Category: Science

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.

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 Alexandera 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.

What libertarianism has become and will become — State Capacity Libertarianism

Having tracked the libertarian “movement” for much of my life, I believe it is now pretty much hollowed out, at least in terms of flow.  One branch split off into Ron Paul-ism and less savory alt right directions, and another, more establishment branch remains out there in force but not really commanding new adherents.  For one thing, it doesn’t seem that old-style libertarianism can solve or even very well address a number of major problems, most significantly climate change.  For another, smart people are on the internet, and the internet seems to encourage synthetic and eclectic views, at least among the smart and curious.  Unlike the mass culture of the 1970s, it does not tend to breed “capital L Libertarianism.”  On top of all that, the out-migration from narrowly libertarian views has been severe, most of all from educated women.

There is also the word “classical liberal,” but what is “classical” supposed to mean that is not question-begging?  The classical liberalism of its time focused on 19th century problems — appropriate for the 19th century of course — but from WWII onwards it has been a very different ballgame.

Along the way, I believe the smart classical liberals and libertarians have, as if guided by an invisible hand, evolved into a view that I dub with the entirely non-sticky name of State Capacity Libertarianism.  I define State Capacity Libertarianism in terms of a number of propositions:

1. Markets and capitalism are very powerful, give them their due.

2. Earlier in history, a strong state was necessary to back the formation of capitalism and also to protect individual rights (do read Koyama and Johnson on state capacity).  Strong states remain necessary to maintain and extend capitalism and markets.  This includes keeping China at bay abroad and keeping elections free from foreign interference, as well as developing effective laws and regulations for intangible capital, intellectual property, and the new world of the internet.  (If you’ve read my other works, you will know this is not a call for massive regulation of Big Tech.)

3. A strong state is distinct from a very large or tyrannical state.  A good strong state should see the maintenance and extension of capitalism as one of its primary duties, in many cases its #1 duty.

4. Rapid increases in state capacity can be very dangerous (earlier Japan, Germany), but high levels of state capacity are not inherently tyrannical.  Denmark should in fact have a smaller government, but it is still one of the freer and more secure places in the world, at least for Danish citizens albeit not for everybody.

5. Many of the failures of today’s America are failures of excess regulation, but many others are failures of state capacity.  Our governments cannot address climate change, much improve K-12 education, fix traffic congestion, or improve the quality of their discretionary spending.  Much of our physical infrastructure is stagnant or declining in quality.  I favor much more immigration, nonetheless I think our government needs clear standards for who cannot get in, who will be forced to leave, and a workable court system to back all that up and today we do not have that either.

Those problems require state capacity — albeit to boost markets — in a way that classical libertarianism is poorly suited to deal with.  Furthermore, libertarianism is parasitic upon State Capacity Libertarianism to some degree.  For instance, even if you favor education privatization, in the shorter run we still need to make the current system much better.  That would even make privatization easier, if that is your goal.

6. I will cite again the philosophical framework of my book Stubborn Attachments: A Vision for a Society of Free, Prosperous, and Responsible Individuals.

7. The fundamental growth experience of recent decades has been the rise of capitalism, markets, and high living standards in East Asia, and State Capacity Libertarianism has no problem or embarrassment in endorsing those developments.  It remains the case that such progress (or better) could have been made with more markets and less government.  Still, state capacity had to grow in those countries and indeed it did.  Public health improvements are another major success story of our time, and those have relied heavily on state capacity — let’s just admit it.

8. The major problem areas of our time have been Africa and South Asia.  They are both lacking in markets and also in state capacity.

9. State Capacity Libertarians are more likely to have positive views of infrastructure, science subsidies, nuclear power (requires state support!), and space programs than are mainstream libertarians or modern Democrats.  Modern Democrats often claim to favor those items, and sincerely in my view, but de facto they are very willing to sacrifice them for redistribution, egalitarian and fairness concerns, mood affiliation, and serving traditional Democratic interest groups.  For instance, modern Democrats have run New York for some time now, and they’ve done a terrible job building and fixing things.  Nor are Democrats doing much to boost nuclear power as a partial solution to climate change, if anything the contrary.

10. State Capacity Libertarianism has no problem endorsing higher quality government and governance, whereas traditional libertarianism is more likely to embrace or at least be wishy-washy toward small, corrupt regimes, due to some of the residual liberties they leave behind.

11. State Capacity Libertarianism is not non-interventionist in foreign policy, as it believes in strong alliances with other relatively free nations, when feasible.  That said, the usual libertarian “problems of intervention because government makes a lot of mistakes” bar still should be applied to specific military actions.  But the alliances can be hugely beneficial, as illustrated by much of 20th century foreign policy and today much of Asia — which still relies on Pax Americana.

It is interesting to contrast State Capacity Libertarianism to liberaltarianism, another offshoot of libertarianism.  On most substantive issues, the liberaltarians might be very close to State Capacity Libertarians.  But emphasis and focus really matter, and I would offer this (partial) list of differences:

a. The liberaltarian starts by assuring “the left” that they favor lots of government transfer programs.  The State Capacity Libertarian recognizes that demands of mercy are never ending, that economic growth can benefit people more than transfers, and, within the governmental sphere, it is willing to emphasize an analytical, “cold-hearted” comparison between government discretionary spending and transfer spending.  Discretionary spending might well win out at many margins.

b. The “polarizing Left” is explicitly opposed to a lot of capitalism, and de facto standing in opposition to state capacity, due to the polarization, which tends to thwart problem-solving.  The polarizing Left is thus a bigger villain for State Capacity Libertarianism than it is for liberaltarianism.  For the liberaltarians, temporary alliances with the polarizing Left are possible because both oppose Trump and other bad elements of the right wing.  It is easy — maybe too easy — to market liberaltarianism to the Left as a critique and revision of libertarians and conservatives.

c. Liberaltarian Will Wilkinson made the mistake of expressing enthusiasm for Elizabeth Warren.  It is hard to imagine a State Capacity Libertarian making this same mistake, since so much of Warren’s energy is directed toward tearing down American business.  Ban fracking? Really?  Send money to Russia, Saudi Arabia, lose American jobs, and make climate change worse, all at the same time?  Nope.

d. State Capacity Libertarianism is more likely to make a mistake of say endorsing high-speed rail from LA to Sf (if indeed that is a mistake), and decrying the ability of U.S. governments to get such a thing done.  “Which mistakes they are most likely to commit” is an underrated way of assessing political philosophies.

You will note the influence of Peter Thiel on State Capacity Libertarianism, though I have never heard him frame the issues in this way.

Furthermore, “which ideas survive well in internet debate” has been an important filter on the evolution of the doctrine.  That point is under-discussed, for all sorts of issues, and it may get a blog post of its own.

Here is my earlier essay on the paradox of libertarianism, relevant for background.

Happy New Year everyone!

Which researchers really work long hours?

No, not work smart but put in what would appear to be lots of extra hours.  Why not measure who submits papers to journals in the off-work hours?:

Main outcome measures Manuscript and peer review submissions on weekends, on national holidays, and by hour of day (to determine early mornings and late nights). Logistic regression was used to estimate the probability of manuscript and peer review submissions on weekends or holidays.

Results The analyses included more than 49 000 manuscript submissions and 76 000 peer reviews. Little change over time was seen in the average probability of manuscript or peer review submissions occurring on weekends or holidays. The levels of out of hours work were high, with average probabilities of 0.14 to 0.18 for work on the weekends and 0.08 to 0.13 for work on holidays compared with days in the same week. Clear and consistent differences were seen between countries. Chinese researchers most often worked at weekends and at midnight, whereas researchers in Scandinavian countries were among the most likely to submit during the week and the middle of the day.

Emphasis added.  Get this, you lazy bastards:

The average probability of a manuscript being submitted at the weekend for both journals was 0.14, and for a peer review it was 0.18. Peer review submissions during holidays had average probabilities of 0.13 (The BMJ) and 0.12 (BMJ Open), which were higher than the probabilities for manuscripts of 0.08 (The BMJ) and 0.10 (BMJ Open).

For weekend paper submission, China appears to be at about 0.22, India at about 0.09, see Figure 1.  France, Italy, Spain, and Brazil all submit quite late in the afternoon, often a bit after 6 p.m.

That is from a new paper by Adrian Barnett, Inger Mewburn, and Sara Schroter.  They do not tell us when they submitted it, but I wrote this blog post a wee bit after 8 p.m.

Via Michelle Dawson.

Comparing meta-analyses and preregistered multiple-laboratory replication projects

Many researchers rely on meta-analysis to summarize research evidence. However, there is a concern that publication bias and selective reporting may lead to biased meta-analytic effect sizes. We compare the results of meta-analyses to large-scale preregistered replications in psychology carried out at multiple laboratories. The multiple-laboratory replications provide precisely estimated effect sizes that do not suffer from publication bias or selective reporting. We searched the literature and identified 15 meta-analyses on the same topics as multiple-laboratory replications. We find that meta-analytic effect sizes are significantly different from replication effect sizes for 12 out of the 15 meta-replication pairs. These differences are systematic and, on average, meta-analytic effect sizes are almost three times as large as replication effect sizes. We also implement three methods of correcting meta-analysis for bias, but these methods do not substantively improve the meta-analytic results.

That is from a new article in Nature Human Behavior by Amanda Kvarven, Eirik Strømland, and Magnus Johannesson.

Charles Murray’s *Human Diversity*

His new book is coming out in January, and the subtitle is The Biology of Gender, Race, and Class. I will get to the details shortly, but my bottom-line review is “Not as controversial as you might think,” but do note the normalization at the end of that phrase.

Here is one bit from p.294 toward the end of the book:

Nothing we are going to learn will diminish our common humanity.  Nothing we learn will justify rank-ordering human groups from superior to inferior — the bundles of qualities that make us human are far too complicated for that.  Nothing we learn will lend itself to genetic determinism.  We live our lives with an abundance of unpredictability, both genetic and environmental.

Most of the book defends ten key propositions, laid out on pp.7-8.  The first four of those propositions concern differences between men and women (“Sex differences in personality are consistent worldwide…”) and I do not find those controversial, so I will not cover them.  The chapters on those propositions provide a good survey of the evidence, and a good answer to the denialists, though I doubt if Murray is the right person to win them over.  Let’s now turn to the other propositions, with my commentary along the way:

5. Human populations are genetically distinctive in ways that correspond to self-identified race and ethnicity.

True, but Murray’s analysis did not push me beyond the usual citations of lactose intolerance, sickle cell anemia, adaptation to high altitudes, and the like.  That said, pp.190-195 offer a very dense discussion of target alleles for various traits, such as schizophrenia, and how those target alleles vary across different groups.  I found those pages difficult to follow, and also wished that discussion had been fifty pages rather than five.  Toward the end of that discussion, Murray does write (p.194): “…proof of the role of natural selection for many genetic differences will remain unobservable without methodological breakthroughs.”  With that I definitely agree.

On p.195 he adds “It is implausible to expect that none of the imbalances will yield evidence of significant genetic differences related to phenotypic differences across continental populations.”  That returns to my core point about this book not shifting my priors.  You could agree with that sentence (noting the ambiguity in the word “significant”) and still have a quite modest vision of what those differences might mean.  In any case, nothing in the book pushes me beyond that sentence in the direction of the geneticists.

And here the contrast with the chapters on men and women becomes (unintentionally?) glaring: those biological differences are relatively easy to demonstrate, so perhaps hard-to-demonstrate biological differences are not so significant.  That too is just a conjecture, but there are multiple ways to play the “absence of evidence” and “how to interpret the residuals” cards, and I wish those had received a more extensive philosophy of science-like discussion.

Now let’s move to the next proposition:

6. Evolutionary selection pressure since humans left Africa has been extensive and mostly local.

That one strikes me as a miswording or misstatement, though I do not see that it corresponds to any actual mistakes in the broader text.  You might think that general, non-local evolutionary selection for all humans has been quite large over the millennia, relative to local selection.  I genuinely do not know the ratio here, but Murray does not seem to address the actual comparison of “across all human groups” vs. “local” as loci of selection pressures.

Next up:

7. Continental population differences in variants associated with personality, abilities, and social behavior are common.

Clearly true, but note this proposition does not claim biological roots for those differences.  The real question comes in the next proposition:

8. The shared environment usually plays a minor role in explaining personalities, abilities, and social behavior.

Here I have what I think is a major disagreement with Murray.  If he means the term “shared environment” in the narrow sense used by say twin studies, he is probably correct.  But in the more literal, Webster-derived conception of “shared environment” I very much disagree.  Culture is a truly major shaper of our personalities, abilities, and social behavior, and self-evidently so. For my taste the book did not contain nearly enough discussion of culture and in fact there is virtually no discussion of the concept or its power, as a look at the index will verify.  The real lesson of “twins studies plus anthropology” is that you have to control almost all of a person’s environment to have a major impact, but a major impact indeed can be had.  I behave very differently than my Irish potato famine ancestors, and not because I am genetically 1/8 from the Madeira Islands.  That said, within the narrower range of environmental variation measured in twins studies…well those studies seem to be fairly accurate.

9. Class structure is importantly based on differences in abilities that have a substantial genetic component.

Correct as stated, but I see those differences as much less genetic than Murray does.  For instance, IQ is to some extent heritable, but how much does that shape economic outcomes?  It is worth turning to Murray’s discussion on p.232 and the associated footnote 17 (pp.428-429).  His main source is what is to me a flawed meta-study on IQ and job performance (Murray to his credit does also cite the best-known critique of such studies).  I would opt more directly for the labor market literature on IQ and individual earnings, based on actual measured wages, which shows fairly modest correlations between IQ and earnings (read here, here and here).  So, at the very least, the inherited IQ-based permanent stratification version of The Bell Curve argument is much more compelling to Murray than it is to me.

10. Outside interventions are inherently constrained in the effects they can have on personality, abilities, and social behavior.

Clearly this is literally true, if only because of the meaning of “constrained.”  But mostly I would repeat my remarks on culture from #8.  Cultures change, and over time they are likely to change a great deal.  For instance, early in the 20th century, Korea, Japan, and China often were described as low work ethic cultures.  As cultures change, in turn those cultures can shape the personalities, abilities, and social behaviors of subsequent generations, in significant ways albeit constrained.  So while Murray is correct as stated, I believe I would disagree with his intended substantive point about the weight of relative forces.

Overall this is a serious and well-written book that presents a great deal of scientific evidence very effectively.  Anyone reading it will learn a lot.  But it didn’t change my mind on much, least of all the most controversial questions in this area.  If anything, in the Bayesian sense it probably nudged me away from geneticist-based arguments, simply because it did not push me any further towards them.

Murray of course will write the book he wants to, but my personal wish list was two-fold: a) a book leaving most of the normal science behind, and focusing only on the uncertain and controversial frontier issues, in great detail, and b) much more discussion of the import of culture.

Most of all, I am happy that America’s culture of achievement is inducing Murray to continue to produce major works at the age of 76, soon to be 77.

You can pre-order here.

Work on these things

Here are some projects I’d like to see funded, some through my own ventures, or others through alternative mechanisms. On these issues, the right person could have an enormous impact, whether through the research side or directly coming up with actionable ideas, including of course creating and building companies.

More studies of super-effective people. Either individually or collectively. If you take the outliers in any domain, what should our intuitions be for understanding the underlying processes determining how many people could have ended up in those positions? How many people had the right genes but had the wrong upbringing? How many people had the right genes and the right upbringing but the wrong luck, or perhaps society failed them in some other manner? The answers to these questions have significant policy implications.

A comprehensive analysis and critique of the NIH and NSF. The US funds more science research than any other country — about $35 billion per year on the NIH and $8 billion per year on the NSF. How exactly do these institutions work? How have they changed over time and have these changes been for good or bad? Based on what we now know, how might we better structure the NIH and NSF? What experiments should we run or what kind of studies should we perform?

Why is life expectancy so long in Hong Kong? Life expectancy in Hong Kong is 84.23 years, more than five years longer than the US and the highest in the world. Hong Kong is not that wealthy (median household income is $38,000 USD); it’s somewhat polluted; people don’t obviously eat what seems like a healthy diet; and they don’t seem to exercise a great deal. What should we learn from this?

Bloomberg Terminal for everything. This might be a nonprofit, a company, or a government project. To state the obvious, many analyses hinge on having the right data. If you’re in finance, getting the right data is often easy: just pull it up on your Bloomberg terminal. But there is no practical way to ask “what most correlates with life expectancy in Hong Kong?” (See above on that topic.) Figure out a way to build a growing corpus of structured data across the broadest variety of domains.

A comprehensive guide to the American healthcare system. The American healthcare system is by far the world’s biggest and also by a considerable margin the world’s most influential. Yet there is no comprehensive, dispassionate, and analytical disaggregation of how it all works. Who are the actors and what are their incentives? To the degree that the relationships between different entities are in equilibrium, what are the forces ensuring they stay there? What is the Sankey diagram of fund flows within the U.S. healthcare system?

Better answers for how to quantify worker productivity. In most knowledge industries, companies have nothing better than highly subjective measures (i.e., supervisors’ assessments) of worker productivity. In theory, it seems significant improvements should be possible. In the short term, is it possible to measure the productivity or efficacy of individual managers, software engineers, educators, scientists? How about teams, and what size of team? And can we do so without creating Goodhart’s Law problems?

What should Widodo do? Indonesia is a large, populous middle-income country. It faces no major near-term security threats. It has a small manufacturing base and no major non-commodity export sectors. What is the best non-bureaucratic 10 page economic development briefing document and set of prescriptions that one could write for Indonesia’s president? For Indonesia, substitute Philippines, Chile, or Morocco. 

A comparative study of foundations and their efficacy. Philanthropic foundations are behind a lot of important work. But how does a foundation decide what it wants and how the resulting grants should be structured? How effective are the programs of that foundation? In practice, how have its institutional mechanisms evolved? Imagine some kind of resource that answered these questions for the major American foundations.

Institutional critiques. More broadly, there is no discipline of institutional criticism. There is a very rich literature of policy criticism in economics, journalism, and non-fiction books. There is also a rich literature of “corporate criticism”: there are thousands of articles about how Facebook (budget: $20 billion) works and how it might be good or bad. But there is relatively little analysis of the most important institutions in our society: government departments. How is the Department of Agriculture (budget: $150 billion) organized and how effective or not is it? How about the Department of Energy (budget: $32 billion)? And why are not those questions paramount in the minds of policymakers?

Cultures of excellence. If you ask informed Filipinos why the street food is mediocre, they will tell you that Philippines lacks a “culture of excellence”. It seems that some kind of “culture of doing things really well” has very persistent and generalizable effects. South Korea and Japan have developed much more rapidly than many Asian countries, despite many others adopting relatively free “Washington Consensus”-style trade policies. Russia still has higher GDP per capita than Mexico despite Mexico’s economic policies having been much better than Russia’s for many, many decades at this point. How should we think about cultures of excellence?

Regeneration at the government layer. Herbert Kaufman (unsurprisingly) concludes in an empirical study that government organizations don’t die. While we might all agree that this is a problem, actionable solutions are in short supply. What can or should we do about this?

IQ paradox. Ron Unz points out that intergenerational variation of IQ may be much higher than is often assumed, citing Ireland and Croatia as examples. For instance, not long ago Ireland had sub-par measured IQ and now that figure is much higher, following growth and prosperity. The policy implications of IQ disparities across nations may therefore be different to what might otherwise obviously follow: perhaps environment matters much more than is assumed. If so, what should we be doing more or less of?

Credible plans for new top-tier universities. 7 of the best 25 universities in the world (Times ranking) were started in the US between 1861 and 1891 by ambitious reformers. It’s probably harder in many ways to start an impactful new university today… but it’s likely not impossible and the returns to doing so successfully might be very high. What might be a good plan? Why have so few of these plans come to fruition? 

Summaries of the state of knowledge in different fields. As a general matter, a lot of oral knowledge in the world is still not readily available, and reflection on this fact might lead one in many interesting directions. One obvious application is helping people more readily understand the present state of affairs in different domains. If I want to know “how we’re doing” in, say, antiviral drug development, I could spend a few hours hunting for top researchers, email a few, and perhaps get on calls to obtain their candid assessments. Are we making good progress? What are the most important open problems? What’s holding things back? And so on. How can we make all of this knowledge publicly available across all fields?

Mechanisms for better matching. One of the single interventions that could do the most to improve global welfare would be to improve the efficiency of the partner/marriage matching ecosystem. Online dating demonstrates that significant change (and maybe even improvement?) is possible, with some figures suggesting that up to two thirds of relationships in the US may now be initiated through online dating services. Accomplished people often seem to struggle with this challenge. Good solutions would be important.

What should Durkan do? Jenny Durkan is the current mayor of Seattle. As cities become more important loci of economic activity in the world, the importance of effective city governance will increase. As with the Widodo challenge, what is the best 10 page briefing document and set of prescriptions that one could write for her? What about Baltimore and St. Louis?