Rick Rubin podcasts with me
Recorded in August in Europe, here is the link. One hour, forty-five minutes, mostly Rick asking me questions about what he would like to hear about from the worlds of economics and politics. We do have I think also another one about music coming up…
Wednesday assorted links
“Life in India is a series of bilateral negotiations”
By Rohit Krishnan:
Life in India is a series of bilateral negotiations conducted a thousand times a day. And that drives the character of life here.
Now, I am seeing the country properly after several years. And it’s a major change.
Visible infrastructure has gotten much better. Roads are good, well maintained, and highways are excellent. They built 7500 miles last year, just as the year before. And they’re fantastic…
But:
Living in a country built off of bilateral negotiations for everything is simultaneously the libertarian dream and an incredibly inefficient way to do most collective things. Ronald Coase told us this in 1960.
“if property rights are well-defined and transaction costs are low, private parties can negotiate solutions to externalities without the need for government intervention”
But Indian life is dominated by transaction costs. Every time a driver pokes his car into a turn when the signal’s not for him it creates friction that ripples through the entire system. Every time someone has to spend effort doing a 1:1 negotiation they lose time and efficiency. Horribly so.
…The reason this isn’t an easy fix is that the ability to negotiate everything is also the positive. When every rule is negotiable you get to push back on silly things like closing off a section of a parking garage with rubber cones by just asking. Life in the West feels highly constricted primarily because of this, we’re all drowning in rules.
Here is the full essay.
How should strong AI alter philanthropy?
That is the theme of my latest Bloomberg column, and here is one bit:
One big change is that AI will enable individuals, or very small groups, to run large projects. By directing AIs, they will be able to create entire think tanks, research centers or businesses. The productivity of small groups of people who are very good at directing AIs will go up by an order of magnitude.
Philanthropists ought to consider giving more support to such people. Of course that is difficult, because right now there are no simple or obvious ways to measure those skills. But that is precisely why philanthropy might play a useful role. More commercially oriented businesses may shy away from making such investments, both because of risk and because the returns are uncertain. Philanthropists do not have such financial requirements.
And this oft-neglected point:
Strong AI capabilities also mean that the world might be much better over some very long time horizon, say 40 years hence. Perhaps there will be amazing new medicines that otherwise would not have come to pass, and as a result people might live 10 years longer. That increases the return — today — to fixing childhood maladies that are hard to reverse. One example would be lead poisoning in children, which can lead to permanent intellectual deficits. Another would be malnutrition. Addressing those problems was already a very good investment, but the brighter the world’s future looks, and the better the prospects for our health, the higher those returns.
The flip side is that reversible problems should probably decline in importance. If we can fix a particular problem today for $10 billion, maybe in 10 years’ time — due to AI — we will be able to fix it for a mere $5 billion. So it will become more important to figure out which problems are truly irreversible. Philanthropists ought to be focused on long time horizons anyway, so they need not be too concerned about how long it will take AI to make our world a fundamentally different place.
Recommended, interesting throughout.
Reflections on Palantir
Here is a new essay by Nabeel Qureshi, excerpt:
The combo of intellectual grandiosity and intense competitiveness was a perfect fit for me. It’s still hard to find today, in fact – many people have copied the ‘hardcore’ working culture and the ‘this is the Marines’ vibe, but few have the intellectual atmosphere, the sense of being involved in a rich set of ideas. This is hard to LARP – your founders and early employees have to be genuinely interesting intellectual thinkers. The main companies that come to mind which have nailed this combination today are OpenAI and Anthropic. It’s no surprise they’re talent magnets.
And this:
Eventually, you had a damn good set of tools clustered around the loose theme of ‘integrate data and make it useful somehow’.
At the time, it was seen as a radical step to give customers access to these tools — they weren’t in a state for that — but now this drives 50%+ of the company’s revenue, and it’s called Foundry. Viewed this way, Palantir pulled off a rare services company → product company pivot: in 2016, descriptions of it as a Silicon Valley services company were not totally off the mark, but in 2024 they are deeply off the mark, because the company successfully built an enterprise data platform using the lessons from those early years, and it shows in the gross margins – 80% gross margins in 2023. These are software margins. Compare to Accenture: 32%.
The rest is interesting throughout. As Nabeel and a few others have noted, there should be many more pieces trying to communicate what various businesses and institutions really are like.
Tuesday assorted links
1. How to improve educational outcomes in developing economies.
2. That was then, this is now: Russia to unveil new Stalin statue.
3. African Urban Lab launched in Zanzibar.
Comment delays
There is a slight glitch in the WordPress system, but your comments will appear eventually. Apologies for any delays!
Google signs contract for small modular nuclear reactors
Since pioneering the first corporate purchase agreements for renewable electricity over a decade ago, Google has played a pivotal role in accelerating clean energy solutions, including the next generation of advanced clean technologies. Today, we’re building on these efforts by signing the world’s first corporate agreement to purchase nuclear energy from multiple small modular reactors (SMRs) to be developed by Kairos Power. The initial phase of work is intended to bring Kairos Power’s first SMR online quickly and safely by 2030, followed by additional reactor deployments through 2035. Overall, this deal will enable up to 500 MW of new 24/7 carbon-free power to U.S. electricity grids and help more communities benefit from clean and affordable nuclear power.
Here is more from Google.
Letters of recommendation
We analyze 6,400 letters of recommendation for more than 2,200 economics and finance Ph.D. graduates from 2018 to 2021. Letter text varies significantly by field of interest, with significantly less positive and shorter letters for Macroeconomics and Finance candidates. Letters for female and Black or Hispanic job candidates are weaker in some dimensions, while letters for Asian candidates are notably less positive overall. We introduce a new measure of letter quality capturing candidates that are recommended to “top” departments. Female, Asian, and Black or Hispanic candidates are all less likely to be recommended to top academic departments, even after controlling for other letter characteristics. Finally, we examine early career outcomes and find that letter characteristics, especially a “top” recommendation have meaningful effects on initial job placements and journal publications.
That is from a new paper by Beverly Hirtle and Anna Kovner. Via the excellent Kevin Lewis.
The economics of haunted houses
Running a haunted house can be a risky—if not scary—business. The barrier to entry is high, the overhead costs can be massive, and the attractions have just one very vulnerable month to earn their money. Like many other small businesses, 60% of them don’t make it past their third year, according to haunted-house review company and directory Scare Factor. Most operators have to keep their day jobs or do side hustles to make ends meet.
“The most successful haunts, the ones that last more than three years, are full-on destinations rather than a 20-minute attraction,” says Scare Factor co-owner Nora Proffet. “Haunters are competing with all other forms of entertainment, and visitors want to spend the whole evening having fun.”
In the past few years, that has meant adding food trucks, ax throwing, bars and escape rooms, Proffet says. Many attractions also create special events—whether scary or not—for Christmas and Valentine’s Day.
Over the past nine years, LaFlamboy—who has been a part of more than a dozen haunted attractions, most not as successful as HellsGate—has added numerous flourishes to lure people to his haunted attraction. Customers can now visit a midway with bonfires, food concessions, two bars, a free photo booth, an escape room, a gift shop and giant movie screen, and roaming actors—playing various creatures—to take selfies.
And:
A National Retail Federation survey showed that approximately 18% of U.S. adults visited a haunted attraction last year, or about 46.5 million people. The Halloween and Costume Association says that haunted houses are a $500 million industry.
That money is split among a relatively small number of attractions. According to Scare Factor, the U.S. is home to 2,100 for-profit haunted attractions—that is around double the number in the 1990s—plus an estimated 1,000 not-for-profit ones, which include pop-ups in cornfields.
Here is more from Heidi Mitchell at the WSJ, via Patrick Moloney.
Kevin Bryan on the contributions of AJR
Acemoglu, Johnson and Robinson Win Nobel Prize for Institutions and Prosperity
The Nobel prize goes to Daron Acemoglu, Simon Johnson and James Robinson for their work on institutions, prosperity, and economic growth. Here is a key piece summarizing their work: Institutions as a Fundamental Cause of Long-Run Growth.
This paper develops the empirical and theoretical case that differences in economic institutions are the fundamental cause of differences in economic development. We first document the empirical importance of institutions by focusing on two “quasi-natural experiments” in history, the division of Korea into two parts with very different economic institutions and the colonization of much of the world by European powers starting in the fifteenth century. We then develop the basic outline of a framework for thinking about why economic institutions differ across countries. Economic institutions determine the incentives of and the constraints on economic actors, and shape economic outcomes. As such, they are social decisions, chosen for their consequences. Because different groups and individuals typically benefit from different economic institutions, there is generally a conflict over these social choices, ultimately resolved in favor of groups with greater political power. The distribution of political power in society is in turn determined by political institutions and the distribution of resources. Political institutions allocate de jure political power, while groups with greater economic might typically possess greater de facto political power…Economic institutions encouraging economic growth emerge when political institutions allocate power to groups with interests in broad-based property rights enforcement, when they create effective constraints on power-holders, and when there are relatively few rents to be captured by power-holders.
See this great MRU video on Institutions for a quick overview! Here from an interview with Acemoglu, is a slightly more pointed perspective. Politics keeps people poor:
Why is it that certain different types of institutions stick?….it wouldn’t make sense, in terms of economic growth, to have a set of institutions that ban private property or create private property that is highly insecure, where I can encroach on your rights. But politically, it might make a lot of sense.
If I have the political power, and I’m afraid of you becoming rich and challenging me politically, then it makes a lot of sense for me to create a set of institutions that don’t give you secure property rights. If I’m afraid of you starting new businesses and attracting my workers away from me, it makes a lot of sense for me to regulate you in such a way that it totally kills your ability to grow or undertake innovations.
So, if I am really afraid of losing political power to you, that really brings me to the politics of institutions, where the logic is not so much the economic consequences, but the political consequences. This means that, say, when considering some reform, what most politicians and powerful elites in society really care about is not whether this reform will make the population at large better off, but whether it will make it easier or harder for them to cling to power.
Those are the sort of issues that become first-order if you want to understand how these things work.
One interesting aspect of this year’s Nobel is that almost all of AJRs Nobel work is accessible to the public because it has come primarily through popular books rather than papers. The Economic Origins of Dictatorship and Democracy, Why Nations Fail, and the The Narrow Corridor all by Acemoglu and Robinson and Power and Progress by Acemoglu and Johnson are all very readable books aimed squarely at the general public. The books are in many ways deeper and more subtle than the academic work which might have triggered the broader ideas (such as the famous Settler Mortality paper). Many of the key papers such as Reversal of Fortune are also very readable.
This is not to say that the authors have not also made many technical contributions to economics, most especially Acemoglu. I think of Daron Acemoglu (GS) as the Wilt Chamberlin of economics, an absolute monster of productivity who racks up the papers and the citations at nearly unprecedented rates. According to Google Scholar he has 247,440 citations and an H-index of 175, which means 175 papers each with more than 175 citations. Pause on that for a moment. Daron got his PhD in 1992 so that’s over 5 papers per year which would be tremendous by itself–but we are talking 5 path-breaking, highly-cited papers per year plus many others! (Of course, most written with excellent co-authors). In addition, he’s the author of a massive textbook on economic growth. More than any other economist Daron has pushed the cutting-edge of technical economics and has also written books of deep scholarship still accessible to the public. In his overview of Daron’s work for the John Bates Clark medal Robert Shimer wrote “he can write faster than I can digest his research.” I believe that is true for the profession as a whole. We are all catching-up to Daron Acemoglu.
Indeed, in reading a book like Why Nations Fail and papers like The Network Origins of Aggregate Fluctuations (one of my favorite Acemoglu papers) and The Uniqueness of Solutions for Nonlinear and Mixed Complementarity Problems it’s difficult to believe they are co-authored by the same person. Acemoglu is as comfortable talking history, politics, and political economy as he is talking about the economics of recessions and abstruse mathematics.
Here are Previous MR posts on Daron Acemoglu including this post on democracy where I find the effect of democracy on growth to be ho-hum. Here is Maxwell Tabarrok on Acemoglu on AI. Here is Conversations with Tyler with Acemoglu and a separate conversation with Simon Johnson.
As noted, one of my favorite Acemoglu papers (with Carvalho, Ozdaglar, and Tahbaz-Salehi) is The Network Origins of Aggregate Fluctuations. Conventional economics models the aggregate economy as if it were a single large firm. In fact, the economy is a network. An auto plants needs steel and oil to operate so fluctuations in the steel and oil industry will influence production in the auto industry. For a long time, the network nature of production has been ignored. In part because there are some situations in which a network can be modeled as if it were a single firm and in part because it’s just much easier to do the math that way. Acemoglu et al. show that aggregate fluctuations can be generated by sector fluctuations and that organization of the network cannot be ignored. This is a modern approach to real business cycles. See also my post on Gabaix and granular fluctuations).
In recent work, Acemoglu and Restrepo have created a new way of modeling production functions which divides work into tasks, some of which are better performed by capital and others by labor. Technological change is not simply about increasing the productivity of labor or capital (modeled in standard economics as making one laborer today worth two of yesterday’s) but about changing which tasks can best be done by capital and which by labor. As a task moves from labor to capital the demand for labor falls but productivity increases which generates demand for other kinds of labor. In addition, as capital replaces labor in some tasks entirely new tasks may be created for which labor has a comparative advantage. A number of interesting points come out of this including the idea that what we have to fear most is not super-robots but mediocre-robots. A super-robot replaces labor but has an immense productivity advantage which generates wealth and demand for labor elsewhere. A mediocre-robot replaces the same labor but doesn’t have a huge productivity advantage. In an empirical breakdown, Acemoglu and Restrepo suggest that what has happened in the 1990s and especially since 2000 is mediocre-robots. As a result, there has been a decline in labor on net. Thus, Acemoglu is more negative than many economists on automation, at least as it has occurred recently. Acemoglu and Restrepo is some of the best recent work going beyond the old tired debates to reformulate how we think of production and to use that reformulation to tie those reformulations to what is actually happening in the economy.
Solow thought of technical change as exogenous which is still the first-pass approach to thinking about technical change. Acemoglu in contrast focuses on price and market size. In particular, the larger the market the greater the incentive to invest in R&D to serve that market (see also my TED talk). Thus, technical change will tend to be cumulative. A sector with a productivity improvement will grow which can make that sector even more remunerative for further technical advances (depending on elasticities). This matters a lot for environmental change because it suggests that a relative small intervention today–including subsidizing research on clean technologies–can have a huge payoff in the future because by directing technical change in the right direction you make it easier to switch later on. (from this interview)
But let’s think of the logic of directed technical change with cumulative research. The less we do on green technology today, the less knowledge is accumulated in the green sector, so the bigger is the gap between fossil-fuel-based technology and energy, and the cleaner energy, so the harder it will be in the future to close that gap. With more proactive, decisive action today, we already start closing the gap, and we’re making it easier to deal with the problem in the future.
Simon Johnson has also written important books on banking and finance including and that was before the big run up in American debt! James Robinson has written widely on African development and colonialism and African development more generally.
Overall, I’d say that this is an award for political science and for popular economics in the very best sense of economics that matters. Go buy their books and read them!
Conversations with the new Laureates
Here is my earlier CWT with Acemoglu, and with Simon Johnson. We’ll have to try to get to James Robinson. Congratulations to the winners!
Monday assorted links
1. Drones over U.S. military bases (WSJ).
2. Eric Topol podcast with the excellent Patrick Hsu.
3. Matt Clancy on AI progress. Excellent thread.
What surprised you the most this year?
What has surprised you the most this year?
The development of decentralized training for AI models, from DiLoCo and DiPaCo from Google DeepMind to Distro coming up from Nous Research. It completely changes the game in terms of how we ought to think about large models and what we can do to train them. This means pure compute thresholds are not going to be very useful, and that we will have even less of a way to centrally control the means of knowledge production.
That is from Rohit Krishnan, who answers other questions as well. Interviewed by Derek Robertson, via Mike Doherty.