Art market fractionalization
From an email sent to me by The Art Newspaper:
Fractionalisation and tokenisation of art are all the rage. While the notion of unlocking the value in an artwork by selling shares in it has been around for over a decade, a slew of new initiatives is taking it to an explosive new level.
Among the splashiest new launches is the Artex Stock exchange out of Liechtenstein, co-founded by financiers Prince Wenceslas von Liechtenstein and Yassir Benjelloun-Touimi, the latter seemingly the driving force. The project buys art (its first acquisition is Bacon’s Three Studies for a Portrait of George Dyer, 1963) bought for $52m in 2017 at Christie’s and now valued at $55m. Investors can buy shares for as little as $100 in the Bacon, which can be traded (or technically, the company that owns it) on the Liechtenstein MTF (an alternative trading platform). Other paintings will follow; trading starts on 21 July.
These ideas seem weird to me. The more wonderful it is to own art, the lower should be its pecuniary rate of return, as recompense. So why buy into fractional shares of an art work? You don’t get to hang it on your work, and at the same time you get the subpar rate of return resulting from the fact that some people do get to hang it on their walls.
Mission Impossible 7 (no real spoilers)
Eh. It has some good scenes, but there was no movie in the movie, as they say. I do understand this is a film and also a visual event, but the evil AI should have been kept abstract, rather than visualized in such a corny, stupid fashion. That reminded me of Hitchcock’s Spellbound, which is also too visually literal. This installation of the MI series uses a bunch of Hitchcock tricks, some expected James Bond, and then a bunch of LOTR moral and “power corrupts” themes, which don’t fit the kinetic emphasis in the on-screen action. Most of all, I am struck by the great lengths they go to in order to portray lone individuals as decisive over the final outcomes. In a Bond movie there is a wire to be cut, a button to be pressed, and a specific villain to be vanquished in a remote island lair, with a stomach punch-invulnerable henchman along the way. OK enough, but here they have to work far harder. They even solve for the strategic game between the AI and the good guys, and it still isn’t clear who is a decisive agent in the action.
And so the movie ends with “part I and uncertainty,” as even 2 hours 40 minutes was not enough to get a single individual to matter. Modelmakers, take note.
Monday assorted links
1. Rasheed Griffith interviews Anton Howes on the Caribbean, Japanese toilets, and much more.
2. Puffin photo.
3. The economic life of cells?
5. Good profile of BAP. In my view, the so-called “New Right” needs to learn to live with feminization, one way or another. I think of BAP as “Cope” for those people who will not or cannot do so.
NBA (CEO) fact of the day
“there are now more NBA players with $30 million annual salaries than CEOs of S&P 500 companies who are guaranteed that much.”
Here is further information.
Shakked Noy and Whitney Zhang on the productivity effects of LLMs
We examined the productivity effects of a generative artificial intelligence (AI) technology, the assistive chatbot ChatGPT, in the context of midlevel professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 453 college-educated professionals and randomly exposed half of them to ChatGPT. Our results show that ChatGPT substantially raised productivity: The average time taken decreased by 40% and output quality rose by 18%. Inequality between workers decreased, and concern and excitement about AI temporarily rose. Workers exposed to ChatGPT during the experiment were 2 times as likely to report using it in their real job 2 weeks after the experiment and 1.6 times as likely 2 months after the experiment.
Those experiments were run a bit earlier, I am keen to see something similar attempted with GPT-4. And I am pleased that Emergent Ventures was able to have supported this work.
Bottlenecks and the productivity slowdown
Despite the rapid pace of innovation in information and communications technologies (ICT) and electronics, aggregate US productivity growth has been disappointing since the 1970s. We propose and empirically explore the hypothesis that slow growth stems in part from an unbalanced sectoral distribution of innovation over the last several decades. Because an industry’s success in innovation depends on complementary innovations among its input suppliers, rapid productivity growth that is concentrated in a subset of sectors may create bottlenecks and consequently fail to translate into commensurate aggregate productivity gains. Using data on input-output linkages, citation linkages, industry productivity growth and patenting, we find evidence consistent with this hypothesis: the variance of suppliers’ Total Factor Productivity growth or innovation adversely affects an industry’s own TFP growth and innovation. Our estimates suggest that a substantial share of the productivity slowdown in the United States (and several other industrialized economies) can be accounted for by a sizable increase in cross-industry variance of TFP growth and innovation. For example, if TFP growth variance had remained at the 1977-1987 level, US manufacturing productivity would have grown twice as rapidly in 1997-2007 as it did—yielding a counterfactual growth rate that would have been close to that of 1977-1987 and 1987-1997.
Here is the new NBER working paper by Daron Acemoglu, David Autor, and Christina Patterson.
Sunday assorted links
Fiona Scott Morton
French ministers lashed out Thursday at the European Commission for picking a U.S. professor for a top antitrust job overseeing U.S. Big Tech firms.
France’s Europe Minister Catherine Colonna said she was “astonished” by the choice of Fiona Scott Morton as chief competition economist, “which deserves to be reconsidered by the Commission.”
Digital Minister Jean-Noël Barrot said the Commission should rethink the hire, which raises “legitimate questions” at a time when the EU is rolling out ambitious digital enforcement legislation.
Here is the full Politico article, and here is her law review piece (with Herbert Hovenkamp) criticizing the Chicago School approach to antitrust law. Here is her piece (with Michael Kades) on interoperability and tech. Her views are not mine, but I am quite sure she would boost the quality of analysis and debate in those EU forums. Perhaps that is not what everyone wants.
Markets in everything those new service sector jobs body doubling edition
Consultant Micha Goebig scrolled through her phone to find receipts and bill clients while on her home computer on Friday afternoon. Nine strangers quietly watched her on a video link while also doing their own solo work.
The small group was gathered online by Flow Club, a subscription service that says it can help home-based workers stay on task and be productive by quietly working in tandem. The online session was built around body doubling, a productivity strategy gaining traction among remote and hybrid employees who say they get more done if others are looking on.
Body doubling, initially adopted by and coined by ADHD therapists, is one of several ways that workers are trying to regain focus and accountability when they aren’t working under the watchful eyes of bosses and colleagues in the office. A number of companies have sprung up to offer what they say is positive peer pressure that can boost productivity.
Here is the full WSJ piece, interesting throughout. Via the excellent Samir Varma.
Tony Kulesa on Paul Graham and Y Combinator
What makes for the magic of Paul Graham? Here is one short excerpt from a new essay by Tony Kulesa:
Therefore one could summarize YC’s structure as:
- Invest in young, smart, energetic, and determined hackers
- Give them enough money to pay for living expenses, but not much more
- Give them a few months to build something, launch it, and see some evidence of growth
And, since finding good ideas is hard, get statistics on your side by batch processing startups – run as many of these experiments as you can in parallel.
The piece is very good throughout, and considers how Paul created a community and an ethos through his writing. Here is a follow-up essay by Eric Gilliam.
Saturday assorted links
1.The cost of trade in books — EU to UK — is rising.
2. “The results suggest an intent-to-treat effect of 0.17 years higher longevity as a result of prohibition. A back-of-an-envelope calculation suggests a minimum treatment-on-treated effect of 1.7 years impact.” Link here.
3. Why did nuclear flop in Britain?
4. MIE: “121-year-old Cadbury coronation chocolates to be sold at auction.“
6. New Bloomberg crypto ecosystem dashboard, “mainstream integration accelerating…” Listen to Jamie Coutts! The future of crypto remains uncertain, but currently reading a person’s comments about crypto is one of the best ways to spot some of said person’s cognitive and emotional biases.
8. Can Claude 2 write like you?
9. Why many men reject the mode of discourse found in therapy (hint: too feminized).
Shopify is a Great Company!
Bloomberg: Time is money, and Shopify Inc. wants its workers to understand that maxim applies to pointless meetings, too.
The Canadian e-commerce company has rolled out a calculator embedded in employees’ calendar app that estimates the cost of any meeting with three or more people. The tool uses average compensation data across roles and disciplines, along with meeting length and attendee count, to put a price tag on the event. A typical 30 minute endeavor with three employees can run from $700 up to $1,600. Adding an executive — like Chief Operating Officer Kaz Nejatian, who built the program during a company-wide hack day — can shoot the cost above $2,000.
….The company is on pace to cut out 322,000 hours and 474,000 discrete events in 2023, according to Nejatian.
This point is especially important:
“No one at Shopify would expense a $500 dinner,” Nejatian said in an interview. “But lots and lots of people spend way more than that in meetings without ever making a decision.
Making good decisions requires taking into account all costs, including opportunity costs, and not just focusing on costs that are visibly priced. Without visible prices, however, it can be difficult to fully appreciate costs. Thus, making hidden opportunity costs visible is an excellent strategy for better decision making.
Hat tip: Joshua Gans.
States rights tortoise nationalism is the worst tortoise nationalism
You can’t bequeath them to just anyone. Veronica Tomlinson, 52, had thought about leaving Walter, a 24-year-old desert tortoise, to East Coast relatives if she and her husband died first. But state laws prohibit people from moving desert tortoises out of the state where they were adopted. The Tomlinsons, of Las Vegas, have instructions in their will for Walter to be returned to the Tortoise Group, a Nevada-based nonprofit that arranges tortoise adoptions. The couple plan to leave the group their savings and life-insurance money, after paying debts and funeral costs, to cover Walter’s care.
Emphasis is added, not in the original. Here is the full WSJ story, via Anecdotal. No Coase theorem for Walter!
On white flight (from the comments)
Are whites fleeing from Asian-heavy California public schools? One recent paper suggested maybe so, but abc raises some doubts:
I don’t want to dismiss the paper out of hand, as I have seen time and again the challenges communities face both in and outside of the school setting in accommodating demographic change.
However, I don’t think the headline result in this paper is particularly credible. First, there isn’t a well-articulated research question to guide the choice of regression. Second, the authors implicitly rely on the “an instrument is always better” fallacy rather than explaining why their instrument yields more reliable estimates than naive OLS for the (unstated) question of interest. Taken together, the paper is undergrad-thesis level material elevated only by a click bait topic and result. If we want to make bold claims about White animosity towards Asians (a claim that also constructive of such animosity and counter-animosity from Asians towards Whites) we should demand substantive evidence. This paper does not present such evidence.
Some key takeaways:
(1) The authors note that a mechanical housing market replacement would suggest a one-for-one effect, but say that their -1.47 effect is above that threshold. However, if we check the confidence interval using a conservative 1.96 critical value and the estimated standard error of the coefficient estimate, we have -1.47 + 1.96*0.268 = -0.96 so that we are not statistically significantly different from -1 by this measure.
(2) The naive OLS estimate in high-SES regions is -0.6, well below the fixed enrollment effect of -1. The authors speculate that OLS may be biased downward because the error term include unmeasured district quality changes that draw in both Asians and Whites. (Note such a correlation only operates if enrollment is not capped, so inconsistent with that model.) The authors don’t document any of these omitted variable issues, however, and just assert that their instrument will be better.
(3) Authors do not substantively engage issues with their IV. First, the IV doesn’t account for changes in composition of immigrants over time (increasing wealth and education of Asian arrivals relative to earlier waves) nor does it account for movement of second-generation Asian families. If there is no omitted variable bias but the instrumented entry is lower than the actual entry, then mechanically the coefficient will have to be higher to offset this effect and restore least-squares minimization.
(4) The instrumented Asian inflows coefficient could pick up effects from Asian-agglomeration effects. A one unit increase in Asian enrollment from pure fixed-pattern immigration flows made lead to shifts of previously settled Asians or shift the direction of subsequent immigration. For example, a settled Korean in Riverside who sees large increases in Korean population in Orange County may see OC as being more attractive than before and move into the area. This induced shift may be only partially captured by the first-stage prediction, leaving the 2nd stage coefficient of interest to increase in magnitude.
(5) Various sensitivities lead to surprising results. First, the instrument behaves poorly in some subsamples, e.g. the bottom-half of the SES scale. Why should we believe an instrument in one data subset when it plainly fails in the complement? Second, the instrument is insignificant in the Bottom Tercile of the above-median SES group (appendix table 2). Third, the IV estimate is only -0.841 in the top tercile of the above-median SES group, again below the key -1 threshold if enrollment caps are binding. Taken together, are we to think that we can identify white flight using this instrument only for the 66.6th to 83.3th percentile bucket?
(6) There’s just a big background trend issue that one has to worry about here. The theory of white flight begs the question of “flight to where?” However if we just look at Appendix Figure 2 during this time period there is a big drop in total White enrollment (and a small decline in Black enrollment) while Asian and Hispanic enrollment see big increases. To what extent are we just finding that aging out of whites in high-SES regions is being replaced disproportionately by Asians?
(7) A couple other wrinkles: how are mixed-race students handled? how would demographic shifts in total enrollment by district affect the 1-to-1 threshold? If child population is shrinking over time (e.g. because families are leaving CA, children per family is declining) then normal churn would predict more than 1-to-1 replacement of new-cohort race versus previous-cohort race.
So perhaps the right answer is “no”?
Against human-AI collaboration
From a new NBER working paper by Nikhil Agarwal, Alex Moehring, Pranav Rajpurkar, and Tobias Salz:
Radiologists do not fully capitalize on the potential gains from AI assistance because of large deviations from the benchmark Bayesian model with correct belief updating. The observed errors in belief updating can be explained by radiologists’ partially underweighting the AI’s information relative to their own and not accounting for the correlation between their own information and AI predictions. In light of these biases, we design a collaborative system between radiologists and AI. Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI.
I am more optimistic in my views, noting there may well be contexts such as radiology where the collaborations fail. I collaborate with Google’s AI all the time, and I am pretty sure that joint effort does better than either myself or “Google with no human” unaided. Still, this is a cautionary note of some import, as many humans are not good enough to work well with AIs.