Podcast appearances and mentions of Robert Solow

rWotD Episode 2876: Wassily Leontief Welcome to Random Wiki of the Day, your journey through Wikipedia’s vast and varied content, one random article at a time.The random article for Wednesday, 19 March 2025 is Wassily Leontief.Wassily Wassilyevich Leontief (Russian: Васи́лий Васи́льевич Лео́нтьев; August 5, 1905 – February 5, 1999), was a Soviet-American economist known for his research on input–output analysis and how changes in one economic sector may affect other sectors.Leontief won the Nobel Memorial Prize in Economic Sciences in 1973, and four of his doctoral students have also been awarded the prize (Paul Samuelson 1970, Robert Solow 1987, Vernon L. Smith 2002, Thomas Schelling 2005).This recording reflects the Wikipedia text as of 00:06 UTC on Wednesday, 19 March 2025.For the full current version of the article, see Wassily Leontief on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Mastodon at @wikioftheday@masto.ai.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm generative Joanna.

内容简介本期播客介绍了经济学原理如何帮助我们理解和解决古老或现代的问题。通过引用清华经管学院前院长钱颖一的文章《理解经济学思维》，深入探讨了经济学的两个基本出发点——资源稀缺性和个人理性，以及其落脚点——效率。这些核心原则既简单又具有可验证性，帮助我们回归常识，优化资源与时间的使用，理解经济增长的关键驱动力，如资本、劳动力和技术创新。此外，还概括了经济学的三个基本原理：人们会对激励做出反应，市场是有效的资源配置手段，以及创新是推动经济增长的最终力量。这一期是经济学思维的备忘，用于提醒我始终关注原初的问题和简洁的原理。参考 配乐：Kitagaku Moyo. Nazo Nazo 钱颖一.《理解经济学原理》 罗伯特·索洛(Robert Solow). 增长模型 古斯塔夫·勒庞.《乌合之众》 丹尼尔·卡尼曼.行为经济学研究 播客41期：从行为社会科学基本问题看去 播客47期：消费者行为学与增长设计

Welcome to this week's episode of “The Mixtape with Scott”! My podcast tries to capture the personal stories of living economists and create an oral history of the profession from the narratives. And this week, I'm thrilled to welcome Dr. Avinash K. Dixit, a distinguished economist whose life's work has influenced many fields within economics. But let me start by telling you a little about his background.Dr. Dixit is the John J. F. Sherrerd '52 University Professor of Economics Emeritus at Princeton University. He also serves as a Distinguished Adjunct Professor of Economics at Lingnan University in Hong Kong and is a Senior Research Fellow at Nuffield College, Oxford. For his many contributions to science, he has been awarded numerous accolades, including election to the American Academy of Arts and Sciences, the National Academy of Sciences, and the American Philosophical Society. He was also honored with India's Padma Vibhushan in 2016, recognizing his outstanding contributions to literature and education.As he will share, he was born in Mumbai, India and attended St. Xavier's College where he earned a degree in Mathematics and Physics. Afterwards, he earned another degree (also in mathematics) from Cambridge before going to MIT to get his PhD where he was supervised by the late Robert Solow. After graduation, he went to Berkeley, Oxford, Warwick and then Princeton where he's been since 1981. Both the sheer number of contributions he has made to many fields, but also their influence, is incredible. I put in the title for this episode simply “Microeconomics” after his name, but that was a difficult decision as his work spans microeconomic theory, game theory, international trade, industrial organization, and public economics, just to name a few. I could've written any one of those and it would've still been inadequate. His recent work continues to address pressing global issues, such as optimal policies for green power generation and the dynamics of social, political, and economic institutions. He is an example of someone who follows his heart and his mind, even taking risks throughout his career to leave entire fields of inquiry in search of more questions. In addition to his long list of scientific manuscripts, there have also been many influential books, both textbooks but also more ones aimed at a broader population of readers. Things like “Theory of International Trade” (with Victor Norman), “Investment Under Uncertainty” (with Robert Pindyck), “The Art of Strategy” (with Barry Nalebuff), and “Games of Strategy” (with Susan Skeath and David Reiley). So I'll stop there and turn it over to the show's host — myself — and my guest, Dr. Dixit. Thank you for tuning in to this episode of “The Mixtape with Scott.” If you enjoy our conversation, please share the podcast and help us continue to bring you stories from the world of economics.Scott's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Scott's Substack at causalinf.substack.com/subscribe

He's an elder statesman in the worlds of journalism, policy and economics in India -- and he takes the long view. Niranjan Rajadhyaksha joins Amit Varma in episode 388 of The Seen and the Unseen to talk about his life and learnings. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Niranjan Rajadhyaksha on Twitter, Mint and Artha Global. 2. The Rise of India -- NIranjan Rajadhyaksha. 3. Niranjan Rajadhyaksha interviewed in Marathi by Think Bank: Part 1. Part 2. 4. MV Rajadhyaksha and Vijaya Rajadhyaksha. 5. The Times of India obituary of MV Rajadhyaksha. 6. Adventures of a Bystander -- Peter F Drucker. 7. The Theory of Moral Sentiments -- Adam Smith's book that contains the concept of the impartial spectator. 8. The Impartial Spectator columns by Niranjan Rajadhyaksha and Shruti Rajagopalan. 9. Ratatouille -- Brad Bird. 10. The Overton Window. 11. John Maynard Keynes on Alfred Marshall. 12. The Rooted Cosmopolitanism of Sugata Srinivasaraju — Episode 277 of The Seen and the Unseen. 13. The Rise and Fall of the Bilingual Intellectual -- Ramachandra Guha. 14. Understanding India Through Its Languages — Episode 232 of The Seen and the Unseen (w Peggy Mohan). 15. Wanderers, Kings, Merchants: The Story of India through Its Languages — Peggy Mohan. 16. The Heckman Equation -- a website based on James Heckman's work. 17. Select episodes of The Seen and the Unseen with Suyash Rai (1, 2) and Rahul Verma (1, 2). 18. Stri Purush Tulana by Tarabai Shinde on Amazon and Wikipedia. 19. Kalyanche Nishwas by Vibhavari Shirurkar (Malati Bedekar) on Amazon and Wikipedia. 20. Makers of Modern India -- Ramachandra Guha. 21. Simone de Beauvoir (Wikipedia, Britannica, Amazon) and Germaine Greer (Wikipedia, Britannica, Amazon). 22. Gopal Ganesh Agarkar's essay on education for girls. 23. The omnibus volume of BR Nanda's biographies of Gokhale, Gandhi and Nehru. 24. The Adda at the End of the Universe — Episode 309 of The Seen and the Unseen (w Vikram Sathaye and Roshan Abbas). 25. This Be The Verse — Philip Larkin. 26. Rohit Lamba Will Never Be Bezubaan -- Episode 378 of The Seen and the Unseen. 27. Volga Se Ganga (Hindi) (English) -- Rahul Sankritayan. 28. In Service of the Republic — Vijay Kelkar & Ajay Shah. 29. Turning Over the Pebbles: A Life in Cricket and in the Mind -- Mike Brearley. 30. Slow Horses (book one of Slough House) -- Mick Herron. 31. Postwar: A History of Europe Since 1945 -- Tony Judt. 32. On Warne -- Gideon Haigh. 33. The Essential Keynes -- John Maynard Keynes. 34. The Age of Uncertainty — John Kenneth Galbraith. 35. Asian Drama -- Gunnar Myrdal. 36. Aneesh Pradhan on Spotify, Amazon, Instagram, Twitter and his own website. 37. Malini Goyal is the Curious One — Episode 377 of The Seen and the Unseen. 38. The UNIX Episode -- Episode 32 of Everything is Everything. 39. The O-Ring Theory of Economic Development -- Michael Kremer. 40. Why Abhijit Banerjee Had to Go Abroad to Achieve Glory -- Amit Varma. 41. Why Talent Comes in Clusters -- Episode 8 of Everything is Everything. 42. The Dark Knight Rises -- Christopher Nolan. 43. Thinking it Through -- The archives of Amit Varma's column for Mint. 44. Remembering Mr. Shawn's New Yorker -- Ved Mehta. 45. Videhi -- Vijaya Rajadhyaksha. 46. Select pieces on the relationship between Raymond Carver and Gordon Lish: 1, 2, 3, 4. 47. Capitalism, Socialism, and Democracy -- Joseph Schumpeter. 48. Maharashtra Politics Unscrambled — Episode 151 of The Seen and the Unseen (w Sujata Anandan). 49. Complaint Resolution Systems: Experimental Evidence from Rural India -- Chinmaya Kumar and MR Sharan. 50. Parkinson's Law — C Northcote Parkinson. 51. The Importance of the 1991 Reforms — Episode 237 of The Seen and the Unseen (w Shruti Rajagopalan and Ajay Shah). 52. The Life and Times of Montek Singh Ahluwalia — Episode 285 of The Seen and the Unseen. 53. The Forgotten Greatness of PV Narasimha Rao — Episode 283 of The Seen and the Unseen (w Vinay Sitapati). 54. The Life and Times of KP Krishnan — Episode 355 of The Seen and the Unseen. 55. Lant Pritchett Is on Team Prosperity — Episode 379 of The Seen and the Unseen. 56. The Reformers — Episode 28 of Everything is Everything. 57. The Tragedy of Our Farm Bills — Episode 211 of The Seen and the Unseen (w Ajay Shah). 58. Public Choice Theory Explains SO MUCH -- Episode 33 of Everything is Everything. 59. The Logic of Collective Action — Mancur Olson. 60. Ashutosh Salil and the Challenge of Change — Episode 312 of The Seen and the Unseen. 61. Rational Ignorance. 62. The State of Our Farmers — Ep 86 of The Seen and the Unseen (w Gunvant Patil, in Hindi). 63. India's Agriculture Crisis — Ep 140 of The Seen and the Unseen (w Barun Mitra & Kumar Anand). 64. The Indian State Is the Greatest Enemy of the Indian Farmer — Amit Varma. 65. The Worldly Philosophers --  Robert Heilbroner. 66. The Clash of Economic Ideas — Lawrence H White. 67. Capital-Labor Substitution and Economic Efficiency -- Kenneth Arrow, Hollis Chenery, Bagicha Singh Minhas and Robert Solow. 68. Room 666 -- Wim Wenders. 69. Laapataa Ladies -- Kiran Rao. 70. The Brave New Future of Electricity -- Episode 40 of Everything is Everything. 71. What I, as a development economist, have been actively “for” — Lant Pritchett. 72. National Development Delivers: And How! And How? — Lant Pritchett. 73. Economic growth is enough and only economic growth is enough — Lant Pritchett with Addison Lewis. 74. Smoke and Ashes -- Amitav Ghosh. 75. Sata Uttarachi Kahani -- GP Pradhan. 76. Gopal Ganesh Agarkar and Bal Gangadhar Tilak. 77. Collections of VD Savarkar's Marathi essays: 1, 2. 78. Savarkar and the Making of Hindutva -- Janaki Bakhle. 79. Savarkar Te BJP -- SH Deshpande. 80. Sarvakarancha Buddhiwad Ani Hindutvawad -- Sheshrao More. 81. Swatantryaveer Savarkar Ek Rahasya -- DN Gokhale. 82. Shodh Savarkarancha -- YD Phadke. 83. The Taking of Pelham 123 -- Tony Scott. 84. Sriram Raghavan (IMDb) (Wikipedia) and Vijay Anand (IMDb) (Wikipedia). 85. Manorama Six Feet Under -- Navdeep Singh. 86. Agatha Christie and Frederick Forsyth on Amazon. 87. Salil Chowdhury and RD Burman on Spotify. 88. Haikyu -- Haruichi Furudate. 89. Pramit Bhattacharya Believes in Just One Ism — Episode 256 of The Seen and the Unseen. 90. Yes Minister and Yes Prime Minister — Jonathan Lynn and Antony Jay. 91. Dilip José Abreu: an elegant and creative economist — Rohit Lamba. Niranjan would like to inform listeners that Spontaneous Order would be translated to Marathi as उत्सफूर्त व्यवस्था. This episode is sponsored by CTQ Compounds. Check out The Daily Reader and FutureStack. Use the code UNSEEN for Rs 2500 off. Amit's newsletter is active again. Subscribe right away to The India Uncut Newsletter! It's free! Amit Varma and Ajay Shah have launched a new video podcast. Check out Everything is Everything on YouTube. Check out Amit's online course, The Art of Clear Writing. Episode art: ‘The Impartial Spectator' by Simahina.

David Henderson is a research fellow at Stanford University's Hoover Institution and the editor of the Concise Encyclopedia of Economics. He is also an emeritus professor of economics with the Naval Postgraduate School. Today, we talk about another famous economist who has recently passed, Robert Solow. Henderson tells us about the Solow model, a still relevant model used in macroeconomics relating to economic growth, and we discuss its origin and its flaws. He talks to us about Solow's career, his reputation, and his attitude (Solow had a career-long grudge against Milton Friedman). Henderson leads us on a multi-media experience, where he reads us quotes from a book containing and interview of Solow about Friedman, and you can listen to it here, on the podcast!Never miss another AdamSmithWorks update.Follow us on Facebook, Twitter, and Instagram.

David Henderson is a research fellow at Stanford University's Hoover Institution and the editor of the Concise Encyclopedia of Economics. He is also an emeritus professor of economics with the Naval Postgraduate School. Today, we talk about another famous economist who has recently passed, Robert Solow. Henderson tells us about the Solow model, a still relevant model used in macroeconomics relating to economic growth, and we discuss its origin and its flaws. He talks to us about Solow's career, his reputation, and his attitude (Solow had a career-long grudge against Milton Friedman). Henderson leads us on a multi-media experience, where he reads us quotes from a book containing and interview of Solow about Friedman, and you can listen to it here, on the podcast!Never miss another AdamSmithWorks update.Follow us on Facebook, Twitter, and Instagram.

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor.  Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission.   Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree]]>

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School.

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Economic giant Robert Solow died in December 2023. He was a Nobel laureate, and four of his PhD students went on to also receive the Nobel. He is known for the growth model named in his honor. Garett Jones of GMU joins Bob to discuss the work of Solow, focusing on the possible tension between the Solow model's conclusions about capital accumulation vis-à-vis the Austrian School. Join Tom DiLorenzo, Joe Salerno, and Patrick Newman in Tampa on February 17: Mises.org/Tampa2024Use code "Action24" for 15% off admission. Human Action Podcast listeners can get a free copy of Murray Rothbard's Anatomy of the State: Mises.org/HAPodFree

Robert Solow is 98 years old and a giant among economists. He tells Steve about cracking German codes in World War II, why it's so hard to reduce inequality, and how his field lost its way. 

"You can see the computer age everywhere but in the productivity statistics," said Nobel laureate economic Robert Solow in 1987. A decade later, the '90s productivity boom was in full swing. Likewise, it took decades for electrification to have an impact on productivity growth in the early 20th century. Today, artificial intelligence can write a coherent paragraph or generate an image from a simple prompt. But when will AI show up in the statistics, boosting productivity and then economic growth? Avi Goldfarb joins Faster, Please! — The Podcast to discuss that question and more.Avi holds the Rotman Chair in Artificial Intelligence and Healthcare at the University of Toronto's Rotman School Of Management. He's also co-author, along with Ajay Agrawal and Joshua Gans, of 2022's Power and Prediction: The Disruptive Economics of Artificial Intelligence.In This Episode* Prediction at scale (1:34)* How AI has transformed ride hailing and marketing (5:37)* The potential for “system-level” changes (11:26)* When will AI show up in the statistics? (16:12)* The impact of ChatGPT and DALL-E (19:46)Below is an edited transcript of our conversation.Prediction at scaleJames Pethokoukis: What this book is about—and then you can tell me if I've gotten it horribly wrong—this is a book about machines making predictions using advanced statistical techniques. 1) Is that more or less right? And 2) why is that an important capability?Avi Goldfarb: That's more or less right. The only place where I [would offer] a little correction there is, the reason we're talking about artificial intelligence today is almost entirely due to advances in computational statistics. Yes, it is just stats and that sounds kind of unexciting. But once we have prediction at scale, it can be really transformative to all aspects of business in the economy. There's a reason why we're calling computational stats “artificial intelligence” and we didn't use to.Prediction at scale. That's a great three-word description. Probably why you used it. To what extent is that now happening? The name of the book is Power and Prediction: The Disruptive Economics of Artificial Intelligence. Is this prediction at scale already disruptive to some degree or is it, will be disruptive?The technology, for the most part, is pretty close to there, in the sense that we can do prediction at scale because we have the data and we have computational power to do all sorts of amazing things. For the most part, it hasn't been disruptive yet. And it hasn't been disruptive yet, just because we have the technology doesn't mean we know how to use it well and we know how to use it productively in our processes and systems in order to get the most out of it.Are there sectors currently doing this, but they're not doing it well yet? It's in a variety of sectors, but not enough companies doing it? Lots of companies are already using these machine learning tools, but they tend to be using them for things they were already doing before. If you had some prediction process to predict, if you're a bank, whether somebody's going to pay back a loan. In the very old days you'd have some human, the loan officer, look the customer up and down and go with their gut. And then, starting in the 1960s and especially in the ‘90s and beyond, we started to use scoring rules, partly your credit score and partly other things, to get a sense of whether people are going to pay them back. And so we were already doing a prediction task done by a machine. And now increasingly we're using these machine learning tools. We're using what we're calling AI, over the past five to 10 years, to predict whether people are going to pay back a loan. We're seeing those kinds of things all over the place, which is: You had some prediction, maybe you've used even a machine prediction before, and now we're using machine learning. We're using AI to make those predictions a little bit better. Lots of companies are using that.That sounds incremental. That sounds like an incremental advance.It's absolutely an incremental advance. We call these point solutions, which is, you look at your workflow, you identify something that a human is doing. You take out that human; you drop in a machine. You don't mess with a workflow because it's always easier to do things when you don't mess with a workflow. The problem is, when you don't mess with a workflow, there's only so much gain you can get. We've seen AI-based point solutions, prediction point solutions, all over the place. We haven't seen real transformation in very many industries. We've seen it in a couple. We haven't seen it in very many industries because real transformation requires doing things differently.How AI has transformed ride hailing and marketingDo you think that it has happened in one or two industries that you think would actually meet that bar of transformational? Can you give me an example?Absolutely. If you wanted to be a cab driver in the city of London 20 years ago, or even today, it takes three years of schooling. Learning to navigate those streets is really, really hard. And especially learning to navigate and predict where the traffic is going to be is really, really hard. And so there is a really rigorous process to screen people to be taxi drivers. In the US 30 years ago, there was something like 200 or 300 taxi drivers in the whole country. About 15 years ago, two technologies came about. The first one being digital dispatch, which is essentially tools for drivers to find riders, sometimes through prediction and sometimes through other tools. And then the second part was what's been disruptive with respect to that three years of schooling in the city of London, which is prediction tools for navigating a city. This is your GPS system.In the early days, many people selling digital dispatch and navigational predictions were selling them into professional driving companies, into taxi companies. “Hey, your taxi drivers can be 15 percent more efficient if they know the best route at this time.” That's what we call a point solution. You're already doing this, you take out some part of the human process, you drop in a machine, and you do it a little bit better. A couple of companies realized that digital dispatch combined with navigational prediction could create an entirely new type of industry. And this is the ride-hailing industry led by Uber and Lyft and others. That's a totally new kind of way to do personal transportation that made millions of amateur drivers as good as professional because they could navigate the city and find riders.Example number one is the taxi industry. Personal ride-hailing, for lack of a better word, has been transformed partly through digital and really those maps are important—and a big part of those maps is machine learning tools and figuring out where the traffic is, etc. So industry number one.Industry number two is advertising. I don't know if you've seen the TV show Mad Men. That was really how the advertising industry operated well into the ‘90s. Maybe not the soap opera aspect of it. Maybe, maybe not. I don't know. But the idea that there's a lot of wining and dining and charming people to convince them to spend millions of dollars on an ad campaign. And whether a campaign worked or not was largely based on gut feel. And which kinds of customers you targeted and which TV show and which magazine, all of that was priced based on intuition and not much else.Digital advertising came along in the late 1990s, and the first ways we thought about digital advertising was that it was like the magazine industry. So instead of advertising in People magazine, you're going to advertise on Yahoo using the exact same processes you did in People magazine. There was a rate card and it was going to be so many dollars per thousand users. And if you were doing general search, it might be $10, and if you're looking for real estate, it might be $50. And that's exactly how the magazine industry was priced. Some magazines were more than others based on readership and topic. And it was all based on personal selling, intuition, deals, etc.Then people realize that digital advertising created an opportunity to predict who the user was, who might see your ad. A user arrives at a publisher and an ad needs to be served, and you can predict who that user is and what they might want and when they might want it. Based on those predictions, rather than just do the magazine industry old way of doing things, you can now serve the right ad to the right person at the right time. Starting around 2000, there were all these innovations in online advertising that led to an industry that today looks almost nothing like the industry that you saw in Mad Men. Every time a user goes to a website, there is a real-time auction, in fractions of a second, between, in effect, thousands of advertisers for that user's attention. And there are all these intermediary steps, lots and lots of intermediaries—largely led by Google, but some other players that complement Google in that process—to create an entirely new kind of ad industry. The ad industry has had a system-level change because we can now predict, for a given impression or given user who's looking at a page, what they might want and when they might want it. Predictions changed the industry.The potential for “system-level” changesHow confident are you that this technology is powerful enough that we'll see system-level changes across the economy? That this is a general-purpose technology that will be significant? And do we have any idea what those changes will be, or is it, “They'll be big, but we don't know exactly what they are.”The technology itself is pretty extraordinary. And so in lots and lots of contexts, I'm pretty confident the technology's going to get there. There are some constraints on it, which is that you need data on the thing you're trying to predict in order for the predictions to work. But there are lots and lots of industries where we have great data. The technology barriers, I think, are being overcome. In some industries faster than others, but they're being overcome in lots and lots of places.That's not the only barrier. The technology is barrier number one. Think of an industry that I'm particularly excited about the potential of the technology, which is healthcare. Why is it so exciting for healthcare? Because diagnosis is at the center of how healthcare operates. If you know what's wrong with somebody, it's much easier to treat them, it's much less costly to treat them, and you can deliver the right treatment to the right person at the right time. Diagnosis, by the way, is prediction. It wasn't obvious, the way we thought about that in the past. But really, what it is, it can be solved [with] statistical prediction by using the information you have, the data on your symptoms, to fill in the information you don't have, which is what's actually causing your symptoms. If you do a Google Scholar search for something like “artificial intelligence healthcare,” you'll get a few million hits. There are lots of people who've done research producing AI for diagnosis. The technology, in many cases, is there. And in lots of other cases, it's pretty close.That doesn't mean it's going to transform healthcare. Why not? What's an AI doing diagnosis? They're doing a thing that makes doctors special. Yes, a good doctor in their workflow does all sorts of other things — they help patients navigate the stress of the healthcare system, they provide some treatments, etc. — but the thing that they went to school for all those years for, and for many of them the thing that they have that nurses and pharmacists and other medical professionals don't, is the ability to diagnose. When you bring in machine diagnosis into the healthcare system, that's going to be very disruptive to doctors. There are lots of reasons why, then, doctors might resist. First, they might be worried about their own jobs. Second, they might just not trust the machines and believe they're good enough. Because [in] the medical system doctors are a core source of power—they help determine how things work—they're going to resist many of the biggest system-level changes from AI-based diagnosis.And so you may have regulatory barriers, you may have organizational incentive barriers, and you may have barriers from the individual people on the ground who sabotage the machines that are trying to replace them. All of these are reasons — even if the technology is good enough — that AI in healthcare may be a long way away, even though we can see what that vision looks like. In other industries, it might be closer. In lots of retail contexts, you're trying to figure out who wants what and when — Amazon's pretty good at that in lots of ways — and in-store retailers can do that too. And so there are reasons to think that disruption in many retail industries will come faster.I just want to be a little careful here. I see the technology is there. There are some barriers on the technology side. If the payoff is big enough, I think most of the technology-related barriers can be overcome. To give you a sense of this: We hear a lot something like, “We don't want to do AI in our company because it's just so difficult to get the data organized and get the right data to build those predictions.” Well, yeah, it's difficult. But if the payoff is going to be transformative to the company and make the company millions or billions of dollars, then they'll spend thousands or millions in order to make it happen. And so a lot of the challenges aren't tech specific. They're incentives and organization based.When will AI show up in the statistics?I think of the classic Paul David paper about the dynamo. It took a while before factories used electricity, and they actually had to redo how the factory was designed to get full productivity value. And you say that we are sort of in the “between times.” And that makes me think of a classic Solow paradox: We see computers everywhere but in the statistics. He said that in '87. Are we, like, in the 1987 period with this technology? Or are we now in the late ‘90s where it's starting to happen and the boom is about to begin?I think we're in the early ‘80s.Not even the late ‘80s?He said that in 1987. By 1990 it was showing up in the data. So he just missed it.[We're in the early 1980s] in the sense that we don't quite know what the organization of the future looks like. There are reasons to think for many industries it might take a long time, like many years or decades, for it to show up in the productivity stats. While I do say we're in the early ‘80s because we haven't figured it out yet, I'm a little more optimistic that maybe it won't be 30 years to really have the impact. Mostly because we just have the lessons of history. We know from past technologies, and business leaders know from past technologies, electricity and the internet and the steam engine and others, that it requires some system-level change. And we now have the toolkits to think through, how do you build system-level change without destroying your company?When electricity was diffusing in the 1890s, there wasn't really any idea that this might take 40 years to figure out what the factory of the future looks like. It just wasn't on anybody's mind. The management challenges of redesign were unstudied, and there was no easily accessible knowledge to figure that out. Jump forward to the ‘80s and computing: Again, we hadn't even learned the lessons of electricity back then. Paul David's paper came out in 1990. It was a solution to the Solow paradox.But since then, we have a much better understanding of what's required for technological change. There has been decades of economics literature Erik Brynjolfsson, Tim Bresnahan, Paul David, and others. And there's been decades of management literature taking a lot of those ideas from econ and trying to communicate them to a broader audience to say, “Yes, it's hard. But doing nothing can also be a disaster. So being proactive is useful.” Then there's another piece about optimism here, which is that the entrepreneurial ecosystem is different than it used to be. And we have lots and lots of very smart people building tech companies, trying to make the system-level change happen. And that gives us more effectively more kicks at the can to actually figure out what the right system looks like.The impact of ChatGPT and DALL-EChatGPT and these text-to-image generators like DALL-E, are these significant innovations that can cause system change? Or are they toys that can't figure out how many arms people have and are able to produce B-level middle school essays?They're both. What do I mean by that? The technology is incredible. What ChatGPT can do and DALL-E can do is really, at least to me, it's amazing. Especially what ChatGPT can do. It's much better than I… That came much faster than at least I thought it was going to come. When I first saw it, I was blown away. So far it's a toy. So far, most applications have been “Hey, isn't this cool? I can do this kind of thing.” In a handful of places, it's moved beyond a toy to a point solution. Joshua [Gans], Ajay [Agrawal], and I wrote a piece in HBR. We drafted it out, and rather than reread it and edit it 60 times like we normally do, we sent it into ChatGPT and said, “Write this in a way that's easy to read.” And it did. We had to do some final edits afterwards. But like, we are already doing the same thing. It made a piece of our workflow a little bit more efficient. Point solution.A lot of the talk here in universities, “Uh-oh, we have to change the way we do final exams because ChatGPT can write those exams for our students.” Sure. But that's really not thinking through the potential of what the technology can do. What we've seen so far are toys and point solutions, but I do see extraordinary potential for system solutions in both. Both DALL-E and ChatGPT, and all these generative models. ChatGPT, if you think about it, what does it do? One thing it does is it allows anybody to write well. Like I told my students, you no longer have an excuse to write a bad essay with terrible grammar and punctuation that's not structured like a five-paragraph essay. No excuse anymore. It used to be, okay, maybe there's an excuse because there was some time crunch and you had other things due. Or your language skills — you're a math person, not an English person. No excuse anymore. ChatGPT upskills all those people who are good at other things but whose opportunities were constrained by their ability to write. So what's that new system? I don't know. But there are a lot more people around the world who are bad at writing English than are good at writing English. And if now everybody is a B high school-level student, able to write an essay or able to write well in English, an email or whatever it might be, that's going to be amazing. We just have to figure out how to harness that. We haven't yet.You've sort of given us a potential timeframe, broadly, for when we might see this in the data. When we see it in the data, how significant do you think this technology can be? What is, do you think, the potential impact once you can find it in the data, the productivity growth, which is kind of the end goal is here?That's a great question. Let me reframe it and say, the thing I'm worried about is that it won't reach its potential. A lot of people are worried about the impact of AI on jobs and what are people going to do if machines are intelligent? Jason Furman attended our first Economics of AI conference. This was in 2017. He was formerly chair of Obama's Council of Economic Advisors. And the thing I'm worried about is that there's not going to be enough AI. The productivity booms that we've had in history from way back to the steam engine and then electricity and then the computer age and the internet have been driven by system-level change, where we've figured out how to reinvent the economy. And that's led to sustained productivity growth: first the steam engine at 0.5 percent and then maybe 1 percent with electricity and then 2 percent after the war or more. I don't know what the number is going to be. I know you wanted me to give you a number. I don't know what the number's going to be. But this technology has potential to be like all those others, assuming we figure out what that system-level change looks like and we overcome the various sources of resistance.To sum it up, your concern is less about, can we solve the technical problems, versus, will society accept the results?Exactly. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fasterplease.substack.com/subscribe

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: "Heretical Thoughts on AI" by Eli Dourado, published by DragonGod on January 19, 2023 on LessWrong. Abstract Eli Dourado presents the case for scepticism that AI will be economically transformative near term. For a summary and or exploration of implications, skip to "My Take". Introduction Fool me once. In 1987, Robert Solow quipped, “You can see the computer age everywhere but in the productivity statistics.” Incredibly, this observation happened before the introduction of the commercial Internet and smartphones, and yet it holds to this day. Despite a brief spasm of total factor productivity growth from 1995 to 2005 (arguably due to the economic opening of China, not to digital technology), growth since then has been dismal. In productivity terms, for the United States, the smartphone era has been the most economically stagnant period of the last century. In some European countries, total factor productivity is actually declining. Eli's Thesis In particular, he advances the following sectors as areas AI will fail to revolutionise: Housing Most housing challenges are due to land use policy specifically Housing factors through virtually all sectors of the economy He points out that the internet did not break up the real estate agent cartel (despite his initial expectations to the contrary) Energy Regulatory hurdles to deployment There are AI optimisation opportunities elsewhere in the energy pipeline, but the regulatory hurdles could bottleneck the economic productivity gains Transportation The issues with US transportation infrastructure have little to do with technology and are more regulatory in nature As for energy, there are optimisation opportunities for digital tools, but the non-digital issues will be the bottleneck Health > The biggest gain from AI in medicine would be if it could help us get drugs to market at lower cost. The cost of clinical trials is out of control—up from $10,000 per patient to $500,000 per patient, according to STAT. The majority of this increase is due to industry dysfunction. Synthesis: I'll stop there. OK, so that's only four industries, but they are big ones. They are industries whose biggest bottlenecks weren't addressed by computers, the Internet, and mobile devices. That is why broad-based economic stagnation has occurred in spite of impressive gains in IT. If we don't improve land use regulation, or remove the obstacles to deploying energy and transportation projects, or make clinical trials more cost-effective—if we don't do the grueling, messy, human work of national, local, or internal politics—then no matter how good AI models get, the Great Stagnation will continue. We will see the machine learning age, to paraphrase Solow, everywhere but in the productivity statistics. Eli thinks AI will be very transformative for content generation, but that transformation may not be particularly felt in people's lives. Its economic impact will be even smaller (emphasis mine): Even if AI dramatically increases media output and it's all high quality and there are no negative consequences, the effect on aggregate productivity is limited by the size of the media market, which is perhaps 2 percent of global GDP. If we want to really end the Great Stagnation, we need to disrupt some bigger industries. A personal anecdote of his that I found pertinent enough to include in full: I could be wrong. I remember the first time I watched what could be called an online video. As I recall, the first video-capable version of RealPlayer shipped with Windows 98. People said that online video streaming was the future. Teenage Eli fired up Windows 98 to evaluate this claim. I opened RealPlayer and streamed a demo clip over my dial-up modem. The quality was abysmal. It was a clip of a guy surfing, and over the modem and with a struggling CPU I got about 1 fra...

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: "Heretical Thoughts on AI" by Eli Dourado, published by DragonGod on January 19, 2023 on LessWrong. Abstract Eli Dourado presents the case for scepticism that AI will be economically transformative near term. For a summary and or exploration of implications, skip to "My Take". Introduction Fool me once. In 1987, Robert Solow quipped, “You can see the computer age everywhere but in the productivity statistics.” Incredibly, this observation happened before the introduction of the commercial Internet and smartphones, and yet it holds to this day. Despite a brief spasm of total factor productivity growth from 1995 to 2005 (arguably due to the economic opening of China, not to digital technology), growth since then has been dismal. In productivity terms, for the United States, the smartphone era has been the most economically stagnant period of the last century. In some European countries, total factor productivity is actually declining. Eli's Thesis In particular, he advances the following sectors as areas AI will fail to revolutionise: Housing Most housing challenges are due to land use policy specifically Housing factors through virtually all sectors of the economy He points out that the internet did not break up the real estate agent cartel (despite his initial expectations to the contrary) Energy Regulatory hurdles to deployment There are AI optimisation opportunities elsewhere in the energy pipeline, but the regulatory hurdles could bottleneck the economic productivity gains Transportation The issues with US transportation infrastructure have little to do with technology and are more regulatory in nature As for energy, there are optimisation opportunities for digital tools, but the non-digital issues will be the bottleneck Health > The biggest gain from AI in medicine would be if it could help us get drugs to market at lower cost. The cost of clinical trials is out of control—up from $10,000 per patient to $500,000 per patient, according to STAT. The majority of this increase is due to industry dysfunction. Synthesis: I'll stop there. OK, so that's only four industries, but they are big ones. They are industries whose biggest bottlenecks weren't addressed by computers, the Internet, and mobile devices. That is why broad-based economic stagnation has occurred in spite of impressive gains in IT. If we don't improve land use regulation, or remove the obstacles to deploying energy and transportation projects, or make clinical trials more cost-effective—if we don't do the grueling, messy, human work of national, local, or internal politics—then no matter how good AI models get, the Great Stagnation will continue. We will see the machine learning age, to paraphrase Solow, everywhere but in the productivity statistics. Eli thinks AI will be very transformative for content generation, but that transformation may not be particularly felt in people's lives. Its economic impact will be even smaller (emphasis mine): Even if AI dramatically increases media output and it's all high quality and there are no negative consequences, the effect on aggregate productivity is limited by the size of the media market, which is perhaps 2 percent of global GDP. If we want to really end the Great Stagnation, we need to disrupt some bigger industries. A personal anecdote of his that I found pertinent enough to include in full: I could be wrong. I remember the first time I watched what could be called an online video. As I recall, the first video-capable version of RealPlayer shipped with Windows 98. People said that online video streaming was the future. Teenage Eli fired up Windows 98 to evaluate this claim. I opened RealPlayer and streamed a demo clip over my dial-up modem. The quality was abysmal. It was a clip of a guy surfing, and over the modem and with a struggling CPU I got about 1 fra...

durée : 00:04:09 - Le Pourquoi du comment : économie et social - par : Florence Jany-Catrice - Dans les années 1950, l'économiste Robert Solow développa un modèle de référence qui dépendait essentiellement des volumes de travail et de capital. Dépassant ce cadre aujourd'hui, la croissance verte repose sur le progrès technique et le modèle d'une "croissance endogène"

In this episode, Victor Menaldo speaks to Nicolas Wittstock about his forthcoming book on productivity within the US technology sector. Robert Solow famously declared in 1987 that “you can see the computer age everywhere but in the productivity statistics”. Extending this observation to the technologies of the fourth industrial revolution, economists like Robert Gordon have voiced similar skepticism. Victor Menaldo presents preliminary results from his forthcoming book on productivity within the US technology sector.

This week we talk about The Great Stagnation, Robert Solow, and total factor productivity.We also discuss low-hanging economic fruit, technological innovations, and looms. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

This week we talk about The Great Stagnation, Robert Solow, and total factor productivity. We also discuss low-hanging economic fruit, technological innovations, and looms. Become a patron: patreon.com/letsknowthings (patrons get access to an ad-free version of the show, and a bonus episode each month) Support the show: letsknowthings.com/support (monetarily or non-monetarily) Show notes/transcript: letsknowthings.com Daily summary of the previous day's news that I curate: yesterdaysnewsletter.com You might also enjoy my other podcast: brainlenses.com

Giampaolo Galli è vicedirettore dell'Osservatorio sui Conti Pubblici dell'Università Cattolica del Sacro Cuore e Senior Fellow presso la Luiss School of European Political Economy.Laureato all'Università Bocconi, ha conseguito il Ph.D al MIT (1980) sotto la supervisione di Robert Solow e Franco Modigliani.Ha lavorato presso il Servizio Studi della Banca d'Italia e nel 1991 è divenuto responsabile della direzione internazionale e ha rappresentato la Banca d'Italia presso diverse istituzioni internazionali, tra le quali il Comitato Monetario dell'Unione Europea, il Comitato di Politica Economica dell'OCSE, il G10.È stato anche capo economista di Confindustria (1995-2002), Direttore Generale dell'Associazione Nazionale delle Imprese di Assicurazioni (2003-2008), Direttore Generale di Confindustria (2009-2012); deputato eletto come indipendente nella lista del collegio Milano 1 del Partito Democratico, assegnato alla Commissione Bilancio della Camera (2013-2018).

Garett Jones is one of the smartest people I have ever talked to - and he is at his usual brilliant best in this conversation. We started by trying to see how his latest book fit into the context of less developed countries with weak rule of law. I have often remarked that Garett is underrated as a development economist. I still think so.You can listen or download to the podcast on any platform of your choice (some links here), and you can also rate us here.TranscriptTobi: WeIcome to Ideas Untrapped and today I am on with economist, Garett Jones. Garett Jones is a professor of economics at George Mason University and he has written two excellent books "Hive Mind" and "10% Less Democracy". Welcome, Garett.Garett: Thanks very much for having me, Tobi.Tobi: Thinking on the margin is something I admire so much which is what you did quite well in your latest book 10% Less Democracy. Are economists just better at this than everybody else, and if yes, why?Garett: You're right. I do try to do that a lot in the book. I agree with you, Economists, I think, are better at this than other social scientists because it's just so much a part of our training. It's so normal for us to think, do you want to buy two more peanut butter sandwiches or two fewer? Should this company hire three more workers or fire two workers? So that kind of marginalist thinking which is where... the Marginal Revolution of the 19th century embodies that. Yeah, that really helps us think about big social questions in a very productive way. When I applied this to democracy what I realised it was quickly, something that a lot of people know, which is that all of the things we call democracies are a blend of democracy with oligarchy of one form or another. So getting the balance right is more important than an all-or-nothing question. All or nothing is off the table, thank goodness, but getting the blend right between insiders and outsiders, between elite and the masses, that's something that can be evaluated and fortunately, my economist friends have evaluated it. For some people, they'll quite naturally assume that democracy means an independent judiciary, the rule of law, impartial, fairly uncorrupt government. And those things are not democracy. They are good things but they are not democracy. - GJTobi: So given all the trade-offs that are involved with social decisions and reforms, how can we be better at thinking on the margins? We live in an era of protest movement where people want sweeping changes and that's not really how society works, so how can people better train themselves to be marginalist thinkers?Garett: Yeah, you're right. There aren't that many questions where we have to go all or nothing. Most policy questions can be a question of incrementalism, so I think whenever possible we should just ask ourselves 'if I can dial this up a little bit or dial this down a little bit, which would be the better way to work? Which would be the better way to move?' Whether that's thinking about whether I want my judges to have a little bit longer terms, whether I want my voters to have a little bit more information before they walk into the voting booth, whether I want a healthcare program to be available for people who are 62 rather than 65? Just thinking about it in terms of small changes helps us better evaluate...it helps the mind better weigh the benefits versus the cost of the decision. Because when we talk about revolutionary change, there are often just too many things going on for our brains to even weigh them, to even weigh the benefits, and weigh the costs. So marginalism, I think, is better suited for human minds and fortunately most, but not all, political decisions are well suited to a little more vs a little less.Tobi: I read your book, great book by the way...Garett: Thank you very much.Tobi: The writing is fantastic, I'm a big fan. In the book, I know you are applied the 10% Less Democracy framework to rich countries...Garett: Yeah.Tobi: But I've been trying to extrapolate and apply it to developing countries, and one thing I noticed (you can shed more light on this) is, sometimes it feels like low-income and middle-income countries are torn in some kind of institutional paradox. You have multilateral institutions like [the] IMF who have these prescriptions that are bureaucratic but have long-term benefits - Central Bank independence, don't manipulate the exchange rate, keep inflation low, be responsible with your budget, and all that. And on the other side of that, you have think tanks, aid agencies and other foundations (who are also interested in development and give it advice) who seem to favour radical democratisation of everything, basically. So what do you think, as a policymaker, as a voter in a low and middle-income country, how best to approach this paradox on a mental level?Garett: I think a message that I bring up early on in the book is that most of the clear benefits of democracy come from a moderate level of democracy. As Amartya Sen, the Nobel laureate who showed that if you want to avoid famines, what you need is democracy. In modern times there's never been a famine as he defines it in a functioning democracy. But his measure for functioning democracy is a pretty basic one. It's competitive elections and a free press. And that's not too hard a standard for a lot of countries to meet and more countries are meeting it now than they were meeting it, of course, three decades ago though, perhaps, less than a couple of years ago. So if you're pushing for democratisation, I think we should draw on the best evidence we have for what it is, what kind of democracy we need to get the benefits of democracy, and that seems to be a moderate level. Also, there is a lot of lazy talk about democracy where people stuff all of the good things they like into the word democracy. For some people, they'll quite naturally assume that democracy means an independent judiciary, the rule of law, impartial, fairly uncorrupt government. And those things are not democracy. They are good things but they are not democracy. So I think just clearly speaking about what it is that you want is valuable because we realised quickly that the things we like out of modern so-called democracies are a blend of the rule of the people and the rule of insiders, with a third thing that I don't talk much about in the book directly but the rule of law - the impartial rule of law, a bureaucracy that just operates on its own according to [the] rules that have been around for a long time. That is undemocratic and it seems to be very useful. So simply talking clearly about what it is we want in a reform and is this valuable reform truly about the voice of the people or is it more about something like an independent bureaucracy? That would help us get away from this lazy jargon of calling everything good democratic.Tobi: You also discussed the relationship between democracy and growth in your book and you conclude that the evidence is a bit of a muddle. What I was thinking when I was reading that part, I thought about Chile...Garett: Uh-huhTobi: And one of the famous examples when economists and some other thinkers discuss Chile is to look at GDP growth from the Pinochet years and the democratic years, and then they conclude that, oh, GDP growth is higher after Pinochet and hence democracy is better. But again, if you look at that history a bit, you'll see that there were some things, though they weren't palatable and I'm not saying I prefer autocracies here or anything...there were some hard reforms that Pinochet pushed through that clearly had benefits even during the Chilean democracy. One such rule was the inability of the parliament to hike the budget. You either cut it low or you pass it as it is which introduced a lot of fiscal responsibility in the budgeting process. So what do you think is responsible for this muddle in the evidence in the relationship between democracy and growth? Why can't we get a really clear picture?Garett: A big problem is the real-life fact which is just that a lot of autocrats do a terrible job. They come into power and they make the place worse off. So some autocrats come to power and appear to make the country better off, or at least, it predicts better performance and other autocrats come into power and things get worse. So when we stop looking at individual anecdotes and when we pull them together and do something rigorous and statistical, the evidence that autocrats are more likely to create great reforms looked pretty weak. There are plenty of anecdotes, right? We can call them case studies where autocrats are associated with and may have put into place things that look like good pro-growth reforms. Pinochet gets a lot of credit for this kind of stories but also Park Chung-hee of South Korea. The problem is that (a) we don't have a great counterfactual (b) maybe they just got lucky. And that's why using rigorous cross-country comparisons is more useful than individual case studies and when we do that, it's very hard to find evidence that either democracy causes growth or that an autocrat taking over causes growth. It's too much of a coin flip to recommend any particular policy if our goal in choosing a government is economic growth. So this is why we should stick to the things where we have better evidence and so these big changes - autocrat in charge versus free press multi-party democracy, there there's a muddle. That's the reason why the framing in my book 10% Less Democracy is about smaller changes where we have better identified better causal stories with better testings like independent central banks, independent judges. We have more evidence for the small things than we do for the big things.Thinking in marginalist terms is important but also good public education really can improve policy if we teach true important economic ideas to people - GJTobi: Looking at this really well, is it really about voter control? Because I imagine the issues are different, for example, I think voters care a lot about Central Bank independence than, say, a national minimum wage for example. So isn't the case that in some situations or on some issues, again, sticking with Central Bank independence, politicians adjust ill-informed or ignorant and they're not necessarily responding to voter preference?Garett: That's a great point. It's always a good idea to wonder whether the politicians themselves are poorly informed and they don't have an incentive to be very well-informed on most policy issues. A friend of mine had a conversation with the prominent United States politician who I won't name and this person said...the elected official said 'my job isn't to understand all the details of the policy, that's what my staff job is. My job is to keep track of all of the other members of Congress and find out how to cut deals with them.' So their real specialty is deal-making, deal cutting. They don't know that much about the detail of policy. So you are right that basically part of the problem is that the politicians themselves don't know that much, but they need to know enough to be able to pick someone who's good to run some of these things. So running [knowing] someone who is competent to run an independent central bank, it's kind of a hard job but you can outsource that to some staff; and if you're worried about things working pretty well for a long time, then you'll task your staff with picking somebody who seems like a pretty good candidate who won't cause much trouble and who will make the economy look pretty good before the electorate. So this idea that elected officials don't know that much themselves but they do have an incentive to get some things right when they know they're going to be held accountable by the voters.Tobi: But again, wouldn't less democracy, at least, in some cases lead to populist backlash? I mean, we're seeing that with Brexit...Garett: Oh, yes.Tobi: Some part of the American polity is also in that mode. The EU is a very good example where some British voters say 'oh, we are not going to be subjected to Brussels' rule'. There's a case of Africa, also, where people respond quite negatively to what they perceive as external technocratic interventions. So wouldn't less democracy run a risk of populism in the long-term?Garett: Actually, that's a great point and it's the one that I literally never discussed in the book - it's the idea of a populist backlash. Because it's one of these things that is important and too hard to quantify. The risk of a populist backlash to 10% Less Democracy is a little bit like the risk of a doctor being reluctant to give someone tough advice about diet and exercise because the person just might not come to the doctor anymore. So this is an important question - when should informed people, when should people who are relative experts just not push that hard for the best solution because they are afraid the patient won't take the medicine? In a way that's part of the reason I stick to 10% Less Democracy. I just want people to think about a little bit longer term, a little bit more independent Central Bank because I think these things are less likely to provoke that kind of backlash. But, you're right. But know this, to the extent you're right, this should tell us something about the cost of democracy - if one of the costs of democracy is that voters don't want things that are actually good for the voters, this should be part of our understanding [of] what democracy really is. If the problem is that the voters don't want to take advice that's just some person's opinion, 'ok, well, who cares?' Voters shouldn't have to listen to some person's random opinion. But if voters don't want to take [a] medicine that's actually good for the voters themselves, that should be part of our understanding of one of the weaknesses of democracy and something we should try to find a solution to. Maybe it's the solutions I present in my book, maybe it should be something else, but understanding the weaknesses of modern democracy is important to improving it.Tobi: A good illustration of that point is trade policy. I was just reading Matthew Klein and Michael Pettis's book where they basically say that trade wars are class wars.Garett: Uh-huh.Tobi: It's a reaction. And also it's a tactic for politicians to whip up voter sentiments and possibly win votes. How can people, again, I'm quite interested especially on the key issues that matters like central banking, like trade policy... things that affect the welfare and long-term prosperity, how can voters be a lot more informed to know that taking the choices out of my hands does not really mean I'm being oppressed?Garett: Yeah. This is really an important question. Part of it is that there is some evidence that just education in schools really changes people's minds. So when my colleague Bryan Caplan - he wrote a great book Myth of the Rational Voter, and he wrote a follow-up article where he looked at whether education or IQ scores were better predictors of pro-market attitudes. And he found that particularly on free trade, there was evidence that education itself, years of education was a better predictor of pro-trade attitudes than IQ. This is a signal and it's a reminder of something that made a lot of us believe which is that one of the things you learn in school is that people in other countries are good and, sometimes, they are great people and you should care about them. Also, you might learn some complicated ideas like the benefits of free trade. So education that is focused on teaching true and important facts about public policy, I think, can be a big part of this. But there is another element, another solution is just that we should think like a marginalist and go up the marginal cost curve. Push for a little less populism on topics where the voters aren't going to resist as much. Voters around the world have been pretty cool comparatively speaking with independent central banks - letting neutral banks lend out money and respond to financial crisis, (and) I think people can kind of understand why that's better than having one political party trying to lend to its buddies all the time. So, yeah, thinking in marginalist terms is important but also good public education really can improve policy if we teach true important economic ideas to people.Tobi: Let's let's go of the cuff a bit. Why did East Asia converge faster than the rest of the developing world?Garett: This is a great question. I mean part of it you could say that they actually had pretty high levels of productivity before say about 1800. This is part of what the deep roots literature shows, of Putterman and Weil and Bill Easterly at NYU - that, like, before 1500, before the great age of exploration, East Asia was pretty close to the technological frontier for the planet as a whole and so what's happened in the last 50 years in a way is a return to trend. That's not an answer. That's just more of a reminder that sometimes the more things change the more they stay the same. But when I look for the proximate cause, something more like a proximate cause, then I turned back to my first book, Hive Mind, which is that as far back as we have data on test scores, East Asia with particular countries we had good tests on in the ’60s and early 70s - Hong Kong, Taiwan, Japan, and soon after that Singapore - these countries were doing pretty well on standardised tests no matter how we measure it. And I think that good cognitive skills are [a] really important ingredient of being able to jump to the technological frontier, and I think that good cognitive skills are an important part of running a good competent government. Those aren't the whole story, fortunately, China's decision to turn away from communism was one of the best decisions in all of human history; that mattered a lot for well over a billion people. But the fact that as far as we can tell, test scores, human capital as currently measured was pretty high in East Asia in the sixties and early seventies, that gave them a good solid launching pad for modern prosperity.Tobi: You've hinted I think on your Twitter feed, I'm not sure anymore, that economists know the causes of long-run prosperity. What's your explanation for that?Garett: We're really good at the proximate causes, the very nearest causes. These are simple things that come from the Solow growth model. Robert Solow, [the] Nobel Laureate, just helped us think about where do GDP come from and having a lot of machines per worker, having a way for people to use them productively is really crucial. Machines aren't your enemy, they are your friend. A lot of technology isn't your enemy, it's your friend. But when we try to look one step behind that, say, why do some nations wind up with a lot more productivity and lot more capital per worker than others, then there's more debate but a lot of people would jump just straight to something like institutions. Some places have great institutions, good competent governance, neutral rule of law, and that means the capital is willing to flow from around the world to good places. I would also add on human capital however measured - whether it's years of education or test scores are both very quite robust predictors. So economists know what works and the simplest version is one I said years ago which is, have pretty high test scores and don't be communist, and you're probably going to be rich. So if a country can find a way to raise its test scores through better run schools, through better public health, and it can avoid the massive mistake of totalitarian communism, then it's got a pretty good future ahead of it.Tobi: In your view why did you choose test scores, I mean, what's the best case for cognitive ability in human capital and long-run prosperity ahead of all these other proximate causes you mentioned like institutions or geography or industrial policy and all these other factors?Garett: The simplest version is just that it's what shows up in the data when I and others have run very serious horse races. So Eric Hanushek, professor at Stanford, leading education researcher, he's found that test scores whether you call them IQ or math and science scores are very astonishingly robust predictors of national prosperity and they really beat out years of education. There's a lot of emphasis on trying to raise measured use of education but the problem is that we know that there is schooling and then there's schooling. So if you just get a diploma but nobody ever taught you anything, the schooling didn't really make you more productive. Another reason though is because of the well-known finding from psychology research which is that skills predict kills. People who are above average in math tend to be above average in verbal stuff. People who are above average at vocabulary test tend to be better at solving three-dimensional puzzles, and so for reasons that are still poorly understood there is what I call a DaVinci effect and what others call a g-factor across mental skills. So running a modern economy at a high-level involves kind of a little bit of everything. It's a little bit of a smorgasbord, it's a little bit of a casserole. It's probably unlikely to be the case that there is going to be this one simple thing that solves all the problems. What you really want is something that is equivalent of a Swiss army knife, something that's a mediocre tool for everything rather than one tool for just one thing. And cognitive skills whether you called them IQ or g or whatever seem to be this version of sort of Swiss army knife where there's a little bit of everything. So people who do better on standardised tests tend to be a little bit more patient. Groups of folks who do better on standardised tests tend to be more cooperative, they're more likely to see the invincible hand and support market-friendly policies, they are more likely to be tolerant of others who are unlike them and these are all great things for a nation. So like I said, running a modern economy near the frontier of productivity involves a lot of little things - low corruption, competent governance, foresight, voters who understand the benefits of trade; the one thing that I can get us a lot of all those little things is higher cognitive skills.Tobi: You wrote a paper a few years ago which I like very much...Garett: Thank you.Tobi: O-ring sectors and Foolproof sectors. And if I understand your model correctly (and you're welcome to set me straight here)... so thinking about this paper and this model, if I am a high-skilled worker in Nigeria, for example, overall you're saying the returns to skill for my education and my skill level is marginal compared to a low-skilled worker. But if I move to the United States of America where obviously there are a lot more high-skilled workers than in Nigeria, the returns to my skill will still be marginal but then there's this huge gains at the national level between both countries that are pretty large, can you explain how that works?Garett: Yeah, so I'm building here on the work of Michael Kremer who just won the Nobel prize last year. He wrote a great paper about the O-ring theory of economic development. He said that a lot of economic tasks in modern economies, especially the richest economies, are kind of like building a space shuttle where if you make one mistake, even in a very complex process of launching a space shuttle, the space shuttle tend to blow up killing everyone on board. This is actually why the space shuttle challenger was destroyed because of the failure of an O-ring (basically a big piece of big rubber band) that was an important part of keeping the rocket safe. So one small failure can destroy the value of an entire product, of course, that's true with a lot of things that we value like smartphones, automobile transmissions, one broken link can destroy the whole thing. Thing is that economists without even realising it, we use another model, routinely, that's not O-ring often without thinking about it - we kind of assumed that workers of different skill levels can get to be mushed together and it's nice to have skilled workers, but maybe you can throw maybe two high-skilled workers and three low-skilled workers, maybe they are perfect substitutes for each other. You know, just throw more bodies at it and eventually the job will get done, there are certainly jobs like that. So part of what I did and really my contribution in this paper, the O-ring sector and the Foolproof sector, was to say 'what if some parts of the economy work like Michael Kremer's world where things like building a space shuttle or smartphone? Not if other things are like the way economists normally think about the world, the Foolproof sector - where if you throw enough people at it eventually the job will get done.' And I said 'what if workers have to decide which of these two sectors they are going to work in?' So let me think about you as an example... high-skilled worker, and you're trying to decide what sector you're going to work in. Well, one of the great ideas in economics is that you're going to go to where the pay is highest, ignoring all the other complications for your life, and so really the net message is that there's always going to be some combination of workers balancing between the two sectors. So if low-skilled workers are superabundant and high-skilled workers are scarce, just about all of us are going to be taking on these foolproof tasks where perfection, exact precision is not crucial. And if high-skilled workers are really abundant, most of us will be working on O-ring type tasks but there will always be some of us, sort of, in-between. This helps explain why the capital goods, hi-tech goods are made in just a few places in the world and in particular, they're usually made in places where the workers are really really expensive. You would think that firms will try to find the cheapest workers possible for any task but instead hi-tech manufacturing, especially cutting-edge hi-tech manufacturing, tends to happen in countries that are pretty high wage. The only thing that can explain this, if people are rational, is that it must be critical, it must be crucial to have high-skilled workers working on those tasks. So one of the lessons of this is that lower-skilled workers can find something really useful to do in a high wage country because if they come to a high wage country, they are competing against a lot of other high-skill workers, so all they have to do is be an okay substitute for that high-skilled worker in some tasks, maybe it's mowing lawns, maybe it's doing routine legal work and all of a sudden those workers can earn a lot more than they would in their home country. So the O-ring-Foolproof paper is in a way an important message for the value of low-skilled workers in high wage countries, but it also helps explain why cutting edge, frontier technology innovation only happens in the highest skilled countries where workers are super expensive.Tobi: The national returns to skill, how does it work with these two sectors?Garett: Well, there is an element of, sometimes, the real world is more complicated than the model, and that's, of course, true here. I have to say that I suspect there is a critical mass element to high-skilled workers. For instance, if I can bring a million of Japan's best engineers to a lower-skilled country, a million of them could run a lot of fantastic factories, be great workers and end up giving a lot of great employment opportunities to lower-skilled folks, so there is this element of...outside the model of... a critical mass element. But the O-ring-Foolproof story is a reminder that high-skilled workers who are in relatively low-skilled countries are often going to be, like, unable to make use of their full potential. Being able to have an O-ring sector of your own to go work in is really where the magic happens of economic prosperity. The greatest things that are happening and the way that economic frontiers are being built is in these O-ring sectors, and, to me, it's a reminder that this is a case for the brain drain. A case that the brain drain actually helps the world as a whole. Brain drain issues are complicated and there are lots of forces pushing both ways but I want to emphasise that there is this positive element to the brain drain which is getting high-skilled workers into countries that can make great use of high-skilled workers really helps the whole world. Has Michael Clemens has pointed out, one benefit of that is that migrants who go from low-skilled to high-skilled countries send back a lot of remittances and those remittances are super valuable. I'd like to emphasise another point, which is getting those high-skilled workers from low average-skilled countries to higher average-skilled countries means that they can contribute to the growth of ideas which makes the global pie bigger.Tobi: You sort of preempted where I was going with that. There was also this essay by Michael Clemens and I think Justin Sandefur about this brain drain issue where they sort of asserted that another element to the brain drain issue that the incentive to migrate and earn more in high wage countries leads to more production of high-skilled workers even in low-wage countries. So Nigeria exports a lot of doctors to the UK, it means a lot more students would want to be doctors so that they can migrate to the UK or wherever where they can earn a lot more than they would in their home countries. Now, here is my question: isn't there a sort of negative effect to this in that their home countries get stuck in the poverty trap... a lot of these high-growth sectors never gets built and some of these countries just depend on remittances which can be pretty tricky?Garett: Yeah. This is a hard problem. Another problem with the brain drain is that it means that the government which really needs a lot of high-skilled workers to basically run competent bureaucracies and manage difficult technical questions, a lot of those folks are gone. They've gone to move to other countries where they can earn a lot more. So I don't want to pretend that the brain drain issues are simple to resolve. But I think that the point you're making, I tend to think of it as less of a problem because people are very reluctant to move. There's a lot of evidence that people are reluctant to move from their home country and they need a really big wage premium. So if things were even sort of mediocre, if there were some moderately hi-tech positions in the home country, you would have very high rates of retention. I think that's pretty clear from the evidence from the fact that people are very reluctant to move. If they can find any excuse to stay, they stay. That's speaking a little informally but I think the data backs that up. Also though, there is this element of where the threat of exit does make home countries behave a little better. The fact that some people might leave does make a home country government say: well, we want to make ourselves more inviting. If we think about what's happened in China (to give an extreme example) over the last few decades, there's been a lot of brain drain from China as Chinese graduates, high-skilled workers often, have moved to many different countries across Asia and across Europe and North America, and one of the reasons that the Chinese government wants to be somewhat open, somewhat...wants to be unlike its totalitarian past and more like [its] authoritarian present is because they want to feel like they can come back. So the threat of exit does discipline national governments in an important way that we shouldn't forget. Brain drain means it's harder to build these hi-tech sectors that you're pointing out, but a brain drain also gives those home country governments a better incentive to behave well. I think of this as a sort of Tiebout voting with your feet story which economies should always be open to, that people voting with their feet sends a very powerful message to governments and informal and formal evidence, I think, backs up that. Tobi: Can national IQ be deliberately raised on a scale that matters? I know you talked about nutrition in hive mind. Also, I look at things like assortative mating and other things but can it really happen on a scale that moves the needle on national prosperity?Garett: This is a great question. I feel like one reason I wrote Hive Mind was to get more people thinking about the very question you asked. Like, my comparative advantage is what does IQ cause rather than what causes IQ? But I believe the Flynn Effect is real. I believe [it is], at least, substantially real. The Flynn Effect is as you know, but your listeners may not, is the longtime rise intelligence scores that's been documented around the world. Public health interventions, people getting healthier and living longer lives, I think that obviously is increasing people's cognitive skills (like, the public health element has just got to be real). I'm less confident but I'm still fairly confident that good education raises cognitive skills big enough to move the needle. And the third one is this broadly cultural story which is really Flynn, I want to attribute this to Flynn himself. My colleague Tyler Cowen and I talked about this in our podcast back in January when I was on Conversations with Tyler - IQs in East Germany rose at least five points, maybe much more, in the decade or so after the end of communism. I think there are these cultural influences on intelligence that are not just teaching to the tests, there is something about a modern open society that I think challenges the mind and makes it work better in a wide variety of settings. So I want to stick with those three right now that public health interventions are first order, good broad-based education is suggestively very important but I can't say conclusively, and then third; there is more evidence I have to say for this big cultural effect - that when your country becomes more like Popperian open society, more [a] mixture of capitalist and loosely democratic, people seem to use their brains in different ways on a regular basis that shows up on the IQ tests. Tobi: Why and I'm sure you must have experienced this maybe in discussing your work or maybe on social media and in other ways. Why is intelligence still a taboo subject so to speak? When I sit with my friends and we talk about development and I bring up Hive Mind...and people bellyache about 'oh, we can't do this or that' and you mildly suggest that 'hey guys, have you considered that our national IQ is pretty low and maybe, maybe that's why we can't get some of these things done.' There's a natural push back that you get. Why is intelligent still such a taboo subject?Garett: I think part of it is because people assume that when you're talking about intelligence you're talking about something that is supposedly a hundred percent or nearly a hundred percent genetic and something that is essential to a person in some very deep way. So I think it's very essentialist as an explanation. I think that's a mistake, I think the evidence does not support that position. And here's a test of it, because instead of using the word "intelligence", use the word "national test scores" and you talk about how education can raise test scores in an important way, then people get much less defensive about it. People are much more open to these very same ideas, the very same channels. So I think a big part of it is that intelligence sounds like something that is intrinsic to a person, unchangeable, nearly immutable and so any ascription of causation to that is personal. So I think discussing it in a Flynnian way, the way that James Flynn has, which is very evidence-based (and) where we think of intelligence as being something like an intermediate outcome... it's not the deep root cause of everything, it's an intermediate outcome that in turn is caused by other stuff. I think that opens people up to thinking about how people's minds create the economy we live in. I'd much rather talk about how our minds create the world around us than talk about what some deep, supposedly essential thing called an IQ score. Of course, the history of the misuse of IQ test is important. The mistakes and evils that have occurred in the name of intelligence research are important. But there are many other things that have been used in evil ways in the past and we cut them slack, and democracy will be, of course, one of those. But I think something about intelligence makes people think it's intrinsic, it's basically immutable and so you're telling people to despair. And if there's one thing to think about when thinking about human cognitive skills is we shouldn't think about despair, we should think about trying to find ways to improve all of the nations in the world not just the lucky few.Tobi: That's interesting. Tell us about what you're working on right now what's your next big project.Garett: I'm on sabbatical and finishing up right now and I'm writing my third book in what I call my Singapore trilogy. And that's going to be a book really about the deep roots literature which I'd mentioned earlier. I'm interested in why the past is prologue. Hive Mind is a book, in a way, about the short-run. About almost proximate causes. 10% Less Democracy is a book about the rich countries. My third book is going to be a book about the whole world and a book about persistence. A book about why the more things change, the more they stay the same. So, again, this is going to draw on the deep roots literature, it's going to draw on the late Alberto Alesina's work on cultural persistence - how migrants carry their attitudes from their home country to the country they move to to a large degree. It's been a lot of fun to write this book because it's so data-driven and it's based on a lot of research that is very influential within economics and not influential at all outside economics and my job is to change that.Tobi: You've been an advocate so speak of high-skilled migration.Garett: Uh-huh.Tobi: How does your argument square with people like Bryan Caplan and who call for open borders, I mean, just let them come?Garett: So Caplan's comic book where I make an appearance on open borders, that's a great fun read I think people should look at that and give his ideas careful attention. I like to remind people that institutions do not just create themselves ex nihilo. That they are actually created by people and I just really want people to think about that a lot. High-skilled immigration means bringing in more informed voters and low-skilled immigration means, 'well, we really need to put a lot of effort into educating those folks' and hope that they support great institutions that will keep the country rich for a very long time. Fortunately, there is a lot of evidence that even the most optimistic supporters of open borders tend to emphasise that high-skilled immigrants have a lot of positive externalities, it's easy to make that case, and less skilled immigrants have... they are more like a wash, there is probably a plus in the short to medium run but closer to a wash than with high-skilled immigration. So I want people to think hard about where good institutions come from and if people coming to your country are going to support better institutions, that should be great news. And if people [who] come to your country are likely to tear it down, you have a little bit more concern. Caplan addresses this in his book in a number of ways with his keyhole solutions. But I think the next 20 to 30 years of both academic research and historical experience will let us know which way low-skilled immigration is going to shape the government of rich countries. Tobi: Charter Cities. Are you optimistic, are you a fan? How best to think about it from a skill and immigration perspective?Garett: Yes, so, Charter Cities which is an idea that's often associated with the Nobel Laureate Paul Romer. The idea that countries should create small little areas within there that are governed by another country's rules. A country that's well-governed, frontier. So say a poor country could say 'hey, we're going to let Singapore run a small part of our country or let Singaporean legislation or Singaporean case law hold sway in this part of our country. I tend to think that the biggest barrier to charter cities as the revenge of democracy. It's very hard to avoid what voters want. I would like to believe that countries with great institutions could franchise their institutions to other poor countries, but the problem is that institutions are created by people and we need to figure out why the institutions are weak in the first country (the country that's starting the charter city). There's a pretty good chance you're going to get a revenge of democracy there and a reversion to the old ways. Some of my GMU colleagues and I have joked that Singapore should franchise it's government to a lot of countries the way McDonald's franchises it's operating model. It would be great if we could do this but it's hard to avoid the norms of democracy especially when, as I note in 10% Less Democracy, some degree of democracy is really important to have. So Charter Cities being in tension with democracy, that's the real problem we have in making Charter Cities durable. I think the solution is to have moderate Charter Cities. Countries where, say, Singaporean law or Japanese law or South Korean law is the default but the local voters can overrule it with a two-thirds vote. Something like that might be much more durable than a full charter city solution. Starting with the default of some rich country's rules but let the local voters overturn it piece by piece and build that change into the original set of rules so that people don't feel like this is out of their control. Tobi: I'm going to ask you a very specific question. So, say, I win the election in Nigeria and I ask you 'hey, Garett, my country is going to be 300 million people in 2050, what are the policies that we can embark upon right now that can get us to a middle-income country over that time period', what would be your advice?Garett: I think my biggest piece of advice would be: find a way to become a credible, attractive place for massive amounts of high-skill immigration. How do I get five million people from China, a million and [a] half people from South Korea, two million people from America to move to Nigeria? Some of those folks will, perhaps, be people of Nigerian descent, people whose ancestors are Nigerian and who want to come back. Some of those folks will be folks who just saw that there is going to be some great tax deals, some great tax incentives to move back. I think people are policy and becoming an attractive place for high-skilled immigration like Singapore is a great way to make your country richer.Tobi: Thank you very much. I've been speaking with Economist Garett Jones and it's wonderful to have you Garett.Garett: It's been great talking with you, Tobi. This is a public episode. Get access to private episodes at www.ideasuntrapped.com/subscribe

This podcast discusses the drivers of economic growth under the Neoclassical growth theory. The economic output represents the aggregated activity of billions of people, influenced by forces seen and unseen. Despite the difficulties, economists cannot resist trying. Forecasters usually rely on two different predictive approaches. Theory-based: This one is shaped by how economists believe economies behave. Data-based: This one is shaped by how economies have behaved in the past. The simplest of the theoretical bunch is the Solow growth model, named for Robert Solow, a Nobel-prize winning economist. Robert Solow developed a model that explained the contribution of labor, capital, and technology (total factor productivity) to economic growth. The model shows that the economy's productive capacity and potential GDP increase for two reasons: accumulation of such inputs as capital, labor, and raw materials used in production, and discovery and application of new technologies that make the inputs in the production process more productive—that is, able to produce more goods and services for the same amount of input. Solow's growth accounting equation shows that the rate of growth of potential output equals growth in technology plus the weighted average growth rate of labor and capital. He also said that if capital grows faster than labor, capital will become less productive, resulting in slower and slower growth. According to his model, there are two major implications of potential GDP: Long-term sustainable growth cannot rely solely on capital deepening investment that increases the stock of capital relative to labor. This means, increasing the supply of some input(s) relative to other inputs will lead to diminishing returns and cannot be the basis for sustainable growth. Given the relative scarcity and hence high productivity of capital in developing countries, the growth rates of developing countries should exceed those of developed countries. As a result, there should be a convergence of incomes between developed and developing countries over time. I have discussed some excerpts from the book ““Good Economics for Hard Times” by Abhijit V. Banerjee and Esther Duflo. Listeners might learn a lot by reading the book. In this podcast, I tried to relate the Neoclassical growth theory with the empirical evidence shown in the book.

Crecimiento económico y elecciones en EUA (El modelo de Robert Solow); el comportamiento de la tasa de interés; el coronavirus; y la marcha del 9 de marzo en protesta de los feminicidios.

durée : 00:58:45 - Les Cours du Collège de France - Qu'est-ce qu'un modèle de croissance ? Comment confronte-on un modèle aux données? Philippe Aghion, titulaire de la chaire "Économie des institutions, de l'innovation et de la croissance", avant d'aborder la question des institutions, nous présente les travaux du grand professeur Robert Solow. - réalisation : Anne Sécheret - invités : Philippe Aghion Economiste, Professeur au Collège de France.

Please listen to our weekly podcast summarizing our June 22 note and consider becoming a paid subscriber, if you are not already, to read the full noteIP, Innovation, Policy, Productivity and MarginsRobert Gordon in the “Rise and Fall of American Growth” concluded the best innovation is behind us.  Erik Brynjolfsson’s in “The Second Machine Age”, reached the opposite conclusion.  Robert Solow commented decades ago that technology could be seen everywhere except in the productivity statistics.  Finally, Alexander Field in “A Great Leap Forward” described how the Great Depression obfuscated the beginning of a golden age for productivity.  While second revisions to GDP rarely generate much interest, the sharp upward revision to intellectual property product investment was the catalyst for this note. Barry C. KnappManaging PartnerIronsides Macroeconomics LLC908-821-7584https://www.linkedin.com/in/barry-c-knapp/@barryknapp This is a public episode. Get access to private episodes at ironsidesmacro.substack.com/subscribe

A brand new documentary from ‘The Bulletin with UBS’ gathers Nobel Perspectives from some of the brilliant laureates in economic sciences with whom UBS works. Robert Solow, Christopher Sims, Michael Spence and Christopher Pissarides train their spotlight on the future to discuss how it (and our collective prospective economic growth) will be shaped by advances in technology.

It’s pretty rare that I have a legit economist on the show. It’s even rarer when said legit economist is a woman -- WOOT! On this episode we check both those boxes with Linda Yueh, an economist who holds senior academic positions at Oxford University, London Business School, and the London School of Economics and Political Science. Linda joined us to discuss her latest book, What Would the Great Economists Do?: How Twelve Brilliant Minds Would Solve Today's Biggest Problems. Since the days of Adam Smith, economists have grappled with a series of familiar problems – but often their ideas are hard to digest, even before we try to apply them to today's issues. In her latest book, Linda explains the key thoughts of history's greatest economists, how our lives have been influenced by their ideas and how they could help us with the policy challenges that we face today. In the light of the post-Great Recession economy, where growth has not accelerated as fast as in previous expansions, Yueh explores the thoughts of economists from Adam Smith and David Ricardo to contemporary academics Douglass North and Robert Solow. Along the way, she asks, what do the ideas of Karl Marx tell us about the likely future for the Chinese economy? How do the ideas of John Maynard Keynes, who argued for government spending to create full employment, help us think about state intervention? And with globalization in trouble, what can we learn about handling Brexit and Trumpism? Linda is also an accomplished journalist, who has spent time as an anchor/correspondent at the BBC and Bloomberg TV. With a strong social media presence, she’s worth a follow so you don’t miss any of her smart blog posts. “Better Off” is sponsored by Betterment. We love feedback so please leave us a rating or review in Apple Podcasts. "Better Off" theme music is by Joel Goodman, www.joelgoodman.com. Connect with me at these places for all my content: http://www.jillonmoney.com/  https://twitter.com/jillonmoney  https://www.facebook.com/JillonMoney  https://www.instagram.com/jillonmoney/  https://www.youtube.com/c/JillSchlesinger  https://www.linkedin.com/in/jillonmoney/  http://www.stitcher.com/podcast/jill-on-money  https://apple.co/2pmVi50

Guest: Irwin Collier, Free University of Berlin Hosted and produced by Reinhard Schumacher In this episode Irwin Collier, professor of Economics at the John-F.-Kennedy Institute for North American Studies at the Free University of Berlin, talks about his project Economics in the Rear-View Mirror, which recently celebrated its third anniversary. On his website, Irwin is collecting and making available teaching resources used in economics programmes at US universities. These resources include syllabi, exams, and lecture notes. His project is covering the period from roughly 1870 – 1970. So far, the website features more than 750 artefacts, including documents from Joseph Schumpeter, Paul Samuelson, Robert Solow, Frank Knight, and many more well-known and lesser-known economists. Irwin’s website is a treasure for historians of economics, and a treasure that is still growing. The interview covers the motivation and aim of the project, some technical and archival topics, as well as some lessons on the development of economics from 1870–1970 that can be drawn from the project so far.

Shedding light on some of today’s hot economic issues, a panel of experts explore the ideas of influential thinkers from Adam Smith and David Ricardo to contemporary academics Douglass North and Robert Solow.

…or How I Learned to Stop Worrying and Love Inequality. David R. Henderson (http://www.davidrhenderson.com) is a research fellow at Stanford University’s Hoover Institution, and a professor of economics at the Graduate School of Business and Public Policy, Naval Postgraduate School, in Monterey, California. Thomas Piketty’s Capital in the 21st Century (http://amzn.to/1LT9jLG) managed to do something unprecedented among equation-dense economic tomes, it became the #1 selling book on Amazon.com. The book tapped in to a hot topic among politicians and the general public: the high (and possibly rising) wealth and income shares of the top 1%. However, David points out that although the book was a best-seller, it wasn’t actually a best-reader. Amazon logs the sentences people highlight, and the top five most-highlighted sentences in Capital all appear in the first 26 pages (www.wsj.com/articles/the-summers-most-unread-book-is-1404417569). It seems that, at least among kindle readers, most people didn’t make it past the introduction. It appears that people buy the book to back up the views they already hold. David thinks that the huge interest in economic inequality in general and the wealth of the 1% in particular was sparked in the 1990s by politicians, including Al Gore, and picked up by journalists like Sylvia Nasar (https://en.wikipedia.org/wiki/Sylvia_Nasar), before influencing the economics debate. Piketty has been able to ride this wave of public interest at what appears to be its crest. David distinguishes between inequality of wealth, inequality of income, and inequality of power. Income inequality is the difference in the amount of income we each take in in wages, interest, dividends, and government transfers (e.g. welfare or social security payments), the four main sources of income for most people. Wealth should ideally include the total value of a person’s assets in addition to the stream of income he is likely to earn in the future, though this stream is more often ignored in wealth statistics. Wealth inequality is not the same as income inequality. Critically, since people earn variable income throughout their lives, income inequality doesn’t capture what we think of as the gap between “rich” and “poor.” Retired people who own two-million-dollar homes might have low incomes, but they certainly aren’t poor. Or, to use an example that’s relevant to myself, as a PhD student my income probably sits in the bottom quintile, and yet I can expect a much higher income after I graduate. The major factor in both income inequality and wealth inequality (measured by current assets and not expected earnings) is age. Teenagers earn little or nothing, but they grow into adults and gain skills and education, their incomes rise, and they gain wealth through savings. Even if everyone had the same lifetime earnings, there would still be significant inequality in any given year since some people would be young low-earners, while others would be older, wealthier high-earners. And since the older people would have had the chance to accumulate wealth over a lifetime, they would have twenty times the wealth of their younger counterparts. While there is a correlation between wealth and power, that correlation is by no means perfect. David gives the example of Bill Gates who discovered the hard way that when you have too little political influence, it can be costly. Gates was hit with a long and costly antitrust suit, after which he greatly expanded his lobbying efforts; he had learned his lesson. David agrees with Joseph Stiglitz’ argument (http://amzn.to/1LT9dDC), to some extent, that large accumulations of wealth are the result of rent seeking. Local governments restrict the building of new homes and developments that could expand the supply of housing. Thus, they keep real estate prices artificially high to the benefit of those who already own their homes. This is an example of successful rent seeking by homeowners to the detriment of non-homeowners. However, while Stiglitz would argue that this justifies a higher tax rate on the wealthy, David prefers the more direct solution of simply reducing or removing these restrictions. The following are also mentioned in this episode: Wealth Inequality in America (https://www.youtube.com/watch?v=QPKKQnijnsM) Piketty and Saez vs. Burkhauser and Cornell: Who’s right on income inequality and stagnation? (https://www.aei.org/publication/piketty-and-saez-vs-burkhauser-and-cornell-whos-right-on-income-inequality-and-stagnation/) Income and Wealth by Alan Reynolds (http://amzn.to/1LOy1Ma) The Boskin Commission (https://en.wikipedia.org/wiki/Boskin_Commission) Myths of Rich and Poor by W. Michael Cox and Richard Alm (http://amzn.to/1NOvEYR) Mark J. Perry on individual income inequality (https://www.aei.org/publication/sorry-krugman-piketty-and-stiglitz-income-inequality-for-individual-americans-has-been-flat-for-more-than-50-years/) Greg Mankiw’s favourite textbook (http://amzn.to/1Rihq8j) Bernie Madoff (https://en.wikipedia.org/wiki/Bernard_Madoff) The McCulloch chainsaw (https://en.wikipedia.org/wiki/Robert_P._McCulloch) Lyndon B. Johnson (https://en.wikipedia.org/wiki/Lyndon_B._Johnson) David’s review of Capital in the 21st Century for Regulation (http://object.cato.org/sites/cato.org/files/serials/files/regulation/2014/10/regulationv37n3-9.pdf) David’s (unexpectedly) controversial EconLog post about ordinal utility (http://econlog.econlib.org/archives/2015/05/tyler_cowen_on_14.html) Robert Solow’s review of Capital in the 21st Century (http://www.newrepublic.com/article/117429/capital-twenty-first-century-thomas-piketty-reviewed) Matthew Rognlie’s response to Piketty (http://www.newrepublic.com/article/117429/capital-twenty-first-century-thomas-piketty-reviewed) and Randal O’Toole’s comment on Rognlie’s response (http://www.cato.org/blog/housing-wealth-inequality) Branko Milanović’s blog on global inequality (http://glineq.blogspot.ca/) David’s article on The Bottom One Percent (http://www.hoover.org/research/bottom-one-percent) Peter Jaworski (http://explore.georgetown.edu/people/pj87/?action=viewpublications&PageTemplateID=360 Is Government the Source of Monopoly? By Yale Brozen (http://amzn.to/1HdvyI0)

Robert Solow, Professor Emeritus at Massachusetts Institute of Technology and Nobel Laureate, talks with EconTalk host Russ Roberts about his hugely influential theory of growth and inspiration to create a model that better reflected the stable long-term growth of an economy. Solow contends that capital accumulation cannot explain a significant portion of the economic growth we see. He makes a critical distinction between innovation and technology, and then discusses his views on Milton Friedman and John M. Keynes.

Robert Solow, Professor Emeritus at Massachusetts Institute of Technology and Nobel Laureate, talks with EconTalk host Russ Roberts about his hugely influential theory of growth and inspiration to create a model that better reflected the stable long-term growth of an economy. Solow contends that capital accumulation cannot explain a significant portion of the economic growth we see. He makes a critical distinction between innovation and technology, and then discusses his views on Milton Friedman and John M. Keynes.

Dr. Robert Solow, Professor Emeritus of Economics at the Massachusetts Institute of Technology, talks about his life and career with Dr. Peter Berck, SJ Hall Professor of Agricultural and Resource Economics and Policy at the University of California at Berkeley. In this conversation, Dr. Solow discusses growing up in an immigrant family in 1930s Brooklyn, being introduced to literature and ideas at James Madison High School, attending Harvard University on scholarship, and receiving the 1987 Nobel Prize in Economics Laureate.

In 1987 Robert Solow observed that "We see evidence of the computer age everywhere except in the productivity statistics." In 2009, the situation is utterly changed; a large and growing body of evidence reveals that IT is affecting not only productivity, but also competition. And technology's impact is not limited to only a few industries, but is instead being felt throughout the economy. Dr. McAfee will first present evidence of IT's deep and broad impact, then offer an explanation for how the humble computer could be having such a large effect. The "Computer Revolution" in business is actually four distinct but related developments. McAfee will describe each of them, then use case studies to show how leading companies are taking advantage of them to advance within their industries.