Nicholas Whitaker of Brown, general career development grant in the area of Progress Studies.
Coleman Hughes, travel and career development grant.
Michael T. Foster, career development grant to study machine learning to predict which politicians will succeed and advance their careers.
John Strider, a Progress Studies grant on how to reinvent the integrated corporate research lab.
Dryden Brown, to help build institutions and a financial center in Ghana, through his company Bluebook Cities.
Adaobi Adibe, to restructure credentialing, and build infrastructure for a more meritocratic world, helping workers create property rights in the evaluation of their own talent.
Jassi Pannu, medical student at Stanford, to study best policy responses to pandemics.
Vasco Queirós, for his work on a Twitter browser app for superior threading and on-line communication.
Chris, a loyal MR reader, writes to me:
I’ve been turning to your insights on prizes vs. grants over the years. Your Google talk from 2007 is without question the best discussion I’ve found of their respective merits…I was wondering if your thinking on prizes vs. grants has evolved, and in particular [TC has added the numbers here]:
1. In the Google talk, you talked about an equilibrium in which there would be a growing ecosystem of big prizes complementing one another. I’m not sure it has turned out this way. Do you agree, and what happened? Did the “failure” of some high profile prizes (e.g. the Google Lunar XPrize) dampen down the enthusiasm?
2. More generally, there seemed to be an expectation in the 2000s and early 2010s that prizes would take off and become a more significant feature of the R&D funding landscape. Again, I don’t think that has really happened. What explains that?
3. Looking specifically at government funding of R&D, do you think there is an equilibrium in which grants can coexist with prizes? Or do grants squeeze out prizes through some form of adverse selection (the best researchers opting for grants over prizes)?
4. How important do you think public choice reasons are for us being in a grant-dominated equilibrium? It seems that the science sector has done a great job of positioning itself as something other than an interest group, with its interests squarely aligned with the public good. (Even suggesting that the science sector is also an interest group seems slightly heretical. It’s interesting that Dominic Cummings, for all his radicalism, seems to see little need for any reform of the science/research ecosystem beyond ARPA).
First a general remark: I now see the current scientific (and cultural) establishment as having more implicit prizes than I used to realize. In fact, getting a grant is one of the biggest prizes you can receive, if the grant is sufficiently prestigious. By an “implicit prizes,” I mean a prize where the target achievement is not quite spelled out, but if “we” (however defined) judge you to have achieved enough, we will pour grants, status, and high quality social networks into your lap. For instance, Alex and I have received significant “prizes” for writing MR, although none of those prizes have names or bring explicit public recognition, as opposed to general recognition. We have in contrast never received a grant to write MR, so are prizes really so under-provided?
So my current thinking is a bit less “grants vs. prizes,” and somewhat more “implicit prizes vs. explicit prizes, each combined with grants to varying degrees.” Implicit prizes are more flexible, but they also are easier to cheat with, since the standard of achievement is never quite clear. Implicit prizes also are much more valuable to people who can use, build, and exploit their social networks, and of course that is not everyone (but shouldn’t we be giving more prizes to those people?). Implicit prizes also can be revoked through subsequent loss of status. Implicit prizes are more likely “granted” by the hands of social networks rather than judging panels, all of those features being both cost and benefit.
Now to the specific points:
1. As the venture capital ecosystem grows, and as the value of publicity rises (it is easier to monetize scientific and other sources of fame), and there are more “influencers in the broad sense,” there are more implicit prizes to be had. And did the Lunar XPrize fail? If an end is not worth accomplishing, a prize is one way to find that out.
2. In addition to my point about the proliferation of implicit prizes, the scientific, academic, and political communities are far too conservative in the literal sense of that word. How many top schools experiment with different tenure procedures? Different ways of running a department? It is sad how difficult it is to experiment with changes in academia and science, whether the topic be prizes or not.
3. The best researchers get both grants and prizes (one hopes).
By the way, here is a recent piece on the empirics of prizes, mostly positive results.
A torrent of data is being released daily by preprint servers that didn’t even exist a decade ago, then dissected on platforms such as Slack and Twitter, and in the media, before formal peer review begins. Journal staffers are working overtime to get manuscripts reviewed, edited, and published at record speeds. The venerable New England Journal of Medicine (NEJM) posted one COVID-19 paper within 48 hours of submission. Viral genomes posted on a platform named GISAID, more than 200 so far, are analyzed instantaneously by a phalanx of evolutionary biologists who share their phylogenetic trees in preprints and on social media.
“This is a very different experience from any outbreak that I’ve been a part of,” says epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health. The intense communication has catalyzed an unusual level of collaboration among scientists that, combined with scientific advances, has enabled research to move faster than during any previous outbreak. “An unprecedented amount of knowledge has been generated in 6 weeks,” says Jeremy Farrar, head of the Wellcome Trust…
The COVID-19 outbreak has broken that mold. Early this week, more than 283 papers had already appeared on preprint repositories (see graphic, below), compared with 261 published in journals. Two of the largest biomedical preprint servers, bioRxiv and medRxiv, “are currently getting around 10 papers each day on some aspect of the novel coronavirus,” says John Inglis, head of Cold Spring Harbor Laboratory Press, which runs both servers. The deluge “has been a challenge for our small teams … [they] are working evenings and weekends.”
We demonstrate empirically that measures of novelty are correlated with but distinct from measures of scientific impact, which suggests that if also novelty metrics were utilized in scientist evaluation, scientists might pursue more innovative, riskier, projects.
That is from Jay Bhattacharya and Mikko Packalen in a new NBER working paper and scientific innovation and stagnation.
They point out that Eugene Garfield, the scientist behind the development of citation count, did not think it should be used to evaluate individual scientists. Overall, citations encourage too much work in crowded, “approaching peak” areas, rather than developing new ideas. In lieu of citations, the authors suggest using textual analysis to determine how much a paper is building on new ideas rather than on already intensively explored ideas.
We have the transcript live on our Day One Project site: https://www.dayoneproject.org/cowen-kalil-transcript
Here was the video version, with some sound imperfections. And from Schmidt Futures:
…some context on the broader event is here, along with details on our open call for innovation, science, and tech policy ideas to inform the priorities of the next presidential term – your community undoubtedly would have great contributions. We are accepting submissions of these ideas through the Day One Accelerator until March 1.
I am very much looking to my Schmidt Futures event coming up this March.
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.
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.
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.
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.
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.
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.
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.
…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?
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.
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.
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.
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.”
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!
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.
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.