Category: Education
How and why did the Victorians succeed?
From Samuel Hughes, in Works in Progress:
The elites of Victorian Britain operated differently. Their schools and universities were not terribly academic and had very little STEM. As adults, they got up late, drank a lot, and spent a remarkable share of their waking hours partying. They loved feasting, sports, holidays, dancing, and dressing up. Contrary to their stodgy reputation, they were probably a lot of fun.
Here is some further detail:
Most socially elite Victorian boys began formal education at the age of seven or eight, spending about five years in a ‘preparatory school’ before passing on to a ‘public school’ (which, to the enduring confusion of international observers, is a kind of elite private school). There was a hierarchy of public schools, with the nine ‘Clarendon schools’ at the top, and just one, Eton College, clearly at the apex. About 30 percent of nineteenth-century cabinet ministers and 40 percent of prime ministers were Old Etonians.
Most students at both preparatory and public schools were boarders, meaning that elite Victorians were generally sent away from home by the age of eight and spent their childhoods more with peers than with family. In this respect they were distinctive even in their own time: elite families in continental Europe and the United States were far more likely to keep their children at home and educate them at small local private schools. It is plausible that this contributed to the lack of subnational loyalties in the British elite, in contrast to the distinct elites of, for example, the Southern United States, Catalonia in Spain, or Hungary in the Austrian Empire.
Nor did the Victorians wake up early in the morning. Recommended, there is much more at the link, though the answer to the question remains somewhat of a mystery to me.
Against Laissez-Faire Democracy
In a new paper, Brennan and Freiman argue persuasively that:
the arguments against laissez-faire capitalism apply in a rather straight way against laissez-faire democracy. This should be a rather startling result, considering that laissez-faire capitalism is widely rejected, yet laissez-faire democracy is widely accepted.
All the typical market failure arguments–externalities, asymmetric information, principal-agent problems, behavioral issues–apply to democracy. For example:
democracies also suffer from obvious externality problems. Indeed, this is the key difference between markets and politics. Market decisions are usually and mostly internalized; negative externalities are seen as an anomaly to be corrected. Political decisions are by nature public. For instance, about 37% of the eligible
voting public, and 26% of the entire population of the UK voted in favour of Brexit, but the effects of Brexit are borne by everyone else, including losing voters, eligible voters who abstained, children and other citizens ineligible to vote, future generations, and foreigners.…The problem is similar to the pollution of individual automobile drivers or airline passengers. We drive too much and pollute too much because we bear the benefits of these actions but pass the costs onto others. Individually we matter little but collectively we matter a great deal. If externality regulation is prima facie justified in the case of pollution, it is prima facie justified in the case of voting.
They go on to suggest regulations that might apply to democracy. Noting first that:
…pretty much everyone already accepts some government actions which are plausibly seen as vote or democratic regulation. For instance, some issues are put directly to voters, while others are left to elected leaders, while others are left to judges and bureaucracies, and still others are removed from political consideration entirely. The timing and place of elections, who is permitted to vote (at what age), who is permitted to run for office, and so on are kinds of regulation. Government funding of public education is in part meant to be a non-laissez-faire subsidy meant to increase voter competence. Many readers are likely comfortable with government regulation of campaign donations, campaign expenditures and even which kinds of speech are permitted. Probably almost all readers reject straight up markets in votes (Freiman 2014). Each of these things is a kind of government regulation of democracy which inhibits laissez-faire democracy.
The authors sketch possible regulations while stipulating, for the sake of argument, universal, equal suffrage—no weighted votes or knowledge tests for voting eligibility. Possibilities include paying citizens to pass a voluntary civics exam; tracking campaign promises and fining deceptive claims; regulating political advertisements more like drug advertisements; and requiring supermajorities for certain decisions. See the paper for more.
You may object that these regulations would be captured by special interests, be administered by biased officials, or have unintended consequences. Indeed, welcome to the argument for laissez-faire.
Marital sorting by class and race
Americans rarely marry outside their race or class group, a pattern with well-documented implications for inequality and intergenerational mobility. Limited exposure may partly explain these low intergroup marriage rates. We instrument for exposure using variation in childhood neighborhoods based on whether other race and class groups had more opposite-sex children of similar age. Exposure increases interclass (high- and low-parent income) marriage but has no detectable effect on interracial (White and Black) marriage. A spatial marriage model predicts that residential segregation—one of many forms of exposure—accounts for more than one-third of marital sorting by class but less than 5 percent by race.
That paper is from the most recent AER, by Benjamin Goldman, Jamie Gracie, and Sonya R. Porter.
New issue of Econ Journal Watch
Volume 23, Issue 2, September 2026
In this issue:
“China shock” fragility: According to David Autor, David Dorn, and Gordon Hanson (2013), the “China shock” hit the United States from 1990 to 2007. When corrections by Kirill Borusyak et al. (2022) are fully applied, Joseph Francis argues, the harm of Chinese imports to unemployment, labor force participation, wages, and household incomes disappear. “China shock,” Francis argues, illustrates how the “credibility revolution” very much leaves the con in econometrics. (The commented-on authors are hereby invited to reply in a future issue.)
The predictions did not come true: A June 2023 decision, SFFA v. Harvard, held that race-based admissions are unconstitutional. Thirty-three selective colleges forecast Black enrollment declines of 50 to 70 percent. Fifteen research universities assured the Court that race-neutral admissions “would undercut” the diversity they seek. David Card estimated that eliminating racial preferences would cut the Black share of Harvard’s admitted class from 14 percent to 6 percent. Three years on, David Kane reports that the predictions did not come anywhere close to true.
Science confections: Following up on his critique of Jonathan Pekar et al. in Science, Michael Weissman now criticizes Michael Worobey et al. in Science, who claim that case locations and nucleic acid samples showed that SARS-CoV-2 had first been introduced to humans at the Huanan Seafood Market. Several lines of evidence suggest the introduction to humans was earlier and elsewhere. (The commented-on authors are hereby invited to reply in a future issue.)
The fall of Fama-French and momentum factors: Commenting on Clifford Asness, Eugene Fama, and Kenneth French, Michael O’Connor argues that momentum’s circa-2003 demise and that of market capitalization and the book-to-market ratio about a decade later represent a structural break or ‘regime change.’ (The commented-on authors are hereby invited to reply in a future issue.)
All in the family: Kenneth Judd and Karl Schmedders take issue with an article in Journal of Economic Literature by Jesús Fernández-Villaverde. They say that he overstates the newness of neural-network or “deep learning” solvers and that such solvers should be understood as inside the family of projection and weighted-residual solvers. (The commented-on author is hereby invited to reply in a future issue.)
Is chatbot validation valid? Alexis Akira Toda reports on Gemini, Refine, Claude, and ChatGPT on errors in four published papers in mathematical economics. The chatbots generated false positives—endorsing flawed proofs—and varied in how much guidance they needed to find the error. ChatGPT Pro performed best, Claude was middling, and Gemini worst. Corrections of the original errors had already been published at the time of the experiment, and so a chatbot’s success might reflect its canvassing for published commentary. Since the chatbots generated false positives, however, we know that they are fallible one way or another.
How are so-called digital public goods provided? The United Nations and its Digital Public Goods Alliance have constructed an ambitious framework for certifying, funding, and deploying open-source software in developing countries. The soundness of the UN’s approach is, according to Vidar Andre Utvik and coauthors, illustrated by the rapid development of Covid-19 surveillance tools in Sri Lanka and Norway. Here, John Levendis argues that their own evidence reveals that local professionals cooperated voluntarily and that community innovation preceded WHO guidance, not the reverse. (The commented-on authors are hereby invited to reply in a future issue.)
On Haavelmo’s adoption of Neyman-Pearson theory: Trygve Haavelmo’s (1944) monograph “The Probability Approach in Econometrics” is based on a strict adoption of Neyman-Pearson testing theory. Here, Tom Engsted argues that Haavelmo resorted to the notion of a hypothetical infinite population and a distinctly non-frequentist (Bayesian-like) interpretation of probability with the aim of inductive inference, in direct contradiction with Neyman-Pearson theory.
1939–1945: Housing policy in Denmark: In a critique of the primary literature on the subject, Jens Hansen explains the origins of Danish rent control and other housing regulation. The interventions have wrought great hardship and no discernible benefits. The history exemplifies the intervention dynamic.
Where credit is due: Alexis Akira Toda reports on some curious failures to cite predecessors and brings credit to the slighted authors and papers. (The commented-on authors are hereby invited to reply in a future issue.)
Greening on the edge: An article models sustainable product development under crowdfunding and regulation. Here, Marco Sorge argues that the authors disregard corner solutions for environmental quality, which robustly arise across admissible parameter configurations, and conduct comparative statics where no interior solution exists. A complete characterization of the equilibrium corrects, and in some cases contradicts, their conclusions. (The commented-on authors are hereby invited to reply in a future issue.)
Book-manuscript symposium:
Gregory Clark on genetics and social mobility
The third book in Clark’s trilogy is titled To Have and Have Not: The Determinants of Social Status in England, 1600-2026:
- The book manuscript that commenters received.
- Leaders in genetics and social mobility comment:
- Gregory Clark’s reply to commenters.
- Symposium webpage
EJW Audio:
Trump Administration Limits Predatory Lending in Education
The New Republic writes “President Trump is banning students majoring in degrees that don’t make enough money from taking out college loans.” Yes, but do note that no student is banned from any major and the lending rule is mild. Undergraduate programs must show:
that their graduates earn more than the typical high school diploma holder…[and] graduate programs will be required to demonstrate that their graduates earn more than the typical bachelor’s degree holder. (emphasis added).
Think about how low that bar is. The comparison group for an undergraduate program is working adults aged 25-34 with nothing more than a high school diploma. A college program that can’t beat that has almost certainly made its students worse off. For graduate programs the bar is the lowest of several bachelor’s benchmarks, including bachelor’s holders in the same field. A master’s in social work need only beat people with a bachelor’s in social work. A program must also fail in two out of three years before it loses loan eligibility. The Department estimates that about 5% of programs will fail in the first year.
I mocked the term “predatory lending” when it first became common in the financial crisis but in this case predatory lending fits the bill because the real borrower isn’t the individual student. Under income-driven repayment, the taxpayer is a forced co-signer, and it’s the taxpayer who gets predated.
Most expansions of the student loan program have been motivated by the picture of an enterprising student who works hard and wants to major in mechanical engineering or nursing but because of their poor circumstances they can’t afford college. “Credit constraints, asymmetric information, you can’t collateralize human capital,” said the economists. Nice theory, what’s the practice?
The economists wanted loans for good investments and insurance against bad luck but the economists can’t swing the vote and once the government is lending, colleges want more tuition money and students want more forgiveness. The result is a subsidy for programs whose graduates are never likely to repay. As Looney and Yannelis document:
Starting in the late 1990s, policymakers weakened regulations that had constrained institutions from enrolling aid-dependent students. This led to rising enrollment of relatively disadvantaged students, but primarily at poor-performing, low-value institutions whose students systematically failed to complete a degree, struggled to repay their loans, defaulted at high rates, and foundered in the job market. As these new borrowers experienced similarly poor outcomes, their loans piled up, loan performance deteriorated, and with it the finances of the federal program.
Indeed, the program worked in reverse of what was promised. The biggest subsidies went to programs whose graduates were least able to repay, rather than programs with the strongest case for public support. As I wrote earlier:
Looney does a back of the envelope calculation and estimates that typical graduates in Mechanical Engineering will on average get a 0% subsidy but graduates in Music will get a 96% subsidy, in Drama a 99% subsidy and Masseuses a 100% subsidy on average. This of course is exactly the wrong approach. If we are going to subsidize, we should subsidize degrees with plausible positive spillovers not masseuses.
The courts later blocked Biden’s Save plan but the problem is built into income-driven repayment. If music, drama and masseuses are promised a 95%+ subsidy who is paying? The taxpayers. Moreover, it’s even worse than this because the very existence of these loans incentivizes the creation of expensive, useless programs. It’s not just the drama colleges, however. Not surprisingly, the law schools have proven adept at using Public Service Loan Forgiveness (PSLF) to rip off the taxpayer. The school raises tuition, then covers the student’s small income-driven payments for ten years, and the taxpayer forgives the rest. In short, protecting students from the cost of failure rewards colleges for producing it.
Fortunately, the same bill limiting loans ended Grad PLUS loans and capped graduate borrowing. You can see the logic: if taxpayers are going to insure the loans, they need some say over which programs qualify and how much is borrowed. I don’t like giving government that power, but this is the Mises–Higgs intervention ratchet in action: subsidize the loans, absorb the losses, then regulate the programs to limit the losses.
My ideal program would get the government out of the student loan business altogether but until then this is a good first step at limiting one of the most expensive and wasteful programs of the federal government.
Don’t let AI make you dumber
That is the topic of my latest Free Press column, here is one excerpt:
I do not think the skeptics would put it this way, but as I read Conti, I find he has a pretty bleak fundamental view of humanity. Are we all really just looking to veg out and abandon curiosity and inquiry, at least once the machines have taken care of both the basic functions of life and certain higher aims such as scientific research? I think some people are like that—indeed you might say many people—but it does not reflect what I take to be the general human condition.
If I look at most people who might fit into the “middle class” when it comes to intellectual pursuits or educational status, I observe they have a lot of strong interests. This might play with their pets, improve their performance at sports, or learn how to cook better. You do not have to identify those preferences with “the new Athens” or “the next Mozart” to think they are perfectly good and noble ways for people to spend their time.
Most of us want to do something interesting and stimulating with our leisure time, and if we do not, it is often because our jobs are so busy and stressful that we just wish to decompress. Of course, in this radical vision of our AI future, fewer jobs will be so all-consuming and so more of us will use vacations and leisure time to explore and learn rather than to just sit on the beach scrolling our phones. And to the extent some jobs do remain hectic, or become even more so (such as cybersecurity), they will continue to be challenging and intellectually stimulating.
A related worry is that humans may feel they simply cannot compete with the AIs, and thus they might turn away from creative pursuits. It is true that I, more than ever, have given up all hope of proving new theorems in mathematical economics. But many of my intellectual and creative pursuits do not involve competition at all. For instance, I use AI to understand classical music better, asking the models questions before I sit down to listen to a piece. (Such as “which are the best recordings?” and “what should I listen for in the second movement?”) As the models get better and smarter, I am not going to be discouraged in this endeavor, as I was not “competing” with the models to see which of us knew more. Rather, I will gratefully end up much better informed about classical music—my increasing knowledge has already induced me to see more live concerts.
Recommended, and AI saved me time on the proofreading and fact-checking (not the writing!), so I could return to reading China Mieville…
Defining the Unemployment Rate
An updated version of our Marginal Revolution University (MRU) video on defining the unemployment rate. Free to use for anyone but goes best, of course, with Modern Principles of Economics, the best principles of economics textbook.
What should I ask Terence Tao?
Yes, I will be doing a Conversation with him. And he has a new book coming out Six Math Essentials. So what should I ask him?
Dartmouth Provost okie-dokie
On Aug. 26, a Semafor investigation using Pangram to determine whether recent guest columns in major newspapers were written using artificial intelligence identified a column by Schnell as “100% AI-written.” Schnell’s column in The Washington Post — titled “Universities are fighting AI cheating. But there’s a deeper problem.” — argued that “a degree should distinguish what students can do independently from what they can accomplish with AI.
”The Dartmouth subsequently reviewed publications written by Schnell before and after the November 2022 public release of ChatGPT. Pangram 4.0, the latest publicly available version of the AI detector, labeled all of Schnell’s written works prior to 2022 as “100% human-written,” while a test of nine of his works published this year returned a median “AI-written” percentage of 96%. Between 2019 and 2026, Schnell did not publish any academic works or op-eds for which he was the sole author credited, according to his online curriculum vitae.Pangram 4.0 assesses whether a work is AI-written by separating the submitted text into smaller segments, which are then identified as “human,” “AI-assisted” or “AI-generated,” according to a Pangram technical report.
…Schnell’s Sept. 18 email statement to The Dartmouth was itself “100% AI-written,” according to a Pangram test.
Here is the full story, you people crack me up…
The Tragedy of Nikole Hannah-Jones
Nikole Hannah-Jones famously sent her child to a predominantly black/lower-income public school in New York City. Now her child says she shouldn’t have been used as political fodder.
The essay makes for hard reading and I acknowledge NHJ’s courage in writing about her mistakes and the cost to her child. The piece is raw and true when talking about her personal choices, but the larger story in which NHJ embeds her personal history is mostly false. The problem isn’t funding. The failures she documents are failures of management and accountability, not lack of resources. NYC spends upwards of $40k per pupil on average, the highest of any large district in the country. Moreover, schools in NYC with lower-income students get more than the average. PS 307, where NHJ initially sent her child, is currently funded at just over 52k per student, far above the national average.
Nationally, there haven’t been big gaps in funding within states by race or income for decades. NHJ has reckoned with her failing her own child but she has not yet reckoned with the failure of her entire educational philosophy.
The role of ideology in blinding NHJ is dramatic. NHJ’s daughter earns high grades in her math classes but she fails standardized tests. Most parents, especially conservatives, know what that means. The classes are a lie. But NHJ is blinded by ideology and assumes it’s the standardized tests that are biased. It’s only when her daughter demands to go to a better school that she and her daughter confront the truth:
Najya knew very little algebra and had huge gaps in many of the building blocks of math that she should have learned in elementary school, like mastery of long division and fractions.
The confrontation is almost disastrous:
My child’s confidence wilted like an unwatered plant. She started to develop an almost debilitating anxiety around taking tests. She became sullen at school and began to act out. For the first time ever, I got messages from teachers about Najya’s behavior.
Fortunately, Najya battles back and recovers but much more could be said here about the costs of social promotion, DEI and mismatch on the so-called beneficiaries.
Ironically, NHJ invokes the heroic history of Black Americans integrating schools to justify keeping her own child in a segregated one. Painful.
NHJ thought that she and her husband–politically connected, astute people who were not afraid to speak up–could do well for their daughter and the other kids in the school. But in the end she has written a great piece on why voice fails–even a powerful voice–in the absence of the threat of exit. Hannah-Jones could have saved herself some grief and her child a poor education by reading my post The tragedy of Jonathan Kozol.
It’s been said that Ayn Rand’s heroes don’t exist, but her villains do. Case in point. Hannah-Jones says that she and her husband “felt an obligation to make educational decisions based on the collective good.” But she wasn’t just sacrificing herself for her ideology; she was sacrificing her child, who had no choice in the matter. “I don’t think she’s deserving of more than other kids,” she said in 2017. Nearly a decade later, Najya reminded her that in the abstract your child may not deserve more than other children, but she is nevertheless entitled to expect more from you. “Sometimes I wish instead of always thinking about other kids, you would have thought about me.” You don’t have to be a Randian to think that your child should not be sacrificed to the collective good, but it doesn’t hurt.
Emergent Ventures India, 19th cohort
Asit Singh, founder of Syngi, received his grant to develop sub-angstrom optical MEMS sensors for next-generation semiconductor lithography.
Rishabh received his grant to build a platform for real-time updates from Delhi courts, making judicial activity easier to track and understand.
Hari and Naveen received their grant for Archgen AI, an agentic ecosystem connecting legacy EDA tools with AI workflows for semiconductor chip design.
Shubh, 18, received his grant to scale robotics using world models.
Sarthak Ahuja received his grant for Hypertensight, a smartphone-based system for detecting hypertensive retinopathy, now deployed in over 18 Indian villages. His project was also published in the International Journal of Computer Science and Engineering in 2024.
Michaela Talwar, received her grant for AltShows.com, an artist-run ticketing and discovery platform for India’s alternative arts ecosystem.
Avyukt Jindal, 16, received his grant to develop a nonlinear operator framework and pursue related work in condensed matter and other fields.
Anirudh Anilkumar, 29, and AV Surya Vardhan, 26, received their grant for Fe-rry, developing IoT-enabled modular point-of-care diagnostics for blood testing anywhere.
Bhanvi Kumar, a fourth-year Biotechnology and Biochemical Engineering undergraduate at IIT Kharagpur, received her grant to study cellular reprogramming and cancer cell-state transitions.
Samanvya Hooda received his grant for nuclear strategy research in the Indo-Pacific using Mandarin sources and quantitative methods.
Pradyumn Chaudhary received his grant to build a low-cost, customizable augmented-reality prototype for practical use.
Jay Handa, 16, received his grant to build SMAC (Swing Match AI for Coaches), an AI-powered golf swing analysis platform for coaches.
Sajeeva Ratnam Nepala, 28, founder of Helen Bikes, received his grant to develop a series-hybrid pedal system for the first fully electric hubless bicycle.
Hitendra Singh, cofounder of Spantrik, received his grant to develop reusable rockets, including the Leapfrogger demonstrator; he has built and flown over 100 high-powered rockets.
Avi Mohan Kumar Shuklaa, 17, received his grant for a solar-powered AI elephant deterrent using seismic and radar sensor fusion, already field-deployed in Jharkhand.
Surya Uthkarsha, 14, received his grant for Flake AI, infrastructure that converts websites into editable designs and reflects changes in the underlying codebase.
Kritarth Agrawal received his grant for Sakshya, an AI-powered document-intelligence platform for India’s compliance and regulatory ecosystem.
Tejas Pugalia, 13, received his grant to advance his research on the Ramanujan-tau function and prepare for the International Mathematical Olympiad.
Arnav, 17, from Navi Mumbai, received his grant to build a $200 diagnostic pen intended as an alternative to a $3,000 Parkinson’s scan.
Arjan Chaudhary, 15, received his grant for GGameChamps, which structures competitive-gameplay data for analytics and AI use.
Satadru Chakrabarty, Ph.D., received his grant to develop an affordable multiplexed point-of-care diagnostic using de novo antibodies for liver-function testing in rural India.
Varun Bajpai received his grant for the Biological Data Network (BDN-04), a bio-integrated communication architecture exploring human muscle tissue for data transmission.
Semil Vaghasiya, 24, received his grant to develop sensor-free, 3D-printed hoof pads that use wear-deformation to predict equine injuries.
Kanukant Om Gupta and Mansi Gupta received their grant for Clearquik, to make customs clearance in India simpler, more transparent, and legitimate.
Parth Anand, 15, received his grant for research on temporal hallucinations in large language models across Western and non-Western domains. His paper was accepted for an oral presentation by the Society for Study for Artificial Intelligence and Simulation Behaviour XTAI symposium.
Shadman Sakib received his grant for youth development, communication, practical career guidance, and accessible learning initiatives in Bangladesh; his content reaches over 160,000 subscribers.
Sk Reezaal Arafat, 16, received his grant for Project SATARK, a decentralized agricultural fire-interception grid combining AI, IoT sensors, and a local response network in rural West Bengal.
Manan Gupta received his grant for Artemis, a proactive AI layer that lets users control their digital life through a phone call.
Srijal Dutta received his grant for ZSMC Co., an independent research initiative working across emerging technologies and engineering.
Those unfamiliar with Emergent Ventures can learn more here and here. The EV India announcement is here. More about the winners of EV India second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth, thirteenth, fourteenth, and fifteenth , sixteenth, seventeenth and eighteenth cohorts. To apply for EV India, use the EV application, click the “Apply Now” button and select India from the “My Project Will Affect” drop-down menu.
And here is Nabeel’s AI engine for other EV winners. Here are the other EV cohorts.
If you are interested in supporting the India tranche of Emergent Ventures, please write to me or to Shruti at [email protected].
Scott Beaulier interviews me, in part about Wyoming
Those new service sector jobs
Horwitz is in the business of playing a version of mom for local college students. Concierge companies offering student support have existed for decades. But in recent years, a new crop of upstarts — such as Horwitz’s company, MindyKnows; the Bama Mama in Alabama; the GA Mom in Georgia; and Campus Mom in Texas — have met additional demand from a new generation of worried parents…
The specific services vary here and there, but share commonalities. Campus Mom offers “holistic wellness check-ins,” laundry services and sorority recruitment support packages, sent to the sisters to up a child’s odds of acceptance. Carrie Eckhardt, the Bama Mama, will clean students’ dorm rooms and check in if parents haven’t heard from their child in a few days (“just pop in and say hi, and take a picture and send it to their mom”)…
Horwitz, for her part, brings students balloons on their birthdays and chicken soup when they’re sick, sits with them in the emergency room and picks up their prescriptions if they’re busy. She bakes homemade challah, coordinates with the bedbug exterminator, texts photos and updates to faraway parents and doles out recommendations on the best local doctors and landlords.
Here is more from Kristy Alpert at the NYT.
Is it the screens? Or education systems?
The Dark Ages implies television and phones are the main cause of cognitive decline. This fails to explain the patterns in PISA scores. Why did England and Scotland fall so precipitously from 2000 to 2005 whilst America improved? Why did England and Estonia hold steady after 2015 whilst most other OECD countries declined? How have Singapore, Taiwan, Japan avoided decline altogether?
A better explanation is that a country’s education system is more important than its television diffusion.1 East Asian PISA and IQ scores have probably remained constant, or even risen, because of their rigorous education systems and intensive tutoring cultures. The two European countries which avoid PISA-malaise – Estonia and England – have more rigorous education systems than their neighbours. They (more or less) use the knowledge-rich curricula, direct instruction, and systematic phonics – techniques which their more progressive neighbours abandoned between 1975-1990.
Here is much more from Alexander Thompson, recommended.
Diversity Is Our Strength?
Diversity is our strength is a common motto. Indeed it is one of GMU’s core values but what is the scientific evidence for this thesis? A new scoping review:
Recent years have witnessed many strong claims that ‘diversity’ leads to more original and impactful science, which is a science-focused form of what we call “The Diversity Hypothesis.” However, what evidence supports the claim that diversity enhances scientific output or impact? This pre-registered rapid scoping review seeks to collate and evaluate the scientific evidence for the Diversity Hypothesis…
…Based on over 100 scientific articles, we find that only between 15% and 28% of results reported in the literature are consistent with the hypothesis, with the balance of the results not being consistent with it.
…Overall, the results of the analysis section indicate that there is little empirical evidence that diversity improves scientific output and/or impact. In fact, with the possible exception of disciplinary diversity—a type of informational or viewpoint diversity operationalized at the team level—the majority of the evidence seems to point in the other direction. These findings are robust regardless of how the data is parsed and analyzed. The same conclusions can be drawn looking at the full data set, only the population-adjusted results, or only the results of high quality based on the MMAT analysis.
Hat tip: Colin Wright.