Quantifying stereotypes

2025-07-062025-07-08 PNRJ cognitive science, core principles, ethics, heuristics and biases, politics, public policy, tribal paradigm education, ethnicity, gender, history, Islam, Islamist terrorism, LGBT, Muslim, politics, race, stereotypes, terrorism, trans, transgender

Jul 6 JDN 2460863

There are a lot of stereotypes in the world, from the relatively innocuous (“teenagers are rebellious”) to the extremely harmful (“Black people are criminals”).

Most stereotypes are not true.

But most stereotypes are not exactly false, either.

Here’s a list of forty stereotypes, all but one of which I got from this list of stereotypes:

(Can you guess which one? I’ll give you a hint: It’s a group I belong to and a stereotype I’ve experienced firsthand.)

“Children are always noisy and misbehaving.”
“Kids can’t understand complex concepts.”
“Children are tech-savvy.”
“Teenagers are always rebellious.”
“Teenagers are addicted to social media.”
“Adolescents are irresponsible and careless.”
“Adults are always busy and stressed.”
“Adults are responsible.”
“Adults are not adept at using modern technologies.”
“Elderly individuals are always grumpy.”
“Old people can’t learn new skills, especially related to technology.”
“The elderly are always frail and dependent on others.”
“Women are emotionally more expressive and sensitive than men.”
“Females are not as good at math or science as males.”
“Women are nurturing, caring, and focused on family and home.”
“Females are not as assertive or competitive as men.”
“Men do not cry or express emotions openly.”
“Males are inherently better at physical activities and sports.”
“Men are strong, independent, and the primary breadwinners.”
“Males are not as good at multitasking as females.”
“African Americans are good at sports.”
“African Americans are inherently aggressive or violent.”
“Black individuals have a natural talent for music and dance.”
“Asians are highly intelligent, especially in math and science.”
“Asian individuals are inherently submissive or docile.”
“Asians know martial arts.”
“Latinos are uneducated.”
“Hispanic individuals are undocumented immigrants.”
“Latinos are inherently passionate and hot-tempered.”
“Middle Easterners are terrorists.”
“Middle Eastern women are oppressed.”
“Middle Eastern individuals are inherently violent or aggressive.”
“White people are privileged and unacquainted with hardship.”
“White people are racist.”
“White individuals lack rhythm in music or dance.”
“Gay men are excessively flamboyant.”
“Gay men have lisps.”
“Lesbians are masculine.”
“Bisexuals are promiscuous.”
“Trans people get gender-reassignment surgery.”

If you view the above 40 statements as absolute statements about everyone in the category (the first-order operator “for all”), they are obviously false; there are clear counter-examples to every single one. If you view them as merely saying that there are examples of each (the first-order operator “there exists”), they are obviously true, but also utterly trivial, as you could just as easily find examples from other groups.

But I think there’s a third way to read them, which may be more what most people actually have in mind. Indeed, it kinda seems uncharitable not to read them this third way.

That way is:

“This is more true of the group I’m talking about than it is true of other groups.”

And that is not only a claim that can be true, it is a claim that can be quantified.

Recall my new favorite effect size measure, because it’s so simple and intuitive; I’m not much for the official name probability of superiority (especially in this context!), so I’m gonna call it the more down-to-earth chance of being higher.

It is exactly what it sounds like: If you compare a quantity X between group A and group B, what is the chance that the person in group A has a higher value of X?

Let’s start at the top: If you take one randomly-selected child, and one randomly-selected adult, what is the chance that the child is one who is more prone to being noisy and misbehaving?

Probably pretty high.

Or let’s take number 13: If you take one randomly-selected woman and one randomly-selected man, what is the chance that the woman is the more emotionally expressive one?

Definitely more than half.

Or how about number 27: If you take one randomly-selected Latino and one randomly-selected non-Latino (especially if you choose a White or Asian person), what is the chance that the Latino is the less-educated one?

That one I can do fairly precisely: Since 95% of White Americans have completed high school but only 75% of Latino Americans have, while 28% of Whites have a bachelor’s degree and only 21% of Latinos do, the probability of the White person being at least as educated as the Latino person is about 82%.

I don’t know the exact figures for all of these, and I didn’t want to spend all day researching 40 different stereotypes, but I am quite prepared to believe that at least all of the following exhibit a chance of being higher that is over 50%:

1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 21, 24, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 40.

You may have noticed that that’s… most of them. I had to shrink the font a little to fit them all on one line.

I think 30 is an important one to mention, because while terrorists are a tiny proportion of the Middle Eastern population, they are in fact a much larger proportion of that population than they are of most other populations, and it doesn’t take that many terrorists to make a place dangerous. The Middle East is objectively a more dangerous place for terrorism than most other places, and only India and sub-Saharan Africa close (and both of which are also largely driven by Islamist terrorism). So while it’s bigoted to assume that any given Muslim or Middle Easterner is a terrorist, it is an objective fact that a disproportionate share of terrorists are Middle Eastern Muslims. Part of what I’m trying to do here is get people to more clearly distinguish between those two concepts, because one is true and the other is very, very false.

40 also deserves particular note, because the chance of being higher is almost certainly very close to 100%. While most trans people don’t get gender-reassignment surgery, virtually all people who get gender-reassignment surgery are trans.

Then again, you could see this as a limitation of the measure, since we might expect a 100% score to mean “it’s true of everyone in the group”, when here it simply means “if we ask people whether they have had gender-reassignment surgery, the trans people sometimes say yes and the cis people always say no.”

We could talk about a weak or strict chance of being higher: The weak chance is the chance of being greater than or equal to (which is the normal measure), while the strict chance is the chance of being strictly greater. In this case, the weak chance is nearly 100%, while the strict chance is hard to estimate but probably about 33% based on surveys.

This doesn’t mean that all stereotypes have some validity.

There are some stereotypes here, including a few pretty harmful ones, for which I’m not sure how the statistics would actually shake out:
10, 14, 22, 23, 25, 32, 35, 39

But I think we should be honestly prepared for the possibility that maybe there is some statistical validity to some of these stereotypes too, and instead of simply dismissing the stereotypes as false—or even bigoted—we should instead be trying to determine how true they are, and also look at why they might have some truth to them.

My proposal is to use the chance of being higher as a measure of the truth of a stereotype.

A stereotype is completely true if it has a chance of being higher of 100%.

It is completely false if it has a chance of being higher of 50%.

And it is completely backwards if it has a chance of being higher of 0%.

There is a unique affine transformation that does this: 2X-1.

100% maps to 100%, 50% maps to 0%, and 0% maps to -100%.

With discrete outcomes, the difference between weak and strong chance of being higher becomes very important. With a discrete outcome, you can have a 100% weak chance but a 1% strong chance, and honestly I’m really not sure whether we should say that stereotype is true or not.

For example, for the claim “trans men get bottom surgery”, the figures would be 100% and 6% respectively. The vast majority of trans men don’t get bottom surgery—but cis men almost never do. (Unless I count penis enlargement surgery? Then the numbers might be closer than you’d think, at least in the US where the vast majority of such surgery is performed.)

And for the claim “Middle Eastern Muslims are terrorists”, well, given two random people of whatever ethnicity or religion, they’re almost certainly not terrorists—but if it one of them is, it’s probably the Middle Eastern Muslim. It may be better in this case to talk about the conditional chance of being higher: If you have two random people, you know that one is a terrorist and one isn’t, and one is a Middle Eastern Muslim and one isn’t, how likely is it that the Middle Eastern Muslim is the terrorist? Probably about 80%. Definitely more than 50%, but also not 100%. So that’s the sense in which the stereotype has some validity. It’s still the case that 99.999% of Middle Eastern Muslims aren’t terrorists, and so it remains bigoted to treat every Middle Eastern Muslim you meet like a terrorist.

We could also work harder to more clearly distinguish between “Middle Easterners are terrorists” and “terrorists are Middle Easterners”; the former is really not true (99.999% are not), but the latter kinda is (the plurality of the world’s terrorists are in the Middle East).

Alternatively, for discrete traits we could just report all four probabilities, which would be something like this: 99.999% of Middle Eastern Muslims are not terrorists, and 0.001% are; 99.9998% of other Americans are not terrorists, and 0.0002% are. Compared to Muslim terrorists in the US, White terrorists actually are responsible for more attacks and a similar number of deaths, but largely because there just are a lot more White people in America.

These issues mainly arise when a trait is discrete. When the trait is itself quantitative (like rebelliousness, or math test scores), this is less of a problem, and the weak and strong chances of being higher are generally more or less the same.

So instead of asking whether a stereotype is true, we could ask: How true is it?

Using measures like this, we will find that some stereotypes probably have quite high truth levels, like 1 and 4; but others, if they are true at all, must have quite low truth levels, like 14; if there’s a difference, it’s a small difference!

The lower a stereotype’s truth level, the less useful it is; indeed, by this measure, it directly predicts how accurate you’d be at guessing someone’s score on the trait if you knew only the group they belong to. If you couldn’t really predict, then why are you using the stereotype? Get rid of it.

Moreover, some stereotypes are clearly more harmful than others.

Even if it is statistically valid to say that Black people are more likely to commit crimes in the US than White people (it is), the kind of person who goes around saying “Black people are criminals” is (1) smearing all Black people with the behavior of a minority of them, and (2) likely to be racist in other ways. So we have good reason to be suspect of people who say such things, even if there may be a statistical kernel of truth to their claims.

But we might still want to be a little more charitable, a little more forgiving, when people express stereotypes. They may make what sounds like a blanket absolute “for all” statement, but actually intend something much milder—something that might actually be true. They might not clearly grasp the distinction between “Middle Easterners are terrorists” and “terrorists are Middle Easterners”, and instead of denouncing them as a bigot immediately, you could try taking the time to listen to what they are saying and carefully explain what’s wrong with it.

Failing to be charitable like this—as we so often do—often feels to people like we are dismissing their lived experience. All the terrorists they can think of were Middle Eastern! All of the folks they know with a lisp turned out to be gay! Lived experience is ultimately anecdotal, but it still has a powerful effect on how people think (too powerful—see also availability heuristic), and it’s really not surprising that people would feel we are treating them unjustly if we immediately accuse them of bigotry simply for stating things that, based on their own experience, seem to be true.

I think there’s another harm here as well, which is that we damage our own credibility. If I believe that something is true and you tell me that I’m a bad person for believing it, that doesn’t make me not believe it—it makes me not trust you. You’ve presented yourself as the sort of person who wants to cover up the truth when it doesn’t fit your narrative. If you wanted to actually convince me that my belief is wrong, you could present evidence that might do that. (To be fair, this doesn’t always work; but sometimes it does!) But if you just jump straight to attacking my character, I don’t want to talk to you anymore.

How to teach people about vaccines

2025-05-252025-05-19 PNRJ ethics, heuristics and biases, science and academia autism, availability heuristic, dice, education, probability, risk, vaccination, vaccines

May 25 JDN 2460821

Vaccines are one of the greatest accomplishments in human history. They have saved hundreds of millions of lives with minimal cost and almost no downside at all. (For everyone who suffers a side effect from a vaccine, I guarantee you: Someone else would have had it much worse from the disease if they hadn’t been vaccinated.)

It’s honestly really astonishing just how much good vaccines have done for humanity.

Thus, it’s a bit of a mystery how there are so many people who oppose vaccines.

But this mystery becomes a little less baffling in light of behavioral economics. People assess the probability of an event mainly based on the availability heuristic: How many examples can they think of when it happened?

Precisely because vaccines have been so effective at preventing disease, we have now reached a point where diseases that were once commonplace are now virtually eradicated. Thus, parents considering whether to vaccinate their children think about whether they know anyone who has gotten sick from that disease, and they can’t think of anyone, so they assume that it’s not a real danger. Then, someone comes along and convinces them (based on utter lies that have been thoroughly debunked) that vaccines cause autism, and they get scared about autism, because they can think of someone they know who has autism.

But of course, the reason that they can’t think of anyone who died from measles or pertussis is because of the vaccines. So I think we need an educational campaign that makes these rates more vivid for people, which plays into the availability heuristic instead of against it.

Here’s my proposal for a little educational game that might help:

It functions quite similarly to a classic tabletop RPG like Dungeons & Dragons, only here the target numbers are based on real figures.

Gather a group of at least 100 people. (Too few, and the odds become small enough that you may get no examples of some diseases.)

Each person needs 3 10-sided dice. Preferably they would be different colors or somehow labeled, because we want one to represent the 100s digit, one the 10s digit, and one the 1s digit. (The numbers you can roll thus range uniformly from 0 to 999.) In TTRPG parlance, this is called a d1000.

Give each person a worksheet that looks like this:

Disease	Before vaccine: Caught?	Before vaccine: Died?	After vaccine: Caught?	After vaccine: Died?
Diptheria
Measles
Mumps
Pertussis
Polio
Rubella
Smallpox
Tetanus
Hep A
Hep B
Pneumococca
Varicella

In the first round, use the figures for before the vaccine. In the second round, use the figures for after the vaccine.

For each disease in each round, there will be a certain roll that people need to get in order to either not contract the disease: Roll that number or higher, and you are okay; roll below it, and you catch the disease.

Likewise, there will be a certain roll they need to get to survive if they contract it: Roll that number or higher, and you get sick but survive; roll below it, and you die.

Each time, name a disease, and then tell people what they need to roll to not catch it.

Have them all roll, and if they catch it, check off that box.

Then, for everyone who catches it, have them roll again to see if they survive it. If they die, check that box.

Based on the historical incidences which I have converted into lifetime prevalences, the target numbers are as follows:

Disease	Before vaccine: Roll to not catch	Before vaccine: Roll to survive	After vaccine: Roll to not catch	After vaccine: Roll to survive
Diptheria	13	87	0	0
Measles	244	1	0	0
Mumps	66	0	2	0
Pertussis	123	20	4	2
Polio	20	89	0	0
Rubella	19	1	19	0
Smallpox	20	12	0	0
Tetanus	1	800	1	71
Hep A	37	1	4	1
Hep B	22	4	4	4
Pneumococca	19	103	11	119
Varicella	950	1	164	0

What you should expect to see for a group of 100 is something like this (of course the results are random, so it won’t be this exactly):

Disease	Before vaccine: Number caught	Before vaccine: Number died	After vaccine: Number caught	After vaccine: Number died
Diptheria	1	0	0	0
Measles	24	0	0	0
Mumps	7	0	0	0
Pertussis	12	1	0	0
Polio	2	0	0	0
Rubella	2	0	0	0
Smallpox	2	0	0	0
Tetanus	0	0	0	0
Hep A	4	0	0	0
Hep B	2	0	0	0
Pneumococca	2	1	1	1
Varicella	95	0	16	0

You’ll find that not a lot of people have checked those “dead” boxes either before or after the vaccine. So if you just look at death rates, the difference may not seem that stark.

(Of course, over a world as big as ours, it adds up: The difference between the 0.25% death rate of pertussis before the vaccine and 0% today is 20 million people—roughly the number of people who live in the New York City metro area.)

But I think people will notice that a lot more people got sick in the “before-vaccine” world than the “after-vaccine” world. Moreover, those that did get sick will find themselves rolling the dice on dying; they’ll probably be fine, but you never know for sure.

Make sure people also notice that (except for pneumococca), if you do get sick, the roll you need to survive is a lot higher without the vaccine. (If anyone does get unlucky enough to get tetanus in the first round, they’re probably gonna die!)

If anyone brings up autism, you can add an extra round where you roll for that too.

The supposedly “epidemic” prevalence of autism today is… 3.2%.

(Honestly I expected higher than that, but then, I hang around with a lot of queer and neurodivergent people. (So the availability heuristic got me too!))

Thus, what’s the roll to not get autism? 32.

Even with the expansive diagnostic criteria that include a lot of borderline cases like yours truly, you still only need to roll 32 on this d1000 to not get autism.

This means that only about 3 people in your group of 100 should end up getting autism, most likely fewer than the number who were saved from getting measles, mumps, and rubella by the vaccine, comparable to the number saved from getting most of the other diseases—and almost certainly fewer than the number saved from getting varicella.

So even if someone remains absolutely convinced that vaccines cause autism, you can now point out that vaccines also clearly save billions of people from getting sick and millions from dying.

Also, there are different kinds of autism. Some forms might not even be considered a disability if society were more accommodating; others are severely debilitating.

Recently clinicians have started to categorize “profound autism”, the kind that is severely debilitating. This constitutes about 25% of children with autism—but it’s a falling percentage over time, because broader diagnostic criteria are including more people as autistic, but not changing the number who are severely debilitated. (It is controversial exactly what should constitute “profound autism”, but I do think the construct is useful; there’s a big difference between someone like me who can basically function normally with some simple accommodations, and someone who never even learns to talk.)

So you can have the group do another roll, specifically for profound autism; that target number is now only 8.

There’s also one more demonstration you can do.

Aggregating over all these diseases, we can find the overall chance of dying from any of these diseases before and after the vaccine.

Have everyone roll for that, too:

Before the vaccines, the target number is 8. Afterward, it is 1.

If autism was brought up, make that comparison explicit.

Even if 100% of autism cases were caused by vaccines (which, I really must say, is ridiculous, as there’s no credible evidence that vaccines cause autism at all) that would still mean the following:

You are trading off a 32 in 1000 chance of your child being autistic and an 8 in 1000 chance of your child being profoundly autistic, against a 7 in 1000 chance of your child dying.

If someone is still skeptical of vaccines at this point, you should ask them point-blank:

Do you really think that being autistic is one-fifth as bad as dying?

Do you really think that being profoundly autistic is as bad as dying?

Extrapolating the INE

2025-04-062025-04-01 PNRJ critique of neoclassical economics, current events, inequality, public policy CPI, education, GDP per capita, growth, healthcare, housing, income, Index of Necessary Expenditure, INE, inflation, Putin, Trump

Apr 6 JDN 2460772

I was only able to find sufficient data to calculate the Index of Necessary Expenditure back to 1990. But I found a fairly consistent pattern that the INE grew at a rate about 20% faster than the CPI over that period, so I decided to take a look at what longer-term income growth looks like if we extrapolate that pattern back further in time.

The result is this graph:

Using the CPI, real per-capita GDP in the US (in 2024 dollars) has grown from $25,760 in 1950 to $85,779 today—increasing by a factor of 3.33. Even accounting for increased inequality and the fact that more families have two income earners, that’s still a substantial increase.

But using the extrapolated INE, real per-capita GDP has only grown from $43,622 in 1950 to $85,779 today—increasing by only a factor of 1.97. This is a much smaller increase, especially when we adjusted for increased inequality and increased employment for women.

Even without the extrapolation, it’s still clear that real INE-adjusted incomes have were basically stagnant in the 2000s, increased rather slowly in the 2020s, and then actually dropped in 2022 after a bunch of government assistance ended. What looked, under the CPI, like steadily increasing real income was actually more like treading water.

Should we trust this extrapolation? It’s a pretty simplistic approach, I admit. But I think it is plausible when we consider this graph of the ratio between median income and median housing price:

This ratio was around 6 in the 1950s, then began to fall until in the 1970s it stabilized around 4. It began to slowly creep back up, but then absolutely skyrocketed in the 2000s before the 2008 crash. Now it has been rising again, and is now above 7, the highest it has been since the Second World War. (Does this mean we’re due for another crash? I’d bet as much.)

What does this mean? It means that a typical family used to be able to afford a typical house with only four years of their total income—and now would require seven. In that sense, homes are now 75% more expensive today than they were in the 1970s.

Similar arguments can be made for the rising costs of education and healthcare; while many prices have not grown much (gasoline) or even fallen (jewelry and technology), these necessities have continued to grow more and more expensive, not simply in nominal terms, but even compared to the median income.

This is further evidence that our standard measures of “inflation” and “real income” are fundamentally inadequate. They simply aren’t accurately reflecting the real cost of living for most American families. Even in many times when it seemed “inflation” was low and “real income” was growing, in fact it was growing harder and harder to afford vital necessities such as housing, education, and healthcare.

This economic malaise may have been what contributed to the widespread low opinion of Biden’s economy. While the official figures looked good, people’s lives weren’t actually getting better.

Yet this is still no excuse for those who voted for Trump; even the policies he proudly announced he would do—like tariffs and deportations—have clearly made these problems worse, and this was not only foreseeable but actually foreseen by the vast majority of the world’s economists. Then there are all the things he didn’t even say he would do but is now doing, like cozying up to Putin, alienating our closest allies, and discussing “methods” for achieving an unconstitutional third term.

Indeed, it honestly feels quite futile to even reflect upon what was wrong with our economy even when things seemed to be running smoothly, because now things are rapidly getting worse, and showing no sign of getting better in any way any time soon.

Reflections on the Index of Necessary Expenditure

2025-03-242025-03-17 PNRJ inequality, macroeconomics, public policy, statistics college, education, family, healthcare, housing, income, Index of Necessary Expenditure, INE, inequality, inflation, population, prices, student loans

Mar 16 JDN 2460751

In last week’s post I constructed an Index of National Expenditure (INE), attempting to estimate the total cost of all of the things a family needs and can’t do without, like housing, food, clothing, cars, healthcare, and education. What I found shocked me: The median family cannot afford all necessary expenditures.

I have a couple more thoughts about that.

I still don’t understand why people care so much about gas prices.

Gasoline was a relatively small contribution to INE. It was more than clothing but less than utilities, and absolutely dwarfed by housing, food, or college. I thought maybe since I only counted a 15-mile commute, maybe I didn’t actually include enoughgasoline usage, but based on this estimate of about $2000 per driver, I was in about the right range; my estimate for the same year was $3350 for a 2-car family.

I think I still have to go with my salience hypothesis: Gasoline is the only price that we plaster in real-time on signs on the side of the road. So people are constantly aware of it, even though it isn’t actually that important.

The price surge that should be upsetting people is housing.

If the price of homes had only risen with the rate of CPI inflation instead of what it actually did, the median home price in 2024 would be only $234,000 instead of the $396,000 it actually is; and by my estimation that would save a typical family $11,000 per year—a whopping 15% of their income, and nearly enough to make the INE affordable by itself.

Now, I’ll consider some possible objections to my findings.

Objection 1: A typical family doesn’t actually spend this much on these things.

You’re right, they don’t! Because they couldn’t possibly. Even with substantial debt, you just can’t sustainably spend 125% of your after-tax household income.

My goal here was not to estimate how much families actually spend; it was to estimate how much they need to spend in order to live a good life and not feel deprived.

What I have found is that most American families feel deprived. They are forced to sacrifice something really important—like healthcare, or education, or owning a home—because they simply can’t afford it.

What I’m trying to do here is find the price of the American Dream; and what I’ve found is that the American Dream has a price that most Americans cannot afford.

Objection 2: You should use median healthcare spending, not mean.

I did in fact use mean figures instead of median for healthcare expenditures, mainly because only the mean was readily available. Mean income is higher than median income, so you might say that I’ve overestimated healthcare expenditure—and in a sense that’s definitely true. The median family spends less than this on healthcare.

But the reason that the median family spends less than this on healthcare is not that they want to, but that they have to. Healthcare isn’t a luxury that people buy more of because they are richer. People buy either as much as they need or as much as they can afford—whichever is lower, which is typically the latter. Using the mean instead of the median is a crude way to account for that, but I think it’s a defensible one.

But okay, let’s go ahead and cut the estimate of healthcare spending in half; even if you do that, the INE is still larger than after-tax median household income in most years.

Objection 3: A typical family isn’t a family of four, it’s a family of three.

Yes, the mean number of people in a family household in the US is 3.22 (the median is 3).

This is a very bad thing.

Part of what I seem to be finding here is that a family of four is unaffordable—literally impossible to afford—on a typical family income.

But a healthy society is one in which typical families have two or three children. That is what we need in order to achieve population replacement. When families get smaller than that, we aren’t having enough children, and our population will decline—which means that we’ll have too many old people relative to young people. This puts enormous pressure on healthcare and pension systems, which rely upon the fact that young people produce more, in order to pay for the fact that old people cost more.

The ideal average number of births per woman is about 2.1; this is what would give us a steady population. No US state has fertility above this level. The only reason the US population is growing rather than shrinking is that we are taking in immigrants.

This is bad. This is not sustainable. If the reason families aren’t having enough kids is that they can’t afford them—and this fits with other research on the subject—then this economic failure damages our entire society, and it needs to be fixed.

Objection 4: Many families buy their cars used.

Perhaps 1/10 of a new car every year isn’t an ideal estimate of how much people spend on their cars, but if anything I think it’s conservative, because if you only buy a car every 10 years, and it was already used when you bought it, you’re going to need to spend a lot on maintaining it—quite possibly more than it would cost to get a new one. Motley Fool actually estimates the ownership cost of just one car at substantially more than I estimated for two cars. So if anything your complaint should be that I’ve underestimated the cost by not adequately including maintenance and insurance.

Objection 5: Not everyone gets a four-year college degree.

Fair enough; a substantial proportion get associate’s degrees, and most people get no college degree at all. But some also get graduate degrees, which is even more expensive (ask me how I know).

Moreover, in today’s labor market, having a college degree makes a huge difference in your future earnings; a bachelor’s degree increases your lifetime earnings by a whopping 84%. In theory it’s okay to have a society where most people don’t go to college; in practice, in our society, not going to college puts you at a tremendous disadvantage for the rest of your life. So we either need to find a way to bring wages up for those who don’t go to college, or find a way to bring the cost of college down.

This is probably one of the things that families actually choose to scrimp on, only sending one kid to college or none at all. But because college is such a huge determinant of earnings, this perpetuates intergenerational inequality: Only rich families can afford to send their kids to college, and only kids who went to college grow up to have rich families.

Objection 6: You don’t actually need to save for college; you can use student loans.

Yes, you can, and in practice, most people who to college do. But while this solves the liquidity problem (having enough money right now), it does not solve the solvency problem (having enough money in the long run). Failing to save for college and relying on student loans just means pushing the cost of college onto your children—and since we’ve been doing that for over a generation, feel free to replace the category “college savings” with “repaying student loans”; it won’t meaningfully change the results.

The Index of Necessary Expenditure

2025-03-162025-03-16 PNRJ core principles, critique of neoclassical economics, current events, inequality, politics, public policy, statistics childcare, education, healthcare, income, INE, inequality, inflation, median income, necessitization, necessity, prices

Mar 16 JDN 2460751

I’m still reeling from the fact that Donald Trump was re-elected President. He seemed obviously horrible at the time, and he still seems horrible now, for many of the same reasons as before (we all knew the tariffs were coming, and I think deep down we knew he would sell out Ukraine because he loves Putin), as well as some brand new ones (I did not predict DOGE would gain access to all the government payment systems, nor that Trump would want to start a “crypto fund”). Kamala Harris was not an ideal candidate, but she was a good candidate, and the comparison between the two could not have been starker.

Now that the dust has cleared and we have good data on voting patterns, I am now less convinced than I was that racism and sexism were decisive against Harris. I think they probably hurt her some, but given that she actually lost the most ground among men of color, racism seems like it really couldn’t have been a big factor. Sexism seems more likely to be a significant factor, but the fact that Harris greatly underperformed Hillary Clinton among Latina women at least complicates that view.

A lot of voters insisted that they voted on “inflation” or “the economy”. Setting aside for a moment how absurd it was—even at the time—to think that Trump (he of the tariffs and mass deportations!) was going to do anything beneficial for the economy, I would like to better understand how people could be so insistent that the economy was bad even though standard statistical measures said it was doing fine.

Krugman believes it was a “vibecession”, where people thought the economy was bad even though it wasn’t. I think there may be some truth to this.

But today I’d like to evaluate another possibility, that what people were really reacting against was not inflation per se but necessitization.

I first wrote about necessitization in 2020; as far as I know, the term is my own coinage. The basic notion is that while prices overall may not have risen all that much, prices of necessities have risen much faster, and the result is that people feel squeezed by the economy even as CPI growth remains low.

In this post I’d like to more directly evaluate that notion, by constructing an index of necessary expenditure (INE).

The core idea here is this:

What would you continue to buy, in roughly the same amounts, even if it doubled in price, because you simply can’t do without it?

For example, this is clearly true of housing: You can rent or you can own, but can’t not have a house. And nor are most families going to buy multiple houses—and they can’t buy partial houses.

It’s also true of healthcare: You need whatever healthcare you need. Yes, depending on your conditions, you maybe could go without, but not without suffering, potentially greatly. Nor are you going to go out and buy a bunch of extra healthcare just because it’s cheap. You need what you need.

I think it’s largely true of education as well: You want your kids to go to college. If college gets more expensive, you might—of necessity—send them to a worse school or not allow them to complete their degree, but this would feel like a great hardship for your family. And in today’s economy you can’t not send your kids to college.

But this is not true of technology: While there is a case to be made that in today’s society you need a laptop in the house, the fact is that people didn’t used to have those not that long ago, and if they suddenly got a lot cheaper you very well might buy another one.

Well, it just so happens that housing, healthcare, and education have all gotten radically more expensive over time, while technology has gotten radically cheaper. So prima facie, this is looking pretty plausible.

But I wanted to get more precise about it. So here is the index I have constructed. I consider a family of four, two adults, two kids, making the median household income.

To get the median income, I’ll use this FRED series for median household income, then use this table of median federal tax burden to get an after-tax wage. (State taxes vary too much for me to usefully include them.) Since the tax table ends in 2020 which was anomalous, I’m going to extrapolate that 2021-2024 should be about the same as 2019.

I assume the kids go to public school, but the parents are saving up for college; to make the math simple, I’ll assume the family is saving enough for each kid to graduate from with a four-year degree from a public university, and that saving is spread over 16 years of the child’s life. 2*4/16 = 0.5; this means that each year the family needs to come up with 0.5 years of cost of attendance. (I had to get the last few years from here, but the numbers are comparable.)

I assume the family owns two cars—both working full time, they kinda have to—which I amortize over 10 year lifetimes; 2*1/10 = 0.2, so each year the family pays 0.2 times the value of an average midsize car. (The current average new car price is $33226; I then use the CPI for cars to figure out what it was in previous years.)

I assume they pay a 30-year mortgage on the median home; they would pay interest on this mortgage, so I need to factor that in. I’ll assume they pay the average mortgage rate in that year, but I don’t want to have to do a full mortgage calculation (including PMI, points, down payment etc.) for each year, so I’ll say that they amount they pay is (1/30 + 0.5 (interest rate))*(home value) per year, which seems to be a reasonable approximation over the relevant range.

I assume that both adults have a 15-mile commute (this seems roughly commensurate with the current mean commute time of 26 minutes), both adults work 5 days per week, 50 weeks per year, and their cars get the median level of gas mileage. This means that they consume 2*15*2*5*50/(median MPG) = 15000/(median MPG) gallons of gasoline per year. I’ll use this BTS data for gas mileage. I’m intentionally not using median gasoline consumption, because when gas is cheap, people might take more road trips, which is consumption that could be avoided without great hardship when gas gets expensive. I will also assume that the kids take the bus to school, so that doesn’t contribute to the gasoline cost.

That I will multiply by the average price of gasoline in June of that year, which I have from the EIA since 1993. (I’ll extrapolate 1990-1992 as the same as 1993, which is conservative.)

I will assume that the family owns 2 cell phones, 1 computer, and 1 television. This is tricky, because the quality of these tech items has dramatically increased over time.

If you try to measure with equivalent buying power (e.g. a 1 MHz computer, a 20-inch CRT TV), then you’ll find that these items have gotten radically cheaper; $1000 in 1950 would only buy as much TV as $7 today, and a $50 Raspberry Pi‘s 2.4 GHz processor is 150 times faster than the 16 MHz offered by an Apple Powerbook in 1991—despite the latter selling for $2500 nominally. So in dollars per gigahertz, the price of computers has fallen by an astonishing 7,500 times just since 1990.

But I think that’s an unrealistic comparison. The standards for what was considered necessary have also increased over time. I actually think it’s quite fair to assume that people have spent a roughly constant nominal amount on these items: about $500 for a TV, $1000 for a computer, and $500 for a cell phone. I’ll also assume that the TV and phones are good for 5 years while the computer is good for 2 years, which makes the total annual expenditure for 2 phones, a TV, and a computer equal to 2/5*500 + 1/5*500 + 1/2*1000 = 800. This is about what a family must spend every year to feel like they have an adequate amount of digital technology.

I will also assume that the family buys clothes with this equivalent purchasing power, with an index that goes from 166 in 1990 to 177 in 2024—also nearly constant in nominal terms. I’ll multiply that index by $10 because the average annual household spending on clothes is about $1700 today.

I will assume that the family buys the equivalent of five months of infant care per year; they surely spend more than this (in either time or money) when they have actual infants, but less as the kids grow. This amounts to about $5000 today, but was only $1600 in 1990—a 214% increase, or 3.42% per year.

For food expenditure, I’m going to use the USDA’s thrifty plan for June of that year. I’ll use the figures assuming that one child is 6 and the other is 9. I don’t have data before 1994, so I’ll extrapolate that with the average growth rate of 3.2%.

Food expenditures have been at a fairly consistent 11% of disposable income since 1990; so I’m going to include them as 2*11%*40*50*(after-tax median wage) = 440*(after-tax median wage).

The figures I had the hardest time getting were for utilities. It’s also difficult to know what to include: Is Internet access a necessity? Probably, nowadays—but not in 1990. Should I separate electric and natural gas, even though they are partial substitutes? But using these figures I estimate that utility costs rise at about 0.8% per year in CPI-adjusted terms, so what I’ll do is benchmark to $3800 in 2016 and assume that utility costs have risen by (0.8% + inflation rate) per year each year.

Healthcare is also a tough one; pardon the heteronormativity, but for simplicity I’m going to use the mean personal healthcare expenditures for one man and woman (aged 19-44) and one boy and one girl (aged 0-18). Unfortunately I was only able to find that for two-year intervals in the range from 2002 to 2020, so I interpolated and extrapolated both directions assuming the same average growth rate of 3.5%.

So let’s summarize what all is included here:

Estimated payment on a mortgage
0.5 years of college tuition
amortized cost of 2 cars
7500/(median MPG) gallons of gasoline
amortized cost of 2 phones, 1 computer, and 1 television
average spending on clothes
11% of income on food
Estimated utilities spending
Estimated childcare equivalent to five months of infant care
Healthcare for one man, one woman, one boy, one girl

There are obviously many criticisms you could make of these choices. If I were writing a proper paper, I would search harder for better data and run robustness checks over the various estimation and extrapolation assumptions. But for these purposes I really just want a ballpark figure, something that will give me a sense of what rising cost of living feels like to most people.

What I found absolutely floored me. Over the range from 1990 to 2024:

The Index of Necessary Expenditure rose by an average of 3.45% per year, almost a full percentage point higher than the average CPI inflation of 2.62% per year.
Over the same period, after-tax income rose at a rate of 3.31%, faster than CPI inflation, but slightly slower than the growth rate of INE.
The Index of Necessary Expenditure was over 100% of median after-tax household income every year except 2020.
Since 2021, the Index of Necessary Expenditure has risen at an average rate of 5.74%, compared to CPI inflation of only 2.66%. In that same time, after-tax income has only grown at a rate of 4.94%.

Point 3 is the one that really stunned me. The only time in the last 34 years that a family of four has been able to actually pay for all necessities—just necessities—on a typical household income was during the COVID pandemic, and that in turn was only because the federal tax burden had been radically reduced in response to the crisis. This means that every single year, a typical American family has been either going further and further into debt, or scrimping on something really important—like healthcare or education.

No wonder people feel like the economy is failing them! It is!

In fact, I can even make sense now of how Trump could convince people with “Are you better off than you were four years ago?” in 2024 looking back at 2020—while the pandemic was horrific and the disruption to the economy was massive, thanks to the US government finally actually being generous to its citizens for once, people could just about actually make ends meet. That one year. In my entire life.

This is why people felt betrayed by Biden’s economy. For the first time most of us could remember, we actually had this brief moment when we could pay for everything we needed and still have money left over. And then, when things went back to “normal”, it was taken away from us. We were back to no longer making ends meet.

When I went into this, I expected to see that the INE had risen faster than both inflation and income, which was indeed the case. But I expected to find that INE was a large but manageable proportion of household income—maybe 70% or 80%—and slowly growing. Instead, I found that INE was greater than 100% of income in every year but one.

And the truth is, I’m not sure I’ve adequately covered all necessary spending! My figures for childcare and utilities are the most uncertain; those could easily go up or down by quite a bit. But even if I exclude them completely, the reduced INE is still greater than income in most years.

Suddenly the way people feel about the economy makes a lot more sense to me.

Reflections at the crossroads

2024-01-222024-01-16 PNRJ critique of neoclassical economics, experimental economics, inequality, personal, science and academia academia, career, Edinburgh, education, Elon Musk, job, journals, publishing, Reinhart, rejection, research, Rogoff, teaching, UC Irvine, United King, United States, University of Edinburgh, University of Michigan, writing

Jan 21 JDN 2460332

When this post goes live, I will have just passed my 36^th birthday. (That means I’ve lived for about 1.1 billion seconds, so in order to be as rich as Elon Musk, I’d need to have made, on average, since birth, $200 per second—$720,000 per hour.)

I certainly feel a lot better turning 36 than I did 35. I don’t have any particular additional accomplishments to point to, but my life has already changed quite a bit, in just that one year: Most importantly, I quit my job at the University of Edinburgh, and I am currently in the process of moving out of the UK and back home to Michigan. (We moved the cat over Christmas, and the movers have already come and taken most of our things away; it’s really just us and our luggage now.)

But I still don’t know how to field the question that people have been asking me since I announced my decision to do this months ago:

“What’s next?”

I’m at a crossroads now, trying to determine which path to take. Actually maybe it’s more like a roundabout; it has a whole bunch of different paths, surely not just two or three. The road straight ahead is labeled “stay in academia”; the others at the roundabout are things like “freelance writing”, “software programming”, “consulting”, and “tabletop game publishing”. There’s one well-paved and superficially enticing road that I’m fairly sure I don’t want to take, labeled “corporate finance”.

Right now, I’m just kind of driving around in circles.

Most people don’t seem to quit their jobs without a clear plan for where they will go next. Often they wait until they have another offer in hand that they intend to take. But when I realized just how miserable that job was making me, I made the—perhaps bold, perhaps courageous, perhaps foolish—decision to get out as soon as I possibly could.

It’s still hard for me to fully understand why working at Edinburgh made me so miserable. Many features of an academic career are very appealing to me. I love teaching, I like doing research; I like the relatively flexible hours (and kinda need them, because of my migraines).

I often construct formal decision models to help me make big choices—generally it’s a linear model, where I simply rate each option by its relative quality in a particular dimension, then try different weightings of all the different dimensions. I’ve used this successfully to pick out cars, laptops, even universities. I’m not entrusting my decisions to an algorithm; I often find myself tweaking the parameters to try to get a particular result—but that in itself tells me what I really want, deep down. (Don’t do that in research—people do, and it’s bad—but if the goal is to make yourself happy, your gut feelings are important too.)

My decision models consistently rank university teaching quite high. It generally only gets beaten by freelance writing—which means that maybe I should give freelance writing another try after all.

And yet, my actual experience at Edinburgh was miserable.

What went wrong?

Well, first of all, I should acknowledge that when I separate out the job “university professor” into teaching and research as separate jobs in my decision model, and include all that goes into both jobs—not just the actual teaching, but the grading and administrative tasks; not just doing the research, but also trying to fund and publish it—they both drop lower on the list, and research drops down a lot.

Also, I would rate them both even lower now, having more direct experience of just how awful the exam-grading, grant-writing and journal-submitting can be.

Designing and then grading an exam was tremendously stressful: I knew that many of my students’ futures rested on how they did on exams like this (especially in the UK system, where exams are absurdly overweighted! In most of my classes, the final exam was at least 60% of the grade!). I struggled mightily to make the exam as fair as I could, all the while knowing that it would never really feel fair and I didn’t even have the time to make it the best it could be. You really can’t assess how well someone understands an entire subject in a multiple-choice exam designed to take 90 minutes. It’s impossible.

The worst part of research for me was the rejection.

I mentioned in a previous post how I am hypersensitive to rejection; applying for grants and submitting to journals was clearly the worst feelings of rejection I’ve felt in any job. It felt like they were evaluting not only the value of my work, but my worth as a scientist. Failure felt like being told that my entire career was a waste of time.

It was even worse than the feeling of rejection in freelance writing (which is one of the few things that my model tells me is bad about freelancing as a career for me, along with relatively low and uncertain income). I think the difference is that a book publisher is saying “We don’t think we can sell it.”—’we’ and ‘sell’ being vital. They aren’t saying “this is a bad book; it shouldn’t exist; writing it was a waste of time.”; they’re just saying “It’s not a subgenre we generally work with.” or “We don’t think it’s what the market wants right now.” or even “I personally don’t care for it.”. They acknowledge their own subjective perspective and the fact that it’s ultimately dependent on forecasting the whims of an extremely fickle marketplace. They aren’t really judging my book, and they certainly aren’t judging me.

But in research publishing, it was different. Yes, it’s all in very polite language, thoroughly spiced with sophisticated jargon (though some reviewers are more tactful than others). But when your grant application gets rejected by a funding agency or your paper gets rejected by a journal, the sense really basically is “This project is not worth doing.”; “This isn’t good science.”; “It was/would be a waste of time and money.”; “This (theory or experiment you’ve spent years working on) isn’t interesting or important.” Nobody ever came out and said those things, nor did they come out and say “You’re a bad economist and you should feel bad.”; but honestly a couple of the reviews did kinda read to me like they wanted to say that. They thought that the whole idea that human beings care about each other is fundamentally stupid and naive and not worth talking about, much less running experiments on.

It isn’t so much that I believed them that my work was bad science. I did make some mistakes along the way (but nothing vital; I’ve seen far worse errors by Nobel Laureates). I didn’t have very large samples (because every person I add to the experiment is money I have to pay, and therefore funding I have to come up with). But overall I do believe that my work is sufficiently rigorous to be worth publishing in scientific journals.

It’s more that I came to feel that my work is considered bad, that the kind of work I wanted to do would forever be an uphill battle against an implacable enemy. I already feel exhausted by that battle, and it had only barely begun. I had thought that behavioral economics was a more successful paradigm by now, that it had largely displaced the neoclassical assumptions that came before it; but I was wrong. Except specifically in journals dedicated to experimental and behavioral economics (of which prestigious journals are few—I quickly exhausted them), it really felt like a lot of the feedback I was getting amounted to, “I refuse to believe your paradigm.”.

Part of the problem, also, was that there simply aren’t that many prestigious journals, and they don’t take that many papers. The top 5 journals—which, for whatever reason, command far more respect than any other journals among economists—each accept only about 5-10% of their submissions. Surely more than that are worth publishing; and, to be fair, much of what they reject probably gets published later somewhere else. But it makes a shockingly large difference in your career how many “top 5s” you have; other publications almost don’t matter at all. So once you don’t get into any of those (which of course I didn’t), should you even bother trying to publish somewhere else?

And what else almost doesn’t matter? Your teaching. As long as you show up to class and grade your exams on time (and don’t, like, break the law or something), research universities basically don’t seem to care how good a teacher you are. That was certainly my experience at Edinburgh. (Honestly even their responses to professors sexually abusing their students are pretty unimpressive.)

Some of the other faculty cared, I could tell; there were even some attempts to build a community of colleagues to support each other in improving teaching. But the administration seemed almost actively opposed to it; they didn’t offer any funding to support the program—they wouldn’t even buy us pizza at the meetings, the sort of thing I had as an undergrad for my activist groups—and they wanted to take the time we spent in such pedagogy meetings out of our grading time (probably because if they didn’t, they’d either have to give us less grading, or some of us would be over our allotted hours and they’d owe us compensation).

And honestly, it is teaching that I consider the higher calling.

The difference between 0 people knowing something and 1 knowing it is called research; the difference between 1 person knowing it and 8 billion knowing it is called education.

Yes, of course, research is important. But if all the research suddenly stopped, our civilization would stagnate at its current level of technology, but otherwise continue unimpaired. (Frankly it might spare us the cyberpunk dystopia/AI apocalypse we seem to be hurtling rapidly toward.) Whereas if all education suddenly stopped, our civilization would slowly decline until it ultimately collapsed into the Stone Age. (Actually it might even be worse than that; even Stone Age cultures pass on knowledge to their children, just not through formal teaching. If you include all the ways parents teach their children, it may be literally true that humans cannot survive without education.)

Yet research universities seem to get all of their prestige from their research, not their teaching, and prestige is the thing they absolutely value above all else, so they devote the vast majority of their energy toward valuing and supporting research rather than teaching. In many ways, the administrators seem to see teaching as an obligation, as something they have to do in order to make money that they can spend on what they really care about, which is research.

As such, they are always making classes bigger and bigger, trying to squeeze out more tuition dollars (well, in this case, pounds) from the same number of faculty contact hours. It becomes impossible to get to know all of your students, much less give them all sufficient individual attention. At Edinburgh they even had the gall to refer to their seminars as “tutorials” when they typically had 20+ students. (That is not tutoring!)And then of course there were the lectures, which often had over 200 students.

I suppose it could be worse: It could be athletics they spend all their money on, like most Big Ten universities. (The University of Michigan actually seems to strike a pretty good balance: they are certainly not hurting for athletic funding, but they also devote sizeable chunks of their budget to research, medicine, and yes, even teaching. And unlike virtually all other varsity athletic programs, University of Michigan athletics turns a profit!)

If all the varsity athletics in the world suddenly disappeared… I’m not convinced we’d be any worse off, actually. We’d lose a source of entertainment, but it could probably be easily replaced by, say, Netflix. And universities could re-focus their efforts on academics, instead of acting like a free training and selection system for the pro leagues. The University of California, Irvine certainly seemed no worse off for its lack of varsity football. (Though I admit it felt a bit strange, even to a consummate nerd like me, to have a varsity League of Legends team.)

They keep making the experience of teaching worse and worse, even as they cut faculty salaries and make our jobs more and more precarious.

That might be what really made me most miserable, knowing how expendable I was to the university. If I hadn’t quit when I did, I would have been out after another semester anyway, and going through this same process a bit later. It wasn’t even that I was denied tenure; it was never on the table in the first place. And perhaps because they knew I wouldn’t stay anyway, they didn’t invest anything in mentoring or supporting me. Ostensibly I was supposed to be assigned a faculty mentor immediately; I know the first semester was crazy because of COVID, but after two and a half years I still didn’t have one. (I had a small research budget, which they reduced in the second year; that was about all the support I got. I used it—once.)

So if I do continue on that “academia” road, I’m going to need to do a lot of things differently. I’m not going to put up with a lot of things that I did. I’ll demand a long-term position—if not tenure-track, at least renewable indefinitely, like a lecturer position (as it is in the US, where the tenure-track position is called “assistant professor” and “lecturer” is permanent but not tenured; in the UK, “lecturers” are tenure-track—except at Oxford, and as of 2021, Cambridge—just to confuse you). Above all, I’ll only be applying to schools that actually have some track record for valuing teaching and supporting their faculty.

And if I can’t find any such positions? Then I just won’t apply at all. I’m not going in with the “I’ll take what I can get” mentality I had last time. Our household finances are stable enough that I can afford to wait awhile.

But maybe I won’t even do that. Maybe I’ll take a different path entirely.

For now, I just don’t know.

Homeschooling and too much freedom

2023-11-202023-11-12 PNRJ education, personal, public policy, science and academia, tribal paradigm atheism, conflict, diversity, education, evangelical, homeschool, indocrination, paradox of tolerance, paternalism, psychology, religion, Stopped Clock Principle

Nov 19 JDN 2460268

Allowing families to homeschool their children increases freedom, quite directly and obviously. This is a large part of the political argument in favor of homeschooling, and likely a large part of why homeschooling is so popular within the United States in particular.

In the US, about 3% of people are homeschooled. This seems like a small proportion, but it’s enough to have some cultural and political impact, and it’s considerably larger than the proportion who are homeschooled in most other countries.

Moreover, homeschooling rates greatly increased as a result of COVID, and it’s anyone’s guess when, or even whether, they will go back down. I certainly hope they do; here’s why.

A lot of criticism about homeschooling involves academic outcomes: Are the students learning enough English and math? This is largely unfounded; statistically, academic outcomes of homeschooled students don’t seem to be any worse than those of public school students; by some measures, they are actually better.Nor is there clear evidence that homeschooled kids are any less developed socially; most of them get that social development through other networks, such as churches and sports teams.

No, my concern is not that they won’t learn enough English and math. It’s that they won’t learn enough history and science. Specifically, the parts of history and science that contradict the religious beliefs of the parents who are homeschooling them.

One way to study this would be to compare test scores by homeschooled kids on, say, algebra and chemistry (which do not directly threaten Christian evangelical beliefs) to those on, say, biology and neuroscience (which absolutely, fundamentally do). Lying somewhere in between are physics (F=ma is no threat to Christianity, but the Big Bang is) and history (Christian nationalists happily teach that Thomas Jefferson wrote the Declaration of Independence, but often omit that he owned slaves). If homeschooled kids are indeed indoctrinated, we should see particular lacunas in their knowledge where the facts contradict their ideology. In any case, I wasn’t able to find any such studies.

But even if their academic outcomes are worse in certain domains, so what? What about the freedom of parents to educate their children how they choose? What about the freedom of children to not be subjected to the pain of public school?

It will come as no surprise to most of you that I did well in school. In almost everything, really: math, science, philosophy, English, and Latin were my best subjects, and I earned basically flawless grades in them. But I also did very well in creative writing, history, art, and theater, and fairly well in music. My only poor performance was in gym class (as I’ve written about before).

It may come as some surprise when I tell you that I did not particularly enjoy school. In elementary school I had few friends—and one of my closest ended up being abusive to me. Middle school I mostly enjoyed—despite the onset of my migraines. High school started out utterly miserable, though it got a little better—a little—once I transferred to Community High School. Throughout high school, I was lonely, stressed, anxious, and depressed most of the time, and had migraine headaches of one intensity or another nearly every single day. (Sadly, most of that is true now as well; but I at least had a period of college and grad school where it wasn’t, and hopefully I will again once this job is behind me.)

I was good at school. I enjoyed much of the content of school. But I did not particularly enjoy school.

Thus, I can quite well understand why it is tempting to say that kids should be allowed to be schooled at home, if that is what they and their parents want. (Of course, a problem already arises there: What if child and parent disagree? Whose choice actually matters? In practice, it’s usually the parent’s.)

On the whole, public school is a fairly toxic social environment: Cliquish, hyper-competitive, stressful, often full of conflict between genders, races, classes, sexual orientations, and of course the school-specific one, nerds versus jocks (I’d give you two guesses which team I was on, but you’re only gonna need one). Public school sucks.

Then again, many of these problems and conflicts persist into adult life—so perhaps it’s better preparation than we care to admit. Maybe it’s better to be exposed to bias and conflict so that you can learn to cope with them, rather than sheltered from them.

But there is a more important reason why we may need public school, why it may even be worth coercing parents and children into that system against their will.

Public school forces you to interact with people different from you.

At a public school, you cannot avoid being thrown in the same classroom with students of other races, classes, and religions. This is of course more true if your school system is diverse rather than segregated—and all the more reason that the persistent segregation of many of our schools is horrific—but it’s still somewhat true even in a relatively homogeneous school. I was fortunate enough to go to a public school in Ann Arbor, where there was really quite substantial diversity. But even where there is less diversity, there is still usually some diversity—if not race, then class, or religion.

Certainly any public school has more diversity than homeschooling, where parents have the power to specifically choose precisely which other families their children will interact with, and will almost always choose those of the same race, class, and—above all—religious denomination as themselves.

The result is that homeschooled children often grow up indoctrinated into a dogmatic, narrow-minded worldview, convinced that the particular beliefs they were raised in are the objectively, absolutely correct ones and all others are at best mistaken and at worst outright evil. They are trained to reject conflict and dissent, to not even expose themselves to other people’s ideas, because those are seen as dangerous—corrupting.

Moreover, for most homeschooling parents—not all, but most—this is clearly the express intent. They want to raise their children in a particular set of beliefs. They want to inoculate them against the corrupting influences of other ideas. They are not afraid of their kids being bullied in school; they are afraid of them reading books that contradict the Bible.

This article has the headline “Homeschooled children do not grow up to be more religious”, yet its core finding is exactly the opposite of that:

The Cardus Survey found that homeschooled young adults were not noticeably different in their religious lives from their peers who had attended private religious schools, though they were more religious than peers who had attended public or Catholic schools.

No more religious than private religious schools!? That’s still very religious. No, the fair comparison is to public schools, which clearly show lower rates of religiosity among the same demographics. (The interesting case is Catholic schools; they, it turns out, also churn out atheists with remarkable efficiency; I credit the Jesuit norm of top-quality liberal education.) This is clear evidence that religious homeschooling does make children more religious, and so does most private religious education.

Another finding in that same article sounds good, but is misleading:

Indiana University professor Robert Kunzman, in his careful study of six homeschooling families, found that, at least for his sample, homeschooled children tended to become more tolerant and less dogmatic than their parents as they grew up.

This is probably just regression to the mean. The parents who give their kids religious homeschooling are largely the most dogmatic and intolerant, so we would expect by sheer chance that their kids were less dogmatic and intolerant—but probably still pretty dogmatic and intolerant. (Also, do I have to pount out that n=6 barely even constitutes a study!?) This is like the fact that the sons of NBA players are usually shorter than their fathers—but still quite tall.

Homeschooling is directly linked to a lot of terrible things: Young-Earth Creationism, Christian nationalism, homophobia, and shockingly widespread child abuse.

While most right-wing families don’t homeschool, most homeschooling families are right-wing: Between 60% and 70% of homeschooling families vote Republican in most elections. More left-wing voters are homeschooling now with the recent COVID-driven surge in homeschooling, but the right-wing still retains a strong majority for now.

Of course, there are a growing number of left-wing and non-religious families who use homeschooling. Does this mean that the threat of indoctrination is gone? I don’t think so. I once knew someone who was homeschooled by a left-wing non-religious family and still ended up adopting an extremely narrow-minded extremist worldview—simply a left-wing non-religious one. In some sense a left-wing non-religious narrow-minded extremism is better than a right-wing religious narrow-minded extremism, but it’s still narrow-minded extremism. Whatever such a worldview gets right is mainly by the Stopped Clock Principle. It still misses many important nuances, and is still closed to new ideas and new evidence.

Of course this is not a necessary feature of homeschooling. One absolutely could homeschool children into a worldview that is open-minded and tolerant. Indeed, I’m sure some parents do. But statistics suggest that most do not, and this makes sense: When parents want to indoctrinate their children into narrow-minded worldviews, homeschooling allows them to do that far more effectively than if they had sent their children to public school. Whereas if you want to teach your kids open-mindedness and tolerance, exposing them to a diverse environment makes that easier, not harder.

In other words, the problem is that homeschooling gives parents too much control; in a very real sense, this is too much freedom.

When can freedom be too much? It seems absurd at first. But there are at least two cases where it makes sense to say that someone has too much freedom.

The first is paternalism: Sometimes people really don’t know what’s best for them, and giving them more freedom will just allow them to hurt themselves. This notion is easily abused—it has been abused many times, for example against disabled people and colonized populations. For that reason, we are right to be very skeptical of it when applied to adults of sound mind. But what about children? That’s who we are talking about after all. Surely it’s not absurd to suggest that children don’t always know what’s best for them.

The second is the paradox of tolerance: The freedom to take away other people’s freedom is not a freedom we can afford to protect. And homeschooling that indoctrinates children into narrow-minded worldviews is a threat to other people’s freedom—not only those who will be oppressed by a new generation of extremists, but also the children themselves who are never granted the chance to find their own way.

Both reasons apply in this case: paternalism for the children, the paradox of tolerance for the parents. We have a civic responsibility to ensure that children grow up in a rich and diverse environment, so that they learn open-mindedness and tolerance. This is important enough that we should be willing to impose constraints on freedom in order to achieve it. Democracy cannot survive a citizenry who are molded from birth into narrow-minded extremists. There are parents who want to mold their children that way—and we cannot afford to let them.

From where I’m sitting, that means we need to ban homeschooling, or at least very strictly regulate it.

Why we need critical thinking

2023-07-092023-07-08 PNRJ cognitive science, core principles, education, financial fraud, politics, public policy, science and academia critical thinking, deception, education, Facebook, finance, history, lies, media, news, politics, Reddit, religion, scam, school, science, Thomas Jefferson, truth, wisdom

Jul 9 JDN 2460135

I can’t find it at the moment, but awhile ago I read a surprisingly compelling post on social media (I think it was Facebook, but it could also have been Reddit) questioning the common notion that we should be teaching more critical thinking in school.

I strongly believe that we should in fact be teaching more critical thinking in school—actually I think we should replace large chunks of the current math curriculum with a combination of statistics, economics and critical thinking—but it made me realize that we haven’t done enough to defend why that is something worth doing. It’s just become a sort of automatic talking point, like, “obviously you would want more critical thinking, why are you even asking?”

So here’s a brief attempt to explain why critical thinking is something that every citizen ought to be good at, and hence why it’s worthwhile to teach it in primary and secondary school.

Critical thinking, above all, allows you to detect lies. It teaches you to look past the surface of what other people are saying and determine whether what they are saying is actually true.

And our world is absolutely full of lies.

We are constantly lied to by advertising. We are constantly lied to by spam emails and scam calls. Day in and day out, people with big smiles promise us the world, if only we will send them five easy payments of $19.99.

We are constantly lied to by politicians. We are constantly lied to by religious leaders (it’s pretty much their whole job actually).

We are often lied to by newspapers—sometimes directly and explicitly, as in fake news, but more often in subtler ways. Most news articles in the mainstream press are true in the explicit facts they state, but are missing important context; and nearly all of them focus on the wrong things—exciting, sensational, rare events rather than what’s actually important and likely to affect your life. If newspapers were an accurate reflection of genuine risk, they’d have more articles on suicide than homicide, and something like one million articles on climate change for every one on some freak accident (like that submarine full of billionaires).

We are even lied to by press releases on science, which likewise focus on new, exciting, sensational findings rather than supported, established, documented knowledge. And don’t tell me everyone already knows it; just stating basic facts about almost any scientific field will shock and impress most of the audience, because they clearly didn’t learn this stuff in school (or, what amounts to the same thing, don’t remember it). This isn’t just true of quantum physics; it’s even true of economics—which directly affects people’s lives.

Critical thinking is how you can tell when a politician has distorted the views of his opponent and you need to spend more time listening to that opponent speak. Critical thinking could probably have saved us from electing Donald Trump President.

Critical thinking is how you tell that a supplement which “has not been evaluated by the FDA” (which is to say, nearly all of them) probably contains something mostly harmless that maybe would benefit you if you were deficient in it, but for most people really won’t matter—and definitely isn’t something you can substitute for medical treatment.

Critical thinking is how you recognize that much of the history you were taught as a child was a sanitized, simplified, nationalist version of what actually happened. But it’s also how you recognize that simply inverting it all and becoming the sort of anti-nationalist who hates your own country is at least as ridiculous. Thomas Jefferson was both a pioneer of democracy and a slaveholder. He was both a hero and a villain. The world is complicated and messy—and nothing will let you see that faster than critical thinking.

Critical thinking tells you that whenever a new “financial innovation” appears—like mortgage-backed securities or cryptocurrency—it will probably make obscene amounts of money for a handful of insiders, but will otherwise be worthless if not disastrous to everyone else. (And maybe if enough people had good critical thinking skills, we could stop the next “innovation” from getting so far!)

More widespread critical thinking could even improve our job market, as interviewers would no longer be taken in by the candidates who are best at overselling themselves, and would instead pay more attention to the more-qualified candidates who are quiet and honest.

In short, critical thinking constitutes a large portion of what is ordinarily called common sense or wisdom; some of that simply comes from life experience, but a great deal of it is actually a learnable skill set.

Of course, even if it can be learned, that still raises the question of how it can be taught. I don’t think we have a sound curriculum for teaching critical thinking, and in my more cynical moments I wonder if many of the powers that be like it that way. Knowing that many—not all, but many—politicians make their careers primarily from deceiving the public, it’s not so hard to see why those same politicians wouldn’t want to support teaching critical thinking in public schools. And it’s almost funny to me watching evangelical Christians try to justify why critical thinking is dangerous—they come so close to admitting that their entire worldview is totally unfounded in logic or evidence.

But at least I hope I’ve convinced you that it is something worthwhile to know, and that the world would be better off if we could teach it to more people.

Statisticacy

2023-06-112023-06-04 PNRJ core principles, education, financial advice, science and academia, statistics Bill Nye, blackjack, debt, education, error, exponential, growth, inflation, interest, knowledge, mean, median, Monty Hall, percent, percentage, percentage point, poker, probability, statistics

Jun 11 JDN 2460107

I wasn’t able to find a dictionary that includes the word “statisticacy”, but it doesn’t trigger my spell-check, and it does seem to have the same form as “numeracy”: numeric, numerical, numeracy, numerate; statistic, statistical, statisticacy, statisticate. It definitely still sounds very odd to my ears. Perhaps repetition will eventually make it familiar.

For the concept is clearly a very important one. Literacy and numeracy are no longer a serious problem in the First World; basically every adult at this point knows how to read and do addition. Even worldwide, 90% of men and 83% of women can read, at least at a basic level—which is an astonishing feat of our civilization by the way, well worthy of celebration.

But I have noticed a disturbing lack of, well, statisticacy. Even intelligent, educated people seem… pretty bad at understanding statistics.

I’m not talking about sophisticated econometrics here; of course most people don’t know that, and don’t need to. (Most economists don’t know that!) I mean quite basic statistical knowledge.

A few years ago I wrote a post called “Statistics you should have been taught in high school, but probably weren’t”; that’s the kind of stuff I’m talking about.

As part of being a good citizen in a modern society, every adult should understand the following:

1. The difference between a mean and a median, and why average income (mean) can increase even though most people are no richer (median).

2. The difference between increasing by X% and increasing by X percentage points: If inflation goes from 4% to 5%, that is an increase of 20% ((5/4-1)*100%), but only 1 percentage point (5%-4%).

3. The meaning of standard error, and how to interpret error bars on a graph—and why it’s a huge red flag if there aren’t any error bars on a graph.

4. Basic probabilistic reasoning: Given some scratch paper, a pen, and a calculator, everyone should be able to work out the odds of drawing a given blackjack hand, or rolling a particular number on a pair of dice. (If that’s too easy, make it a poker hand and four dice. But mostly that’s just more calculation effort, not fundamentally different.)

5. The meaning of exponential growth rates, and how they apply to economic growth and compound interest. (The difference between 3% interest and 6% interest over 30 years is more than double the total amount paid.)

I see people making errors about this sort of thing all the time.

Economic news that celebrates rising GDP but wonders why people aren’t happier (when real median income has been falling since 2019 and is only 7% higher than it was in 1999, an annual growth rate of 0.2%).

Reports on inflation, interest rates, or poll numbers that don’t clearly specify whether they are dealing with percentages or percentage points. (XKCD made fun of this.)

Speaking of poll numbers, any reporting on changes in polls that isn’t at least twice the margin of error of the polls in question. (There’s also a comic for this; this time it’s PhD Comics.)

People misunderstanding interest rates and gravely underestimating how much they’ll pay for their debt (then again, this is probably the result of strategic choices on the part of banks—so maybe the real failure is regulatory).

And, perhaps worst of all, the plague of science news articles about “New study says X”. Things causing and/or cancer, things correlated with personality types, tiny psychological nudges that supposedly have profound effects on behavior.

Some of these things will even turn out to be true; actually I think this one on fibromyalgia, this one on smoking, and this one on body image are probably accurate. But even if it’s a properly randomized experiment—and especially if it’s just a regression analysis—a single study ultimately tells us very little, and it’s irresponsible to report on them instead of telling people the extensive body of established scientific knowledge that most people still aren’t aware of.

Basically any time an article is published saying “New study says X”, a statisticate person should ignore it and treat it as random noise. This is especially true if the finding seems weird or shocking; such findings are far more likely to be random flukes than genuine discoveries. Yes, they could be true, but one study just doesn’t move the needle that much.

I don’t remember where it came from, but there is a saying about this: “What is in the textbooks is 90% true. What is in the published literature is 50% true. What is in the press releases is 90% false.” These figures are approximately correct.

If their goal is to advance public knowledge of science, science journalists would accomplish a lot more if they just opened to a random page in a mainstream science textbook and started reading it on air. Admittedly, I can see how that would be less interesting to watch; but then, their job should be to find a way to make it interesting, not to take individual studies out of context and hype them up far beyond what they deserve. (Bill Nye did this much better than most science journalists.)

I’m not sure how much to blame people for lacking this knowledge. On the one hand, they could easily look it up on Wikipedia, and apparently choose not to. On the other hand, they probably don’t even realize how important it is, and were never properly taught it in school even though they should have been. Many of these things may even be unknown unknowns; people simply don’t realize how poorly they understand. Maybe the most useful thing we could do right now is simply point out to people that these things are important, and if they don’t understand them, they should get on that Wikipedia binge as soon as possible.

And one last thing: Maybe this is asking too much, but I think that a truly statisticate person should be able to solve the Monty Hall Problem and not be confused by the result. (Hint: It’s very important that Monty Hall knows which door the car is behind, and would never open that one. If he’s guessing at random and simply happens to pick a goat, the correct answer is 1/2, not 2/3. Then again, it’s never a bad choice to switch.)

The mental health crisis in academia

2023-04-302023-04-24 PNRJ personal, science and academia academia, anxiety, COVID, depression, education, extraversion, graduate student, intelligence, Mensa, monopsony, research, United Kingdom

Apr 30 JDN 2460065

Why are so many academics anxious and depressed?

Depression and anxiety are much more prevalent among both students and faculty than they are in the general population. Unsurprisingly, women seem to have it a bit worse than men, and trans people have it worst of all.

Is this the result of systemic failings of the academic system? Before deciding that, one thing we should consider is that very smart people do seem to have a higher risk of depression.

There is a complex relationship between genes linked to depression and genes linked to intelligence, and some evidence that people of especially high IQ are more prone to depression; nearly 27% of Mensa members report mood disorders, compared to 10% of the general population.

(Incidentally, the stereotype of the weird, sickly nerd has a kernel of truth: the correlations between intelligence and autism, ADHD, allergies, and autoimmune disorders are absolutely real—and not at all well understood. It may be a general pattern of neural hyper-activation, not unlike what I posit in my stochastic overload model. The stereotypical nerd wears glasses, and, yes, indeed, myopia is also correlated with intelligence—and this seems to be mostly driven by genetics.)

Most of these figures are at least a few years old. If anything things are only worse now, as COVID triggered a surge in depression for just about everyone, academics included. It remains to be seen how much of this large increase will abate as things gradually return to normal, and how much will continue to have long-term effects—this may depend in part on how well we manage to genuinely restore a normal way of life and how well we can deal with long COVID.

If we assume that academics are a similar population to Mensa members (admittedly a strong assumption), then this could potentially explain why 26% of academic faculty are depressed—but not why nearly 40% of junior faculty are. At the very least, we junior faculty are about 50% more likely to be depressed than would be explained by our intelligence alone. And grad students have it even worse: Nearly 40% of graduate students report anxiety or depression, and nearly 50% of PhD students meet the criteria for depression. At the very least this sounds like a dual effect of being both high in intelligence and low in status—it’s those of us who have very little power or job security in academia who are the most depressed.

This suggests that, yes, there really is something wrong with academia. It may not be entirely the fault of the system—perhaps even a well-designed academic system would result in more depression than the general population because we are genetically predisposed. But it really does seem like there is a substantial environmental contribution that academic institutions bear some responsibility for.

I think the most obvious explanation is constant evaluation: From the time we are students at least up until we (maybe, hopefully, someday) get tenure, academics are constantly being evaluated on our performance. We know that this sort of evaluation contributes to anxiety and depression.

Don’t other jobs evaluate performance? Sure. But not constantly the way that academia does. This is especially obvious as a student, where everything you do is graded; but it largely continues once you are faculty as well.

For most jobs, you are concerned about doing well enough to keep your job or maybe get a raise. But academia has this continuous forward pressure: if you are a grad student or junior faculty, you can’t possibly keep your job; you must either move upward to the next stage or drop out. And academia has become so hyper-competitive that if you want to continue moving upward—and someday getting that tenure—you must publish in top-ranked journals, which have utterly opaque criteria and ever-declining acceptance rates. And since there are so few jobs available compared to the number of applicants, good enough is never good enough; you must be exceptional, or you will fail. Two thirds of PhD graduates seek a career in academia—but only 30% are actually in one three years later. (And honestly, three years is pretty short; there are plenty of cracks left to fall through between that and a genuinely stable tenured faculty position.)

Moreover, our skills are so hyper-specialized that it’s very hard to imagine finding work anywhere else. This grants academic institutions tremendous monopsony power over us, letting them get away with lower pay and worse working conditions. Even with an economics PhD—relatively transferable, all things considered—I find myself wondering who would actually want to hire me outside this ivory tower, and my feeble attempts at actually seeking out such employment have thus far met with no success.

I also find academia painfully isolating. I’m not an especially extraverted person; I tend to score somewhere near the middle range of extraversion (sometimes called an “ambivert”). But I still find myself craving more meaningful contact with my colleagues. We all seem to work in complete isolation from one another, even when sharing the same office (which is awkward for other reasons). There are very few consistent gatherings or good common spaces. And whenever faculty do try to arrange some sort of purely social event, it always seems to involve drinking at a pub and nobody is interested in providing any serious emotional or professional support.

Some of this may be particular to this university, or to the UK; or perhaps it has more to do with being at a certain stage of my career. In any case I didn’t feel nearly so isolated in graduate school; I had other students in my cohort and adjacent cohorts who were going through the same things. But I’ve been here two years now and so far have been unable to establish any similarly supportive relationships with colleagues.

There may be some opportunities I’m not taking advantage of: I’ve skipped a lot of research seminars, and I stopped going to those pub gatherings. But it wasn’t that I didn’t try them at all; it was that I tried them a few times and quickly found that they were not filling that need. At seminars, people only talked about the particular research project being presented. At the pub, people talked about almost nothing of serious significance—and certainly nothing requiring emotional vulnerability. The closest I think I got to this kind of support from colleagues was a series of lunch meetings designed to improve instruction in “tutorials” (what here in the UK we call discussion sections); there, at least, we could commiserate about feeling overworked and dealing with administrative bureaucracy.

There seem to be deep, structural problems with how academia is run. This whole process of universities outsourcing their hiring decisions to the capricious whims of high-ranked journals basically decides the entire course of our careers. And once you get to the point I have, now so disheartened with the process of publishing research that I can’t even engage with it, it’s not at all clear how it’s even possible to recover. I see no way forward, no one to turn to. No one seems to care how well I teach, if I’m not publishing research.

And I’m clearly not the only one who feels this way.

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Human Economics

Cognitive development economics: Understanding the mind in order to feed the world

education