# 2020 is almost over

Dec27 JDN 2459211

I don’t think there are many people who would say that 2020 was their favorite year. Even if everything else had gone right, the 1.7 million deaths from the COVID pandemic would already make this a very bad year.

As if that weren’t bad enough, shutdowns in response to the pandemic, resulting unemployment, and inadequate fiscal policy responses have in a single year thrown nearly 150 million people back into extreme poverty. Unemployment in the US this year spiked to nearly 15%, its highest level since World War 2. Things haven’t been this bad for the US economy since the Great Depression.

And this Christmas season certainly felt quite different, with most of us unable to safely travel and forced to interact with our families only via video calls. New Year’s this year won’t feel like a celebration of a successful year so much as relief that we finally made it through.

Many of us have lost loved ones. Fortunately none of my immediate friends and family have died of COVID, but I can now count half a dozen acquaintances, friends-of-friends or distant relatives who are no longer with us. And I’ve been relatively lucky overall; both I and my partner work in jobs that are easy to do remotely, so our lives haven’t had to change all that much.

Yet 2020 is nearly over, and already there are signs that things really will get better in 2021. There are many good reasons for hope.

There are now multiple vaccines for COVID that have been successfully fast-tracked, and they are proving to be remarkably effective. Current forecasts suggest that we’ll have most of the US population vaccinated by the end of next summer.

Maybe the success of this vaccine will finally convince some of the folks who have been doubting the safety and effectiveness of vaccines in general. (Or maybe not; it’s too soon to tell.)

Perhaps the greatest reason to be hopeful about the future is the fact that 2020 is a sharp deviation from the long-term trend toward a better world. That 150 million people thrown back into extreme poverty needs to be compared against the over 1 billion people who have been lifted out of extreme poverty in just the last 30 years.

Those 1.7 million deaths need to be compared against the fact that global life expectancy has increased from 45 to 73 since 1950. The world population is 7.8 billion people. The global death rate has fallen from over 20 deaths per 1000 people per year to only 7.6 deaths per 1000 people per year. Multiplied over 7.8 billion people, that’s nearly 100 million lives saved every single year by advances in medicine and overall economic development. Indeed, if we were to sustain our current death rate indefinitely, our life expectancy would rise to over 130. There are various reasons to think that probably won’t happen, mostly related to age demographics, but in fact there are medical breakthroughs we might make that would make it possible. Even according to current forecasts, world life expectancy is expected to exceed 80 years by the end of the 21st century.

There have also been some significant environmental milestones this year: Global carbon emissions fell an astonishing 7% in 2020, though much of that was from reduced economic activity in response to the pandemic. (If we could sustain that, we’d cut global emissions in half each decade!) But many other milestones were the product of hard work, not silver linings of a global disaster: Whales returned to the Hudson river, Sweden officially terminated their last coal power plant, and the Great Barrier Reef is showing signs of recovery.

Yes, it’s been a bad year for most of us—most of the world, in fact. But there are many reasons to think that next year will be much better.

# The evolution of cuteness

Dec20 JDN 2459204

I thought I’d go for something a little more light-hearted for this week’s post. It’s been a very difficult year for a lot of people, though with Biden winning the election and the recent FDA approval of a COVID vaccine for emergency use, the light at the end of the tunnel is now visible. I’ve also had some relatively good news in my job search; I now have a couple of job interviews lined up for tenure-track assistant professor positions.

So rather than the usual economic and political topics, I thought I would focus today on cuteness. First of all, this allows me the opportunity to present you with a bunch of photos of cute animals (free stock photos brought to you by pexels.com):

Beyond the joy I hope this brings you in a dark time, I have a genuine educational purpose here, which is to delve into the surprisingly deep evolutionary question: Why does cuteness exist?

Well, first of all, what is cuteness? We evaluate a person or animal (or robot, or alien) as cute based on certain characteristics like wide eyes, a large head, a posture or expression that evokes innocence. We feel positive feelings toward that which we identify as cute, and we want to help them rather than harm them. We often feel protective toward them.

It’s not too hard to provide an evolutionary rationale for why we would find our own offspring cute: We have good reasons to want to protect and support our own offspring, and given the substantial amounts of effort involved in doing so, it behooves us to have a strong motivation for committing to doing so.

But it’s less obvious why we would feel this way about so many other things that are not human. Dogs and cats have co-evolved along with us as they became domesticated, dogs starting about 40,000 years ago and cats starting around 8,000 years ago. So perhaps it’s not so surprising that we find them cute as well: Becoming domesticated is, in many ways, simply the process of maximizing your level of cuteness so that humans will continue to feed and protect you.

But why are non-domesticated animals also often quite cute? That red panda, penguin, owl, and hedgehog are not domesticated; this is what they look like in the wild. And yet I personally find the red panda to be probably the cutest among an already very cute collection.

Some animals we do not find cute, or at least most people don’t. Here’s a collection of “cute snakes” that I honestly am not getting much cuteness reaction from. These “cute snails” work a little better, but they’re assuredly not as cute as kittens or red pandas. But honestly these “cute spiders” are doing a remarkably good job of it, despite the general sense I have (and I think I share with most people) that spiders are not generally cute. And while tentacles are literally the stuff of Lovecraftian nightmares, this “adorable octopus” lives up to the moniker.

The standard theory is that animals that we find cute are simply those that most closely resemble our own babies, but I don’t really buy it. Naked mole rats have their moments, but they are certainly not as cute as puppies or kittens, despite clearly bearing a closer resemblance to the naked wrinkly blob that most human infants look like. Indeed, I think it’s quite striking that babies aren’t really that cute; yes, some are, but many are not, and even the cutest babies are rarely as cute as the average kitten or red panda.

It actually seems to me more that we have some idealized concept of what a cute creature should look like, and maybe it evolved to reflect some kind of “optimal baby” of perfect health and vigor—but most of our babies don’t quite manage to meet that standard. Perhaps the cuteness of penguins or red pandas is sheer coincidence; out of the millions of animal species out there, some of them were bound to send our cuteness-detectors into overdrive. Dogs and cats, then, started as such coincidence—and then through domestication they evolved to fit our cuteness standard better and better, because this was in fact the primary determinant of their survival. That’s how you can get the adorable abomination that is a pug:

Such a creature would never survive in the wild, but we created it because we liked it (or enough of us did, anyway).

There are actually important reasons why having such a strong cuteness response could be maladaptive—we’re apex predators, after all. If finding animals cute prevents us from killing and eating them, that’s an important source of nutrition we are passing up. So whatever evolutionary pressure molded our cuteness response, it must be strong enough to overcome that risk.

Indeed, perhaps the cuteness of cats and dogs goes beyond not only coincidence but also the co-opting of an impulse to protect our offspring. Perhaps it is something that co-evolved in us for the direct purpose of incentivizing us to care for cats and dogs. It has been long enough for that kind of effect—we evolved our ability to digest wheat and milk in roughly the same time period. Indeed, perhaps the very cuteness response that makes us hesitant to kill a rabbit ourselves actually made us better at hunting rabbits, by making us care for dogs who could do the hunting even better than we could. Perhaps the cuteness of a mouse is less relevant to how we relate to mice than the cuteness of the cat who will have that mouse for dinner.

This theory is much more speculative, and I admit I don’t have very clear evidence of it; but let me at least say this: A kitten wouldn’t get cuter by looking more like a human baby. The kitten already seems quite well optimized for us to see it as cute, and any deviation from that optimum is going to be downward, not upward. Any truly satisfying theory of cuteness needs to account for that.

I also think it’s worth noting that behavior is an important element of cuteness; while a kitten will pretty much look cute no matter what it’s doing, where or not a snail or a bird looks cute often depends on the pose it is in.

There is an elegance and majesty to the tiger below, but I wouldn’t call them cute; indeed, should you encounter either one in the wild, the correct response is for you to run for your life.

Cuteness is playful, innocent, or passive; aggressive and powerful postures rapidly undermine cuteness. A lion make look cute as it rubs against a tree—but not once it turns to you and roars.

The truth is, I’m not sure we fully grasp what is going on in our brains when we identify something as cute. But it does seem to brighten our days.

# Hyper-competition

Dec13 JDN 2459197

This phenomenon has been particularly salient for me the last few months, but I think it’s a common experience for most people in my generation: Getting a job takes an awful lot of work.

Over the past six months, I’ve applied to over 70 different positions and so far gone through 4 interviews (2 by video, 2 by phone). I’ve done about 10 hours of test work. That so far has gotten me no offers, though I have yet to hear from 50 employers. Ahead of me I probably have about another 10 interviews, then perhaps 4 of what would have been flyouts and in-person presentations but instead will be “comprehensive interviews” and presentations conducted online, likely several more hours of test work, and then finally, maybe, if I’m lucky, I’ll get a good offer or two. If I’m unlucky, I won’t, and I’ll have to stick around for another year and do all this over again next year.

Aside from the limitations imposed by the pandemic, this is basically standard practice for PhD graduates. And this is only the most extreme end of a continuum of intensive job search efforts, for which even applying to be a cashier at Target requires a formal application, references, and a personality test.

This wasn’t how things used to be. Just a couple of generations ago, low-wage employers would more or less hire you on the spot, with perhaps a resume or a cursory interview. More prestigious employers would almost always require a CV with references and an interview, but it more or less stopped there. I discussed in an earlier post how much of the difference actually seems to come from our chronic labor surplus.

Is all of this extra effort worthwhile? Are we actually fitting people to better jobs this way? Even if the matches are better, are they enough better to justify all this effort?

It is a commonly-held notion among economists that competition in markets is good, that it increases efficiency and improves outcomes. I think that this is often, perhaps usually, the case. But the labor market has become so intensely competitive, particularly for high-paying positions, that the costs of this competitive effort likely outweigh the benefits.

How could this happen? Shouldn’t the free market correct for such an imbalance? Not necessarily. Here is a simple formal model of how this sort of intensive competition can result in significant waste.

Note that this post is about a formal mathematical model, so it’s going to use a lot of algebra. If you are uninterested in such things, you can read the next two paragraphs and then skip to the conclusions at the end.

The overall argument is straightforward: If candidates are similar in skill level, a complicated application process can make sense from a firm’s perspective, but be harmful from society’s perspective, due to the great cost to the applicants. This can happen because the difficult application process imposes an externality on the workers who don’t get the job.

All right, here is where the algebra begins.

I’ve included each equation as both formatted text and LaTeX.

Consider a competition between two applicants, X and Z.

They are each asked to complete a series of tasks in an application process. The amount of effort X puts into the application is x, and the amount of effort Z puts into the application is z. Let’s say each additional bit of effort has a fixed cost, normalized to 1.

Let’s say that their skills are similar, but not identical; this seems quite realistic. X has skill level hx, and Z has skill level hz.

Getting hired has a payoff for each worker of V. This includes all the expected benefits of the salary, benefits, and working conditions. I’ll assume that these are essentially the same for both workers, which also seems realistic.

The benefit to the employer is proportional to the worker’s skill, so letting h be the skill level of the actually hired worker, the benefit of hiring that worker is hY. The reason they are requiring this application process is precisely because they want to get the worker with the highest h. Let’s say that this application process has a cost to implement, c.

Who will get hired? Well, presumably whoever does better on the application. The skill level will amplify the quality of their output, let’s say proportionally to the effort they put in; so X’s expected quality will be hxx and Z’s expected output will be hzz.

Let’s also say there’s a certain amount of error in the process; maybe the more-qualified candidate will sleep badly the day of the interview, or make a glaring and embarrassing typo on their CV. And quite likely the quality of application output isn’t perfectly correlated with the quality of actual output once hired. To capture all this, let’s say that having more skill and putting in more effort only increases your probability of getting the job, rather than actually guaranteeing it.

In particular, let’s say that the probability of X getting hired is P[X] = hxx/(hxx + hzz).

$P[X] = \frac{h_x}{h_x x + h_z z}$

This results in a contest function, a type of model that I’ve discussed in some earlier posts in a rather different context.

The expected payoff for worker X is:

E[Ux] = hxx/(hxx + hzz) V – x

$E[U_x] = \frac{h_x x}{h_x x + h_z z} V – x$

Maximizing this with respect to the choice of effort x (which is all that X can control at this point) yields:

hxhzz V = (hxx + hzz)2

$h_x h_z x V = (h_x x + h_z z)^2$

A similar maximization for worker Z yields:

hxhzx V = (hxx + hzz)2

$h_x h_z z V = (h_x x + h_z z)^2$

It follows that x=z, i.e. X and Z will exert equal efforts in Nash equilibrium. Their probability of success will then be contingent entirely on their skill levels:

P[X] = hx/(hx + hz).

$P[X] = \frac{h_x}{h_x + h_y}$

Substituting that back in, we can solve for the actual amount of effort:

hxhzx V = (hx + hz)2x2

$h_x h_z x V = (h_x + h_z)^2 x^2$

x = hxhzV/(hx + hz)2

$x = \frac{h_x h_z}{h_x + h_z} V$

Now let’s see what that gives for the expected payoffs of the firm and the workers. This is worker X’s expected payoff:

E[Ux] = hx/(hx + hz) V – hxhzV/(hx + hz)2 = (hx/(hx + hz))2 V

$E[U_x] = \frac{h_x}{h_x + h_z} V – \frac{h_x h_z}{(h_x + h_z)^2} V = \left( \frac{h_x}{h_x + h_z}\right)^2 V$

Worker Z’s expected payoff is the same, with hx and hz exchanged:

E[Uz] = (hz/(hx + hz))2 V

$E[U_z] = \left( \frac{h_z}{h_x + h_z}\right)^2 V$

What about the firm? Their expected payoff is the the probability of hiring X, times the value of hiring X, plus the probability of hiring Z, times the value of hiring Z, all minus the cost c:

E[Uf] = hx/(hx + hz) hx Y + hz/(hx + hz) hz Y – c= (hx2 + hz2)/(hx + hz) Y – c

$E[U_f] = \frac{h_x}{h_x + h_z} h_x Y + \frac{h_z}{h_x + h_z} h_z Y – c = \frac{h_x^2 + h_z^2}{h_x + h_z} Y – c$

To see whether the application process was worthwhile, let’s compare against the alternative of simply flipping a coin and hiring X or Z at random. The probability of getting hired is then 1/2 for each candidate.

Expected payoffs for X and Z are now equal:

E[Ux] = E[Uz] = V/2

$E[U_x] = E[U_z] = \frac{V}{2}$

The expected payoff for the firm can be computed the same as before, but now without the cost c:

E[Uf] = 1/2 hx Y + 1/2 hz Y = (hx + hz)/2 Y

$E[U_f] = \frac{1}{2} h_x Y + \frac{1}{2} h_z Y = \frac{h_x + h_z}{2} Y$

This has a very simple interpretation: The expected value to the firm is just the average quality of the two workers, times the overall value of the job.

Which of these two outcomes is better? Well, that depends on the parameters, of course. But in particular, it depends on the difference between hx and hz.

Consider two extremes: In one case, the two workers are indistinguishable, and hx = hz = h. In that case, the payoffs for the hiring process reduce to the following:

E[Ux] = E[Uz] = V/4

$E[U_x] = E[U_z] = \frac{V}{4}$

E[Uf] = h Y – c

$E[U_f] = h Y – c$

Compare this against the payoffs for hiring randomly:

E[Ux] = E[Uz] = V/2

$E[U_x] = E[U_z] = \frac{V}{2}$

E[Uf] = h Y

$E[U_f] = h Y$

Both the workers and the firm are strictly better off if the firm just hires at random. This makes sense, since the workers have identical skill levels.

Now consider the other extreme, where one worker is far better than the other; in fact, one is nearly worthless, so hz ~ 0. (I can’t do exactly zero because I’d be dividing by zero, but let’s say one is 100 times better or something.)

In that case, the payoffs for the hiring process reduce to the following:

E[Ux] = V

E[Uz] = 0

$E[U_x] = V$

$E[U_z] = 0$

X will definitely get the job, so X is much better off.

E[Uf] = hx Y – c

$E[U_f] = h_x Y – c$

E[Ux] = E[Uz] = V/2

$E[U_x] = E[U_z] = \frac{V}{2}$

E[Uf] = hY/2

$E[U_f] = \frac{h}{2} Y$

As long as c < hY/2, both the firm and the higher-skill worker are better off in this scenario. (The lower-skill worker is worse off, but that’s not surprising.) The total expected benefit for everyone is also higher in this scenario.

Thus, the difference in skill level between the applicants is vital. If candidates are very different in skill level, in a way that the application process can accurately measure, then a long and costly application process can be beneficial, not only for the firm but also for society as a whole.

In these extreme examples, it was either not worth it for the firm, or worth it for everyone. But there is an intermediate case worth looking at, where the long and costly process can be worth it for the firm, but not for society as a whole. I will call this case hyper-competition—a system that is so competitive it makes society overall worse off.

This inefficient result occurs precisely when:
c < (hx2 + hz2)/(hx + hz) Y – (hx + hz)/2 Y < c + (hx/(hx + hz))2 V + (hz/(hx + hz))2 V

$c < \frac{h_x^2 + h_z^2}{h_x + h_z} Y – \frac{h_x + h_z}{2} Y < c + \left( \frac{h_x}{h_x + h_z}\right)^2 V + \left( \frac{h_z}{h_x + h_z}\right)^2 V$

This simplifies to:

c < (hx – hz)2/(2hx + 2hz) Y < c + (hx2 + hz2)/(hx + hz)2 V

$c < \frac{(h_x – h_z)^2}{2 (h_x + h_z)} Y < c + \frac{(h_x^2 + h_z^2)}{(h_x+h_z)^2} V$

If c is small, then we are interested in the case where:

(hx – hz)2 Y/2 < (hx2 + hz2)/(hx + hz) V

$\frac{(h_x – h_z)^2}{2} Y < \frac{h_x^2 + h_z^2}{h_x + h_z} V$

This is true precisely when the difference hx – hz is small compared to the overall size of hx or hz—that is, precisely when candidates are highly skilled but similar. This is pretty clearly the typical case in the real world. If the candidates were obviously different, you wouldn’t need a competitive process.

For instance, suppose that hx = 10 and hz = 8, while V = 180, Y = 20 and c = 1.

Then, if we hire randomly, these are the expected payoffs:

E[Uf] = (hx + hz)/2 Y = 180

E[Ux] = E[Uz] = V/2 = 90

If we use the complicated hiring process, these are the expected payoffs:

E[Ux] = (hx/(hx + hz))2 V = 55.5

E[Uz] = (hz/(hx + hz))2 V = 35.5

E[Uf] = (hx2 + hz2)/(hx + hz) Y – c = 181

The firm gets a net benefit of 1, quite small; while the workers face a far larger total expected loss of 90. And these candidates aren’t that similar: One is 25% better than the other. Yet because the effort expended in applying was so large, even this improvement in quality wasn’t worth it from society’s perspective.

This conclude’s the algebra for today, if you’ve been skipping it.

In this model I’ve only considered the case of exactly two applicants, but this can be generalized to more applicants, and the effect only gets stronger: Seemingly-large differences in each worker’s skill level can be outweighed by the massive cost of making so many people work so hard to apply and get nothing to show for it.

Thus, hyper-competition can exist despite apparently large differences in skill. Indeed, it is precisely the typical real-world scenario with many applicants who are similar that we expect to see the greatest inefficiencies. In the absence of intervention, we should expect markets to get this wrong.

Of course, we don’t actually want employers to hire randomly, right? We want people who are actually qualified for their jobs. Yes, of course; but you can probably assess that with nothing more than a resume and maybe a short interview. Most employers are not actually trying to find qualified candidates; they are trying to sift through a long list of qualified candidates to find the one that they think is best qualified. And my suspicion is that most of them honestly don’t have good methods of determining that.

This means that it could be an improvement for society to simply ban long hiring processes like these—indeed, perhaps ban job interviews altogether, as I can hardly think of a more efficient mechanism for allowing employers to discriminate based on race, gender, age, or disability than a job interview. Just collect a resume from each applicant, remove the ones that are unqualified, and then roll a die to decide which one you hire.

This would probably make the fit of workers to their jobs somewhat worse than the current system. But most jobs are learned primarily through experience anyway, so once someone has been in a job for a few years it may not matter much who was hired originally. And whatever cost we might pay in less efficient job matches could be made up several times over by the much faster, cheaper, easier, and less stressful process of applying for jobs.

Indeed, think for a moment of how much worse it feels being turned down for a job after a lengthy and costly application process that is designed to assess your merit (but may or may not actually do so particularly well), as opposed to simply finding out that you lost a high-stakes die roll. Employers could even send out letters saying one of two things: “You were rejected as unqualifed for this position.” versus “You were qualified, but you did not have the highest die roll.” Applying for jobs already feels like a crapshoot; maybe it should literally be one.

People would still have to apply for a lot of jobs—actually, they’d probably end up applying for more, because the lower cost of applying would attract more applicants. But since the cost is so much lower, it would still almost certainly be easier to do a job search than it is in the current system. In fact, it could largely be automated: simply post your resume on a central server and the system matches you with employers’ requirements and then randomly generates offers. Employers and prospective employees could fill out a series of forms just once indicating what they were looking for, and then the system could do the rest.

What I find most interesting about this policy idea is that it is in an important sense anti-meritocratic. We are in fact reducing the rewards for high levels of skill—at least a little bit—in order to improve society overall and especially for those with less skill. This is exactly the kind of policy proposal that I had hoped to see from a book like The Meritocracy Trap, but never found there. Perhaps it’s too radical? But the book was all about how we need fundamental, radical change—and then its actual suggestions were simple, obvious, and almost uncontroversial.

Note that this simplified process would not eliminate the incentives to get major, verifiable qualifications like college degrees or years of work experience. In fact, it would focus the incentives so that only those things matter, instead of whatever idiosyncratic or even capricious preferences HR agents might have. There would be no more talk of “culture fit” or “feeling right for the job”, just: “What is their highest degree? How many years have they worked in this industry?” I suppose this is credentialism, but in a world of asymmetric information, I think credentialism may be our only viable alternative to nepotism.

Of course, it’s too late for me. But perhaps future generations may benefit from this wisdom.

# The necessitization of American consumption

Dec6 JDN 2459190

Why do we feel poorer than our parents?

Over the last 20 years, real per-capita GDP has risen from $46,000 to$56,000 (in 2012 dollars):

It’s not just increasing inequality (though it is partly that); real median household income has increased over the same period from $62,500 to$68,700 (in 2019 dollars):

The American Enterprise Institute has utterly the wrong interpretation of what’s going on here, but their graph is actually quite informative if you can read it without their ideological blinders:

Over the past 20 years, some industries have seen dramatic drops in prices, such as televisions, cellphones, toys, and computer software. Other industries have seen roughly constant prices, such as cars, clothing, and furniture. Still other industries have seen modest increases in prices that tracked overall inflation, such as housing and food. And then there are some industries where prices have exploded to staggering heights, such as childcare, college education, and hospital services.

Since wages basically kept up with inflation, this is the relevant comparison: A product or service is more expensive in real terms if its price grew faster than inflation.

It’s not inherently surprising that some prices would rise faster than inflation and some would rise slower; indeed, it would be shocking if that were not the case, since inflation essentially just is an average of all price changes over time. But if you look closely at the kinds of things that got cheaper versus more expensive, you can begin to see why the statistics keep saying we are getting richer but we don’t feel any richer.

The things that increased the most in price are things you basically can’t do without: Education, childcare, and healthcare. Yes, okay, theoretically you could do without these things, but the effects on your life would be catastrophic—indeed, going without healthcare could literally kill you. They are necessities.

The things that decreased the most in price are things that people have done without for most of human history: Cellphones, software, and computer software. They are newfangled high-tech goods that are now ubiquitous, but not at all long ago didn’t even exist. Going without these goods would be inconvenient, but hardly catastrophic. Indeed, they largely only feel as necessary as they are because everyone else already has them. They are luxuries.

This even explains why older generations can be convinced that we are richer than the statistics say: We have all these fancy new high-tech toys that they never had. But what good does that do us when we can’t afford our health insurance?

Housing is also an obvious necessity, and while it has not on average increased in price faster than inflation, this average washes out important geographic variation.

San Francisco has seen housing prices nearly triple in the last 20 years:

Over the same period, Detroit’s housing prices plummeted, then returned to normal, and are now only 30% higher than they were 20 years ago (comparable to inflation):

It’s hardly surprising that the cities where the most people are moving to are the most expensive to live in; that’s basic supply and demand. But the magnitude of the difference is so large that most of us are experiencing rising housing prices, even though on average housing prices aren’t really rising.

Put this all together, and we can see that while by the usual measures our “standard of living” is increasing, our financial situation feels ever more precarious, because more and more of our spending is immediately captured by things we can’t do without. I suggest we call this effect necessitization; our consumption has been necessitized.

Healthcare is the most extreme example: In 1960, healthcare spending was only 5% of US GDP. As recently as 2000, it was 13%. Today, it is 18%. Medical technology has greatly improved over that time period, increasing our life expectancy from 70 years in 1960 to 76 years in 2000 to 78 years today, so perhaps this additional spending is worth it? But if we compare 2000 to 2020, we can see that an additional 5% of GDP in the last 20 years has only bought us two years of life. So we have spent an additional 5% of our income to gain 2.6% more life—that doesn’t sound like such a great deal to me. (Also, if you look closely at the data, most of the gains in life expectancy seem to be from things like antibiotics and vaccines that aren’t a large part of our healthcare spending, while most of the increased spending seems to be on specialists, testing, high-tech equipment, and administrative costs that don’t seem to contribute much to life expectancy.)

Moreover, even if we decide that all this healthcare spending is worth it, it doesn’t make us richer in the usual sense. We have better health, but we don’t have greater wealth or financial security.

AEI sees that the industries with the largest price increases have the most government intervention, and blames the government; this is clearly confusing cause with effect. The reason the government intervenes so much in education and healthcare is because these are necessities and they are getting so expensive. Removing those interventions wouldn’t stop prices from rising; they’d just remove the programs like Medicaid and federal student loans that currently allow most people to (barely) afford them.

But they are right about one thing: Prices have risen much faster in some industries than others, and the services that have gotten the most expensive are generally the services that are most important.

Why have these services gotten so expensive? A major reason seems to be that they are difficult to automate. Manufacturing electronics is very easy to automate—indeed, there’s even a positive feedback loop there: the better you get at automating making electronics, the better you get at automating everything, including making electronics. But automating healthcare and education is considerably more difficult. Yes, there are MOOCs, and automated therapy software, and algorithms will soon be outperforming the average radiologist; but there are a lot of functions that doctors, nurses, and teachers provide that are very difficult to replace with machines or software.

Suppose we do figure out how to automate more functions of education and healthcare; would that solve the problem? Maybe—but only if we really do manage to automate the important parts.

Right now, MOOCs are honestly terrible. The sales pitch is that you can get taught by a world-class professor from anywhere in the world, but the truth is that the things that make someone a world-class professor don’t translate over when you are watching recorded video lectures and doing multiple-choice quizzes. Really good teaching requires direct interaction between teacher and student. Of course, a big lecture hall full of hundreds of students often lacks such interaction—but so much the worse for big lecture halls. If indeed that’s the only way colleges know how to teach, then they deserve to be replaced by MOOCs. But there are better ways of teaching that online courses currently cannot provide, and if college administrators were wise, they would be focusing on pressing that advantage. If this doesn’t happen, and education does become heavily automated, it will be cheaper—but it will also be worse.

Similarly, some aspects of healthcare provision can be automated, but there are clearly major benefits to having actual doctors and nurses physically there to interact with patients. If we want to make healthcare more affordable, we will probably have to find other ways (a single-payer health system comes to mind).

For now, it is at least worth recognizing that there are serious limitations in our usual methods of measuring standard of living; due to effects like necessitization, the statistics can say that we are much richer even as we hardly feel richer at all.