The Case Against Education
Article
The Case Against Education is a recurring book in the Astral Codex Ten archive, appearing 4 times across 4 issues between February 18, 2021 and June 27, 2025. The archive places it in contexts such as “Bryan Caplan’s The Case Against Education”; “I’m not going throw out my copy of The Case Against Education just yet”; “There’s been renewed debate around Bryan Caplan’s The Case Against Education recently”. It most often appears alongside Bryan Caplan, 1984, Bay Area.
Metadata
- Category: Books
- Mention count: 4
- Issue count: 4
- First seen: February 18, 2021
- Last seen: June 27, 2025
Appears In
- Book Review: The Cult Of Smart
- Links For February
- A Theoretical “Case Against Education”
- Your Review: Alpha School
Related Pages
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- Bryan Caplan (3 shared issues)
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- 1984 (2 shared issues)
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- Bay Area (2 shared issues)
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- Montessori (2 shared issues)
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- No Child Left Behind (2 shared issues)
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- Shakespeare (2 shared issues)
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- Ukraine (2 shared issues)
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- United States (2 shared issues)
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- 10,000 hour rule (1 shared issues)
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- 1984 Calendar Meme (1 shared issues)
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- 2 Hour Learning, Inc (1 shared issues)
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- 2-hour Learning (1 shared issues)
External Links
Source Context
Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.
The Cult Of Smart invites comparisons with Bryan Caplan's The Case Against Education. Both use largely the same studies to argue that education doesn't do as much as we thought. Caplan very reasonably thinks maybe that means we should have less education. DeBoer...definitely doesn't think that:
...ency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity.” 23: I’m not going throw out my copy of The Case Against Education just yet - I haven’t checked this study but I bet there are lots of possible confounders. Still, this would be fun for somebody more interested to analyze in depth: 24:...
.... “Why should we care? Brain efficiency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity.” 23: I’m not going throw out my copy of The Case Against Education just yet - I haven’t checked this study but I bet there are lots of possible confounders. Still, this would be fun for somebody more interested to analyze in depth: 24: Best of S...
There’s been renewed debate around Bryan Caplan’s The Case Against Education recently, so I want to discuss one way I think about this question.
Why this time might be different: Most promising educational initiatives fail to have impact when expanded beyond their initial studies. Bryan Caplan might argue this is because most education education is just signaling anyway (“The Case Against Education”). He also argues that most parental interventions have no impact (“Selfish Reasons to Have More Kids”) – He claims that how kids turn out is a combination of genetics and non-shared environment (randomness; nothing to do with parenting choices). How can we reconcile Caplan’s buttoned-up data with the idea that the “parenting choice” to educate your kids differently (like with Alpha) might result in different outcomes than would be expected from genetics alone? WHY could Alpha work?
Inline links: The Case Against Education, Selfish Reasons to Have More Kids
They keep the kids motivated so they put in the daily effort and don’t get burned out What Alpha is doing is not rocket science. They are just “following the science” for what has been proven to work, and then designing a school around the best way (or “a way”) to deliver that science - personalized instruction, mastery focus, spaced repetition and incentives. It should not be too surprising that when it all comes together it spits out measurable results. But will it hold? Part Six: A Response to Bryan Caplan "When the data and anecdotes disagree, the anecdotes are usually right. It’s usually not that the data is being miscollected. It’s usually that you’re not measuring the right thing." — Jeff Bezos (on multiple occasions) Not only does Bryan Caplan convincingly argue that education is mostly signalling in his book “The Case Against Education”, he goes even further to pour cold buckets of water on aspirational parents in his book “Selfish Reasons to have more Kids”. In that latter book he makes a compelling case with unimpeachable data that how kids turn out is almost entirely due to their genes plus “non-shared environment” (i.e., random things not having to do with parenting). According to Caplan helicopter-parenting does not hurt your kids, it is just a waste of everyone’s time (and maybe their enjoyment during their childhood). You might be able to influence some of your kids' behavior in the short term, but once they become an adult and move out of your house they will revert to the biases of their genes. As Caplan says, the most important parenting decision you can make that will affect how your kids turn out is your choice of spouse (or more accurately your choice of the genes you use to build your kids). Caplan does put one caveat on his data: range restriction. He admits that all of his adoption studies focus on middle class Americans (and Europeans). He is the first to admit that if you take a baby out of extreme poverty in the developing world and raise him in a middle class American family, he will have better economic outcomes than if you leave him in rural Mauritania (see his “Open Borders” book). He may even grant that moving from the poorest broken families in America to the middle class also may make a difference – since all the data available comes from families who were approved by administrators as acceptable to raise adopted kids. But is the same thing true when you move from the middle of the bell curve to the right? When the Data Set Gets Bigger Raj Chetty’s neighbourhood-impact study cracked the range challenge open. Chetty had access to all IRS filing data for generations. He was able to focus on families with multiple children that moved to significantly different zip codes, and follow those children over extended periods of time. By having millions of data points he could tease apart the impact of moving to a “better” zip code for older vs younger siblings. The younger sibling had the same family environment (and 50% of the same genes), but some number of more years in the “better” neighborhood. Chetty found that better neighborhoods made a difference to long term outcomes. But isn’t the neighborhood where a family lives in a “shared environment”? Clearly some adopted families lived in better neighborhoods than others? Why didn’t Caplan’s adoption studies pick that up? I think part of the answer is noise. Chetty had millions of data points vs hundreds of thousands for the adoption studies. But mostly I think the reason Chetty found this impact while the adoption studies did not is that he was looking for different things. No one took the adoption studies and grouped the zip codes as the relevant input variable. As Bezos says, the data wasn’t miscollected, they were just looking at the wrong things. So what does a good zip code look like? Chetty summarizes a good zip code as: Short commutes