appears to be narrative; more or less we store everything not as audio or video (that’s short-term memory, and quite literally so), but as stories.

How much compression can you get by storing things as narrative? Think about The Lord of the Rings. The extended edition of the films runs to 6 discs of movie (9 discs of other stuff), where a Blu-Ray disc can store about 50 GB. So that’s 300 GB. Compressed into narrative form, we have the books (which, if you’ve read them, are clearly not optimally compressed—no, we do not need five paragraphs about the trees, and I’m gonna say it, Tom Bombadil is totally superfluous and Peter Jackson was right to remove him), which run about 500,000 words altogether. If the average word is 10 letters (normally it’s less than that, but this is Tolkien we’re talking about), each word will take up about 10 bytes (because in ASCII or Unicode a letter is a byte). So altogether the total content of the entire trilogy, compressed into narrative, can be stored in about 5 million bytes, that is, 5 MB. So the compression from HD video to narrative takes us all the way from 300 GB to 5 MB, which is a factor of 60,000. Sixty thousand. I believe that this is the proper order of magnitude for the compression capability of the human brain.

Even more interesting is the fact that the human brain is almost certainly in some sense holographic storage; damage to a small part of your brain does not produce highly selective memory loss as if you had some bad sectors of your hard drive, but rather an overall degradation of your total memory processing as if you in some sense stored everything everywhere—that is, holographically. How exactly this is accomplished by the brain is still very much an open question; it’s probably not literally a hologram in the quantum sense, but it definitely seems to function like a hologram. (Although… if the human brain is a quantum computer that would explain an awful lot—it especially helps with the binding problem. The problem is explaining how a biological system at 37 C can possibly maintain the necessary quantum coherences.) The data storage capacity of holograms is substantially larger than what can be achieved by conventional means—and furthermore has similar properties to human memory in that you can more or less always add more, but then what you had before gradually gets degraded. Since neural nets are much closer to the actual mechanics of the brain as we know them, understanding human memory will probably involve finding ways to simulate holographic storage with neural nets.

With these facts in mind, the amount of information we can usefully take in and store is probably not 0.2 petabytes—it’s probably more like 10 exabytes. The human brain can probably hold just about as much as the NSA’s National Cybersecurity Initiative Data Center in Utah, which is itself more or less designed to contain the Internet. (The NSA is at once awesome and terrifying.)

But okay, maybe that’s not fair if we’re comparing human brains to computers; even if you can compress all your data by a factor of 100,000, that isn’t the same thing as having 100,000 times as much storage.

So let’s use that smaller figure, 0.2 petabytes. That’s how much we can store; how much can we process?

The next thing to understand is that our processing architecture is fundamentally difference from that of computers.

Computers generally have far more storage than they have processing power, because they are bottlenecked through a CPU that can only process 1 thing at once (okay, like 8 things at once with a hyperthreaded quad-core; as you’ll see in a moment this is a trivial difference). So it’s typical for a new computer these days to have processing power in gigaflops (It’s usually reported in gigahertz, but that’s kind of silly; hertz just tells you clock cycles, while what you really wanted to know is calculations—and that you get from flops. They’re generally pretty comparable numbers though.), while they have storage in terabytes—meaning that it would take about 1000 seconds (about 17 minutes) for the computer to process everything in its entire storage once. In fact it would take a good deal longer than that, because there are further bottlenecks in terms of memory access, especially from hard-disk drives (RAM and solid-state drives are faster, but would still slow it down to a couple of hours).

The human brain, by contrast, integrates processing and memory into the same system. There is no clear distinction between “memory synapses” and “processing synapses”, and no single CPU bottleneck that everything has to go through. There is however something like a “clock cycle” as it turns out; synaptic firings are synchronized across several different “rhythms”, the fastest of which is about 30 Hz. No, not 30 GHz, not 30 MHz, not even 30 kHz; 30 hertz. Compared to the blazing speed of billions of cycles per second that goes on in our computers, the 30 cycles per second our brains are capable of may seem bafflingly slow. (Even more bafflingly slow is the speed of nerve conduction, which is not limited by the speed of light as you might expect, but is actually less than the speed of sound. When you trigger the knee-jerk reflex doctors often test, it takes about a tenth of a second for the reflex to happen—not because your body is waiting for anything, but because it simply takes that long for the signal to travel to your spinal cord and back.)

The reason we can function at all is because of our much more efficient architecture; instead of passing everything through a single bottleneck, we do all of our processing in parallel. All of those 100 billion neurons with 500 trillion synapses storing 2 quadrillion bits work simultaneously. So whereas a computer does 8 things at a time, 3 billion times per second, a human brain does 2 quadrillion things at a time, 30 times per second. Provided that the tasks can be fully parallelized (vision, yes; arithmetic, no), a human brain can therefore process 60 quadrillion bits per second—which turns out to be just over 6 petaflops, somewhere around 6,000,000,000,000,000 calculations per second.

So, like I said, a few petaflops.

The Parable of the Dishwasher

JDN 2456478

Much like free trade, technological unemployment is an issue where the consensus opinion among economists diverges quite sharply from that of the general population.

Enough people think that “robots taking our jobs” is something bad that I’ve seen a fair number of memes like this:

EVERY TIME you use the Self Checkout you are ELIMINATING JOBS!

But like almost all economists, I think that self-checkouts, robots, and automation in general are a pretty good thing. They do have a few downsides, chiefly in terms of forcing us to make transitions that are costly and painful; but in general I want more robots, not fewer.

To help turn you toward this view, I offer a parable.

Suppose we have a family, the (stereo)typical American family with a father, a mother, and two kids, a boy named Joe and a girl named Sue.

The kids do chores for their allowance, and split them as follows: Joe always does the dishes, and Sue always vacuums the carpet. They both spend about 1 hour per week and they both get paid $10 a week.

But one day, Dad decides to buy a dishwasher. This dramatically cuts down the time it takes Joe to do the dishes; where he used to spend 1 hour washing dishes, now he can load the dishwasher and get it done in 5 minutes.

  1. Mom suggests they just sell back the dishwasher to get rid of the problem.
  2. Dad says that Joe should now only be paid for the 5 minutes he works each week, so he would now be paid $0.83 per week. (He’s not buying a lot of video games on that allowance.)
  3. Joe protests that he gets the same amount of work done, so he should be paid the same $10 for doing it.
  4. Sue says it would be unfair for her to have to work so much more than Joe, and has a different solution: They’ll trade off the two sets of chores each week, and they should of course get paid the same amount of money for getting the same amount of work done—$10 per kid per week, for an average of 32.5 minutes of work each.

Which of those solutions sounds the most sensible to you?

Mom’s solution is clearly the worst, right? It’s the Luddite solution, the one that throws away technological progress and makes everything less efficient. Yet that is the solution being offered by people who say “Don’t use the self-checkout machine!” Indeed, anyone who speaks of the virtues of “hard work” is really speaking Mom’s language here; they should be talking about the virtues of getting things done. The purpose of washing dishes is to have clean dishes, not to “work hard”. And likewise, when we construct bridges or make cars or write books or solve equations, our goal should be to get that thing done—not to fulfill some sense of moral obligation to prove our worthiness through hard work.

Joe’s solution is what neoclassical economics says should happen—higher productivity should yield higher wages, so the same amount of production should yield the same pay. This seems like it could work, but empirically it rarely happens. There’s also something vaguely unfair about it; if productivity increases in your industry but not in someone else’s, you get to cut your work hours dramatically while they are stuck working just as hard as before.

Dad’s “solution” is clearly terrible, and makes no sense at all. Yet this is what we actually tend to observe—capital owners appropriate all (or nearly all) the benefits of the new technology, and workers get displaced or get ever-smaller wages. (I talked about that in a recent post.)

It’s Sue’s solution that really seems to make the most sense, isn’t it? When one type of work becomes more efficient, people should shift into different types of labor so that people can work fewer hours—and wages should rise enough that incomes remain the same. “Baumol’s disease” is not a disease—it is the primary means by which capitalism raises human welfare. (That’s why I prefer to use the term “Baumol Effect” instead.)

One problem with this in practice is that sometimes people can’t switch into other industries. That’s a little hard to imagine in this case, but let’s stipulate that for some reason Joe can’t do the vacuuming. Maybe he has some sort of injury that makes it painful to use the vacuum cleaner, but doesn’t impair his ability to wash dishes. Or maybe he has a severe dust allergy, so bad that the dust thrown up by the vacuum cleaner sends him into fits of coughing.

In that case I think we’re back to Joe’s solution; he should get paid the same for getting the same amount of work done. I’m actually tempted to say that Sue should get paid more, to compensate her for the unfairness; but in the real world there is a pretty harsh budget constraint there, so we need to essentially pretend that Dad only has $20 per week to give out in allowances. A possible compromise would be to raise Sue up to $12 and cut Joe down to $8; Joe will probably still be better off than he was, because he has that extra 55 minutes of free time each week for which he only had to “pay” $2. This also makes the incentives work out better—Joe doesn’t have a reason to malinger and exaggerate his dust allergy just to get out of doing the vacuuming, since he would actually get paid more if he were willing to do the vacuuming; but if his allergy really is that bad, he can still do okay otherwise. (There’s a lesson here for the proper structure of Social Security Disability, methinks.)

But you know what really seems like the best solution? Buy a Roomba.

Buy a Roomba, make it Sue’s job to spend 5 minutes a week keeping the Roomba working at vacuuming the carpet, and continue paying both kids $10 per week. Give them both 55 minutes more per week to hang out with their friends or play video games. Whether you think of this $10 as a “higher wage” for higher productivity or simply an allowance they get anyway—a basic income—ultimately doesn’t matter all that much. The point is that everyone gets enough money and nobody has to work very much, because the robots do everything.

And now, hopefully you see why I think we need more robots, not fewer.

Of course, like any simple analogy, this isn’t perfect; it may be difficult to reduce the hours in some jobs or move more people into them. There are a lot of additional frictions and complications that go into the real-world problem of achieving equitable labor markets. But I hope I’ve gotten across the basic idea that robots are not the problem, and could in fact be the solution–not just to our current labor market woes, but to the very problem of wage labor itself.

My ultimate goal is a world where “work” itself is fundamentally redefined—so that it always means the creative sense “This painting is some of my best work.” and not the menial sense “Sweeping this floor is so much work!”; so that human beings do things because we want to do them, because they are worth doing, and not because some employer is holding our food and housing hostage if we don’t.

But that will require our whole society to rethink a lot of our core assumptions about work, jobs, and economics in general. We’re so invested in this idea that “hard work” is inherently virtuous that we forgot the purpose of an economy was to get things done. Work is not a benefit; work is a cost. Costs are to be reduced. Puritanical sexual norms have been extremely damaging to American society, but time will tell if Puritanical work ethic actually does more damage to our long-term future.

Do we always want to internalize externalities?

JDN 2457437

I often talk about the importance of externalitiesa full discussion in this earlier post, and one of their important implications, the tragedy of the commons, in another. Briefly, externalities are consequences of actions incurred upon people who did not perform those actions. Anything I do affecting you that you had no say in, is an externality.

Usually I’m talking about how we want to internalize externalities, meaning that we set up a system of incentives to make it so that the consequences fall upon the people who chose the actions instead of anyone else. If you pollute a river, you should have to pay to clean it up. If you assault someone, you should serve jail time as punishment. If you invent a new technology, you should be rewarded for it. These are all attempts to internalize externalities.

But today I’m going to push back a little, and ask whether we really always want to internalize externalities. If you think carefully, it’s not hard to come up with scenarios where it actually seems fairer to leave the externality in place, or perhaps reduce it somewhat without eliminating it.

For example, suppose indeed that someone invents a great new technology. To be specific, let’s think about Jonas Salk, inventing the polio vaccine. This vaccine saved the lives of thousands of people and saved millions more from pain and suffering. Its value to society is enormous, and of course Salk deserved to be rewarded for it.

But we did not actually fully internalize the externality. If we had, every family whose child was saved from polio would have had to pay Jonas Salk an amount equal to what they saved on medical treatments as a result, or even an amount somehow equal to the value of their child’s life (imagine how offended people would get if you asked that on a survey!). Those millions of people spared from suffering would need to each pay, at minimum, thousands of dollars to Jonas Salk, making him of course a billionaire.

And indeed this is more or less what would have happened, if he had been willing and able to enforce a patent on the vaccine. The inability of some to pay for the vaccine at its monopoly prices would add some deadweight loss, but even that could be removed if Salk Industries had found a way to offer targeted price vouchers that let them precisely price-discriminate so that every single customer paid exactly what they could afford to pay. If that had happened, we would have fully internalized the externality and therefore maximized economic efficiency.

But doesn’t that sound awful? Doesn’t it sound much worse than what we actually did, where Jonas Salk received a great deal of funding and support from governments and universities, and lived out his life comfortably upper-middle class as a tenured university professor?

Now, perhaps he should have been awarded a Nobel Prize—I take that back, there’s no “perhaps” about it, he definitely should have been awarded a Nobel Prize in Medicine, it’s absurd that he did not—which means that I at least do feel the externality should have been internalized a bit more than it was. But a Nobel Prize is only 10 million SEK, about $1.1 million. That’s about enough to be independently wealthy and live comfortably for the rest of your life; but it’s a small fraction of the roughly $7 billion he could have gotten if he had patented the vaccine. Yet while the possible world in which he wins a Nobel is better than this one, I’m fairly well convinced that the possible world in which he patents the vaccine and becomes a billionaire is considerably worse.

Internalizing externalities makes sense if your goal is to maximize total surplus (a concept I explain further in the linked post), but total surplus is actually a terrible measure of human welfare.

Total surplus counts every dollar of willingness-to-pay exactly the same across different people, regardless of whether they live on $400 per year or $4 billion.

It also takes no account whatsoever of how wealth is distributed. Suppose a new technology adds $10 billion in wealth to the world. As far as total surplus, it makes no difference whether that $10 billion is spread evenly across the entire planet, distributed among a city of a million people, concentrated in a small town of 2,000, or even held entirely in the bank account of a single man.

Particularly a propos of the Salk example, total surplus makes no distinction between these two scenarios: a perfectly-competitive market where everything is sold at a fair price, and a perfectly price-discriminating monopoly, where everything is sold at the very highest possible price each person would be willing to pay.

This is a perfectly-competitive market, where the benefits are more or less equally (in this case exactly equally, but that need not be true in real life) between sellers and buyers:

elastic_supply_competitive_labeled

This is a perfectly price-discriminating monopoly, where the benefits accrue entirely to the corporation selling the good:

elastic_supply_price_discrimination

In the former case, the company profits, consumers are better off, everyone is happy. In the latter case, the company reaps all the benefits and everyone else is left exactly as they were. In real terms those are obviously very different outcomes—the former being what we want, the latter being the cyberpunk dystopia we seem to be hurtling mercilessly toward. But in terms of total surplus, and therefore the kind of “efficiency” that is maximize by internalizing all externalities, they are indistinguishable.

In fact (as I hope to publish a paper about at some point), the way willingness-to-pay works, it weights rich people more. Redistributing goods from the poor to the rich will typically increase total surplus.

Here’s an example. Suppose there is a cake, which is sufficiently delicious that it offers 2 milliQALY in utility to whoever consumes it (this is a truly fabulous cake). Suppose there are two people to whom we might give this cake: Richie, who has $10 million in annual income, and Hungry, who has only $1,000 in annual income. How much will each of them be willing to pay?

Well, assuming logarithmic marginal utility of wealth (which is itself probably biasing slightly in favor of the rich), 1 milliQALY is about $1 to Hungry, so Hungry will be willing to pay $2 for the cake. To Richie, however, 1 milliQALY is about $10,000; so he will be willing to pay a whopping $20,000 for this cake.

What this means is that the cake will almost certainly be sold to Richie; and if we proposed a policy to redistribute the cake from Richie to Hungry, economists would emerge to tell us that we have just reduced total surplus by $19,998 and thereby committed a great sin against economic efficiency. They will cajole us into returning the cake to Richie and thus raising total surplus by $19,998 once more.

This despite the fact that I stipulated that the cake is worth just as much in real terms to Hungry as it is to Richie; the difference is due to their wildly differing marginal utility of wealth.

Indeed, it gets worse, because even if we suppose that the cake is worth much more in real utility to Hungry—because he is in fact hungry—it can still easily turn out that Richie’s willingness-to-pay is substantially higher. Suppose that Hungry actually gets 20 milliQALY out of eating the cake, while Richie still only gets 2 milliQALY. Hungry’s willingness-to-pay is now $20, but Richie is still going to end up with the cake.

Now, if your thought is, “Why would Richie pay $20,000, when he can go to another store and get another cake that’s just as good for $20?” Well, he wouldn’t—but in the sense we mean for total surplus, willingness-to-pay isn’t just what you’d actually be willing to pay given the actual prices of the goods, but the absolute maximum price you’d be willing to pay to get that good under any circumstances. It is instead the marginal utility of the good divided by your marginal utility of wealth. In this sense the cake is “worth” $20,000 to Richie, and “worth” substantially less to Hungry—but not because it’s actually worth less in real terms, but simply because Richie has so much more money.

Even economists often equate these two, implicitly assuming that we are spending our money up to the point where our marginal willingness-to-pay is the actual price we choose to pay; but in general our willingness-to-pay is higher than the price if we are willing to buy the good at all. The consumer surplus we get from goods is in fact equal to the difference between willingness-to-pay and actual price paid, summed up over all the goods we have purchased.

Internalizing all externalities would definitely maximize total surplus—but would it actually maximize happiness? Probably not.

If you asked most people what their marginal utility of wealth is, they’d have no idea what you’re talking about. But most people do actually have an intuitive sense that a dollar is worth more to a homeless person than it is to a millionaire, and that’s really all we mean by diminishing marginal utility of wealth.

I think the reason we’re uncomfortable with the idea of Jonas Salk getting $7 billion from selling the polio vaccine, rather than the same number of people getting the polio vaccine and Jonas Salk only getting the $1.1 million from a Nobel Prize, is that we intuitively grasp that after that $1.1 million makes him independently wealthy, the rest of the money is just going to sit in some stock account and continue making even more money, while if we’d let the families keep it they would have put it to much better use raising their children who are now protected from polio. We do want to reward Salk for his great accomplishment, but we don’t see why we should keep throwing cash at him when it could obviously be spent in better ways.

And indeed I think this intuition is correct; great accomplishments—which is to say, large positive externalities—should be rewarded, but not in direct proportion. Maybe there should be some threshold above which we say, “You know what? You’re rich enough now; we can stop giving you money.” Or maybe it should simply damp down very quickly, so that a contribution which is worth $10 billion to the world pays only slightly more than one that is worth $100 million, but a contribution that is worth $100,000 pays considerably more than one which is only worth $10,000.

What it ultimately comes down to is that if we make all the benefits incur to the person who did it, there aren’t any benefits anymore. The whole point of Jonas Salk inventing the polio vaccine (or Einstein discovering relativity, or Darwin figuring out natural selection, or any great achievement) is that it will benefit the rest of humanity, preferably on to future generations. If you managed to fully internalize that externality, this would no longer be true; Salk and Einstein and Darwin would have become fabulously wealthy, and then somehow we’d all have to continue paying into their estates or something an amount equal to the benefits we received from their discoveries. (Every time you use your GPS, pay a royalty to the Einsteins. Every time you take a pill, pay a royalty to the Darwins.) At some point we’d probably get fed up and decide we’re no better off with them than without them—which is exactly by construction how we should feel if the externality were fully internalized.

Internalizing negative externalities is much less problematic—it’s your mess, clean it up. We don’t want other people to be harmed by your actions, and if we can pull that off that’s fantastic. (In reality, we usually can’t fully internalize negative externalities, but we can at least try.)

But maybe internalizing positive externalities really isn’t so great after all.