In this paper we merge individual income data, firm-level data, patenting data, and IQ data in Finland over the period 1988–2012 to analyze the returns to invention for inventors and their coworkers or stakeholders within the same firm. We find that: (i) inventors collect only 8 percent of the total private return from invention; (ii) entrepreneurs get over 44 percent of the total gains; (iii) bluecollar workers get about 26 percent of the gains and the rest goes to white-collar workers. Moreover, entrepreneurs start with significant negative returns prior to the patent application, but their returns subsequently become highly positive.
The reason I think polygenicity is important in this case is that it means there is a huge mutational target that natural selection has to keep an eye on. The constant production of new mutations in sperm and egg cells, the fact that so many of them could affect intelligence, and the fact that they will tend to do so negatively, should, in my opinion, make it harder to push intelligence consistently upwards, when new mutations will constantly be pulling it back down.
Again, I would argue this is a different situation to many other traits. For any trait, new mutations are likely to degrade, rather than improve, the developmental program and biological pathways underlying it. But for some traits, the “goal” of that program is to hit a species-optimal set point. Mutations affecting that program could mean you miss high or miss low – there’s no reason to expect to go one way or the other, really (as far as I can see).
For intelligence, following my argument above, the goal is to hit the maximal level possible. New mutations will thus not just replenish genetic variation affecting the trait (in either direction, as in standard models of stabilising selection); they will tend to push it downwards.
Now, maybe someone will tell me why that actually doesn’t matter, but it seems to me that this will tend to oppose any efforts of directional selection to push intelligence upwards in any given population. Whether that is true or not (or the size of the effect it could have) may depend on how much the trait is dominated by the effects of rare mutations. Various lines of evidence suggest that the collective influence of such mutations on intelligence is very substantial.
That is from Kevin Mitchell. I do not feel qualified to judge his claims, but nonetheless found the discussion of interest.
From Eric A. Posner and E. Glen Weyl, that was then:
Self-styled American and European radicals, for example, helped end monarchy and expand the franchise. The free-labor ideology of European radicals and American Radical Republicans helped abolish serfdom and slavery and establish a new basis for industrial labor relations. The late 18th and 19th centuries also witnessed the liberal reformism of Jeremy Bentham, Smith, James and John Stuart Mill, and the Marquis de Condorcet; the socialist revolutionary ideologies of Pierre-Joseph Proudhon and Marx; the labor unionism of Beatrice and Sydney Webb; and, influential at the time but now mostly forgotten, the competitive common ownership ideology of Henry George and Léon Walras. This ideology shaped the Progressive movement in the United States, the “New Liberalism” of David Lloyd George in Britain, the radicalism of Georges Clemenceau in France, even the agenda of the Nationalist Chinese revolutionary leader Sun Yat-Sen. The Keynesian and welfare-state reforms of the early 20th century set the stage for the longest and most broadly shared period of growth in human history.
And this is now:
So where are the heirs of the political economists? Political economy has fragmented into a series of disparate fields, none of which has the breadth, creativity, or courage to support the reformist visions that were crucial to navigating past crises.
…Yet even as economists retreated from visionary social theory, the power they wielded over detailed policy decisions grew. A notable feature of this policy guidance was that it shared the narrowness of economists’ research methods. Policy reforms advocated by mainstream economists were almost always what we call “liberal technocratic” — either center-left or center-right. Economists suggested a bit higher or lower minimum wage or interest rate, a bit more or less regulation, depending on their external political orientation and evidence from their research. But they almost never proposed the sort of sweeping, creative transformations that had characterized 19th-century political economy.
How to explain this timidity? As with many professions endowed with power (like the military), economics developed strict codes of internal discipline and conformity to ensure that this power was wielded consistent with community standards…
The upshot is that economics has played virtually no role in all the major political movements of the past half-century, including civil rights, feminism, anticolonialism, the rights of sexual minorities, gun rights, antiabortion politics, and “family values” debates.
There is much more at the link. I am not sure I have a single endorsement or criticism in response, other than to say that I view MR as, among other things, a fifteen-year running commentary on the economics profession and its ups and downs. In any case, beware complacency!
And do not forget about the authors’ new and stimulating book Radical Markets.
Hat tip goes to Bonnie Kavoussi.
My academic output for the semester: about 30 pages of reports for University committees, 36 pages of replies to referees and editors, 3 pages of data replication readmes, 12 new pages of online appendices, and net net MINUS 4 pages of research papers.
That is from the very smart Judy Chevalier.
1. Applied micro researchers have access to more data than ever and have access to more computing power and more easy to use and sophisticated econometric methods than ever before. Improved canned software in Stata allows applied researchers to relax many of the statistical assumptions that researchers made in previous years. Such “robust” estimates allow us to march towards learning the truth.
2. Due to “natural experiments”, discontinuities, and explicit randomizations, we now have more variation in “cause variables” (the X’s) than ever before.
3. The advent of Google and the rise of Economics in Europe and outside of Western nations means that the current set of applied micro researchers are aware in “real time” about what findings are emerging in the top 5 journals and NBER and IZA and CEPR working papers. Now that there are so many applied micro economists working around the world, this competition fosters innovation and progress.
4. Replication is rising as an important piece of our advance as a “science”.
5. Leading firms such as Amazon highly value quantitative training. Undergraduates are aware of this and they are investing in the math/computer programming and economics and stats training to have the option to pursue this. Some of these young people will opt into doing a PHD in economics and applied micro grows stronger due to this influx of talent.
6. Thanks to scholars such as Raj Chetty, the power of using administrative data (such as IRS tax data) are now more clearly seen all over the world. I expect that more government officials who “know that they do not know” the answers for unlocking economic development will increasingly partner with the J-PAL and other economists to help them to experiment and learn. This is Hayek as applied microeconomist at its best.
The rest of his post lists four concerns.
High skilled workers gain from face to face interactions. If the skilled can move at higher speeds, then knowledge diffusion and idea spillovers are likely to reach greater distances. This paper uses the construction of China’s high speed rail (HSR) network as a natural experiment to test this claim. HSR connects major cities, that feature the nation’s best universities, to secondary cities. Since bullet trains reduce cross-city commute times, they reduce the cost of face-to-face interactions between skilled workers who work in different cities. Using a data base listing research paper publication and citations, we document a complementarity effect between knowledge production and the transportation network. Co-authors’ productivity rises and more new co-author pairs emerge when secondary cities are connected by bullet train to China’s major cities.
That is from Xiaofang Dong, Siqi Zheng, and Matthew E. Kahn. Of course, supersonic air travel should be next…
What happens when a simulated system becomes more real than the system itself? Will the internet become “more real” than the world of ideas it is mirroring? Do we academics live in a simulacra? If the “alt right” exists mainly on the internet, does that make it more or less powerful? Do all innovations improve system quality, and if so why is a lot of food worse than before and home design was better in 1910-1930? How does the world of ideas fit into this picture?
By the end I considered whether we might make science better by first making it “worse.” I also covered The Phantom Tyler Cowen, and whether attempted refutation is the best way to approach a new idea.
…the workers wear caps to monitor their brainwaves, data that management then uses to adjust the pace of production and redesign workflows, according to the company.
The company said it could increase the overall efficiency of the workers by manipulating the frequency and length of break times to reduce mental stress.
Hangzhou Zhongheng Electric is just one example of the large-scale application of brain surveillance devices to monitor people’s emotions and other mental activities in the workplace, according to scientists and companies involved in the government-backed projects.
Concealed in regular safety helmets or uniform hats, these lightweight, wireless sensors constantly monitor the wearer’s brainwaves and stream the data to computers that use artificial intelligence algorithms to detect emotional spikes such as depression, anxiety or rage.
The technology is in widespread use around the world but China has applied it on an unprecedented scale in factories, public transport, state-owned companies and the military to increase the competitiveness of its manufacturing industry and to maintain social stability.
That is from STephen Chen at SCMP, via someone forgotten over at Twitter.
The author is Nick Chater and the subtitle is The Illusion of Mental Depth and the Improvised Mind. I found this to be one of the most interesting books on the mind I have read. Overall the message is that your hidden inner life ain’t what you think:
According to our common-sense view, the senses map the outer world into some kind of inner copy, so that, when perceiving a book, table or coffee cup, our minds are conjuring up a shadowy ‘mental’ book, table or coffee cup. The mind is a ‘mirror’ of nature. But this can’t be right. There can’t be a 3D ‘mental copy’ of these objects — because they don’t make sense in 3D. They are like 3D jigsaw puzzles whose pieces simply don’t fit together. The mind-as-mirror metaphor can’t possibly be right; we need a very different viewpoint — that perception requires inference.
Take that Thomas Reid! By the way:
This perspective has a further, intriguing and direct prediction: that we can only count colours slowly and laboriously…the apparent richness of colour is itself a trick — that our brains seem to be able to encode no more than one colour (or shape, or orientation) at a time. But this is what the data tell us.
Here is perhaps the clincher:
…all of us perceive the world through a remarkably narrow channel — roughly a single word, object, pattern or property at a time.
So much of the rest is the top-down processing function of our minds filling in the gaps.
By the way, if you are told to shake your head up and down, nodding in agreement, while reciting a plausible argument, you will assign a higher truth value to that claim. And emotion is more a “creation of the moment” rather than “an inner revelation.” If you cross a dangerous bridge to meet up with a woman, thus raising your adrenalin levels, you are more likely to develop a crush on her, that sort of thing.
I cannot evaluate all of the claims in this book, and indeed I am partly skeptical in light of the rather scanty treatment given to cross-sectional variation across heterogeneous individuals. Still, the author cites evidence for his major claims and applies reasonable and scientific arguments throughout. I can definitely recommend this book to those interested in serious popular science treatments of the mind, and it is not simply a rehash of other popular science books on the mind.
The top link above is for U.S. Amazon orders, due out in August, I was very happy to have ordered from AmazonUK.
I believe this book was first recommended to me by Tim Harford.
If you’re doing a specific therapy for a specific problem (as opposed to just trying to vent or organize your thoughts), studies generally find that doing therapy out of a textbook works just as well as doing it with a real therapist.
That is from Scott Alexander, who considers ways of saving money on mental health care.
Small numbers are processed on a linear scale, while large numbers are processed on a logarithmic scale. In this paper, we show that financial analysts process small prices and large prices differently. When they are optimistic (pessimistic), analysts issue more optimistic (pessimistic) target prices for small price stocks than for large price stocks. Our results are robust when controlling for the usual risk factors such as size, book-to-market, momentum, profitability and investments. They are also robust when we control for firm and analyst characteristics, or for other biases such as the 52-week high bias, the preference for lottery-type stocks and positive skewness, and the analyst tendency to round numbers. Finally, we show that analysts become more optimistic after stock splits. Overall, our results suggest that a deeply-rooted behavioral bias in number processing drives analysts’ return expectations.
That is from Tristan Roger, Patrick Roger, and Alain Schatt, via the excellent Kevin Lewis.
Here is the transcript and audio, and this is the intro:
Marc Andreessen has described Balaji as the man who has more good ideas per minute than anyone else in the Bay Area. He is the CEO of Earn.com, where we’re sitting right now, a board partner at Andreessen Horowitz, formerly a general partner. He has cofounded the company Counsyl in addition to many other achievements.
Here is one excerpt:
COWEN: Why is the venture capital model so geographically clustered? So much of it is out here in the Bay Area. It’s spreading to other parts of the country. Around the world, you see Israel, in some ways, as being number two, per capita number one. But that’s a very small country. Why is it so hard to get venture capital off the ground in so many areas?
SRINIVASAN: That’s actually now changed with the advent of ICOs and Ethereum and crypto. Historically, the reason for it was companies would come to Sand Hill Road. One maybe slightly less appreciated aspect is, if you come to Sand Hill Road and you get VC financing, the VC who invests in your company typically takes a board seat. A VC does not want to fly 6,000 miles for every board seat if they’ve got 10 board seats and four board meetings a year per company.
What a VC would like in general, all else being equal, is for you to be within driving distance. Not only does that VC like it, so does the next VC in the B round and the next VC in the C round. That factor is actually one of the big things that constrains people to the Bay Area, is VC driving distance, [laughs] because VCs don’t want to do investments that are an entire world away.
With the advent of Ethereum and ICOs, we have finally begun to decentralize the last piece, which was funding. Now, that regulatory environment needs to be worked out. It’s going to be worked out in different ways in different countries.
But the old era where you had to come to Sand Hill to get your company funded and then go to Wall Street to exit is over. That’s something where it’s going to increasingly decentralize. It already has decentralized worldwide, and that’s going to continue.
COWEN: With or without a board seat, doesn’t funding require a face-to-face relationship? It’s common for VC companies to even want the people they’re funding to move their endeavor to the Bay Area in some way, not only for the board meeting. They want to spend time with those people.
We’re doing this podcast face to face. We could have done it over Skype. There’s something significant about actually having an emotionally vivid connection with someone right there in the room. How much can we get around that as a basic constraint?
And here is another:
COWEN: Right now, I pay financial fees to my mutual funds, to Merrill Lynch, all over. Anytime I save money, I’m paying a fee to someone. Which of those fees will go away?
SRINIVASAN: Good question. Maybe all of them.
COWEN: Why? What will they do that we haven’t thought of?
SRINIVASAN: Construction. There’s different kinds of drones. They’re not just flying drones. There’s swimming drones and there’s walking drones and so on.
Like the example I mentioned where you can teleport into a robot and then control that, Skype into a robot and control that on other side of the world. That’s going to be something where maybe you’re going to have it in drone mode so it walks to the destination. You’ll be asleep and then you wake up and it’s at the destination.
Drones are going to be a very big deal. There’s this interesting movie called Surrogates, which actually talks about what a really big drone/telepresence future would look like. People never leave their homes because, instead, they just Skype into a really good-looking drone/telepresent version of themselves, and they walk around in that.
If they’re hit by a car, it doesn’t matter because they can just rejuvenate and create a new one. I think drones are very, very underrated in terms of what they’re going to do.
Do read or listen to the whole thing.
Nice empirical confirmation of something I've repeatedly heard said by Economists. It goes with their obsessive focus on "home runs". Although, given that Jl. Ec. Psych has an impact factor of 1.2, the authors should probably leave it off their CVs. https://t.co/og3XtuiHGV pic.twitter.com/gUgyDC8fNx
— Kieran Healy (@kjhealy) April 21, 2018
This award strikes me as the last remaining award, at least in the near term, from the matching/market design boom of the past 20 years. As Becker took economics out of pure market transactions and into a wider world of rational choice under constraints, the work of Al Roth and his descendants, including Parag Pathak, has greatly expanded our ability to take advantage of choice and local knowledge in situations like education and health where, for many reasons, we do not use the price mechanism. That said, there remains quite a bit to do on understanding how to get the benefits of decentralization without price – I am deeply interested in this question when it comes to innovation policy – and I don’t doubt that two decades from now, continued inquiry along these lines will have fruitfully exploited the methods and careful technique that Parag Pathak embodies.
The post is excellent throughout.
Although the concept of randomized assignment to control for extraneous factors reaches back hundreds of years, the first empirical use appears to have been in an 1835 trial of homeopathic medicine. Throughout the 19th century, there was primarily a growing awareness of the need for careful comparison groups, albeit often without the realization that randomization could be a particularly clean method to achieve that goal. In the second and more crucial phase of this history, four separate but related disciplines introduced randomized control trials within a few years of one another in the 1920s: agricultural science, clinical medicine, educational psychology, and social policy (specifically political science). Randomized control trials brought more rigor to fields that were in the process of expanding their purviews and focusing more on causal relationships. In the third phase, the 1950s through the 1970s saw a surge of interest in more applied randomized experiments in economics and elsewhere, in the lab and especially in the field.
That is from a Julian C. Jamison paper done at the World Bank, via various people in my Twitter feed.