Category: Science
My Conversation with Tim Harford
Here is the transcript and audio, here is part of the summary:
Tim joined Tyler to discuss the role of popular economics in a politicized world, the puzzling polarization behind Brexit, why good feedback is necessary (and rare), the limits of fact-checking, the “tremendously British” encouragement he received from Prince Charles, playing poker with Steve Levitt, messiness in music, the underrated aspect of formal debate, whether introverts are better at public speaking, the three things he can’t live without, and more.
Here is one bit near the opening:
COWEN: These are all easy questions. Let’s think about public speaking, which you’ve done quite a bit of. On average, do you think extroverts or introverts are better public speakers?
HARFORD: I am an introvert. I’ve never seen any research into this, so it should be something that one could test empirically. But as an introvert, I love public speaking because I like being alone, and you’re never more alone than when you’re on the stage. No one is going to bother you when you’re up there. I find it a great way to interact with people because they don’t talk back.
COWEN: What other non-obvious traits do you think predict being good at public speaking?
HARFORD: Hmmm. You need to be willing to rehearse and also willing to improvise and make stuff up as you go along. And I think it’s hard for somebody to be willing to do both. I think the people who like to rehearse end up rehearsing too much and being too stiff and not being willing to adapt to circumstances, whereas the people who are happy to improvise don’t rehearse enough, and so their comments are ill formed and ill considered. You need that capacity to do both.
And another segment:
HARFORD: …Brian Eno actually asked me a slightly different question, which I found interesting, which was, “If you were transported back in time to the year 700, what piece of technology would you take — or knowledge or whatever — what would you take with you from the present day that would lead people to think that you were useful, but would also not cause you to be burned as a witch?”
COWEN: A hat, perhaps.
HARFORD: A hat?
COWEN: If it’s the British Isles.
HARFORD: Well, a hat is useful. I suggested the Langstroth beehive. The Langstroth beehive was invented in about 1850. It’s an enormously important technology in the domestication of bees. It’s a vast improvement on pre-Langstroth beehives, vast improvement on medieval beehives. Yet, it’s fairly straightforward to make and to explain to people how it works and why it works. I think people would appreciate it, and everybody likes honey, and people have valued bees for a long time. So that would have been my answer.
And:
COWEN: I’ve read all of your books. I’ve read close to all of your columns, maybe all of them in fact, and I’m going to ask you a question I also asked Reid Hoffman. You know the truths of economics, plenty of empirical papers. Why aren’t you weirder? I’ve read things by you that I disagreed with, but I’ve never once read anything by you that I thought was outrageous. Why aren’t you weirder?
The conversation has many fine segments, definitely recommended, Tim was in top form. I very much enjoyed our “Brexit debate” as well, too long to reproduce here, but I made what I thought was the best case for Brexit possible and Tim responded.
Reversing the STEM gender gap in Israel?
A new study compares Hebrew-speaking with some Arabic-speaking communities, here is the abstract:
In the past three decades in high‐income countries, female students have outperformed male students in most indicators of educational attainment. However, the underrepresentation of girls and women in science courses and careers, especially in physics, computer sciences, and engineering, remains persistent. What is often neglected by the vast existing literature is the role that schools, as social institutions, play in maintaining or eliminating such gender gaps. This explorative case study research compares two high schools in Israel: one Hebrew‐speaking state school that serves mostly middleclass students and exhibits a typical gender gap in physics and computer science; the other, an Arabic‐speaking state school located in a Bedouin town that serves mostly students from a lower socioeconomic background. In the Arabic‐speaking school over 50% of the students in the advanced physics and computer science classes are females. The study aims to explain this seemingly counterintuitive gender pattern with respect to participation in physics and computer science. A comparison of school policies regarding sorting and choice reveals that the two schools employ very different policies that might explain the different patterns of participation. The Hebrew‐speaking school prioritizes self‐fulfillment and “free‐choice,” while in the Arabic‐speaking school, staff are much more active in sorting and assigning students to different curricular programs. The qualitative analysis suggests that in the case of the Arabic‐speaking school the intersection between traditional and collectivist society and neoliberal pressures in the form of raising achievement benchmarks contributes to the reversal of the gender gap in physics and computer science courses.
The article is “Explaining a reverse gender gap in advanced physics and computer science course‐taking: An exploratory case study comparing Hebrew‐speaking and Arabic‐speaking high schools in Israel” by Halleli Pinson, Yariv Feniger, and Yael Barak.
Via the excellent Kevin Lewis.
*Seven Worlds, One Planet*
That is the new David Attenborough BBC nature show, available on streaming or buy the discs from the UK. Believe it or not it has better footage than the earlier BBC nature shows, while remaining inside the basic template of what such shows attempt to accomplish. Here is a very good Guardian review. Here is a somewhat snotty NYT review, bemoaning Attenborough’s tone of “polite optimism.” Strongly recommended.
*Leonhard Euler: Mathematical Genius of the Enlightenment*
By Ronald S. Calinger, what a beautiful book, clearly written, conceptual in nature, placing Euler in the broader history of mathematics, the funding of science, and the Enlightenment, all in a mere 536 pp. of text. Here is one bit:
At midcentury Leonard Euler was at the peak of his career. Johann I (Jean I) Bernoulli had saluted him as “the incomparable L. Euler, the prince among mathematicians” in 1745, and Henri Poincaré’s later description of him as the “god of mathematics” attests to his supremacy in the mathematical sciences. Euler continued to center his research on making seminal contributions to differential and integral calculus and rational mechanics, and producing substantial advances in astronomy, hydrodynamics, and geometrical optics; the state projects of Frederick II required attention especially to hydraulics, cartography, lotteries, and turbines. At midcentury, when d’Alembert and Alexis Claude Clairaut in Paris, Euler in Berlin, Colin Maclaurin in Scotland, and Daniel Bernoulli in Basel dominated the physical sciences, Euler was their presiding genius.
Nor had I known that Rameau sent his treatise on the fundamental mathematics of music to Euler for comments.
Definitely recommended, you can order it here.
Is it harder to become a top economist?
Mathis Lohaus writes to me:
Thanks for doing the Conversations. I greatly enjoyed Acemoglu, Duflo, and Banerjee in short succession after the Christmas break. Your question about “top-5 journals” and the bits about graduate training reminded of something I’ve had on my mind for a while now:
For the average PhD student, how hard is it to become a tenured economist — compared to 10, 20, 30, 40 … years ago? (And how about someone in the top 10% of talent/grit?)
Publication requirements have clearly become tougher in absolute terms. But how difficult is it to write a few “very good” papers in the first place? On twitter, people will sometimes say things like “oh, it must have been nice to get tenure back in 1997 based on 1 top article, which in turn was based on a simple regression with n = 60”. I wonder if that criticism is fair, because I imagine the learning curve for quantitative methods must have been challenging. And what about the formal models etc.? Surely those were always hard. (I vaguely remember a photo showing difficult comp exam questions…)
More broadly, early career scholars now have tons of data and inspiring research at their fingertips all the time. Also, nepotism and discrimination might be less powerful than in earlier decades…? On the other hand, you have to take into account that many more PhDs are awarded than ever before. I suspect that alone is a huge factor, but perhaps less acute if we focus only on people who “really, really want to stay in academia”.
A different way to ask the question: When would have been the best point in time to try to become an econ professor (in the USA)?
I would love to hear about your thoughts, and/or input from MR readers.
I always enjoy questions that somewhat answer themselves. I would add these points:
1. The skills of networking and finding new data sets are increasingly important, all-important you might say, at least for those in the top tier of ability/effort.
2. Fundraising matters more too, because the project might cost a lot, RCTs being the extreme case here.
3. Managing your research team matters much more, and the average size of research team for influential work is much larger. Once upon a time, three authors on a paper was considered slightly weird (the claim was one of them virtually always did nothing), now four is quite normal and the background research support is much higher as well.
Recently I was speaking to someone on the job market, wondering if he should be an academic. I said: “In the old days you spent a higher percentage of your time doing economics. Nowadays, you spend a higher percentage of your time managing a research team doing economics. You hardly do economics at all. So if you are mainly going to be a manager, why not manage for the higher rather than the lower salary?”
That was tongue in cheek of course.
On the bright side, learning today through the internet is so much easier. For instance, I find YouTube a good way to learn/refresh on new ideas in econometrics, easier than just trying to crack the final published paper.
What else?
Toward a more general theory of task complexity
That is a theme running throughout my latest Bloomberg column, here are some excerpts:
Why so many of America’s best and brightest college graduates go into management consulting, finance or law school is a perennial question. There are some compelling theories, which I will get to, but first I would like to turn the question around: Why are so many people in top positions, whether in the public or private sector, so old?
I submit that these two trends — and a third, declining productivity growth — are related: Many tasks have become increasingly complex in America, often more complex than people can learn in just a few years. By the time you have experience enough to perform them, you are less interested in taking risks. In your young adventurous years, by contrast, the only jobs you can get are those that don’t reward (or allow) adventure. The result of all this is a less audacious America.
And:
…the smart graduates of America’s top universities will seek relatively thick, liquid job markets, with high upside but also protection on the downside. Management consulting is perfect. If you are intelligent and hard-working, you can signal that quickly, and the entry-level tasks are sufficiently anodyne that few very specific skills are required. These jobs are designed to attract talent, so the consulting companies have an eventual option on promoting the best candidates. The same is true of law and the less quantitative parts of finance.
In the short term, this system seems to work for everyone. If you don’t like those vocations after a few years of trying, you still have elite connections and credentials that you can take somewhere else.
On net, America is selling its talented young people insurance value — but at the expense of long-term innovation. It might be better for the country if more of these individuals started businesses, tried their hand at chemistry or materials science, or worked in obscure corners of manufacturing in the Midwest. Of course, rates of failure or stagnation are higher in those areas, while glamour is often lower. Who wants to work on mastering a complex task for 10 or 15 years, with no real guarantee of commercial success?
And:
The slower rates of growth in scientific progress are part of this picture. Older scientists are more likely to be in charge, but they also make fewer conceptual breakthroughs. Younger scientists are more temperamentally inclined to be revolutionaries, but that is hard when it may take you until your late 20s just to learn the basics of your field. Most areas are too complex for a 23-year-old to make new scientific advances, no matter how brilliant he or she may be.
Tech of course is an exception. And please do note that de-bureaucratization could do a great deal to lower this task complexity, while other parts of it are inescapable — I didn’t have the space for that point in the column but will return to it and what might be done. Finally, I thank a number of people who contributed ideas and examples to my argument.
China, Texas fact of the day
When officials at the Texas A&M University System sought to determine how much Chinese government funding its faculty members were receiving, they were astounded at the results—more than 100 were involved with a Chinese talent-recruitment program, even though only five had disclosed their participation.
A plant pathologist at the Texas system, where the median annual salary for such scientists employed by the state is around $130,000, told officials that the researcher had been offered $250,000 in compensation and more than $1 million in seed money to start a lab in China through one of the talent programs. The researcher ultimately rejected the offer, according to the Texas system’s chief research security officer, Kevin Gamache, who led the recent 18-month review that has garnered praise from U.S. officials.
That is from Aruna Viswanatha and Kate O’Keeffe at the WSJ. As for Harvard:
Charles Lieber, a pioneer in nanotechnology, allegedly signed a contract with Chinese counterparts under which he would be paid around $50,000 a month, plus another $150,000 a year for personal expenses; he was also promised—and received—more than $1.5 million to establish a research lab at the Wuhan University of Technology, according to prosecutors.
He is specifically charged with deliberately lying to U.S. government investigators when asked if he received Chinese talent-plan funding, rather than simply omitting the information on forms.
Peter Thiel on the funding of science
At a keynote address at the Precision Medicine World Conference, Thiel argued for enabling riskier research grant-making via institutions such as the NIH, as well as abandoning the scientific staple of the double-blind trial and encouraging the U.S. FDA to further accelerate its regulatory evaluations. He said that these deficiencies are inhibiting the ability of scientists to make major advances, despite the current environment that is flooded with capital and research talent.
Make science great again?
“There’s a story we can tell about what happened historically in how processes became bureaucratized. Early science funding was very informal – DARPA’s a little bit different – but in the 1950s and 1960s, it was very generative,” said Thiel. “You just had one person [who] knew the 20 top scientists and gave them grants – there was no up-front application process. Then gradually, as things scaled, they became formalized.
“One question is always how things scale,” he continued. “There are certain types of businesses where they work better and better at bigger and bigger scales,” he said, pointing to big tech.. “And, if big tech is an ambiguous term, I wonder whether big science is simply an oxymoron.”
He then cited the success of major scientific programs – such as the development of the atomic bomb in the Manhattan Project, the Apollo space program and Watson and Crick’s discovery of DNA – that hinged on having “preexisting, idiosyncratic, quirky, decentralized scientific culture[s]” and were accelerated rapidly by a major infusion of cash.
And:
When I invest in biotech, I have a sort of a model for the type of person I’m looking to invest in,” said Thiel. “There’s sort of a bimodal distribution of scientists. You basically have people who are extremely conventional and will do experiments that will succeed but will not mean anything. These will not actually translate into anything significant, and you can tell that it is just a very incremental experiment. Then you have your various people who are crazy and want to do things that are [going to] make a very big difference. They’re, generally speaking, too crazy for anything to ever work.”
“You want to … find the people who are roughly halfway in between. There are fewer of those people because of … these institutional structures and whatnot, but I don’t think they’re nonexistent,” he continued. “My challenge to biotech venture capitalists is to find some of those people who are crazy enough to try something bold, but not so crazy that it’s going to be this mutation where they do 100 things differently.”
Here is the full story, via Bonnie Kavoussi.
Marginal Revolution University video for Anna Schwartz
It is excellent, one of my favorite MRU videos to date:
Here is some text from the release email:
The second episode of Women In Economics is out today! Join Harvard’s Claudia Goldin, UC Berkeley’s Christina Romer, and more on an insightful, engaging look at Anna Jacobson Schwartz’s life and achievements.
Did you know that Anna graduated from high school at 15?
Or that her dissertation couldn’t be published because of paper rationing during World War II? Yet despite this setback, she went on to coauthor one of the most important books about monetary policy and the Great Depression. Because of her work, she was hailed as one of the leading monetary economists of the 20th century by the end of her career!
We’re so excited to share Schwartz’s incredible story—click here to watch the video!
We’re also excited to announce our next video in our Women in Econ series, about Janet Yellen, will be released on March 8th. It will feature Yellen in her own words, along with Ben Bernanke and Christina Romer. Stay tuned!
Recommended.
How economics has changed
Panel A illustrates a virtually linear rise in the fraction of papers, in both the NBER and top-five series, which make explicit reference to identification. This fraction has risen from around 4 percent to 50 percent of papers.
And:
Currently, over 40 percent of NBER papers and about 35 percent of top-five papers make reference to randomized controlled trials (RCTs), lab experiments, difference-in-differences, regression discontinuity, event studies, or bunching…The term Big Data suddenly sky-rockets after 2012, with a more recent uptick in the top five.
Note that about one-quarter of NBER working papers in applied micro make references to difference-in differences. And:
The importance of figures relative to tables has increased substantially over time…
And about five percent of top five papers were RCTs in 2019. Note also that “structural models” have been on the decline in Labor Economics, but on the rise in Public Economics and Industrial Organization.
That is all from a recent paper by Janet Currie, Henrik Kleven, and Esmee Zwiers, “Technology and Big Data are Changing Economics: Mining Text to Track Methods.”
Via Ilya Novak.
Damir Marusic and Aaron Sibarium interview me for *The American Interest*
It was far-ranging, here is the opening bit:
Damir Marusic for TAI: Tyler, thanks so much for joining us today. One of the themes we’re trying to grapple with here at the magazine is the perception that liberal democratic capitalism is in some kind of crisis. Is there a crisis?
TC: Crisis, what does that word mean? There’s been a crisis my whole lifetime.
And:
TC: I think addiction is an underrated issue. It’s stressed in Homer’s Odyssey and in Plato, it’s one of the classic problems of public order—yet we’ve been treating it like some little tiny annoyance, when in fact it’s a central problem for the liberal order.
And:
AS: What about co-determination?
TC: There are too many people with the right to say no in America as it is. We need to get things done speedier, with fewer obstacles that create veto points. So no, I don’t favor that.
And:
AS: John Maynard Keynes.
TC: I suppose underrated. He was a polymath. Polymaths tend to be underrated, and Keynes was a phenomenal writer. I’m not a Keynesian on macroeconomics, but when you read him, it’s so fresh and startling and just fantastic. So I’d say underrated.
And:
AS: Slavoj Zizek, the quirky communist philosopher you debated recently.
TC: Way underrated. I had breakfast with Zizek before my dialogue with him, and he’s one of the 10 people I’ve met who knows the most and can command it. Now that said, he speaks in code and he’s kind of “crazy,” and his style irritates many people because he never answers any question directly. You get his Hegelian whatever. He has his partisans who are awful, but ordinary intellectuals don’t notice him and he’s pretty phenomenal actually. So I’d say very underrated.
Here is the full interview, a podcast version is coming too.
Big Data+Small Bias << Small Data+Zero Bias
Among experts it’s well understood that “big data” doesn’t solve problems of bias. But how much should one trust an estimate from a big but possibly biased data set compared to a much smaller random sample? In Statistical paradises and paradoxes in big data, Xiao-Li Meng provides some answers which are shocking, even to experts.
Meng gives the following example. Suppose you want to estimate who will win the 2016 US Presidential election. You ask 2.3 million potential voters whether they are likely to vote for Trump or not. The sample is in all ways demographically representative of the US voting population but potential Trump voters are a tiny bit less likely to answer the question, just .001 less likely to answer (note they don’t lie, they just don’t answer).
You also have a random sample of voters where here random doesn’t simply mean chosen at random (the 2.3 million are also chosen at random) but random in the sense that Trump voters are as likely to answer as are other voters. Your random sample is of size n.
How big does n have to be for you to prefer (in the sense of having a smaller mean squared error) the random sample to the 2.3 million “big data” sample? Stop. Take a guess….
The answer is…here. Which is to say that your 2.3 million “big data” sample is no better than a random sample of that number minus 1!
On the one hand, this illustrates the tremendous value of a random sample but it also shows how difficult it is in the social sciences to produce a truly random sample.
Meng goes on to show that the mathematics of random sampling fool us because it seems to deliver so much from so little. The logic of random sampling implies that you only need a small sample to learn a lot about a big population and if the population is much bigger you only need a slightly larger sample. For example, you only need a slightly larger random sample to learn about the Chinese population than about the US population. When the sample is biased, however, then not only do you need a much larger sample you need it to large relative to the total population. A sample of 2.3 million sounds big but it isn’t big relative to the US population which is what matters in the presence of bias.
A more positive way of thinking about this, at least for economists, is that what is truly valuable about big data is that there are many more opportunities to find random “natural experiments” within the data. If we have a sample of 2.3 million, for example, we can throw out huge amounts of data using an instrumental variable and still have a much better estimate than from a simple OLS regression.
How is Twitter disrupting academia?
Kris on Twitter asks that question. I have a few hypotheses, none confirmed by any hard data, other than my “lyin’ eyes”:
1. Twitter exists as a kind of parallel truth/falsehood mechanism, and it is encroaching on traditional academic processes, for better or worse.
2. Hypotheses blaming people or institutions for failures and misdeeds will be more popular on Twitter than in academia, but over time they are spreading in academia too, in part because of their popularity on Twitter. Blame makes for a more popular tweet.
3. Often the number of Twitter followers resembles a Power law, and thus Twitter raises the influence of very well known contributors. Twitter also raises the influence of the relatively busy, compared to say the 2009 world where blogs held more of that influence. Writing blog posts required more time than does issuing tweets.
4. I believe Twitter raises the relative influence of women. For one thing, women can coordinate with each other on Twitter more easily than they can in academic life across different universities.
5. Twitter can damage the career prospects of some of the more impulsive tweeting white males.
6. On Twitter is is easier to judge people by their (supposed) intentions than in academia, so many more people will be accused of acting and writing in bad faith.
7. On Twitter more people do in fact act in bad faith.
8. Hardly anyone looks better on Twitter, so that contributes to the polarization of many professions, especially economics and those professions linked to political issues. Top economists don’t seem so glamorous any more, not even in their areas of specialization.
9. Academic fields related to current events will rise in status and attention, and those topics will garner the Power law retweets. Right now that means political science most of all but of course this will vary over time.
10. Twitter lowers the power of institutions more broadly, as institutions typically are bad at Twitter.
What else?
Is scholarly refereeing productive at the margin?
No, basically:
In economics many articles are subjected to multiple rounds of refereeing at the same journal, which generates time costs of referees alone of at least $50 million. This process leads to remarkably longer publication lags than in other social sciences. We examine whether repeated refereeing produces any benefits, using an experiment at one journal that allows authors to submit under an accept/reject (fast-track or not) or the usual regime. We evaluate the scholarly impacts of articles by their subsequent citation histories, holding constant their sub-fields, authors’ demographics and prior citations, and other characteristics. There is no payoff to refereeing beyond the first round and no difference between accept/reject articles and others. This result holds accounting for authors’ selectivity into the two regimes, which we model formally to generate an empirical selection equation. This latter is used to provide instrumental estimates of the effect of each regime on scholarly impact.
That is from a new NBER paper by Aboozar Hadavand, Daniel S. Hamermesh, and Wesley W. Wilson. This is exactly the kind of work — critical, data-driven self-reflection about science — what Progress Studies wishes to see more of.
Emergent Ventures, sixth cohort
Sonja Trauss of YIMBY, assistance to publish Nicholas Barbon, A Defence of the Builder.
Parnian Barekatain. To fund her synthetic biology research at MIT Media Lab.
Anna Gát, for development as a public intellectual and also toward the idea and practice of spotting and mobilizing talent in others.
M.B. Malabu, travel grant to come to the D.C. area for helping in setting up a market-oriented think tank in Nigeria.
Eric James Wang and Jordan Fernando Alexander, a joint award for their work on the project Academia Mirmidón, to help find, mobilize, and market programming and tech talent in Mexico.
Gonzalo Schwarz, Archbridge Institute, for research and outreach work to improve policy through reforms in Uruguay and Brazil.
Nolan Gray, urban planner from NYC, to be in residence at Mercatus and write a book on YIMBY, Against Zoning.
Samarth Jajoo, an Indian boy in high school, to assist his purchase of study materials for math, computer science, and tutoring. Here is his new book gifting project.
One other, not yet ready to be announced. But a good one.
And EV winner Harshita Arora co-founded AtoB, a startup building a sustainable transportation network for intercity commuters using buses.
Here are previous MR posts on Emergent Ventures.