You don’t always save money by putting your investments into a 401(k).

When you invest money inside a 401(k), you don’t have to pay taxes on any returns earned by your investments. But you also have to pay a fee to your 401(k) provider.

• If you buy and hold index funds in a taxable account, you don’t have to pay any capital gains tax on price increases until you sell.
• In a 401(k), the annual fee adds up every year and may eventually exceed the tax savings.

So the taxes cap out at the capital gains tax rate (15% or 20% depending on your tax bracket),¹ whereas the expenses of a 401(k) continue to accumulate.

However, in a taxable account, you do still have to pay taxes on dividends (and bond payouts) every year, and those taxes might cost you more than the 401(k) fees.²

Below is a calculator to determine how many years before the 401(k) fees exceed the tax savings, if ever.

employer matching (%)
total investment return including dividends (nominal) (%)
dividend yield (%)
401(k) fee (%)
capital gains tax rate (%)
income tax rate today (%)
income tax rate in retirement (%)

A 401(k) falls behind a taxable account after:

This calculator assumes you buy index funds and hold them forever. If you trade stocks within a taxable account, you have to pay taxes every time you make a trade.

Something else to consider: If you quit your job, your old employer’s 401(k) provider will let you roll your 401(k) into an IRA. You don’t have to pay any fees on an IRA.³ So even if the 401(k) fees exceed the tax benefits after (say) 30 years, that’s not a problem if you expect to quit your job after less than 30 years. Realistically, few people stay at one job for so long that the 401(k) fees exceed the tax savings.

(If you change jobs, usually you can roll your old 401(k) into your new 401(k), but I wouldn’t do that because it means you have to keep paying 401(k) fees. It’s almost always better to roll your old 401(k) into an IRA.)

Notes

I did another caffeine experiment on myself. This time I tested if I could have caffeine 4 days a week without getting habituated.

Last time, when I took caffeine 3 days a week, I didn’t get habituated but the results were weird. This time, with the more frequent dose, I still didn’t get habituated, and the results were weird again!

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Here are some things I’ve changed my mind about. Most of the changes are recent (because I can remember recent stuff more easily) but some of them happened 5+ years ago.

I’m a little nervous about writing this because a few of my old beliefs were really dumb. But I don’t think it would be fair to include only my smart beliefs.

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I recently read Eat, Drink, and Be Healthy: The Harvard Medical School Guide to Healthy Eating. As I understand, it’s the book that does the best job of representing the mainstream scientific perspective on nutrition for a lay audience. Here are the notes I took.

Last updated 2024-05-31.

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Last time, I found that the healthiest BMI range for all-cause mortality is 20–22. But BMI doesn’t tell the whole story. Most obviously, it doesn’t account for body fat vs. lean mass. All else equal, you’d rather have more muscle¹ and less fat.

So what’s the healthiest combination of lean mass + fat mass?

I’m not going to answer that question because I can’t. Instead, I will explain why I can’t, and then give a rough guess at the answer.

Scientists have been measuring and collecting data on BMI for decades. You can find plenty of giant BMI studies with three million participants in various countries.

We have much sparser data on body fat. Scientists didn’t start collecting data on body fat until the last few decades. And body fat is harder to measure—we have various methods for estimating body fat, but they’re all more complicated than calculating BMI.

I managed to scrounge together some studies on body fat and mortality. My best guess: the average woman should aim for a BMI of 21 with 20% body fat, and the average man a BMI of 21 with 10% body fat. (Subject to individual variation due to genetics and whatnot.)

Trans men should probably target the same body fat % as cis men, and likewise for trans women and cis women, because hormone therapy alters body fat distribution (Spanos et al. (2020)²).

The evidence weakly suggests that there is no lower bound on healthy fat mass, and no upper bound on healthy lean mass. We have so little mortality data on extremely lean + muscular people that we can’t say how healthy they are.

A more in-depth analysis would look at a variety of health indicators (blood pressure, HDL cholesterol, etc.) and use that to predict mortality. I didn’t do that, I just looked at mortality data.

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TLDR: 20 to 22.

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Last updated 2024-07-26 to clarify wording.

Confidence: Likely.

I conducted an experiment on myself to see if I would develop a tolerance to caffeine from taking it three days a week. The results suggest that I didn’t. Caffeine had just as big an effect at the end of my four-week trial as it did at the beginning.

This outcome is statistically significant (p = 0.016), but the data show a weird pattern: caffeine’s effectiveness went up over time instead of staying flat. I don’t know how to explain that, which makes me suspicious of the experiment’s findings.

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In 2012, Scott Alexander defended social sciences against the claim that they can’t figure anything out. He gave a long list of well-established findings across a variety of social science disciplines.

12 years later, how well did that list hold up?

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Recently, Saar Wilf, creator of Rootclaim, had a high-profile debate against Peter Miller on whether COVID originated from a lab. Peter won and Saar lost.

Rootclaim’s mission is to “overcome the flaws of human reasoning with our probabilistic inference methodology.” Rootclaim assigns odds to each piece of evidence and perfoms Bayesian updates to get a posterior probability. When Saar lost the lab leak debate, some people considered this a defeat not just for the lab leak hypothesis, but for Rootclaim’s whole approach.

In Scott Alexander’s coverage of the debate, he wrote:

While everyone else tries “pop Bayesianism” and “Bayes-inspired toolboxes”, Rootclaim asks: what if you just directly apply Bayes to the world’s hardest problems? There’s something pure about that, in a way nobody else is trying.

Unfortunately, the reason nobody else is trying this is because it doesn’t work. There’s too much evidence, and it’s too hard to figure out how to quantify it.

Don’t give up so easily! We as a society have spent approximately 0% of our collective decision-making resources on explicit Bayesian reasoning. Just because Rootclaim used Bayesian methods and then lost a debate doesn’t mean those methods will never work. That would be like saying, “randomized controlled trials were a great idea, but they keep finding that ESP exists. Oh well, I guess we should give up on RCTs and just form beliefs using common sense.”

(And it’s not even like the problems with RCTs were easy to fix. Scott wrote about 10 known problems with RCTs and 10 ways to fix them, and then wrote about an RCT that fixed all 10¹ of those problems and still found that ESP exists. If we’re going to give RCTs more than 10 tries, we should extend the same courtesy to Bayesian reasoning.)

I’m optimistic that we can make explicit Bayesian analysis work better. And I can already think of ways to improve on two problems with it.

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Confidence: Likely.

If you take caffeine every day, does it stop working? If it keeps working, how much of its effect does it retain?

There are many studies on this question, but most of them have severe methodological limitations. I read all the good studies (on humans) I could find. Here’s my interpretation of the literature:

• Caffeine almost certainly loses some but not all of its effect when you take it every day.
• In expectation, caffeine retains 1/2 of its benefit, but this figure has a wide credence interval.
• The studies on cognitive benefits all have some methodological issues so they might not generalize.
• There are two studies on exercise benefits with strong methodology, but they have small sample sizes.

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