Category: Education
Parents should believe in upward mobility
There is a new paper on this topic, with multiple authors by led by Rebecca Ryan. Here is the abstract:
Research in economics and psychology shows that individuals are sensitive to cues about economic conditions in ways that affect attitudes, beliefs, and behavior. We provide causal evidence that parents’ beliefs about economic mobility prospects shape parental investments of time and money in children. To do so we conduct an on-line information experiment with ~ 1,000 socioeconomically diverse parents of children ages 5-15. The information treatment aimed to manipulate parents’ beliefs in the possibility for future upward (downward) economic mobility in US society. The experimental results yield three conclusions. First, parents are highly sensitive to signals about future economic mobility prospects. Second, parents who are induced to believe in the likely possibility of future upward mobility increase their beliefs about the return on their own investments of time and money. Using a novel measure of time investment we developed, these parents also increase their time investments in the service of boosting children’s skill. Finally, they report being more willing to pay for resources that would boost their child’s skill development. Third, these patterns are true for economically advantaged and disadvantaged families alike. We discuss the implication of these results in terms of reports showing that Americans are losing faith in “The American Dream.”
No, researchers should not lie, but perhaps this gives some additional perspective on who exactly is harming the world. There can be a cost to publishing neurotic, untrue ideas.
Via the excellent Kevin Lewis.
How badly do humans misjudge AIs?
We study how humans form expectations about the performance of artificial intelligence (AI) and consequences for AI adoption. Our main hypothesis is that people project human-relevant problem features onto AI. People then over-infer from AI failures on human-easy tasks, and from AI successes on human-difficult tasks. Lab experiments provide strong evidence for projection of human difficulty onto AI, predictably distorting subjects’ expectations. Resulting adoption can be sub-optimal, as failing human-easy tasks need not imply poor overall performance in the case of AI. A field experiment with an AI giving parenting advice shows evidence for projection of human textual similarity. Users strongly infer from answers that are equally uninformative but less humanly-similar to expected answers, significantly reducing trust and engagement. Results suggest AI “anthropomorphism” can backfire by increasing projection and de-aligning human expectations and AI performance.
That is from a new paper by Raphael Raux, job market candidate from Harvard. The piece is co-authored with Bnaya Dreyfuss.
How well does bar exam performance predict subsequent success as a lawyer?
Eh:
How well does bar exam performance predict lawyering effectiveness? Is performance on some components of the bar exam more predictive? The current study, the first of its kind to measure the relationship between bar exam scores and a new lawyer’s effectiveness, evaluates these questions by combining three unique datasets—bar results from the State Bar of Nevada, a survey of recently admitted lawyers, and a survey of supervisors, peers, and judges who were asked to evaluate the effectiveness of recently-admitted lawyers. We find that performance on both the Multistate Bar Examination (MBE) and essay components of the Nevada Bar have little relationship with the assessed lawyering effectiveness of new lawyers, calling into question the usefulness of these tests.
Here is the full article by Jason M. Scott, Stephen N. Goggin, and David Faigman. Via the excellent Kevin Lewis.
Difficult to pronounce names
We test for labor market discrimination based on an understudied characteristic: name fluency. Analysis of recent economics PhD job candidates indicates that name difficulty is negatively related to the probability of landing an academic or tenure-track position and research productivity of initial institutional placement. Discrimination due to name fluency is also found using experimental data from prior audit studies. Within samples of African Americans (Bertrand and Mullainathan 2004) and ethnic immigrants (Oreopoulos 2011), job applicants with less fluent names experience lower callback rates, and name complexity explains roughly between 10 and 50 percent of ethnic name penalties. The results are primarily driven by candidates with weaker résumés, suggesting that cognitive biases may contribute to the penalty of having a difficult-to-pronounce name.
That is from a new AEJ piece by Qi Ge and Stephen Wu.
Human Capital Accumulation in China and India in 20th Century
By Nitin Kumar Bharti and Li Yang:
Abstract. The education system of a country is instrumental in its long-run development. This paper compares the historical evolution of the education systems in the two largest emerging economies- China and India, between 1900 and 2018. We create a novel time-series data of educational statistics related to enrolment, graduates, teachers and expenditure based on historical statistical reports. China adopted a bottom-up approach in expanding its education system, compared to India’s top-down approach in terms of enrolment. While India had a head-start in modern education, it has gradually been overtaken by China- at Primary education in the 1930’s Middle/Secondary level in the 1970s and Higher/Tertiary level in the 2010s. It resulted in the lower cohort-wise average education and higher education inequality in India since 1907. Vocational education is a central component of the Chinese education system, absorbing half of the students in higher education. In India, the majority of the students pursue traditional degree courses (Bachelors, Masters etc.), with 60% in Humanities courses. Though India is known as the “land of engineers”, China produces a higher share of engineers. We conjecture that the type of human capital in China through engineering and vocational education helped develop its manufacturing sector. Utilizing micro-survey data since the 1980s, we show that education expansion has been an inequality enhancer in India. This is due to both the unequal distribution of educational attainment and higher individual returns to education in India.
Interesting throughout, via Pseudoerasmus.
Higher education is getting cheaper
That is the topic of my latest Bloomberg column, here is one excerpt:
There are a lot of numbers, but here is the comparison I find most impressive: Adjusting for grants, rather than taking sticker prices at face value, the inflation-adjusted tuition cost for an in-state freshman at a four-year public university is $2,480 for this school year. That is a 40% decline from a decade ago…
As might be expected, the trajectory for student debt is down as well. About half of last year’s graduates had no student debt. In 2013, only 40% did. That famous saying from economics — if something cannot go on forever, it will stop — is basically true. Due to changes in the formula, aid for Pell Grants is up, which helps to limit both student debt and the expenses of college.
Is quality going down? Probably a bit, but with a caveat:
,,,various adjustments kick in to limit the scope of the potential damage. Rather than cutting classes in computer science, a university might decide (as mine did) not to field a football team. Or a school might rely less on full-time professors and more on adjuncts. That is often a negative, but again schools can and do adjust, for instance by paying their adjuncts more and putting more effort into finding and keeping the good ones. A school might also reduce courses that attract few students and put more emphasis on subject areas with high enrollments.
Granted, none of this is ideal. But such adjustments can keep much of the damage at manageable levels. Many schools also are easing off their DEI bureaucracies.
And students will make adjustments of their own. If their classes give them less than what they want, they may turn more to the internet — to online education or, these days, AI. To argue that a large-language model is not as good as a professor is to miss the point. These innovations only have to make up some of the marginal deteriorations of quality.
With apologies to Peter Thiel, I believe U.S. higher education is going to muddle through.
Increasing the Supply of Very High-IQ Workers
I have argued that there are on the order of just 164 thousand very high-IQ workers in the United States. How do we get more? Ian Calaway on the job market from Stanford has an interesting paper arguing that early math mentors can be a force multiplier for students with superior math abilities. Calaway estimates that having a math mentor at a school, someone who runs a math club and organizes entry into top math competitions, increases the number of students earning PhDs and pursing careers as scientists and professors. Not every school has such a math mentor but Calaway estimates (after taking into account underlying abilities, he’s not naive) that over 27 years, math mentors identified 9,092 American Math Competitions students (the cream of the crop) but there were 11,168 missing students of very high ability.
These 11,168 additional students represent the missing exceptional math talents who would have participated in the AMC and been identified as exceptional if they had access to a mentor…these mentors would have increased the number of these students attending selective universities (3,017 students), majoring in STEM (3,465 students), earning PhDs (1,652 students), and pursuing careers as scientists and professors (1,850 students) during this twenty-seven year period.
11,168 missing students of very high ability over 27 years may not sound like much but we are talking about the very top talent level. A footnote illustrates:
Sergey Brin (Google), Mark Zuckerberg (Meta), Peter Thiel (PayPal), and Sam Altman (OpenAI) were all top AMC scorers (Committee on the American Mathematics
Competitions, 1980–2023)
High-IQ individuals don’t simply vanish without mentorship; they likely still have decent careers. However, even if you are skeptical about the social value of earning a PhD, the number of mentored individuals who go on to start firms or earn patents appears substantial. Just as athletic talent can wither without guidance, it seems that intellectual talent may also be underutilized without proper mentorship, with many high-IQ individuals failing to reach their full potential.
Emergent Ventures winners, 38th cohort
Sandro Luna, Austin, easier ways of getting blood pressure readings.
Divyan Bavan, Ontario, 17, machine learning for biology.
Michael Domarkas, 17, Surrey, UK, general career support for the biosciences.
Saras Agrawal, 17, Alberta, AI to monitor heart attack risk.
Charmaine Lee, NYC, music composition and performance.
Jodi Ettenberg, Ottawa, podcast on how to deal with adversity.
Jiya Singhal, Stavanger, Norway, high school, AI to detect skin cancer.
Janine Leger, Texas, for building full-time communities around the globe.
Rishi Mehta, Toronto, a device to limit falls of the elderly.
Ivan Lin, Sydney, 16, travel grant to the Bay Area.
Fearghal Desmond and Ryan Morrissey, Cork and Limerick, Induct, and a travel grant to SF.
Filip Cerny, 18, Prague, general career support, building out entrepreneurship in Czechia.
James Vitali, London, to write a book about the political future of the UK, general career support.
Harry Law, Cambridge, UK, historian of science, to write a history of AI.
Joshua Muthu, Warwick, UK, economic models of cities and building.
Kyla Scanlon, Venice, CA, to produce content on economics, including a new documentary, and also for travel support.
Pieter Garicano, WDC and Europe, general career support, writing on Europe, progress, and technology.
Ukraine cohort:
Nazar Drugov, Cambridge, MA, and MIT, and Ukraine, 17, to make Khanmigo fully functional in Ukrainian.
Aleksandra Peeva, Berlin, to study Russian sanctions.
Maria Marinichenko, physics and math instruction in Ukrainian for Ukrainians.
Anastasiya Dobrobabenko, STEM education for a school near Kyiv.
The Immigration Rap Battle
From the team that brought you Hayek v. Keynes we have the immigration rap battle featuring “George Borjas,” “Garett Jones” and “Stephen Miller” on team build the wall and “Bryan Caplan” and “Alex Nowrasteh” on open the border. I wouldn’t say the actors (AI?), look very much like their real world counterparts but much respect to the author of the rap lyrics who has brilliantly captured the essence of the ideas economically and thematically.
The evolution of nepotism in academia, 1088-1800
We have constructed a comprehensive database that traces the publications of father–son pairs in the premodern academic realm and examined the contribution of inherited human capital versus nepotism to occupational persistence. We find that human capital was strongly transmitted from parents to children and that nepotism declined when the misallocation of talent across professions incurred greater social costs. Specifically, nepotism was less common in fields experiencing rapid changes in the knowledge frontier, such as the sciences and within Protestant institutions. Most notably, nepotism sharply declined during the Scientific Revolution and the Enlightenment, when departures from meritocracy arguably became both increasingly inefficient and socially intolerable.
That is from a new paper by David de la Croix & Marc Goñi. Via the excellent Kevin Lewis.
Principles of Economics Textbooks and the Market for Ice Cream
Rey Hernández-Julián and Frank Limehouse writing in the Journal of Economics Teaching write that very few principles of economics textbooks deal with modern information and digital tech industries:
The main takeaways of our review are highlighted by two stand-alone textboxes found in Mankiw’s (2023) textbook. This textbook has been regarded as one of the most dominant players in the principles of economics textbook market for over 20 years. In the introductory chapter of the 10th Edition (2023), “Ten Principles of Economics” there is a stand-alone textbox with the Netflix logo with the following caption: “Many movie streaming services set the marginal cost of a movie equal to zero”. However, there is no further explanation of this statement in the chapter and no presentation of the concept of zero marginal cost pricing in the remainder of the entire textbook. In Chapter 2 (“Thinking Like an Economist”), there is an In the News article from the New York Times, “Why Tech Companies Hire Economists”, but very little coverage in the text on how to apply microeconomic concepts to the tech industry. These two discussions of the tech industry in Mankiw’s text exemplify many of our findings from other texts….updated examples from the modern economy seem to be afterthoughts and detached from the central discussion of the text.
…There are some notable exceptions. The most significant coverage of these questions is in Chapter 16 of Cowen and Tabarrok’s Modern Principles of Microeconomics, 5th edition (2021). In this chapter, the authors discuss platform service providers, such as Facebook, Amazon, Google, Visa, and Uber, and the role they play in competing “for the market,” instead of “in the market.” They also discuss why the prevailing product is not necessarily the best one, how music is a network good, and why these platform services may give away goods for ‘free’.
I would also point out that our example of a constant-cost industry (flat long-run supply curve) is domain name registration! As we write in Modern Principles:
Now consider what happens when the demand for domain names increases. In 2005, there were more than 60 million domain names. Just one year later, as the Internet exploded in popularity, there were more than 100 million domain names. If the demand for oil nearly doubled, the price of oil would rise dramatically, but despite nearly doubling in size, the price of registering a domain name did not increase…the expansion of old firms and the entry of new firms quickly pushed the price back down to average cost.
In short, it’s called Modern Principles for a reason! Tyler and I are committed to keeping up with the times and not just adding the occasional box and resting on our laurels.
See Hernández-Julián and Limehouse for some further examples of how to introduce modern industries into principles of economics.
What should I ask Paula Byrne?
Paula Jayne Byrne, Lady Bate…is a British biographer, novelist, and literary critic.
Byrne has a PhD in English literature from the University of Liverpool, where she also studied for her MA, having completed a BA in English and Theology at West Sussex Institute of Higher Education (now Chichester University).
Byrne is the founder and chief executive of a small charitable foundation, ReLit: The Bibliotherapy Foundation, dedicated to the promotion of literature as a complementary therapy in the toolkit of medical practitioners dealing with stress, anxiety and other mental health conditions. She is also a practicing psychotherapist, specializing in couples and family counseling.
Byrne, who is from a large working-class Roman Catholic family in Birkenhead, is married to Sir Jonathan Bate, Shakespeare scholar and former Provost of Worcester College, Oxford
Her books cover Jane Austen, Mary Robinson, Evelyn Waugh, Barbara Pym, JFK’s sister, two novels, and her latest is a study of Thomas Hardy’s women, both in his life and in his fiction, namely Hardy’s Women: Mother, Sister, Wives, Muses. Here is her home page. Here is Paula on Twitter.
Science and politics podcast
From the Institute for Progress, here is the link, the participants were Caleb Watney, Dylan Matthews, Alexander Berger, and myself. Excerpt:
Tyler Cowen: I would stress just how decentralized science funding is in the United States. The public universities are run at the state level. We have tax incentives for donations where you have to give to a nonprofit, but there’s otherwise very little control over what counts as a viable nonprofit.
One specific issue that I think has become quite large is how much we run our universities through an overhead system. On federal grants and many other kinds of grants, an overhead is charged. The overhead rates are very high, and well above what the actual marginal overhead costs.
You might think that’s a crazy system, and in some ways it is crazy. It means there’s intense pressure on professors to bring in contracts, regardless of the quality of the work. That’s clearly a major negative. Everyone complains about this.
But the hidden upside is that when universities fund themselves through overhead, there’s a kind of indirect free speech privilege because they can spend the overhead how they want. Now, I actually think they are violating the implicit social contract right now by spending the overhead poorly. But for a long while, this was why our system worked well. You had very indirect federal appropriations: some parts of which went to science, other parts of which went to education. It was done on a free speech basis.
But like many good systems, it doesn’t last forever. It gets abused. If we try to clean up the mess — which now in my view clearly is a mess — well, I’m afraid we’ll get a system where Congress or someone else is trying to dictate all the time how the funds actually should be allocated.
That’s a question I’ve thought through a good amount: how or whether we should fix the overhead system? I feel we’ve somehow painted ourselves into a corner where there is no good political way out in any direction. But I think you’ll find case by case that the specifics are really going to matter.
Dylan Matthews: Let’s get into some of the specifics. Do you have an example of the overhead system breaking down that is motivating for you here?
Tyler Cowen: Well, universities are spending more and more of their surplus on staff and facilities — on ends that even if you think they’re defensible in some deep sense like “Oh, we need this building,” it’s about the university. It’s about what leads to long run donations, but it’s seen as a violation of public trust.
The money is neither being spent on possibly useful research, nor educating students. The backlash against universities is huge, most of all in Florida, Texas, and North Carolina. It seems to me that where we are at isn’t stable. How we fund science through universities is, in some ways, collapsing in bad ways. The complaints are often justified, but odds are that we’ll end up with something worse.
Recommended, interesting throughout.
What predicts success in science?
How does a person’s childhood socioeconomic status (SES) influence their chances to participate and succeed in science? To investigate this question, we use machine-learning methods to link scientists in a comprehensive biographical dictionary, the American Men of Science (1921), with their childhood home in the US Census and with publications. First, we show that children from low-SES homes were already severely underrepresented in the early 1900s. Second, we find that SES influences peer recognition, even conditional on participation: Scientists from high-SES families have 38% higher odds of becoming stars, controlling for age, publications, and disciplines. Using live-in servants as an alternative measure for SES confirms the strong link between childhood SES and becoming a star. Applying text analysis to assign scientists to disciplines, we find that mathematics is the only discipline in which SES influences stardom through the number and the quality of a scientist’s publications. Using detailed data on job titles to distinguish academic from industry scientists, we find that industry scientists have lower odds of being stars. Controlling for industry employment further strengthens the link between childhood SES and stardom. Elite undergraduate degrees explain more of the correlation between SES and stardom than any other control. At the same time, controls for birth order, family size, foreign-born parents, maternal education, patents, and connections with existing stars leave estimates unchanged, highlighting the importance of SES.
That is from a new NBER working paper by Anna Airoldi and Petra Moser.
The Declining Relative Quality of the Child Care Workforce
Although it is widely acknowledged that high-skilled teachers are integral to service quality and young children’s well-being in child care settings, little is known about the qualifications and skills of the child care workforce. This paper combines data from multiple sources to provide a comprehensive assessment of the quality of individuals employed in the child care sector. I find that today’s workforce is relatively low-skilled: child care workers have less schooling than those in other occupations, they score substantially lower on tests of cognitive ability, and they are among the lowest-paid individuals in the economy. I also show that the relative quality of the child care workforce is declining, in part because higher-skilled individuals increasingly find the child care sector less attractive than other occupations. Furthermore, I provide evidence that at least three other factors may be associated with the decline in worker quality. First, the recent proliferation of community college programs offering child care-related certificates and degrees may divert students away from attending four-year schools. Second, those majoring in child care-related fields are negatively selected for their cognitive skills, thereby decreasing the quality of the child care labor pool. Third, I show that the increased availability of outside employment options for high-skilled women had a detrimental effect on the quality of the child care workforce.
That is from a new paper by Chris M. Herbst. Via the excellent Kevin Lewis.