Podcasts about economic literature

Melvyn Bragg and guests discuss the Austrian-British economist Friedrich Hayek's The Road to Serfdom (1944) in which Hayek (1899-1992) warned that the way Britain was running its wartime economy would not work in peacetime and could lead to tyranny. His target was centralised planning, arguing this disempowered individuals and wasted their knowledge, while empowering those ill-suited to run an economy. He was concerned about the support for the perceived success of Soviet centralisation, when he saw this and Fascist systems as two sides of the same coin. When Reader's Digest selectively condensed Hayek's book in 1945, and presented it not so much as a warning against tyranny as a proof against socialism, it became phenomenally influential around the world. With Bruce Caldwell Research Professor of Economics at Duke University and Director of the Center for the History of Political EconomyMelissa Lane The Class of 1943 Professor of Politics at Princeton University and the 50th Professor of Rhetoric at Gresham College in LondonAndBen Jackson Professor of Modern History and fellow of University College at the University of OxfordProducer: Simon TillotsonReading list:Angus Burgin, The Great Persuasion: Reinventing Free Markets Since the Depression (Harvard University Press, 2012)Bruce Caldwell, Hayek's Challenge: An Intellectual Biography of F.A. Hayek (University of Chicago Press, 2004)Bruce Caldwell, ‘The Road to Serfdom After 75 Years' (Journal of Economic Literature 58, 2020)Bruce Caldwell and Hansjoerg Klausinger, Hayek: A Life 1899-1950 (University of Chicago Press, 2022)M. Desai, Marx's Revenge: The Resurgence of Capitalism and the Death of Statist Socialism (Verso, 2002)Edward Feser (ed.), The Cambridge Companion to Hayek (Cambridge University Press, 2006)Andrew Gamble, Hayek: The Iron Cage of Liberty (Polity, 1996)Friedrich Hayek, Collectivist Economic Planning (first published 1935; Ludwig von Mises Institute, 2015), especially ‘The Nature and History of the Problem' and ‘The Present State of the Debate' by Friedrich HayekFriedrich Hayek (ed. Bruce Caldwell), The Road to Serfdom: Text and Documents: The Definitive Edition (first published 1944; Routledge, 2008. Also vol. 2 of The Collected Works of F. A. Hayek, University of Chicago Press, 2007)Friedrich Hayek, The Road to Serfdom: Condensed Version (Institute of Economic Affairs, 2005; The Reader's Digest condensation of the book)Friedrich Hayek, ‘The Use of Knowledge in Society' (American Economic Review, vol. 35, 1945; vol. 15 of The Collected Works of F. A. Hayek, University of Chicago Press) Friedrich Hayek, Individualism and Economic Order (first published 1948; University of Chicago Press, 1996), especially the essays ‘Economics and Knowledge' (1937), ‘Individualism: True and False' (1945), and ‘The Use of Knowledge in Society' (1945)Friedrich Hayek, The Constitution of Liberty (first published 1960; Routledge, 2006) Friedrich Hayek, Law. Legislation and Liberty: A new statement of the liberal principles of justice and political economy (first published 1973 in 3 volumes; single vol. edn, Routledge, 2012)Ben Jackson, ‘Freedom, the Common Good and the Rule of Law: Hayek and Lippmann on Economic Planning' (Journal of the History of Ideas 73, 2012)Robert Leeson (ed.), Hayek: A Collaborative Biography Part I (Palgrave, 2013), especially ‘The Genesis and Reception of The Road to Serfdom' by Melissa LaneIn Our Time is a BBC Studios Audio Production

Cada vez mais jovens altamente qualificados mudam-se para os EUA, o Canadá ou os Países Baixos fazendo da exportação de talento um dos maiores «assets» de Portugal dos últimos anos.Esta tendência é uma novidade para um país que nas décadas de 1960, 1970 e 1980 se caracterizava por exportar mão-de-obra pouco qualificada.Mas já é bem conhecida noutras geografias. E a globalização tornou mais fácil essa mobilidade. A livre circulação de pessoas com recursos especializados ou com vontade de os melhorar abriu-se aos jovens portugueses: «já temos um milhão de bebés Erasmus», refere o economista José Alberto Ferreira, a propósito dos efeitos secundários desta diáspora de cérebros.Contudo, a ‘fuga' de talento levanta questões sobre o impacto económico no país. Se é uma realidade que se perdem empreendedores e oportunidades para a criação de empresas, também é verdade que o conhecimento continua a circular entre os que vão e os que ficam.Por outro lado, as empresas portuguesas precisam de evoluir no que toca à valorização destas pessoas. Será que vamos conseguir reter jovens qualificados em Portugal?REFERÊNCIAS E LINKS ÚTEISPires, R. P., Vidigal, I., Pereira, C., Azevedo, J., & Veiga, C. M. (2024). Emigração Portuguesa 2023: Relatório Estatístico. Observatório da Emigração e Rede Migra, CIES-Iscte.Instituto Nacional de Estatística, I.P. (2023). O que nos dizem os Censos sobre a população de nacionalidade estrangeira residente em Portugal. Três estudos sobre a nova emigração portuguesa (pp. 7–36). Observatório da Emigração, CIES-Iscte.Docquier, F., & Rapoport, H. (2012). Globalization, brain drain, and development. Journal of Economic Literature, 50(3), 681–730.Gibson, J., & McKenzie, D. (2011). Eight questions about brain drain. Journal of Economic Perspectives, 25(3), 107–128.Breschi, S., Lissoni, F., & Miguelez, E. (2017). Foreign-origin inventors in the USA: Testing for diaspora and brain gain effects. Journal of Economic Geography, 17(5), 1009–1038.Choudhury, P., Ganguli, I., & Gaulé, P. (2023). Top talent, elite colleges, and migration: Evidence from the Indian Institutes of Technology. Journal of Development Economics, 164, 103120.In Pertinente Economia: Dicionário de Inovação, Ensino Superior - Para todos?,  Como ajudar um pequeno negócio a crescer?BIOSMARIANA ALVIMLocutora da rádio RFM há 15 anos. Depois de quase 10 a fazer o «Café da Manhã», agora leva os ouvintes a casa, com Pedro Fernandes, no «6PM». É autora de livros para adolescentes e criou o podcast «Vale a Pena», no qual entrevista artistas enquanto leitores.JOSÉ ALBERTO FERREIRADoutorando em Economia no Instituto Universitário Europeu, em Florença. Trabalhou no Banco Central Europeu, com foco na investigação em modelos de política monetária e macroprudencial.

Online behavioral advertising has raised privacy concerns due to its dependence on extensive tracking of individuals' behaviors and its potential to influence them. Those concerns have been often juxtaposed with the economic value consumers are expected to gain from receiving behaviorally targeted ads. Those purported economic benefits, however, have been more frequently hypothesized than empirically demonstrated. We present the results of two online experiments designed to assess some of the consumer welfare implications of behaviorally targeted advertising using a counterfactual approach. Study 1 finds that products in ads targeted to a sample of online participants were more relevant to them than randomly picked products but were also more likely to be associated with lower quality vendors and higher product prices compared to competing alternatives found among search results. Study 2 replicates the results of Study 1. Additionally, Study 2 finds the higher product relevance of products in targeted ads relative to randomly picked products to be driven by participants having previously searched for the advertised products. The results help evaluate claims about the direct economic benefits consumers may gain from behavioral advertising. About the speaker: Alessandro Acquisti is the Trustees Professor of Information Technology and Public Policy at Carnegie Mellon University's Heinz College. His research combines economics, behavioral research, and data mining to investigate the role of privacy in a digital society. His studies have promoted the revival of the economics of privacy, advanced the application of behavioral economics to the understanding of consumer privacy valuations and decision-making, and spearheaded the investigation of privacy and disclosures in social media.Alessandro has been the recipient of the PET Award for Outstanding Research in Privacy Enhancing Technologies, the IBM Best Academic Privacy Faculty Award, the IEEE Cybersecurity Award for Innovation, the Heinz College School of Information's Teaching Excellence Award, and numerous Best Paper awards. His studies have been published in journals across multiple disciplines, including Science, Proceedings of the National Academy of Science, Journal of Economic Literature, Management Science, Marketing Science, and Journal of Consumer Research. His research has been featured in global media outlets including the Economist, the New York Times, the Wall Street Journal, NPR, CNN, and 60 Minutes. His TED talks on privacy and human behaviour have been viewed over 1.5 million times.Alessandro is the director of the Privacy Economics Experiments (PeeX) Lab, the Chair of CMU Institutional Review Board (IRB), and the former faculty director of the CMU Digital Transformation and Innovation Center. He is an Andrew Carnegie Fellow (inaugural class), and has been a member of the Board of Regents of the National Library of Medicine and a member of the National Academies' Committee on public response to alerts and warnings using social media and associated privacy considerations. He has testified before the U.S. Senate and House committees and has consulted on issues related to privacy policy and consumer behavior with numerous agencies and organizations, including the White House's Office of Science and Technology Policy (OSTP), the US Federal Trade Commission (FTC), and the European Commission.He has received a PhD from UC Berkeley and Master degrees from UC Berkeley, the London School of Economics, and Trinity College Dublin. He has held visiting positions at the Universities of Rome, Paris, and Freiburg (visiting professor); Harvard University (visiting scholar); University of Chicago (visiting fellow); Microsoft Research (visiting researcher); and Google (visiting scientist).His research interests include privacy, artificial intelligence, and Nutella. In a previous life, he has been a soundtrack composer and a motorcycle racer (USGPRU).

The Serbian-American economist Branko Milanovic is one of the world's leading authorities on inequality. In this KEEN ON America conversation, we talked about Milanovic's interpretation of the history of American economic inequality - from slavery to contemporary capitalism. Why has America become so much unequal over the last fifty years, I asked. And today, in what Milanovic sees as a post neo-liberal age, how does he imagine the future of economic inequality?Branko Milanovic obtained his Ph.D. in economics (1987) from the University of Belgrade with a dissertation on income inequality in Yugoslavia. He served as lead economist in the World Bank's Research Department for almost 20 years, leaving to write his book on global income inequality, Worlds Apart (2005). He was a senior associate at the Carnegie Endowment for International Peace in Washington (2003-2005) and has held teaching appointments at the University of Maryland (2007-2013) and at the Paul H. Nitze School of Advanced International Studies at Johns Hopkins University (1997- 2007). He was a visiting scholar at All Souls College in Oxford, and Universidad Carlos III in Madrid (2010-11). Professor Milanovic's main area of work is income inequality, in individual countries and globally, including in preindustrial societies. He has published articles in Economic Journal, Review of Economics and Statistics, Journal of Economic Literature, Journal of Development Economics, and Journal of Political Philosophy, among others. His book The Haves and the Have-nots (2011) was selected by The Globalist as the 2011 Book of the Year. Global Inequality (2016) was awarded the Bruno Kreisky Prize for the best political book of 2016 and the Hans Matthöfer Prize in 2018, and was translated into 16 languages. It addresses economic and political effects of globalization and introduces the concept of successive “Kuznets waves” of inequality. In March 2018, Milanovic was awarded (jointly with Mariana Mazzucato) the 2018 Leontief Prize for Advancing the Frontiers of Economic Knowledge. His most recent books are Capitalism, Alone, published in 2019, and Visions of Inequality, published in 2023..Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Professor Guido Alfani of Bocconi University discusses economic inequality in preindustrial times, centralized in Europe and beyond. Alfani was recently published in the Journal of Economic Literature. Professor Ted Seto of Loyola Law School provides commentary, and UVA Law professor Ruth Mason and Oxford University professor Tsilly Dagan also discuss the work. This event was held as part of the “Tax Meets Non-Tax” Oxford-Virginia Legal Dialogs workshop series that builds bridges from tax to other kinds of scholarship. (University of Virginia School of Law, May 24, 2024)

Timely publication of research in peer-reviewed journals is critical for economists seeking tenure and important for audiences looking for high-quality, trustworthy studies. But in recent decades, there has been an increasing concern that the pace of publishing in economics is too slow. In a paper in the Journal of Economic Literature, authors Aboozar Hadavand, Daniel S. Hamermesh, and Wesley W. Wilson analyzed the publication lag in top economics journals and compared it to other fields. They found that economics publishing takes nearly twice as long as comparable fields in the other social sciences. However, Hamermesh says that some innovative journals, such as AER: Insights, are taking steps to shorten the time between submission and publication. He recently spoke with Tyler Smith about the pace of publishing in economics, how to fix it, and some advice for young economists trying to publish their work.

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: On what research policymakers actually need, published by MondSemmel on April 24, 2024 on LessWrong. I saw this guest post on the Slow Boring substack, by a former senior US government official, and figured it might be of interest here. The post's original title is "The economic research policymakers actually need", but it seemed to me like the post could be applied just as well to other fields. Excerpts (totaling ~750 words vs. the original's ~1500): I was a senior administration official, here's what was helpful [Most] academic research isn't helpful for programmatic policymaking - and isn't designed to be. I can, of course, only speak to the policy areas I worked on at Commerce, but I believe many policymakers would benefit enormously from research that addressed today's most pressing policy problems. ... most academic papers presume familiarity with the relevant academic literature, making it difficult for anyone outside of academia to make the best possible use of them. The most useful research often came instead from regional Federal Reserve banks, non-partisan think-tanks, the corporate sector, and from academics who had the support, freedom, or job security to prioritize policy relevance. It generally fell into three categories: New measures of the economy Broad literature reviews Analyses that directly quantify or simulate policy decisions. If you're an economic researcher and you want to do work that is actually helpful for policymakers - and increases economists' influence in government - aim for one of those three buckets. New data and measures of the economy The pandemic and its aftermath brought an urgent need for data at higher frequency, with greater geographic and sectoral detail, and about ways the economy suddenly changed. Some of the most useful research contributions during that period were new data and measures of the economy: they were valuable as ingredients rather than as recipes or finished meals... These data and measures were especially useful because the authors made underlying numbers available for download. And most of them continue to be updated monthly, which means unlike analyses that are read once and then go stale, they remain fresh and can be incorporated into real-time analyses. Broad overviews and literature reviews Most academic journal articles introduce a new insight and assume familiarity with related academic work. But as a policymaker, I typically found it more useful to rely on overviews and reviews that summarized, organized, and framed a large academic literature. Given the breadth of Commerce's responsibilities, we had to be on top of too many different economic and policy topics to be able to read and digest dozens of academic articles on every topic... Comprehensive, methodical overviews like these are often published by think-tanks whose primary audience is policymakers. There are also two academic journals - the Journal of Economic Perspectives and the Journal of Economic Literature - that are broad and approachable enough to be the first (or even only) stop for policymakers needing the lay of the research land. Analysis that directly quantify or simulate policy decisions With the Administration's focus on industrial policy and place-based economic development - and Commerce's central role - I found research that quantified policy effects or simulated policy decisions in these areas especially useful... Another example is the work of Tim Bartik, a labor economist and expert on local economic development. In a short essay, he summarized a large academic literature and estimated how effective different local economic development policies are in terms of the cost per job created. Cleaning up contaminated sites for redevelopment creates jobs at a much lower cost per job than job training, which in turn is much more cos...

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: On what research policymakers actually need, published by MondSemmel on April 24, 2024 on LessWrong. I saw this guest post on the Slow Boring substack, by a former senior US government official, and figured it might be of interest here. The post's original title is "The economic research policymakers actually need", but it seemed to me like the post could be applied just as well to other fields. Excerpts (totaling ~750 words vs. the original's ~1500): I was a senior administration official, here's what was helpful [Most] academic research isn't helpful for programmatic policymaking - and isn't designed to be. I can, of course, only speak to the policy areas I worked on at Commerce, but I believe many policymakers would benefit enormously from research that addressed today's most pressing policy problems. ... most academic papers presume familiarity with the relevant academic literature, making it difficult for anyone outside of academia to make the best possible use of them. The most useful research often came instead from regional Federal Reserve banks, non-partisan think-tanks, the corporate sector, and from academics who had the support, freedom, or job security to prioritize policy relevance. It generally fell into three categories: New measures of the economy Broad literature reviews Analyses that directly quantify or simulate policy decisions. If you're an economic researcher and you want to do work that is actually helpful for policymakers - and increases economists' influence in government - aim for one of those three buckets. New data and measures of the economy The pandemic and its aftermath brought an urgent need for data at higher frequency, with greater geographic and sectoral detail, and about ways the economy suddenly changed. Some of the most useful research contributions during that period were new data and measures of the economy: they were valuable as ingredients rather than as recipes or finished meals... These data and measures were especially useful because the authors made underlying numbers available for download. And most of them continue to be updated monthly, which means unlike analyses that are read once and then go stale, they remain fresh and can be incorporated into real-time analyses. Broad overviews and literature reviews Most academic journal articles introduce a new insight and assume familiarity with related academic work. But as a policymaker, I typically found it more useful to rely on overviews and reviews that summarized, organized, and framed a large academic literature. Given the breadth of Commerce's responsibilities, we had to be on top of too many different economic and policy topics to be able to read and digest dozens of academic articles on every topic... Comprehensive, methodical overviews like these are often published by think-tanks whose primary audience is policymakers. There are also two academic journals - the Journal of Economic Perspectives and the Journal of Economic Literature - that are broad and approachable enough to be the first (or even only) stop for policymakers needing the lay of the research land. Analysis that directly quantify or simulate policy decisions With the Administration's focus on industrial policy and place-based economic development - and Commerce's central role - I found research that quantified policy effects or simulated policy decisions in these areas especially useful... Another example is the work of Tim Bartik, a labor economist and expert on local economic development. In a short essay, he summarized a large academic literature and estimated how effective different local economic development policies are in terms of the cost per job created. Cleaning up contaminated sites for redevelopment creates jobs at a much lower cost per job than job training, which in turn is much more cos...

Technology has advanced by leaps and bounds in the past few centuries, but much of that progress is still limited to the richest countries. Why don't new technologies spread quickly throughout the world, benefiting billions of people? In this podcast, we'll focus on one particular answer: new technologies improve productivity, but they improve productivity more when paired with knowledge on how to use them. If this is true, new technologies will be less beneficial to recipients who don't have the knowledge to use them effectively - and thus, they may not spread as much as we expected. This podcast is an audio read through of the (initial draft) of Training enhances the value of new technology, published on New Things Under the Sun. This is a collaboration with Karthik Tadepalli, an economics PhD student at the University of California, Berkeley. See here for more on New Things Under the Sun's collaboration policy.Articles mentionedComin, Diego, and Martí Mestieri. 2014. Technology Diffusion: Measurement, Causes and Consequences. In Handbook of Economic Growth, Vol. 2, eds. Philippe Aghion and Steven Durlauf. Elsevier. 565-622. https://doi.org/10.1016/B978-0-444-53540-5.00002-1Verhoogen, Eric. 2023. Firm-Level Upgrading in Developing Countries. Journal of Economic Literature 61(4): 1410-64. https://doi.org/10.1257/jel.20221633Giorcelli, Michela. 2019. The Long-Term Effect of Management and Technology Transfers. American Economic Review109(1): 121-152. https://doi.org/10.1257/aer.20170619Giorcelli, Michela, and Bo Li. 2023. Technology Transfer and Early Industrial Development: Evidence from the Sino-Soviet Alliance. SSRN Working Paper. https://doi.org/10.2139/ssrn.3758314

Self-preferencing by digital platforms has become ubiquitous in today's antitrust discourse. But has enough focus been put on economic analysis to understand the procompetitive benefits of self-preferencing conduct? Emilie Feyler and Dr. Veronica Postal, Senior Consultants at NERA Economic Consulting, speak with Jaclyn Phillips and Barry Nigro about their assessment of the economic literature on self-preferencing. Listen to this episode to learn more about what the empirical research has to say and if there are still gaps to fill. With special guests: Emilie Feyler, Senior Consultant, NERA Economic Consulting and Dr. Veronica Postal, Senior Consultant, NERA Economic Consulting Related Links: Can Self-Preferencing Algorithms Be Procompetitive? Hosted by: Jaclyn Phillips, White & Case LLP and Barry Nigro, Fried, Frank, Harris, Shriver & Jacobson LLP

Much of the world's population lives in countries in which little research happens. Is this a problem? According to classical economic models of the “ideas production function,” ideas are universal; ideas developed in one place are applicable everywhere. This is probably true enough for some contexts; but not all. In this post we'll look at four domains - agriculture, health, the behavioral sciences, and program evaluation research - where new discoveries do not seem to have universal application across all geographies.This podcast is an audio read through of the (initial version of the) article "When research over there isn't helpful here," originally published on New Things Under the Sun.Articles mentionedComin, Diego, and Marti Mestieri. 2014. Technology diffusion: Measurement, causes, and consequences. In Handbook of economic growth, Vol. 2, 565-622. Elsevier. https://doi.org/10.1016/B978-0-444-53540-5.00002-1Verhoogen, Eric. Forthcoming. Firm-level upgrading in developing countries. Journal of Economic Literature. (link)Moscona, Jacob, and Karthik Sastry. 2022. Inappropriate technology: Evidence from global agriculture. SSRN working paper. https://doi.org/10.2139/ssrn.3886019Wilson, Mary Elizabeth. 2017. The geography of infectious diseases. Infectious Diseases: 938–947.e1. https://doi.org/10.1016%2FB978-0-7020-6285-8.00106-4Wang, Ting, et al. 2022. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604(7906): 437-446. https://doi.org/10.1038/s41586-022-04601-8Hotez, Peter J., David H. Molyneux, Alan Fenwick, Jacob Kumaresan, Sonia Ehrlich Sachs, Jeffrey D. Sachs, and Lorenzo Savioli. 2007. Control of neglected tropical diseases. New England Journal of Medicine 357(10): 1018-1027. https://doi.org/10.1056/NEJMra064142Henrich, Joseph, Steven J. Heine, and Ara Norenzayan. 2010. The weirdest people in the world? Behavioral and Brain Sciences 33(2-3): 61-83. https://doi.org/10.1017/S0140525X0999152XApicella, Coren, Ara Norenzayan, and Joseph Henrich. 2020. Beyond WEIRD: A review of the last decade and a look ahead to the global laboratory of the future. Evolution and Human Behavior 41(5): 319-329. https://doi.org/10.1016/j.evolhumbehav.2020.07.015Klein Richard A., et al. 2018. Many Labs 2: Investigating Variation in Replicability Across Samples and Settings. Advances in Methods and Practices in Psychological Science. 2018;1(4):443-490. https://doi.org/10.1177/2515245918810225Schimmelpfennig, Robin, et al. 2023. A Problem in Theory and More: Measuring the Moderating Role of Culture in Many Labs 2. PsyArXiv. https://doi.org/10.31234/osf.io/hmnrx.Vivalt, Eva. 2020. How much can we generalize from impact evaluations? Journal of the European Economic Association18(6): 3045-3089. https://doi.org/10.1093/jeea/jvaa019Vivalt, Eva, Aidan Coville, and K. C. Sampada. 2023. Tacit versus Formal Knowledge in Policy Decisions.

EPISODE 1823: In this KEEN ON show, Andrew talks to Branko Milanovic, author of VISIONS OF INEQUALITY, about how different economists have made sense of economic inequality over the last 250 yearsBRANKO MILANOVIC is a Senior Scholar at the Stone Center on Socio-Economic Inequality at the CUNY Graduate Center and the author of the forthcoming Visions of Inequality: From the French Revolution to the End of the Cold War. Branko's main area of work is income inequality, in individual countries and globally, including in pre-industrial societies. He has published articles in The Economic Journal, Review of Economics and Statistics, Journal of Economic Literature, Economic History Review, and Journal of Political Philosophy, among others. His book, The Haves and the Have-nots (2011) was selected by The Globalist as the 2011 Book of the Year. His book Global Inequality (2016), was awarded the Bruno Kreisky Prize for the best political book of 2016, and Hans Matthöfer Prize in 2018, and was translated into sixteen languages. It addresses economic and political effects of globalization and introduces the concept of successive “Kuznets waves” of inequality. In March 2018, Branko was awarded (jointly with Mariana Mazzucato) the 2018 Leontief Prize for Advancing the Frontiers of Economic Knowledge. His new book Capitalism, Alone was published in September 2019. He has contributed numerous op-eds and essays to Social Europe, VoxEU, The Guardian, Foreign Affairs, Foreign Policy, Vox, The Financial Times, Le Monde, El Pais, La Vanguardia, Le Monde Diplomatique and blogs ProMarket (U of Chicago), Global Policy (Durham University), Brave New Europe (Berlin). His blog posts are regularly translated into Spanish (Letras Libres), German (Makronom), Italian (Fata Turchina) and French (Atlanico).Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.

In this episode, Xavier Bonilla has a dialogue with Melissa Kearney about the two-parent privilege. They define what is the two-parent privilege, the class gap in families and importance of college educated parents. They talk about conservative family values and views on two-parent households, two parents working outside of the home, working moms, stay-at-home moms, and single moms. They talk about the child tax credit, childhood poverty, birth rates, and many more topics. Melissa S. Kearney is the Neil Moskowitz Professor of Economics at the University of Maryland and Director of the Aspen Economic Strategy Group. She is a Research Associate at the National Bureau of Economic Research (NBER); a non-resident Senior Fellow at Brookings; a scholar affiliate and member of the board of the Notre Dame Wilson-Sheehan Lab for Economic Opportunities (LEO); and a scholar affiliate of the MIT Abdul Jameel Poverty Action Lab (J-PAL). She is an editorial board member of the American Economic Journal: Economic Policy and Journal of Economic Literature, and a former co-editor of the Journal of Human Resources and Senior Editor of Future of Children. She holds a BA in Economics from Princeton University and a PhD in Economics from the Massachusetts Institute of Technology. She is the author of the book, The Two-Parent Privilege: How Americans Stopped Getting Married and Started Falling Behind. Website: http://econweb.umd.edu/~kearney/melissa_website/index.htmlTwitter: @kearney_melissa Get full access to Converging Dialogues at convergingdialogues.substack.com/subscribe

The costs of Alzheimer's disease are significant. In 2021, it affected nearly 6 million Americans and accounted for an estimated 8 percent of total US health-care spending—about as much as cancer and heart disease combined. And those numbers are only expected to increase as the population ages. In a paper in the Journal of Economic Literature, authors Amitabh Chandra, Courtney Coile, and Corina Mommaerts explain how economists can help provide insights into the numerous policy issues that Alzheimer's disease raises. However, Mommaerts says that the disease also challenges core assumptions in the standard economics tool kit. She recently spoke with Tyler Smith about cognitive constraints, incentives for providers, and encouraging more innovative treatments for Alzheimer's disease.

De que maneira a Economia Digital difere das outras Economias?É o tal ‘papão' que nos vai engolir, ou tem trazido vantagens importantes?E, ao nível da inovação, que mudanças implementou a nível global?Será que o mundo ficou mais ‘pequeno': será que, através dela, negócios e pessoas podem agora estar mais próximos? O economista Hugo Figueiredo tem uma perspetiva otimista acerca da Economia Digital e vai explicá-la com clareza ao Hugo van der Ding. Neste episódio, vai-se falar de como a Economia Digital permitiu reduzir custos de mobilidade e favoreceu negócios de aglomeração (o famoso ‘Bundling'). De como se abriu a possibilidade de se poder trabalhar de qualquer parte do mundo para todas as partes dele. Das empresas ‘superestrela' e da criação de novos mercados. Bem-vindo ao futuro do nosso presente.REFERÊNCIAS E LINKS ÚTEISEconomia Digital:Goldfarb, Avi, and Catherine Tucker. 2019. "Digital Economics." Journal of Economic Literature, 57 (1): 3-43.Economia da Inteligência ArtificialAgrawal, A., Gans, J., & Goldfarb, A. (2016). The simple economics of machine intelligence. Harvard Business Review, 17(1), 2-5.Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic policy for artificial intelligence. Innovation policy and the economy, 19(1), 139-159.Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Harvard Business Press.Plataformas:Spulber, D. F. (2019). The economics of markets and platforms. Journal of Economics & Management Strategy, 28(1), 159-172.Belleflamme, P., & Peitz, M. (2021). The Economics of Platforms. Cambridge University Press.Tecnologias Digitais e o Paradoxo da Produtividade:Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda (pp. 23-57). University of Chicago Press. Gordon, R. (2013). The death of innovation, the end of growth [Video]. TED Conferences.Brynjolfson, E. (2013). The key to growth? Race with the machines[Video]. TED Conferences.BIOSHUGO VAN DER DINGHugo van der Ding é muitas personagens. Locutor, criativo e desenhador acidental. Uma espécie de cartunista de sucesso instantâneo a quem bastou uma caneta Bic, uma boa ideia e uma folha em branco. Criador de personagens digitais de sucesso como a Criada Malcriada e Cavaca a Presidenta, também autor de um dos podcasts mais ouvidos em Portugal, Vamos Todos Morrer, podemos encontrá-lo, ou melhor ouvi-lo, todas as manhãs na Antena 3 ou por detrás dos bonecos que nos surgem todos os dias por aqui e ali. HUGO FIGUEIREDOÉ professor de Economia na Universidade de Aveiro, investigador do CIPES - Centro de Investigação em Políticas do Ensino Superior e colaborador do GOVCOPP – Unidade de Investigação em Governança, Competitividade e Políticas Públicas. É licenciado em Economia pela Universidade do Porto e doutorado em Ciências Empresariais pela Universidade de Manchester. Os seus interesses de investigação centram-se nas áreas da economia do trabalho, da educação e do ensino superior.

Episode #33 Guilhem Delon-Saumier est analyste en investissements chez OCO Global, entreprise de conseil en investissements directs à l'étranger et intelligence économique. Dans cet entretien nous parlons de la réalité des investissements directs étrangers sur le terrain : les facteurs d'attraction, les retombées économiques, les rôles respectifs des entrepreneurs et des agences publiques, etc. Nous abordons aussi le versant politique de l'investissement étranger, et notamment des ambivalences autour des mérites économiques des importations, des exportations, et des investissements étrangers. Enfin nous faisons un point sur la situation actuelle qui remet en cause le modèle d'économie mondialisée tel que nous le connaissons depuis plusieurs décennies. Production et animation par Pierre Schweitzer pour Contrepoints. Programme : 00:00 - Introduction 01:19 - Présentation de l'invité 03:40 - La stabilité institutionnelle plus importante que la compétitivité fiscale ?  06:11 - Le commerce international en pratique ressemble-t-il à la théorie ? 10:25 - Quelle efficacité des agences de promotion des investissements internationaux ? 14:32 - Qu'est qu'un Investissement Direct Etranger (IDE) ? 18:46 - Les acteurs du commercial mondial sont-ils économiquement "schizophrènes" ? 23:40 - L'Etat a-t-il un effet d'éviction sur les initiatives privées d'attraction des investissements ? 29:27 - Le libre-échange unilatéral est-il souhaitable et acceptable ? 34:11 - L'importance variable des critères ESG dans l'investissement 37:02 - Les exportations sont-elles possibles sans importations ? 43:00 - Les limites du commerce international 49:45 - Jusqu'où le contexte de guerre et de pandémie fait-il reculer le commerce international ? 54:28 - Quels pays se referment le plus ? 56:54 - La menace de l'inflation sur la mondialisation 1:02:59 - Conclusion Pour aller plus loin : « Economie Globale, Prospérité Locale », ouvrage de notre invité disponible gratuitement chez Librairal et préfacé par Jean-Marc Daniel https://www.librairal.org/wiki/Guilhem_Delon-Saumier:Economie_mondiale,_prospérité_locale  Les moteurs du développement économique (Cours en ligne de l'économiste Emmanuel Martin) https://youtube.com/playlist?list=PLHMqffn5cyaDh1NSYT7J39us8oArpzvd1  « Moi le crayon » (texte original de Leonard E. Reed) http://herve.dequengo.free.fr/Read/Read1.htm  « Moi le crayon » (version vidéo avec Milton Friedman, sous-titres français) https://www.youtube.com/watch?v=PvX13GCcVTA « Trade Costs » (article académique mentionné dans l'épisode) J. Anderson et E.v. Wincoop : Journal of Economic Literature , Sep., 2004, Vol. 42, No. 3 (Sep., 2004), pp. 691- 751. https://www.nber.org/system/files/working_papers/w10480/w10480.pdf Suivez-nous sur le web : https://www.contrepoints.org https://twitter.com/contrepoints https://fr.linkedin.com/company/contrepoints https://www.facebook.com/Contrepoints

Graduate school should be about learning how to push the frontiers of knowledge. Many students, however, also learn that getting a PhD can push them into emotional and psychological trouble. In a paper in the Journal of Economic Literature, authors Valentin Bolotnyy, Matthew Basilico, and Paul Barreira surveyed eight top-ranked economics PhD programs across the country and found high levels of significant depression and anxiety symptoms among students. Their survey indicates that some norms in the field, such as working alone and downplaying emotional distress, may be exacerbating the profession's mental health issues. Bolotnyy recently spoke with Tyler Smith about the prevalence of mental distress among economics PhD students and what universities can do to remedy the situation, such as encouraging a more collaborative research environment.

One of my goals in this newsletter is to help you uncover the ways wealth and power shape public policy. Today, I'm going to focus on a topic that may seem wonky to you — but that's exactly the point. Its very wonkiness disguises the power dynamic lying behind it. The issue is how we measure the economy. Start with the rate of inflation — how fast prices are rising. That number is now driving the Federal Reserve, our central bank, to raise interest rates — which in turn is causing mortgage and bank loans to soar, the dollar to reach new heights against foreign currencies, and the stock market to plunge. It is also likely to drive us into a recession. That number is issued every month, from two places. Midway through the month, the Labor Department's Bureau of Labor Statistics announces the consumer price index. Near the end of each month, the Commerce Department's Bureau of Economic Analysis releases the Personal Consumption Expenditures Price Index. The two measurements are done slightly differently, but the important point is they're released every month, like clockwork. Meanwhile, on the first Friday of each month, the Bureau of Labor Statistics tells us how many new jobs have been produced in the previous month and what's happened to wages. (Economists and business columnists are already bracing for this Friday's report, covering September's jobs and wages.)There you have it: Prices, jobs, and wages. These are the three variables we learn about repeatedly because they are announced each month. The media repeat them, analyze them, frame stories around them. The three variables are used by policymakers at the Fed and in Congress and the White House. They're viewed as the core criteria for how the economy is doing. In short, these three variables drive the national economic conversation. But what about corporate profits? There's no monthly report on them. Without a regular monthly report on profits, it's been easy for the media and much of the economic establishment to ignore them — and ignore the record upsurge in corporate profits that occurred over the last two years (in a moment, I'll tell you how we know about that). Every month we hear about inflation resulting from wages pushing up prices, but we don't hear about record-high profits pushing up prices. Why is there no report on profits? That answer is found in history — and in power. As historian Eli Cook recounts, the first bureau of labor statistics in the United States was established on June 23, 1869 by the Massachusetts state legislature. It was supposed to collect data on jobs, wages, prices, and profits in that state. But when the new bureau sent out a prepared questionnaire to business owners seeking information on their profits, not a single one was returned. The bureau then tried to estimate profits by publishing a report on the amount of money deposited by wealthy Bostonians in local savings banks. Boston elites went nuts. “Astonished at the audacity” of this “unspeakably mischievous” report, they made sure the bureau chiefs were fired in 1873. The bureau chiefs were replaced by Carroll Wright, who soon went to Washington to head up a new federal agency then called the Bureau of Labor (eventually the Bureau of Labor Statistics) — and did so for the next twenty years. Wright devoted his life to comparing wage rates to cost-of-living indices as a way to measure what were then novel concepts such as “price levels” and “standards of living.” Presumably, to avoid the minefield his predecessors in Massachusetts ran into, Wright never investigated profit rates. And to this day, we know far less about profits than we do about prices, jobs, and wages. As Cook points out, profits continue to be a neglected topic in economics. No Nobel Prize in Economics has ever been given to the study of profits, presumably because we know so little about them. Economists classify publications into many categories (the Journal of Economic Literature's J3 code stands for “wages, compensation and labor costs”) but no category exists for profits. The last time The American Economic Review published an article with the word “profits” in the title was in 2014 (it was about the Japanese textile industry at the turn of the 20th century). Carroll Wright's Bureau of Labor Statistics is still going strong — one of the crown jewels of the federal government. But there is no comparable Bureau of Capital Statistics with the power to gather profit data from corporations. (The Commerce Department's Bureau of Economic Analysis does publish quarterly estimates of corporate profits but those estimates are based on samples of shareholder reports, IRS filings, and corporate income statements. They're guesswork at best because corporations notoriously shift some profits to nations with lower tax rates, depreciate assets like crazy, low-ball profits when reporting to the IRS and exaggerate them when communicating with Wall Street, and use every accounting gimmick imaginable.)So the only monthly, reliable reports we get are on prices, jobs, and wages. If we measured corporate profits more often and more reliably, Americans might be getting a story about inflation centered not on workers' power to get wage gains but on corporations' power to get price gains. There might be far more discussion about what appear to be record profit margins and their effects on price increases across the land. So rather than assume the Fed must hike interest rates to cool the economy by weakening workers' purchasing power — lowering their wages and causing them to lose jobs — we might discuss ways to weaken corporations' pricing power: such as windfall profits taxes, price controls, and tougher antitrust enforcement.Never underestimate how certain measurements, issued regularly and reliably, frame the national debate. And always ask why these measures, and not others, are chosen. For the answer, look to history — and power. Please note: Subscribers to this newsletter are keeping it going. Thank you! I also appreciate you sharing this content with others and leaving your thoughts in the comments. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit robertreich.substack.com/subscribe

In this week’s episode of The Mixtape with Scott, I had the pleasure of interviewing Kyle Kretschman, Head of Economics at Spotify. It was a great opportunity for me because Kyle is one of the first economists I have spoken to who didn’t enter tech as a senior economist (e.g., John List, Susan Athey, Michael Schwarz, Steve Tadelis). Kyle entered tech straight out of graduate school. He spent much of his career at Amazon, a firm that has more PhD economists than can be easily counted. Under Pat Bajari’s leadership there, Kyle grew and his success was noticed such that he was then hired away by Spotify to lead up their economics team. At the end of the interview, I asked Kyle an economics article that has haunted his memories and he said “BLP”, which is affectionate shorthand that “Automobile Prices in Market Equilibrium” by Berry, Levinsohn and Pakes 1995 Econometrica goes by. I really enjoyed this interview, and despite the less than ideal sound quality at times, I hope you will too.But before I conclude, I wanted to share some more of my thoughts. This series I’ve been doing on “economists in tech”, which has included interviews with John List, Susan Athey, Michael Schwarz and Steve Tadelis, comes from a complex place inside me. First there is the sheer curiosity I have about it as a part of the labor market for PhD economists. As I have said before on here, the tech sector has exploded in the last decade and the demand for PhD economists has grown steadily year over year. Tech demand selects on PhD economists with promising academic style research inclinations. There is substantial positive selection in this market as firms seek out strong candidates can be produce value for them. This is reflected in both junior market salaries, but also senior. Job market candidates are economists with technical skills in econometrics and economic theory, not to mention possess competent computer programming skills in at least one but often several popular coding languages. They are also candidates who were often entertaining careers within academia at the time they entered tech, and in those academic careers, they envisioned themselves writing academic articles about research they found personally and scientifically important and meaningful. Going into tech, therefore, would at least seem to involve choice that may go far beyond merely that of taking one job over another. It may involve a choice between a career in academia and a career outside it, which for many of us can feel permanent, as though we are leaving academia. And for many economists, it may be the first time they have ever contemplated such a thing. If they do internalize the story that way, if they do see taking a job in tech as “leaving academia”, then I can imagine that for at least some economists, that may be complicated, at least. But there’s another reason I have been wanting to talk to economists in tech and that is I am very concerned about the welfare of our PhD students. In a recent article published in the Journal of Economic Literature, economists interviewed graduate students in top economics programs. They found there incredibly high rates of depression, anxiety, loneliness and even suicidality. This is a common feature of graduate studies, but it is interesting that PhD economists have incredibly good employment opportunities and yet the depression and anxiety plague there too. One of the things that struck me in that study was the disconnect between what graduate students felt about their work and what their advisors felt about their own work. Many students, for instance, do not feel they are properly supported by advisers, do not believe their advisers care about their research success and do not even care about them as a person. Whereas most Americans (and faculty) feel that their work has a positive impact on society, only 20% of PhD students in economics feel that way. (I discussed the article as well as my own research on the mental health of PhD students here.) I suppose part of me feels a great sigh of relief to see the labor market for PhD economists expanding in light of those troubling statistics. If students know that life is full of infinite possibilities, then perhaps they can begin to process earlier what they want to do in the short years they have on this small spinning ball of rock we call Earth. If students do not in the end want to become professors, if they do not have the opportunities to become one, they should know that there is no “failure” involved there. Careers are just that — careers. They do not tell us who we are. The sooner a student can detach from the unhelpful story that our value is linked to a vita listing our accomplishments, the sooner they can begin their own life work of choosing their meaning. Can having more labor market opportunities with more employers competing for them help do that? Well no, not really. At least, not exactly. It can disrupt certain equilibrium, but then the new equilibrium can just as easily cover that up too. Still, I do like the idea that to keep students in academia, universities and departments must fight harder for them, pay attention to them, and invest in them as people. I like the idea that students have more options and that the options are diverse. Will it help their depression? Well, that’s another matter, as that’s complex. And presumably the economists in the survey I mentioned were themselves well aware of the career options they had since they were coming from the nation’s top 10 PhD programs in economics. I suppose my point is that ultimately, the burden of life really cannot be resolved with money or career. We are trained to look there because we have boundless appetites. But ultimately the hard work of navigating life can only be helped so much by a job. We must still decide for ourselves what meaning we will choose for ourselves. But one thing I know, and one thing which I think our profession is profoundly bad at saying out loud, is that if we make our identity connected to vitas, we will not just be miserable, we will be hopeless, and probably poisoned. Such a mindset leads to endless laps on a brutalizing treadmill of meaningless performance in which a person chases for first place in a race they don’t remember signing up for and which they cannot win. They compare themselves with others running, not knowing that they too are brutalized by their own treadmill, not realizing that it is impossible to catch up with someone else as there is always someone else ahead of us. The sooner we learn that the joy we long for will not come when we get a top 5, the sooner we can look elsewhere. It has taken me many years to relearn a lesson I learned decades ago — I am whole now. I am complete now. I still run, and I still chase, but I am not chasing completeness. I am not chasing my own wholeness. Being whole and complete has nothing to do with a career. Careers are ultimately orthogonal to hope, which does not mean they do not matter — they absolutely matter. But if asked to deliver meaning, we will find that our jobs are as weak as wet spaghetti at such a task as that.So, I suppose in some ways I simply want to announce — there are incredible opportunities for economists inside government, commerce and academia. But the weight of this life is not likely to be lighter in any one of them, for the weight we feel in life is largely self imposed, inside us, in the stories we tell about who we are and for many of us who we are not. Those stories are real, because we feel them and because we believe them, but they are not true. All stories are wrong, but some are useful, and the story that our lives can only matter if we have certain types of jobs or certain types of success, while it may be useful to getting a paper out or accomplishing something important, in a much bigger sense it is hollow at best and pure poison at worst. TRANSCRIPTThis transcript will be updated once the more complete transcript is finished; for now it was transcribed using voice-to-text machine learning.Kyle Kretschman:Might not have prepared myself well enough to be attractive for some of the most pop most top tier schools. Scott Cunningham:In this week's episode of the mix tape with Scott, I had the pleasure of interviewing Kyle kretchma the head of economics at the streaming platform. Spotify. Before I dive into the interview, though, I wanted to give you a bit of a heads up about the sound quality. Unfortunately, the sound quality in the interview on Kaza side is a bit muffled. We discussed refilming. It tried to find a way to tweak it, but there were certain constraints on the actual sound itself that kept us from being able to do it. And we didn't feel that refilming, it would be good because we thought that the interview had a lot of serendipitous kind of spontaneous tangents and things spoken about that. We thought students and people in academia would want to know, would need maybe even need to know. And I doubted that I could recreate it, cuz I don't even know why it happened. Scott Cunningham:So I'm gonna post a video version of this at my subs, for those who feel that a video version would help them kind of follow it in so far as the audio might be at times challenging. So check out the subst for those of you that wanna watch, watch it instead of just listen to it, hopefully that'll help. I won't say much here by way of introduction, except to say a few things about Kyle, because I wanted to let Kyle tell you his story in his own words, cuz it's his story to tell. And it's an interesting story. Kyle's a PhD economist though from the university of Texas Austin, which is down the road from where I live and work at Baylor, where he wrote on topics in graduate school and applied econometrics, empirical industrial organization or empirical IO and public choice after graduating, Kyle went to Amazon, not academia. Scott Cunningham:In fact, given we might start the boom of tech hiring PhD economists in the early to mid 20 2010s. You could say Kyle maybe was sort of one of the earlier hires among that second wave of PhD economists that went there. He worked for several years at Amazon before being hired away by Spotify to head up and lead a new economics team there, perhaps this is part of a broader trend of tech firms building up more internal teams, not just of data scientists, but like Amazon departments of economists who knows recall though from an earlier interview with Susan athe where, when I asked Susan why she said pat Maja had done something amazing at Amazon, she said he made economists productive. And in time he made many of them productive and very in productive from what I've been able to follow. And Kyle is from what I can gather someone whose skills matured and deepened under the leadership of Papa jar at Amazon and other leaders at and other economists at Amazon. Scott Cunningham:And he was ultimately hunted down by a major tech term to create an economics team there I'm by no means an expert on the labor market for PhD economists. I just have been very intrigued and curious by the, the, the Mar the labor market for PhD economists in tech, because well, partly because of realizing first that cause of inference was really valued in tech, but then to sort of realize that there was just this very large community of economists there, but I don't think it's controversial to say over the last 10 to 15 years, the tech industry really has been disruptive in the labor market for PhD economists. They continue to hire at the junior and senior market in larger and larger volume selecting more and more on people who likely would've gone into academia into tenure track or tenured positions. They pay very high wages, some of the very, some of the highest wages in the country, both at the junior level and especially at the, at the higher end at the, at the more advanced levels, people can earn compensation packages by the, in the, by the time they're in their thirties, that many of us didn't know were possible. Scott Cunningham:It's in my mind, historically novel, and I might be wrong about this, but it, it seems historically novel that the PhD economists who likely would've produced academic research papers in tenured and tenure track jobs have begun to branch out of academia, but maintain those skills and maintain that research output. It's partly driven best. I can tell, buy Amazon, I might be wrong, but by Amazon and paja, as well as Jeff Bezos own view, that economists are what I guess we would just say value added for many firms. Therefore I'm continuing to wanna speak with economists in tech to help better trace out the story. This interview with Kyle follows on the back of earlier interviews with people in tech like John list, you know, a, a distinguished professor of economics at the university of Chicago, but also the former chief economist that Lyft and Uber now Walmart Michael Schwartz, former professor of economics at Harvard. Now, chief economist at Microsoft and Susan athe former chief economist at Microsoft professor at Stanford and now chief economist at the DOJ. I hope you find this to be an interesting dive into the industry. Learn a little bit more about economists there, but by, by learning the about one particular important economist, there a, a young man named Kyle crutch, head of economics at Spotify, my name's Scott Cunningham. And this is the mix tape with Scott. Scott Cunningham:Well, it's my pleasure today to have, as my guest on the mix tape with Scott, Kyle crutch, Kyle, thanks so much for being on the call. Kyle Kretschman:Hey Scott, thanks for having me really appreciate the time to talk Scott Cunningham:Well before we get started with your career and, and everything. I was wondering if you could just tell us your name and your title and where you work. Kyle Kretschman:Sure. Yeah. As you said, I'm Kyle kretchma, I'm the head of economics at Spotify, Scott Cunningham:Head of economics at Spotify. Awesome. Okay. I can't wait to talk. So let me, let me, let's get started. I was wondering if you could just tell me where you grew up. Kyle Kretschman:Sure. So most of the time I grew up in outside of Pittsburgh, Pennsylvania, about an hour north of the city, real real small town probably had one stop light. And maybe the, the funny story that I can share is what I took my wife there. She asked where's the Starbucks. And I said, no Starbucks here. There's no Scott Cunningham:Starbucks. Kyle Kretschman:Yeah. So pretty small town called Chippewa township in Pennsylvania. Scott Cunningham:Oh, okay. Is that near like Amish stuff or anything like that? Kyle Kretschman:No, that's the other side of the state. So this would be Western Pennsylvania about near the end of the turnpike, about five minutes from the Ohio border. Scott Cunningham:Oh, okay. Okay. You said, but you, did you mention, you kind of grew up in different places? Kyle Kretschman:Yeah. So before that, my father worked in civil engineering and so would do build roads and bridges basically across every, across the nation. So I was actually born in Louisiana, lived there with, I think for a whole two, three weeks. I don't quite remember. Cause I was pretty young obviously, but then Michigan and then spent some time in Philadelphia before moving out to Pittsburgh around second grade. Scott Cunningham:Oh, that's kinda like, that's like when people described their parents being in the military, just kind of moving around a lot. Kyle Kretschman:Yeah. A little bit. So, but Scott Cunningham:Then you settled in the second grade Kyle Kretschman:That's right. Yeah. So outside of Pittsburgh and then stayed in Pittsburgh through high school and even through undergrad. Scott Cunningham:Oh, okay. Oh, you went to undergrad in Pennsylvania. Kyle Kretschman:Yeah, I did. So I went to undergrad at the university of Pittsburgh. Oh, okay. It was, yeah. If, I guess maybe continuing the story growing up in a town with no Starbucks. I was, I was pretty intrigued by going to a city. Yeah. And find out that lifestyle and yeah, we might have lived pretty close, like an hour away, but we didn't go down to the city very much. So Pittsburgh was just really, really enticing for a city to, for, to go to undergrad in. And so I basically looked at all schools that were in cities and so the proximity plus then the, the ability to just spread my wings and explore what it's like to be in a city was really, really enticing. Scott Cunningham:Did any of your friends go to pit with you? Kyle Kretschman:Yeah, so there's probably, I grew, I graduated from a class of about a little over 200 people in high school and I think there was like five or six people from high school that went to pit for my class. So definitely had some really good friends who went and kept in touch with, through undergrad. Scott Cunningham:Mm. Yeah. So it wasn't, were you sort of an early generation or you weren't, were you a first generation college student in your family or did your parents go to college Kyle Kretschman:Combination? So my dad went to Penn state civil engineer, as I mentioned, me and my mom actually graduated from undergrad the same week. So my mom went back to school later in life after me, after we went to school. And so yeah, we, we were able to celebrate graduation cuz she went to a small private school right outside of the city also. Scott Cunningham:Oh, okay. Okay. Yeah. Well, so what did you like to do in high school? Kyle Kretschman:So I played a lot of sports before high school and then I kind of switched into, and this was a traditional sports of football, basketball, baseball, but then I switched into tennis in high school. And so that kept me busy, but along with a lot of academics and really, really liked computer science. So played a lot of video games growing up, really enjoyed like that aspect in combination. Scott Cunningham:What games were your, were you, did you play on a, on a video game, plat platform? Like an Nintendo or did you play? Kyle Kretschman:Yeah, no, we played a lot of plays very much into like role playing games. Some of the arcade games like Marvel versus Capcom. So yeah. Yeah. Very, very interested in gaming. Yeah. Maybe I was a little too early for that. Cause you know, every, everybody in the 1990s was like, oh, I could make pu money playing video games, which wasn't true back, which wasn't true back then, but that's right. You know, nowadays Scott Cunningham:You can that's right. Yeah. You know, that's right. You can do it. There's all kinds of ways you can make money doing things today that nobody knew was possible 10, 10 or 15 years ago. Even Kyle Kretschman:My Scott Cunningham:That's cool. Yeah. I, I, it's funny, you know, computer games can keep a, keep a kid in high school going, you know, like especially I think they're kind of misunderstood. I, I had a lot of friends that, well, I mean, I, I, I had, when I didn't have a lot of, we moved from a small town in Mississippi to Memphis and I, those, those that first year when I didn't have a friends, I did bulletin boards and played Sierra online games like Kings quest. And it's like, it's like, you know, not intertemporal smoothing, but like inner temporal socializing, smoothing, you know, so that you just kind of get through some periods that would otherwise be a little lonelier. Kyle Kretschman:Yeah, for sure. And I mean, I mean for this audience, like most video games are some sort of form of constrained optimization. So there was, there was the inkling that I, I liked understanding how economies worked in high school through this and yeah. Going back to my mom, my mom always said like she encouraged it and she encouraged education. And there was actually kind of like that nexus, whenever I took economics in high school, it was like, oh, you know, some of these games really are full economies that are constrained and constrained in a way that you can understand and complete in, you know, under a hundred hours. Right. But there was that combination that was kind of showing itself of computer science, computer gains and economics of putting itself together. Scott Cunningham:So you were kind of thinking even in high school about economics in that kind of like, you know, optimizing something and like, like almost that modern theory that we get in graduate school. Kyle Kretschman:I think more, I had the intuition when I didn't have know how to say what it was in high school because my high school was pretty forward and that it offered both advanced computer science courses that could get you through definitely through first year of undergrad, maybe even through second year with advanced placement. And then they also offered advanced placement economics. And so I, I ended up taking advanced place in economics my junior year when most people took senior year. And so whenever I was going small Scott Cunningham:Town, even in that small town, they had, you had good your high school. Good econ. Kyle Kretschman:Yeah. It was a real, it was a really good high school that would put together good curriculum that did a lot of college preparatory work though. They, wow. They really leaned into the advanced placement, the AP courses to get students ready to go to school. Scott Cunningham:Wow. Wow. So even at, as a junior, you're taking AP econ, you know, you don't have to take AP econ. That kind of is say that, that sounds like somebody that was kind of interested in it. Kyle Kretschman:Yeah, very much. Yeah. And again, as soon as I, I definitely didn't get to the graduate level of understanding, like, you know, LaGrange multipliers, but the, the micro and macro sequence just made intuitive sense to me. It was like, it was kind of where I was like, yeah, this fit. And this is how I think. And some people might criticize me now that I think too much like an economist. Right. Like, but at the same time, it just like, it started to put together that language and even more so some of the frameworks that really kind of drew me into it. Scott Cunningham:Well, did you, did you, did you notice that you had this interest in computer science and this interest in economics and that they might be one, did you get a feeling that they could be in conversation with each other? Kyle Kretschman:Not Scott Cunningham:At first, our ancestors a hundred years ago. Didn't, you know, those economists didn't think that way, but now it's just so natural for this generation of economists to be almost one half, you know, one third mathematician, one third economist, one third computer scientist. Kyle Kretschman:Yeah. So not at first, but I, I feel like I made have like lucked into it, honestly, because whenever I chose to go to Pitt, I chose to start as computer science because I knew what that pass was. I was inspired by my older brother, the great teacher in high school. And like, I was definitely like, okay, a software software development engineer career is great. It's cutting edge. It's there. But after probably like the first year, it just didn't feel that end state didn't feel right. And so I made kind of the hard decision to choose, honestly, to switch into economics as a major, because I wasn't sure what the end state would be, where I was going with it. Cuz it was definitely felt more amorphous, you know, it's a social science, so yeah. It didn't feel like it was gonna be as clear cut and as, and have as much certainty. But pretty quickly, like after a year was like, oh, well we're doing, we're using E views at the time. All right, this is coding. I know how to do this. This is great. Right. And starting, starting to see some of that in undergrad was like the, kind of the aha moment that like, yeah, this is, this is a place where I can apply this love of coding and problem solving, but problems and solutions that I find really, really hard and interesting. Scott Cunningham:It was because of econometrics though. It was in that. Kyle Kretschman:Exactly. Yeah, yeah. Scott Cunningham:Yeah. Wow. That's, that's really interesting because you know, I think it's still the case that, you know, you can easily end up with an econometrics class that remains purely theoretical and doesn't end up, you know, exposing the student with a lot of actual coding, but it sounds like your professors were, were getting you into working with data. Kyle Kretschman:That's correct. Yeah. Both. Both within the class. So like I said, we used E views at the time. Yeah. And again, kind of like learning as a go, I, I don't think I really knew what I was doing whenever we were typing commands and E views, but the computer scientist in me was like, okay, well this is a function. I know functions. Didn't put outputs, but definitely didn't understand necessarily things that were going under the hoods or you know, all of the theory that goes with it. Oh, right, right, right. So it was, you Scott Cunningham:Knew the coding part, you knew you were coding, but you did, but like the, the actual statistical modeling was kind of the new part, but that was a way for you to kind of engage it a little bit. Kyle Kretschman:Yep, exactly. Scott Cunningham:Oh, that's interesting. That's interesting. Well, so what were you gonna have to choose between a computer science and an econ major did or did you end up doing both? Kyle Kretschman:So I chose an econ major, but then I had what I would call basically minors or concentrations in computer science, but then also in statistics and also in math, because once, once I had an internship at a bank and was doing data entry and I was like, eh, I don't think this is what I wanna use my economics degree for. Yeah. I had a couple professors at pit named Steve Houston and Frank Giani who brought me on as a research assistant, an undergrad to start being part of some of like their survey projects and data collection. And even, even one of 'em I don't, Steve was crazy, but he even let me TA classes on undergrad, so oh, wow. But he kinda, I mean, I, I say that jokingly because it was formative for me, it was like, okay, this is great. How do I do more of this? And he was like, well, you go get your econ PhD. And I was like, so I can be a teacher with computer science and doing economics altogether. He goes, yeah, let's do that. And so it was with the help and support of some of these really good professors and education to kind push me on this path consider to get Ancon PhD. Scott Cunningham:Mm. And that's when you were like, so how, how, what, what year would you have been in your program? Kyle Kretschman:Probably. I think I was in my junior year where I was starting to explore this. And then in my senior year is where I was like, okay, I'm actually gonna be doing more more of this and applying to grad school because going back, as I said, I entered with some credits. So my senior year was very, I didn't need a full course load. So I was looking for other things to keep me busy, which maybe, maybe that's one of the themes of this conversation is I kinda kind of like the variety and really have variety seeking behavior too. Yeah, Scott Cunningham:Yeah, yeah. Yeah. So you graduate, was there like a field that you were mostly interested in? Kyle Kretschman:I thought I would be going into macro economics. Macro. Yep. Yeah, because Steve worked on the council of economic advisors and I was really inspired by that and the application of economics within, within policy and just again, always applied economics, not necessarily theoretical. So yeah. Then again was, that would be sort of like labor and macro was like the initial idea, but finally Scott, I didn't do all my homework and like, think about like what grad school looked like or all it looked like. I kind of went a little bit more naive than I think other people with, again, ideas of how I could become like a teacher, an educator with some of these tools versus like how disciplined and single thread you need to be on research to be within an econ PhD program and to see that. Scott Cunningham:So you, so you kind of were like, so when you were thinking about graduate schools, what, how, what, what did you sort of, can you walk me through like what you were thinking and how you went about trying to apply to graduate school and where you ultimately chose? Kyle Kretschman:Yeah, sure. So applied probably the, the top 10 and the top 10 probably said no thanks. But also then was targeting specific schools that we had relationships with that I knew would provide computer science and macros. So university at the Iowa at the time, this was 2000 and had a really strong macro program. And then also at the university of Texas with Dean Corbe there, they also had one in Russ Cooper. And so those were like the two that I was like targeting at outside of what the top schools were. But yeah, as I, I kind of mentioned, I, I might not have prepared myself well enough to be attractive for some of the most pop with top tier schools because kind of, you know, as I said, bounced around and would be yeah, a little bit working on it a little bit different things and have computer science versus being solely focused on like economics and math and things that might be more of what the top tier schools were looking for. Scott Cunningham:Yeah. Yeah. You know, you know, it's like the, I mean, I'm the same way. I didn't ha have any econ classes in college. I was a English major, but the, the, the diff there's so many students that sort of seem to almost for whatever reason, know a lot sooner what they want to do and then like make those choices. And then there's just many of us that are, you know, in a process of search yeah. That when you're in a process of search, well, you, you know, by definition, that's like you're using that time to search. Kyle Kretschman:That's exactly right. As Scott Cunningham:Opposed to saying, I've gotta take, I've gotta become a triple major computer science, math, econ, and have to do like, you know, these set of these set of steps that, you know, there's no way I could even have known to do it unless somebody had told me it's weird. I mean, it's just funny how the little things can have such big repercussions for your whole life, but it's, but it, it worked out great. So you end up, where do you end up going? Kyle Kretschman:I went to the university of Texas at Austin. Scott Cunningham:Yeah. Yeah. What year was that? And Kyle Kretschman:So, so this would've been 2002. Scott Cunningham:Oh, okay. So you go to oh 6 0 7. Kyle Kretschman:Okay. And so ended up working. So I ended up working a lot with Jason, Ava. Yeah. And who came in and became the, the head of the department. Yeah. Applied econometrician who just did an amazing job going back to whenever I said, I didn't know how things worked under the hood, in those formulas. He didn't even let us use those formulas. So anytime we were doing applied econometric econometrics with them, not only we learning to teach, we're learning the theory, but he said, you have to code it yourself. You have to do the matrix algebra, you have to calculate standard errors. You can't really call those functions. So that was probably again, that wasn't until the third year, but yeah, in the first year to go back a little bit, Scott Cunningham:I, that played to your strengths though. I bet that played to your strengths. Yeah. Just at the end of the day, wanting to be someone that, that wrote down the raw code. Kyle Kretschman:That's exactly right. And, but the first year I didn't play my strength. Yeah. Yeah. So the first year I felt, I felt a little bit outta water and I was like, this is, I remember when we were proving what local non association. And I was like, this is, this is one hard, but also like, again, going back to like, that is this actually how I wanna be spending my time and right. I, I was like, yes, I do. But I was like, I, I knew that I needed to get to those applied applications. Yeah. And so that's, again, why I was thankful to be able to work with Jason and Steve Trayo and a few other, they applied econometricians at Texas that really encouraged me to explore starting in the second year. They didn't us like pin it down. And so I, I thought I, at the second year I worked like wrote the first, a paper on school choice and trying to see if I could find some sort of instrument on school selection on public versus private. And again, so that led to like that idea of like applied econometrics was really, really the thing that like, I was like, okay, now this fits again. Once we got into second and third year Scott Cunningham:Was, was picking up that intuition, that kind of like labor style identification, causal inference kind of approach. Was that something you picked up from Jason or was that just like from your labor people? Oh, okay. Kyle Kretschman:Yeah. That's yeah. From Jason and Steve a lot. They did a great job of doing that. And yeah. So then, yeah. Then I, then I threw in, I knew threw a little bit of a switch in there also, and my co-author Nick master and Arti and closest friend and classmate in Texas was very theoretical and very interested in applied empirical IO. And so we started working in that field also together. And so then I got to work with the Han me vet and Ken Hendrix on using empirical IO. So, oh, wow. Yeah. And so again, Scott Cunningham:This is the more structural, more structural econometric. So you've got this like reduced, you've kind of got this like traditional labor reduced form type of, part of your brain. And then you've got this empirical IO structural part of your brain kind of emerging at the same time. Kyle Kretschman:That's right. That's exactly right. Yeah. And then we threw, we threw everybody for a loop. I also saying we wanted to study study politics and how money turns into vote using both using all these tools. So yeah, I can see here kind of saying in hindsight, like it all makes sense in this story that I'm telling you, but at the time it was more of what you were talking about. It was searching. It was, I wanna be working on really interesting applied problems. I love the toolkit that economics provides in framing. And yeah. I have to be coding to be able to utilize these tools that I've had built up in the past. Scott Cunningham:Yeah, yeah. Yeah. So, so matching with Nick was really important Kyle Kretschman:Very much. Scott Cunningham:And why, if you hadn't to match with Nick, I mean, just kind of outta curiosity, if you could articulate the value added of that whole partnership, what was it? Kyle Kretschman:Yes. Sure. So, so we matched basically from math camp going into, going into the first year because Nick came both from the pure math and physics background and also had some experience in the air force. So the air force was sending him to Texas and he, we were, we were definitely, we definitely didn't have a lot of vend overlap on the fact. He's like, well, I would have the intuition and some of the computer skills, Nick would have the theoretical math skills, Scott Cunningham:The theoretical math skills. Yep. Kyle Kretschman:And then we just had, we had the common factor that we wanted to work hard together and learn together and we're willing to, we're willing to intellectually hash out really tough things together. Yeah. So yeah, he huge credit to him through being able to put up with me. And he says, he says the same thing once in a while. But again, matching with somebody that had the, the more real analysis proof based understanding of math was so valuable for me. And especially, Scott Cunningham:I think some empirical IO, especially empirical IO, just being able to, you know, think like an economist in the area of IO is thinking real deep about, you know, a rich set of models and modeling approaches. Kyle Kretschman:That's Scott Cunningham:Exactly right. That's definitely not what you're learning in your econometrics classes, even though they might go together. Kyle Kretschman:Yep. So, so yeah, it was just a, it was a really good match from the beginning. And so we complimented each other and we're, we're able to build a strong enough relationship to be able to be able to hash out, have really long nights yelling at each other, we say in the office, but it never, it was always for educational purposes and lifting each other up. Scott Cunningham:Was that different than what you thought grad school was gonna be like? Kyle Kretschman:Yeah. So I knew the research component a little bit. I just didn't under understand the unstructured research on how that was gonna go and like the cadence and where it was gonna and how that was gonna be so required to develop your own viewpoint. Yeah. I thought it would be more directed cuz as a 22 year old, that was the experience I had generally. So that was the big one was the undirected and I liked it, but it was also very difficult. Scott Cunningham:How would you describe what you're talking about to your college self? Who kind of like, you know, he, he doesn't really, he doesn't even have the vocabulary for what you're describing. What would you say? It was like, Kyle Kretschman:I think you use a good term. You have to be not only wanting to search, you have to be willing to search, but you also, then you have to put in the guardrails yourself to keep it focused because you're not necessarily gonna have those external guardrails that you will have from an alternative path of going to either like a master's program that's gonna be more structured or going in an industry or going to get a job. Right. Like I mentioned at a bank for like a 22 year old where entry level jobs are gonna be more structured. Yeah. So yeah, I just, I, I probably knew it, but I didn't know what it meant to be and what, what it meant to experience it. Scott Cunningham:So how did Jason and, and Steve kind of, and any other faculty, how, how did they, how did they, I, so I did this interview with Susan athe and she was saying that, you know, the amazing thing that pat Maja did at Amazon was he managed to make economists productive, which kind it was kind of a weird, weird way of saying it. And so in a way it could, in a way you could imagine a department that sort of has like a, you know, this idea of like research has got to come. There's like a, there's like a, a journey that a graduate student has to come on to just to basically make a decision to be a researcher. Yeah. You know, and you could imagine that creating the conditions for that is, is involves faculty member, doing stuff that's not necessarily obvious. What, how did they, how do you think they contributed to that for you personally? Kyle Kretschman:For me personally, at the time, again, it goes back to encourage the exploration versus mandating or saying that I need to be on one path. So like even Nick and I at the time explore the idea of a private company and how, what, what that would be into like pinching, pitching a venture capitalist on, on that. So all those things, again, in grad school, they, they were encouraged, but they weren't structured at the time. Yeah. So yeah, I can, I can, I understand Susan's comment because I was, I was one of those economists who started pretty early with pat and we, we have a lot of good mechanisms that we've learned and built at Amazon when I was there at the time through pat, through lay other people who were willing to make the jump into this entrepreneurial space that hit the election and the, of coalesce of economists doing open book, empirical research, along with data science. Right. Just becoming more and more valuable and applicable, but is kind of what Susan piloting that we can, we can talk more about if you Scott Cunningham:Want. Yeah. I do wanna talk about that. I wanna talk about the, the decision though, you know, to, to be, because you, you sort of started off in college, you know, you said things like, oh, you can become an educator and then you've gone in this non-academic direction and you know, it, it, and that's like a, that's a more common story now, you know, right. Of, of top talent, very talented PhDs that you could have easily seen 20 years ago, would've been an academia. Their counterfactuals are, are following you. And so, you know, it's, it's a, it's a big part of our, you know, collective story as economists that this, this new labor market that didn't, that didn't exist historically now exists and draws in so much talent. And I was just curious in a way you're kind of like a, a first generation person like that, you know, when you think about it, right. Cause text's not very old, right. Facebook, Facebook, what it's like 2007. And so, you know, so you've got this, you, you, you've got this, this chance to kind of say like, it must have been, so I don't wanna put words in your mouth, but I guess I was just wondering, what were the feelings like as you considered not taking an academic track and when did it start to be something in your mind that you thought that's gonna be something I'm explore Kyle Kretschman:Probably pretty early, because if you wanna really trace the roots of like tech economists back, it starts obviously with Hal varying at Google and me and Nick, actually, we, we sent an email to Hal, probably 2008 saying, do you have any, have any use for some summer interns who can do some empirical IO? And he said, no, not, not at this time, but so, but he Scott Cunningham:Answered the email. Kyle Kretschman:He did answer the email. Yeah. It was nice, nice of him to answer. Cause we knew he was probably pretty busy, but so it, honestly, when Amazon started hiring economists, I was probably searching for about a year to move into tech. If you wanna move back to the decision point coming outta grad school, honestly it was a challenging labor or a challenging job market for me, somebody who is a lover variety, who is working on empirical IO problems with campaign, policy, campaign, finance reform, policy recognition. That's, that's not fitting a lot of the standard application process. Yeah. Once again, that's so that's probably a theme for me. And again, at the time it was hard. I was, I was in the running for jobs at VA wakes force that I thought would be really good fit because they're the EDU the emphasis would be on education with the research ability to do research and work on problems that were more widely probably policy oriented. Yeah. But neither neither of them came through. So I just always knew that I industry was gonna be an option. And so Scott Cunningham:What year is this? What, Kyle Kretschman:What, what this would've been in this would've been in Scott Cunningham:20 11, 20 11. Okay. Oh, so you moved through the, you moved through the program or kind of relatively quickly. Oh 7, 4, 4, 5 years. Okay. Kyle Kretschman:Five years. Yeah. Five years. Yeah. Oh six to 11. Okay. But so for about a year, about six. Yeah. Yeah. And so starting in 2013 is whenever I started applying to the first tech job as a data scientist and got it went great until I talked to the VP who was a business part, like pure business person. When I was talking to the hiring manager at the time, it was a company who was providing college counseling as a software service. And so they would do this at their, their clients were both for profit and not for profit companies. And we were talking like, we'd get into details about treatment effects models and how we could measure the impact of their intervention. It went great. But then I had the flyout scheduled, but then the interview with the VP, he said, well, how am I gonna monetize your algorithm? Right. And I was like, I'm not sure I know what algorithm means, but right. I, I wasn't prepared for that language and that application and how you turn econometric modeling and measurement into, into business impact at the time. Yes. Right. So spent another year looking around with different opportunities like that and honestly learning again. So, so whenever Amazon, so this would've been in 2014 and then Amazon was hiring its first big cohort with pat. So this was a cohort that was about, I think there was about 13 of us. It was a no brainer. Kyle Kretschman:Whenever, whenever we did the interview, it just was like, all right, this is exactly right for me. I was hop. I was hoping it was right on the other side. And I could probably tell you some funny stories about the interview process, but I was like, this is, this is what's meant to be. Yeah. So it, it, it was like a 10 year journey from 2004 when I switched outta computer science into 2014 being like this, just this fit. Scott Cunningham:Right. Right. Right. So outta curiosity, you know, is, is there, is there something that you think is supposed to be learned by the fact that when you were on the job market and you had that interview with that, that gig and the, and you get to the VP and he articulates questions that are not traditional econ questions, or even econometrics questions like business profitability to act, it's kind of ironic, isn't it like to everybody? That's not an economist. That's actually what we, they think we do, you know, is like, they think we do all that stuff. And then they don't know that we're like, like you said, you know, trying to set up a Lara and solve, solve it, like what's a Lara, but do you think your competition at that time did know how to answer questions like that? Like non-economists in those positions Kyle Kretschman:Probably at an inflection point. Yeah. Because this is the same time. Wherever machine learning is becoming more common toolkit with an industry. So there would be like machine learning algorithms that are designed for, you know, prediction, problem sequencing, anything like that that are specifically designed to be used in a business setting to monitor. Scott Cunningham:So they, they not only know machine learning, it's like, they also can kind of immediately articulate why this would be profitable. Kyle Kretschman:I think so. Yeah, because again, the computer, so it's like in learning the language and this is the language that would probably be more understood within a machine learning computer science version is okay, well, I'm gonna use this to change the recommendation engine right. Is very common one. Yeah. That's obviously gonna be, so how are you gonna monetize it? I'm gonna improve the match and the recommendation engine it's gonna have this. So I think at the time there was a little bit of it, but, you know, hopefully I think, I think I learned pretty quick that you can, you can use econometrics in a similar vein. As I said, it's a flavor of data science, Scott Cunningham:Have you had to become a blue collar machine learner? Kyle Kretschman:I've had to understand it, but not, I think you mean by blue collar, you mean like implementing it Scott Cunningham:And yeah, I just, when I, I usually say blue collar in the sense of like, you know, you, don't like, you know, you basically are picking up these skills, but you weren't like, you know, you didn't get a PhD in computer science. You know, Kyle Kretschman:The answer was then that answer is definitely yes. So like as we, as our cohort and as we grew, the economics discipline at Amazon, that was a big part of it is how one could we bring in some machine learning scientist help educate and teach us. Mm. And yeah. So, and even in, sometimes in lecture style, we would do that because it was so important, but then even more so learning to so that you can interact with different stakeholders specifically, like machine learning scientists. Mm. Then understanding when you can actually implement it and marry it within the econometric models was definitely a huge part of the education process. Scott Cunningham:So you go to Amazon, is that right? That's like your first entry into tech Kyle Kretschman:That's Scott Cunningham:Right. Is Amazon, what's your title? Kyle Kretschman:So Scott Scott Cunningham:A scientist or economist. Kyle Kretschman:I, it was something like business intelligence engineer. There wasn't an economist job family. There was, as you said, it was kinda the forefront. I think it was this. Yeah. I think that's what it was, but Scott Cunningham:Cause it is now right. Baja has a that's Kyle Kretschman:Right. Scott Cunningham:He created a job title called economist. Kyle Kretschman:That's right. Yeah. And that got set up about a year in, so like, and I was part of the group. So we would set these, we would set up like these people and process mechanisms that allow economists to be so influential and productive within Amazon. Scott Cunningham:Mm, okay. So how is he doing it? Why, why is Susan saying he performed a miracle by making economist productive? Can you kind of describe, like, if you had to just guess at like the counterfactual, if it hadn't been, you know, pat, it hadn't even been an economist that was hired into Pat's position. Like, what is it that he, what, what is it that he, or Amazon or whatever is making you go transform and become this new version of yourself? Kyle Kretschman:There's, there's a lot of factors and I could probably spend an hour on this, but I'll, I'll try to, I'll try to reduce it down to like some key mechanisms and ideas. The first is that Amazon is probably the most data driven company. I know. Mm. They are so focused on measurement, both of things you can directly measure. And, but they are. So they were very early interested in economic measurements that are UN observables either coming from like coming from econometric models. That, that was whenever pat demonstrated some of those that was like the light bulb went off the, so, because again, it, Amazon was run by and still generally is people with operation science background. And so this over index on measuring as, as coly and as precisely as possible, well that's that's economics. So that, that was part of it. Another part of it is culturally Amazon operates that makes decisions based on six page white papers, you wanna make some economists really productive, have them write a six page white paper instead of giving them a presentation, especially to people like who may be in the background with MBAs or other people who have a comparative advantage, we economists have a care advantage in writing. Kyle Kretschman:So it was little bit of like a surprise, but you might hear these anecdotes where it's true. Like whenever you go into a, a decision making meeting, you come in with your six page white paper that says here's the business decision to be made here is my recommendation. And here's why, and people sit there and it can be a room for five people can be a room of 25 executives. They sit and read the paper and they read the whole thing. Is there an append that can go on forever depending on how big the meeting is. Sure. But that structure of, of data driven decision making, combined with how you're presenting your argument is written seems like, seems like economists should be pretty good at that. Right? Scott Cunningham:Is that a pat thing? He came up with work, the work he made, Kyle Kretschman:What was the six page idea was from Jeff Bezos. And so that was, would Scott Cunningham:Those be circulated throughout the, throughout the, the, the firm, Kyle Kretschman:The stakeholders who needed to be part of the decision making they be circulated. But again, this is every, like everybody's writing six pages. PowerPoint is basically outlawed at, at Amazon. And again, that happened mid 2000. Sometimes people can Google it to find out, but that six page culture and decision making culture, just again, fit economists. Scott Cunningham:So how is a six page paper similar to the kinds of writing that, you know, you sort of associate with economists and how is it different? Kyle Kretschman:So its I'll start with the differences. So one with the six page versus like a 30 page academic, you are not going to be able to share the research process. You are not supposed to share the research process. You're supposed to share the clear recommendation and how you got to that recommendation. Right? So if you think about like a 30 page academic paper XT, be condensed down into those six pages. In my view, they're just, that's just not how the industry operates, but you probably would know better than me on that where, but so again, where it's the same is again, it's a data driven argument. The purpose of this paper, the abstract here is the hypothesis that I have that and here's how I tested it. And here's how I'm making my conclusion. So what I always found really honestly easy was I felt like I was doing the scientific process. Like I felt I, I was with business decision making it generally work within what is the hypothesis? How are we doing this? How are we testing it? What are we think some alternative conclusions could be, but what are we making towards it? So yeah, yeah. Again, it was closer to what I felt like would be a scientific paper in and that hold of day driven mindset is again, that's more, it's very common. Amazon have a common Spotify now Scott Cunningham:Has that been influential throughout, throughout industry? Has that, how have you noticed Amazon influencing Kyle Kretschman:Some Scott Cunningham:Yeah. Like most people don't understand. Kyle Kretschman:Yeah. There there's some companies who definitely have completely adopted it. There's some companies who haven't, but the, the six pager again, that's, this is not a, this isn't a concept just to economist and tech. This is the concept is, is held up as one of the key mechanisms for all of Amazon. Scott Cunningham:Mm mm Hmm. Kyle Kretschman:One other. Scott Cunningham:How often were you writing those? Kyle Kretschman:Depends on what level you were farther in my career. That's the only thing I did was write six page papers and it would be part of like, my team would help, but again, anytime you have a key business decision to be made or an update, like you're gonna be writing the six page. So yeah, it's again, the farther, the more seniority you have though, the more that becomes your job is to communicate side and guide through these business decisions. Scott Cunningham:Do they, to you, Kyle Kretschman:They belong to the team because it's always Scott Cunningham:Put 'em on a, you can't they're like proprietary though to Amazon. Kyle Kretschman:Oh, correct. Yeah. No, they, they're not publicly available. They're Scott Cunningham:Proprietary. Like it must is it what's that feel like to do something? What's it, what's it feel like to, to do something that creative in that kind of like scientific that's siloed within the firm? Does that feel strange? Kyle Kretschman:No, it didn't. Because what it enables is to be able to work on some of the hardest questions without having to worry about without having to worry about com communication strategies or right. For press release. So no, it felt like we were able, and this is going back to like some of the things that pat and we did at Amazon make successful. We worked on some of the hardest problems at Amazon from a very early stage because we said that it wouldn't be publicly available. Right. So that's gonna do that. And Scott Cunningham:That's been a key part. Yeah. Because okay. I get it. Okay. That, that makes a lot of sense. Yeah. So who did you discover? You were, go ahead. Sorry, Kyle. Kyle Kretschman:No, I was gonna say maybe the last me to highlight. Cause again, I, I, we could probably spend this whole interview on this, but the, the other key mechanism that pat pioneered was the proliferation of economists as a job family was not pat saying and us saying, go do this. And I can give through my own personal example. It was the other business executives, seeing the measurement, seeing the results on product, just saying, okay, I want that. So it really was a demand, AKA demand, internal demand for more economists, that was gonna say, I want this with my business decision making process and want these people who can do this and collaborate across the difference. It was not a, oh, we're gonna put economist in the siloed function that everybody's gonna come here. And that was, that was my story. But the very first year I worked on projects directly for the consumer CFO, basically the whole year. It wasn't necessarily by design, but it was what happened. And at the end of the year, year and a half, the, the VP of finance said, come over here and do this with me and come build, come build an economics team and an economics function here within my organization. And that's really is again, that's the real key was it was business decision makers, demanding the ability to understand this and demanding the skill set, just like they would data science, machine learning because of demonstrated value. Scott Cunningham:What were they witnessing with their own eyes that was so compelling that they would Inc that it would increase demand. Kyle Kretschman:So both I'll call it like ad hoc economic analysis on maybe big strategy projects, but also then the introduction of econometric systems into product. Scott Cunningham:Mm. What does that mean? Introduction of econometric systems into products. Kyle Kretschman:So say you have a product that is gonna, let's go back to the recommended system. And I use that again as an abstract, but within there you might make a change to it and you might make a change with the recommender system. That's gonna cause a treatment effect. Right. So, okay. So we can do that one off to estimate that, but you could also then build an economic system. That's gonna measure those treatment effects and changes like an AB platform or things like that. So maybe people might be more common and familiar with like experimental platforms. This would also be then econom. This would be sub out the AB part of it and sub in an economic model, that's going to be doing always on measurement sometimes at a, you know, service level. So sometimes within like individual pages, sometimes it's gonna be at a monthly level, but the integration of econometric models into the product. Scott Cunningham:Right, right. Wow. So how are you a different economist because of that experience at Amazon, if you had to guess, what was it the treatment effect? Kyle Kretschman:Oh, it mean it was, it was incredibly formative because it to tie like it put the fit together with the application to where I could understand and really to where it is, my job is to take a business question, turn it into a scientific process that can be solved with econometrics. And then also be thinking about, is this a problem that needs a scalable solution? Right. So, so Amazon taught me business integration taught me so many different languages, taught me leadership and management taught me how to work with stakeholders in collaborative ways, but then even more so how to deliver the value through econometric measurement, both again, as I said, not only, not only just in ad hoc research papers or one off analysis, but also then where does this fit directly within the products that we build in tech? Scott Cunningham:Yeah. So where'd you go, seems like people don't stay very long in tech. That's like normal. Whereas like, is, is that right? People kind of like, it, it's less normal to stay your whole career at Amazon unless is that wrong or, Kyle Kretschman:I mean, it's got it still do. So it's probably tough to say that because really the, the field started, like you said, really proliferated in 2012. So I stayed at Amazon for six years and I thought I'd be staying even longer. But Spotify came with the opportunity to one work on something I care very deeply about, which is the music industry. I'm a huge music fan. They also came with the idea to build again. So, you know, that was the part that really enticed me was Spotify did not have any PhD economists who were in an and, and economist roles. They had like one in a data science role, but they didn't have the structured economic discipline that they were seeing that Amazon was proliferating. And also then going into like Uber, Airbnb and the other tech companies. And so they said, can you build again? Kyle Kretschman:And I said, yeah, I'm, I'm excited to build. And then last one, all these there's definitely personal considerations here too. And Spotify just really did a great job showing how the company as a whole has Swedish cultures and values. And at the time I had a nine month old and they said, this is a great place to come be a father with the balance and that, and I said, all right, let's make the jump and come to Spotify. And so now I've been here about two years. So cuz I, I actually went to Spotify in may of 2020. Scott Cunningham:So remind me again, your job title at Spotify. Kyle Kretschman:So I'm head of economics. Scott Cunningham:Is, is that the, is that, is that like chief economist? I, I feel like I see different, different job titles and I don't know exactly what, what everything, Kyle Kretschman:Yeah. It, it it's on the path to it. So I'm, I'm the highest ranking PhD economist at Spotify. Scott Cunningham:I see. Okay. I've been there for two years. Okay, go ahead. Sorry. Kyle Kretschman:Yeah. Cause again, that's what I was brought into build was to build, like we did at Amazon was overall integration of PhD economists within the different business units. Scott Cunningham:So this is the part I'm, I'm having some hard time, like, you know, putting, visualizing or putting in my own words. What exactly will it look like if you have been successful in five years at that goal and what would it look like if you had been a complete, complete bust? What are the two things that are like empirical that I would be able to, to observe? Kyle Kretschman:Yeah. A complete bust is probably that an economics discipline is not, is not part of Spotify and there's not, there's not a job family. So a complete bus would've been, I, I moved to Spotify, an economics discipline. I either in, or I'm working data science job, what success looks like is actually what we put first from a, so I'll talk about the people in process, discipline success. We, I came into was Scott Cunningham:Real quick. So Kyle Kretschman:Foundation on basically. Yeah. Scott Cunningham:So, so failure actually would mean that the economist community within Spotify just never materialized, is that what you're saying? And that, and that means like this, having groups of economists that, that think and use the kinds of training we had in graduate school, but in a way that is actually productive in the firm is, is that, is that right? Kyle Kretschman:So, so yeah, and again, that's, Scott Cunningham:The job is successful if you're able to actually create internal demand for economists. Kyle Kretschman:Yep. That's right. And that's, that's what I would say against from the process side. And then from the product side, that's using econometric research in the ways that I've been talking about it's using it both not only for individual analysis, but also then building econometric measurement systems that improve the product to get towards Spotify's mission of, of billion listeners and fans who can connect with over a million creative artists who are making a living. So that's, so it's a combination, it's the combination people process. Do we have the people set up? Do we have this integrated system of economists working alongside all these different types of stakeholders along with the product side of, do we have these measurement techniques that we're applying in a way that is important to Spotify's not only Spotify's business, but all the stakeholders that have an interest in Bon life. Scott Cunningham:So I feel like, you know, I think to academics that, that, and, and maybe even to some degree students, maybe I'm, maybe I'm completely an outlier here and I'm wrong, but you know, I think there's this like really shallow is a negative word. It, I mean, shallow, literally more and just like, it's just the thinnest knowledge possible of what exactly, you know, the, the, the core skillset of a successful economist is in tech. You know, and for many people they think, I think they, they think it's such a primitive level. They're like, it needs to be somebody that can code, you know, it's a data scientist, but, but it, but it, but that's not what I associate with economics. Right. So what would you, what would you articulate? It is, Kyle Kretschman:So it's the ability to do econom applied econometric research. That's applied to business problems. Mm. So within that is coding. Yes. Scott Cunningham:Right, right. Within that is coding. Kyle Kretschman:I, the vast majority, I won't say everyone, but the vast majority of tech economists are gonna have some level of coding and maybe they're not coding anymore. Like I'm not doing any coding anymore, but like they, they have that ability. So that's just again, that's, that's a skillset, but the real ability is doing long-term economic research. Because the questions that we get asked are very hard and difficult, and they are maybe in the academic setting, maybe they are publication worthy, takes that take three years, four years to actually solve with the right model. Yeah. But it's the ability to take that three year research roadmap

In this week's episode of The Mixtape with Scott, I had the pleasure of interviewing Kyle Kretschman, Head of Economics at Spotify. It was a great opportunity for me because Kyle is one of the first economists I have spoken to who didn't enter tech as a senior economist (e.g., John List, Susan Athey, Michael Schwarz, Steve Tadelis). Kyle entered tech straight out of graduate school. He spent much of his career at Amazon, a firm that has more PhD economists than can be easily counted. Under Pat Bajari's leadership there, Kyle grew and his success was noticed such that he was then hired away by Spotify to lead up their economics team. At the end of the interview, I asked Kyle an economics article that has haunted his memories and he said “BLP”, which is affectionate shorthand that “Automobile Prices in Market Equilibrium” by Berry, Levinsohn and Pakes 1995 Econometrica goes by. I really enjoyed this interview, and despite the less than ideal sound quality at times, I hope you will too.But before I conclude, I wanted to share some more of my thoughts. This series I've been doing on “economists in tech”, which has included interviews with John List, Susan Athey, Michael Schwarz and Steve Tadelis, comes from a complex place inside me. First there is the sheer curiosity I have about it as a part of the labor market for PhD economists. As I have said before on here, the tech sector has exploded in the last decade and the demand for PhD economists has grown steadily year over year. Tech demand selects on PhD economists with promising academic style research inclinations. There is substantial positive selection in this market as firms seek out strong candidates can be produce value for them. This is reflected in both junior market salaries, but also senior. Job market candidates are economists with technical skills in econometrics and economic theory, not to mention possess competent computer programming skills in at least one but often several popular coding languages. They are also candidates who were often entertaining careers within academia at the time they entered tech, and in those academic careers, they envisioned themselves writing academic articles about research they found personally and scientifically important and meaningful. Going into tech, therefore, would at least seem to involve choice that may go far beyond merely that of taking one job over another. It may involve a choice between a career in academia and a career outside it, which for many of us can feel permanent, as though we are leaving academia. And for many economists, it may be the first time they have ever contemplated such a thing. If they do internalize the story that way, if they do see taking a job in tech as “leaving academia”, then I can imagine that for at least some economists, that may be complicated, at least. But there's another reason I have been wanting to talk to economists in tech and that is I am very concerned about the welfare of our PhD students. In a recent article published in the Journal of Economic Literature, economists interviewed graduate students in top economics programs. They found there incredibly high rates of depression, anxiety, loneliness and even suicidality. This is a common feature of graduate studies, but it is interesting that PhD economists have incredibly good employment opportunities and yet the depression and anxiety plague there too. One of the things that struck me in that study was the disconnect between what graduate students felt about their work and what their advisors felt about their own work. Many students, for instance, do not feel they are properly supported by advisers, do not believe their advisers care about their research success and do not even care about them as a person. Whereas most Americans (and faculty) feel that their work has a positive impact on society, only 20% of PhD students in economics feel that way. (I discussed the article as well as my own research on the mental health of PhD students here.) I suppose part of me feels a great sigh of relief to see the labor market for PhD economists expanding in light of those troubling statistics. If students know that life is full of infinite possibilities, then perhaps they can begin to process earlier what they want to do in the short years they have on this small spinning ball of rock we call Earth. If students do not in the end want to become professors, if they do not have the opportunities to become one, they should know that there is no “failure” involved there. Careers are just that — careers. They do not tell us who we are. The sooner a student can detach from the unhelpful story that our value is linked to a vita listing our accomplishments, the sooner they can begin their own life work of choosing their meaning. Can having more labor market opportunities with more employers competing for them help do that? Well no, not really. At least, not exactly. It can disrupt certain equilibrium, but then the new equilibrium can just as easily cover that up too. Still, I do like the idea that to keep students in academia, universities and departments must fight harder for them, pay attention to them, and invest in them as people. I like the idea that students have more options and that the options are diverse. Will it help their depression? Well, that's another matter, as that's complex. And presumably the economists in the survey I mentioned were themselves well aware of the career options they had since they were coming from the nation's top 10 PhD programs in economics. I suppose my point is that ultimately, the burden of life really cannot be resolved with money or career. We are trained to look there because we have boundless appetites. But ultimately the hard work of navigating life can only be helped so much by a job. We must still decide for ourselves what meaning we will choose for ourselves. But one thing I know, and one thing which I think our profession is profoundly bad at saying out loud, is that if we make our identity connected to vitas, we will not just be miserable, we will be hopeless, and probably poisoned. Such a mindset leads to endless laps on a brutalizing treadmill of meaningless performance in which a person chases for first place in a race they don't remember signing up for and which they cannot win. They compare themselves with others running, not knowing that they too are brutalized by their own treadmill, not realizing that it is impossible to catch up with someone else as there is always someone else ahead of us. The sooner we learn that the joy we long for will not come when we get a top 5, the sooner we can look elsewhere. It has taken me many years to relearn a lesson I learned decades ago — I am whole now. I am complete now. I still run, and I still chase, but I am not chasing completeness. I am not chasing my own wholeness. Being whole and complete has nothing to do with a career. Careers are ultimately orthogonal to hope, which does not mean they do not matter — they absolutely matter. But if asked to deliver meaning, we will find that our jobs are as weak as wet spaghetti at such a task as that.So, I suppose in some ways I simply want to announce — there are incredible opportunities for economists inside government, commerce and academia. But the weight of this life is not likely to be lighter in any one of them, for the weight we feel in life is largely self imposed, inside us, in the stories we tell about who we are and for many of us who we are not. Those stories are real, because we feel them and because we believe them, but they are not true. All stories are wrong, but some are useful, and the story that our lives can only matter if we have certain types of jobs or certain types of success, while it may be useful to getting a paper out or accomplishing something important, in a much bigger sense it is hollow at best and pure poison at worst. TRANSCRIPTThis transcript will be updated once the more complete transcript is finished; for now it was transcribed using voice-to-text machine learning.Kyle Kretschman:Might not have prepared myself well enough to be attractive for some of the most pop most top tier schools. Scott Cunningham:In this week's episode of the mix tape with Scott, I had the pleasure of interviewing Kyle kretchma the head of economics at the streaming platform. Spotify. Before I dive into the interview, though, I wanted to give you a bit of a heads up about the sound quality. Unfortunately, the sound quality in the interview on Kaza side is a bit muffled. We discussed refilming. It tried to find a way to tweak it, but there were certain constraints on the actual sound itself that kept us from being able to do it. And we didn't feel that refilming, it would be good because we thought that the interview had a lot of serendipitous kind of spontaneous tangents and things spoken about that. We thought students and people in academia would want to know, would need maybe even need to know. And I doubted that I could recreate it, cuz I don't even know why it happened. Scott Cunningham:So I'm gonna post a video version of this at my subs, for those who feel that a video version would help them kind of follow it in so far as the audio might be at times challenging. So check out the subst for those of you that wanna watch, watch it instead of just listen to it, hopefully that'll help. I won't say much here by way of introduction, except to say a few things about Kyle, because I wanted to let Kyle tell you his story in his own words, cuz it's his story to tell. And it's an interesting story. Kyle's a PhD economist though from the university of Texas Austin, which is down the road from where I live and work at Baylor, where he wrote on topics in graduate school and applied econometrics, empirical industrial organization or empirical IO and public choice after graduating, Kyle went to Amazon, not academia. Scott Cunningham:In fact, given we might start the boom of tech hiring PhD economists in the early to mid 20 2010s. You could say Kyle maybe was sort of one of the earlier hires among that second wave of PhD economists that went there. He worked for several years at Amazon before being hired away by Spotify to head up and lead a new economics team there, perhaps this is part of a broader trend of tech firms building up more internal teams, not just of data scientists, but like Amazon departments of economists who knows recall though from an earlier interview with Susan athe where, when I asked Susan why she said pat Maja had done something amazing at Amazon, she said he made economists productive. And in time he made many of them productive and very in productive from what I've been able to follow. And Kyle is from what I can gather someone whose skills matured and deepened under the leadership of Papa jar at Amazon and other leaders at and other economists at Amazon. Scott Cunningham:And he was ultimately hunted down by a major tech term to create an economics team there I'm by no means an expert on the labor market for PhD economists. I just have been very intrigued and curious by the, the, the Mar the labor market for PhD economists in tech, because well, partly because of realizing first that cause of inference was really valued in tech, but then to sort of realize that there was just this very large community of economists there, but I don't think it's controversial to say over the last 10 to 15 years, the tech industry really has been disruptive in the labor market for PhD economists. They continue to hire at the junior and senior market in larger and larger volume selecting more and more on people who likely would've gone into academia into tenure track or tenured positions. They pay very high wages, some of the very, some of the highest wages in the country, both at the junior level and especially at the, at the higher end at the, at the more advanced levels, people can earn compensation packages by the, in the, by the time they're in their thirties, that many of us didn't know were possible. Scott Cunningham:It's in my mind, historically novel, and I might be wrong about this, but it, it seems historically novel that the PhD economists who likely would've produced academic research papers in tenured and tenure track jobs have begun to branch out of academia, but maintain those skills and maintain that research output. It's partly driven best. I can tell, buy Amazon, I might be wrong, but by Amazon and paja, as well as Jeff Bezos own view, that economists are what I guess we would just say value added for many firms. Therefore I'm continuing to wanna speak with economists in tech to help better trace out the story. This interview with Kyle follows on the back of earlier interviews with people in tech like John list, you know, a, a distinguished professor of economics at the university of Chicago, but also the former chief economist that Lyft and Uber now Walmart Michael Schwartz, former professor of economics at Harvard. Now, chief economist at Microsoft and Susan athe former chief economist at Microsoft professor at Stanford and now chief economist at the DOJ. I hope you find this to be an interesting dive into the industry. Learn a little bit more about economists there, but by, by learning the about one particular important economist, there a, a young man named Kyle crutch, head of economics at Spotify, my name's Scott Cunningham. And this is the mix tape with Scott. Scott Cunningham:Well, it's my pleasure today to have, as my guest on the mix tape with Scott, Kyle crutch, Kyle, thanks so much for being on the call. Kyle Kretschman:Hey Scott, thanks for having me really appreciate the time to talk Scott Cunningham:Well before we get started with your career and, and everything. I was wondering if you could just tell us your name and your title and where you work. Kyle Kretschman:Sure. Yeah. As you said, I'm Kyle kretchma, I'm the head of economics at Spotify, Scott Cunningham:Head of economics at Spotify. Awesome. Okay. I can't wait to talk. So let me, let me, let's get started. I was wondering if you could just tell me where you grew up. Kyle Kretschman:Sure. So most of the time I grew up in outside of Pittsburgh, Pennsylvania, about an hour north of the city, real real small town probably had one stop light. And maybe the, the funny story that I can share is what I took my wife there. She asked where's the Starbucks. And I said, no Starbucks here. There's no Scott Cunningham:Starbucks. Kyle Kretschman:Yeah. So pretty small town called Chippewa township in Pennsylvania. Scott Cunningham:Oh, okay. Is that near like Amish stuff or anything like that? Kyle Kretschman:No, that's the other side of the state. So this would be Western Pennsylvania about near the end of the turnpike, about five minutes from the Ohio border. Scott Cunningham:Oh, okay. Okay. You said, but you, did you mention, you kind of grew up in different places? Kyle Kretschman:Yeah. So before that, my father worked in civil engineering and so would do build roads and bridges basically across every, across the nation. So I was actually born in Louisiana, lived there with, I think for a whole two, three weeks. I don't quite remember. Cause I was pretty young obviously, but then Michigan and then spent some time in Philadelphia before moving out to Pittsburgh around second grade. Scott Cunningham:Oh, that's kinda like, that's like when people described their parents being in the military, just kind of moving around a lot. Kyle Kretschman:Yeah. A little bit. So, but Scott Cunningham:Then you settled in the second grade Kyle Kretschman:That's right. Yeah. So outside of Pittsburgh and then stayed in Pittsburgh through high school and even through undergrad. Scott Cunningham:Oh, okay. Oh, you went to undergrad in Pennsylvania. Kyle Kretschman:Yeah, I did. So I went to undergrad at the university of Pittsburgh. Oh, okay. It was, yeah. If, I guess maybe continuing the story growing up in a town with no Starbucks. I was, I was pretty intrigued by going to a city. Yeah. And find out that lifestyle and yeah, we might have lived pretty close, like an hour away, but we didn't go down to the city very much. So Pittsburgh was just really, really enticing for a city to, for, to go to undergrad in. And so I basically looked at all schools that were in cities and so the proximity plus then the, the ability to just spread my wings and explore what it's like to be in a city was really, really enticing. Scott Cunningham:Did any of your friends go to pit with you? Kyle Kretschman:Yeah, so there's probably, I grew, I graduated from a class of about a little over 200 people in high school and I think there was like five or six people from high school that went to pit for my class. So definitely had some really good friends who went and kept in touch with, through undergrad. Scott Cunningham:Mm. Yeah. So it wasn't, were you sort of an early generation or you weren't, were you a first generation college student in your family or did your parents go to college Kyle Kretschman:Combination? So my dad went to Penn state civil engineer, as I mentioned, me and my mom actually graduated from undergrad the same week. So my mom went back to school later in life after me, after we went to school. And so yeah, we, we were able to celebrate graduation cuz she went to a small private school right outside of the city also. Scott Cunningham:Oh, okay. Okay. Yeah. Well, so what did you like to do in high school? Kyle Kretschman:So I played a lot of sports before high school and then I kind of switched into, and this was a traditional sports of football, basketball, baseball, but then I switched into tennis in high school. And so that kept me busy, but along with a lot of academics and really, really liked computer science. So played a lot of video games growing up, really enjoyed like that aspect in combination. Scott Cunningham:What games were your, were you, did you play on a, on a video game, plat platform? Like an Nintendo or did you play? Kyle Kretschman:Yeah, no, we played a lot of plays very much into like role playing games. Some of the arcade games like Marvel versus Capcom. So yeah. Yeah. Very, very interested in gaming. Yeah. Maybe I was a little too early for that. Cause you know, every, everybody in the 1990s was like, oh, I could make pu money playing video games, which wasn't true back, which wasn't true back then, but that's right. You know, nowadays Scott Cunningham:You can that's right. Yeah. You know, that's right. You can do it. There's all kinds of ways you can make money doing things today that nobody knew was possible 10, 10 or 15 years ago. Even Kyle Kretschman:My Scott Cunningham:That's cool. Yeah. I, I, it's funny, you know, computer games can keep a, keep a kid in high school going, you know, like especially I think they're kind of misunderstood. I, I had a lot of friends that, well, I mean, I, I, I had, when I didn't have a lot of, we moved from a small town in Mississippi to Memphis and I, those, those that first year when I didn't have a friends, I did bulletin boards and played Sierra online games like Kings quest. And it's like, it's like, you know, not intertemporal smoothing, but like inner temporal socializing, smoothing, you know, so that you just kind of get through some periods that would otherwise be a little lonelier. Kyle Kretschman:Yeah, for sure. And I mean, I mean for this audience, like most video games are some sort of form of constrained optimization. So there was, there was the inkling that I, I liked understanding how economies worked in high school through this and yeah. Going back to my mom, my mom always said like she encouraged it and she encouraged education. And there was actually kind of like that nexus, whenever I took economics in high school, it was like, oh, you know, some of these games really are full economies that are constrained and constrained in a way that you can understand and complete in, you know, under a hundred hours. Right. But there was that combination that was kind of showing itself of computer science, computer gains and economics of putting itself together. Scott Cunningham:So you were kind of thinking even in high school about economics in that kind of like, you know, optimizing something and like, like almost that modern theory that we get in graduate school. Kyle Kretschman:I think more, I had the intuition when I didn't have know how to say what it was in high school because my high school was pretty forward and that it offered both advanced computer science courses that could get you through definitely through first year of undergrad, maybe even through second year with advanced placement. And then they also offered advanced placement economics. And so I, I ended up taking advanced place in economics my junior year when most people took senior year. And so whenever I was going small Scott Cunningham:Town, even in that small town, they had, you had good your high school. Good econ. Kyle Kretschman:Yeah. It was a real, it was a really good high school that would put together good curriculum that did a lot of college preparatory work though. They, wow. They really leaned into the advanced placement, the AP courses to get students ready to go to school. Scott Cunningham:Wow. Wow. So even at, as a junior, you're taking AP econ, you know, you don't have to take AP econ. That kind of is say that, that sounds like somebody that was kind of interested in it. Kyle Kretschman:Yeah, very much. Yeah. And again, as soon as I, I definitely didn't get to the graduate level of understanding, like, you know, LaGrange multipliers, but the, the micro and macro sequence just made intuitive sense to me. It was like, it was kind of where I was like, yeah, this fit. And this is how I think. And some people might criticize me now that I think too much like an economist. Right. Like, but at the same time, it just like, it started to put together that language and even more so some of the frameworks that really kind of drew me into it. Scott Cunningham:Well, did you, did you, did you notice that you had this interest in computer science and this interest in economics and that they might be one, did you get a feeling that they could be in conversation with each other? Kyle Kretschman:Not Scott Cunningham:At first, our ancestors a hundred years ago. Didn't, you know, those economists didn't think that way, but now it's just so natural for this generation of economists to be almost one half, you know, one third mathematician, one third economist, one third computer scientist. Kyle Kretschman:Yeah. So not at first, but I, I feel like I made have like lucked into it, honestly, because whenever I chose to go to Pitt, I chose to start as computer science because I knew what that pass was. I was inspired by my older brother, the great teacher in high school. And like, I was definitely like, okay, a software software development engineer career is great. It's cutting edge. It's there. But after probably like the first year, it just didn't feel that end state didn't feel right. And so I made kind of the hard decision to choose, honestly, to switch into economics as a major, because I wasn't sure what the end state would be, where I was going with it. Cuz it was definitely felt more amorphous, you know, it's a social science, so yeah. It didn't feel like it was gonna be as clear cut and as, and have as much certainty. But pretty quickly, like after a year was like, oh, well we're doing, we're using E views at the time. All right, this is coding. I know how to do this. This is great. Right. And starting, starting to see some of that in undergrad was like the, kind of the aha moment that like, yeah, this is, this is a place where I can apply this love of coding and problem solving, but problems and solutions that I find really, really hard and interesting. Scott Cunningham:It was because of econometrics though. It was in that. Kyle Kretschman:Exactly. Yeah, yeah. Scott Cunningham:Yeah. Wow. That's, that's really interesting because you know, I think it's still the case that, you know, you can easily end up with an econometrics class that remains purely theoretical and doesn't end up, you know, exposing the student with a lot of actual coding, but it sounds like your professors were, were getting you into working with data. Kyle Kretschman:That's correct. Yeah. Both. Both within the class. So like I said, we used E views at the time. Yeah. And again, kind of like learning as a go, I, I don't think I really knew what I was doing whenever we were typing commands and E views, but the computer scientist in me was like, okay, well this is a function. I know functions. Didn't put outputs, but definitely didn't understand necessarily things that were going under the hoods or you know, all of the theory that goes with it. Oh, right, right, right. So it was, you Scott Cunningham:Knew the coding part, you knew you were coding, but you did, but like the, the actual statistical modeling was kind of the new part, but that was a way for you to kind of engage it a little bit. Kyle Kretschman:Yep, exactly. Scott Cunningham:Oh, that's interesting. That's interesting. Well, so what were you gonna have to choose between a computer science and an econ major did or did you end up doing both? Kyle Kretschman:So I chose an econ major, but then I had what I would call basically minors or concentrations in computer science, but then also in statistics and also in math, because once, once I had an internship at a bank and was doing data entry and I was like, eh, I don't think this is what I wanna use my economics degree for. Yeah. I had a couple professors at pit named Steve Houston and Frank Giani who brought me on as a research assistant, an undergrad to start being part of some of like their survey projects and data collection. And even, even one of 'em I don't, Steve was crazy, but he even let me TA classes on undergrad, so oh, wow. But he kinda, I mean, I, I say that jokingly because it was formative for me, it was like, okay, this is great. How do I do more of this? And he was like, well, you go get your econ PhD. And I was like, so I can be a teacher with computer science and doing economics altogether. He goes, yeah, let's do that. And so it was with the help and support of some of these really good professors and education to kind push me on this path consider to get Ancon PhD. Scott Cunningham:Mm. And that's when you were like, so how, how, what, what year would you have been in your program? Kyle Kretschman:Probably. I think I was in my junior year where I was starting to explore this. And then in my senior year is where I was like, okay, I'm actually gonna be doing more more of this and applying to grad school because going back, as I said, I entered with some credits. So my senior year was very, I didn't need a full course load. So I was looking for other things to keep me busy, which maybe, maybe that's one of the themes of this conversation is I kinda kind of like the variety and really have variety seeking behavior too. Yeah, Scott Cunningham:Yeah, yeah. Yeah. So you graduate, was there like a field that you were mostly interested in? Kyle Kretschman:I thought I would be going into macro economics. Macro. Yep. Yeah, because Steve worked on the council of economic advisors and I was really inspired by that and the application of economics within, within policy and just again, always applied economics, not necessarily theoretical. So yeah. Then again was, that would be sort of like labor and macro was like the initial idea, but finally Scott, I didn't do all my homework and like, think about like what grad school looked like or all it looked like. I kind of went a little bit more naive than I think other people with, again, ideas of how I could become like a teacher, an educator with some of these tools versus like how disciplined and single thread you need to be on research to be within an econ PhD program and to see that. Scott Cunningham:So you, so you kind of were like, so when you were thinking about graduate schools, what, how, what, what did you sort of, can you walk me through like what you were thinking and how you went about trying to apply to graduate school and where you ultimately chose? Kyle Kretschman:Yeah, sure. So applied probably the, the top 10 and the top 10 probably said no thanks. But also then was targeting specific schools that we had relationships with that I knew would provide computer science and macros. So university at the Iowa at the time, this was 2000 and had a really strong macro program. And then also at the university of Texas with Dean Corbe there, they also had one in Russ Cooper. And so those were like the two that I was like targeting at outside of what the top schools were. But yeah, as I, I kind of mentioned, I, I might not have prepared myself well enough to be attractive for some of the most pop with top tier schools because kind of, you know, as I said, bounced around and would be yeah, a little bit working on it a little bit different things and have computer science versus being solely focused on like economics and math and things that might be more of what the top tier schools were looking for. Scott Cunningham:Yeah. Yeah. You know, you know, it's like the, I mean, I'm the same way. I didn't ha have any econ classes in college. I was a English major, but the, the, the diff there's so many students that sort of seem to almost for whatever reason, know a lot sooner what they want to do and then like make those choices. And then there's just many of us that are, you know, in a process of search yeah. That when you're in a process of search, well, you, you know, by definition, that's like you're using that time to search. Kyle Kretschman:That's exactly right. As Scott Cunningham:Opposed to saying, I've gotta take, I've gotta become a triple major computer science, math, econ, and have to do like, you know, these set of these set of steps that, you know, there's no way I could even have known to do it unless somebody had told me it's weird. I mean, it's just funny how the little things can have such big repercussions for your whole life, but it's, but it, it worked out great. So you end up, where do you end up going? Kyle Kretschman:I went to the university of Texas at Austin. Scott Cunningham:Yeah. Yeah. What year was that? And Kyle Kretschman:So, so this would've been 2002. Scott Cunningham:Oh, okay. So you go to oh 6 0 7. Kyle Kretschman:Okay. And so ended up working. So I ended up working a lot with Jason, Ava. Yeah. And who came in and became the, the head of the department. Yeah. Applied econometrician who just did an amazing job going back to whenever I said, I didn't know how things worked under the hood, in those formulas. He didn't even let us use those formulas. So anytime we were doing applied econometric econometrics with them, not only we learning to teach, we're learning the theory, but he said, you have to code it yourself. You have to do the matrix algebra, you have to calculate standard errors. You can't really call those functions. So that was probably again, that wasn't until the third year, but yeah, in the first year to go back a little bit, Scott Cunningham:I, that played to your strengths though. I bet that played to your strengths. Yeah. Just at the end of the day, wanting to be someone that, that wrote down the raw code. Kyle Kretschman:That's exactly right. And, but the first year I didn't play my strength. Yeah. Yeah. So the first year I felt, I felt a little bit outta water and I was like, this is, I remember when we were proving what local non association. And I was like, this is, this is one hard, but also like, again, going back to like, that is this actually how I wanna be spending my time and right. I, I was like, yes, I do. But I was like, I, I knew that I needed to get to those applied applications. Yeah. And so that's, again, why I was thankful to be able to work with Jason and Steve Trayo and a few other, they applied econometricians at Texas that really encouraged me to explore starting in the second year. They didn't us like pin it down. And so I, I thought I, at the second year I worked like wrote the first, a paper on school choice and trying to see if I could find some sort of instrument on school selection on public versus private. And again, so that led to like that idea of like applied econometrics was really, really the thing that like, I was like, okay, now this fits again. Once we got into second and third year Scott Cunningham:Was, was picking up that intuition, that kind of like labor style identification, causal inference kind of approach. Was that something you picked up from Jason or was that just like from your labor people? Oh, okay. Kyle Kretschman:Yeah. That's yeah. From Jason and Steve a lot. They did a great job of doing that. And yeah. So then, yeah. Then I, then I threw in, I knew threw a little bit of a switch in there also, and my co-author Nick master and Arti and closest friend and classmate in Texas was very theoretical and very interested in applied empirical IO. And so we started working in that field also together. And so then I got to work with the Han me vet and Ken Hendrix on using empirical IO. So, oh, wow. Yeah. And so again, Scott Cunningham:This is the more structural, more structural econometric. So you've got this like reduced, you've kind of got this like traditional labor reduced form type of, part of your brain. And then you've got this empirical IO structural part of your brain kind of emerging at the same time. Kyle Kretschman:That's right. That's exactly right. Yeah. And then we threw, we threw everybody for a loop. I also saying we wanted to study study politics and how money turns into vote using both using all these tools. So yeah, I can see here kind of saying in hindsight, like it all makes sense in this story that I'm telling you, but at the time it was more of what you were talking about. It was searching. It was, I wanna be working on really interesting applied problems. I love the toolkit that economics provides in framing. And yeah. I have to be coding to be able to utilize these tools that I've had built up in the past. Scott Cunningham:Yeah, yeah. Yeah. So, so matching with Nick was really important Kyle Kretschman:Very much. Scott Cunningham:And why, if you hadn't to match with Nick, I mean, just kind of outta curiosity, if you could articulate the value added of that whole partnership, what was it? Kyle Kretschman:Yes. Sure. So, so we matched basically from math camp going into, going into the first year because Nick came both from the pure math and physics background and also had some experience in the air force. So the air force was sending him to Texas and he, we were, we were definitely, we definitely didn't have a lot of vend overlap on the fact. He's like, well, I would have the intuition and some of the computer skills, Nick would have the theoretical math skills, Scott Cunningham:The theoretical math skills. Yep. Kyle Kretschman:And then we just had, we had the common factor that we wanted to work hard together and learn together and we're willing to, we're willing to intellectually hash out really tough things together. Yeah. So yeah, he huge credit to him through being able to put up with me. And he says, he says the same thing once in a while. But again, matching with somebody that had the, the more real analysis proof based understanding of math was so valuable for me. And especially, Scott Cunningham:I think some empirical IO, especially empirical IO, just being able to, you know, think like an economist in the area of IO is thinking real deep about, you know, a rich set of models and modeling approaches. Kyle Kretschman:That's Scott Cunningham:Exactly right. That's definitely not what you're learning in your econometrics classes, even though they might go together. Kyle Kretschman:Yep. So, so yeah, it was just a, it was a really good match from the beginning. And so we complimented each other and we're, we're able to build a strong enough relationship to be able to be able to hash out, have really long nights yelling at each other, we say in the office, but it never, it was always for educational purposes and lifting each other up. Scott Cunningham:Was that different than what you thought grad school was gonna be like? Kyle Kretschman:Yeah. So I knew the research component a little bit. I just didn't under understand the unstructured research on how that was gonna go and like the cadence and where it was gonna and how that was gonna be so required to develop your own viewpoint. Yeah. I thought it would be more directed cuz as a 22 year old, that was the experience I had generally. So that was the big one was the undirected and I liked it, but it was also very difficult. Scott Cunningham:How would you describe what you're talking about to your college self? Who kind of like, you know, he, he doesn't really, he doesn't even have the vocabulary for what you're describing. What would you say? It was like, Kyle Kretschman:I think you use a good term. You have to be not only wanting to search, you have to be willing to search, but you also, then you have to put in the guardrails yourself to keep it focused because you're not necessarily gonna have those external guardrails that you will have from an alternative path of going to either like a master's program that's gonna be more structured or going in an industry or going to get a job. Right. Like I mentioned at a bank for like a 22 year old where entry level jobs are gonna be more structured. Yeah. So yeah, I just, I, I probably knew it, but I didn't know what it meant to be and what, what it meant to experience it. Scott Cunningham:So how did Jason and, and Steve kind of, and any other faculty, how, how did they, how did they, I, so I did this interview with Susan athe and she was saying that, you know, the amazing thing that pat Maja did at Amazon was he managed to make economists productive, which kind it was kind of a weird, weird way of saying it. And so in a way it could, in a way you could imagine a department that sort of has like a, you know, this idea of like research has got to come. There's like a, there's like a, a journey that a graduate student has to come on to just to basically make a decision to be a researcher. Yeah. You know, and you could imagine that creating the conditions for that is, is involves faculty member, doing stuff that's not necessarily obvious. What, how did they, how do you think they contributed to that for you personally? Kyle Kretschman:For me personally, at the time, again, it goes back to encourage the exploration versus mandating or saying that I need to be on one path. So like even Nick and I at the time explore the idea of a private company and how, what, what that would be into like pinching, pitching a venture capitalist on, on that. So all those things, again, in grad school, they, they were encouraged, but they weren't structured at the time. Yeah. So yeah, I can, I can, I understand Susan's comment because I was, I was one of those economists who started pretty early with pat and we, we have a lot of good mechanisms that we've learned and built at Amazon when I was there at the time through pat, through lay other people who were willing to make the jump into this entrepreneurial space that hit the election and the, of coalesce of economists doing open book, empirical research, along with data science. Right. Just becoming more and more valuable and applicable, but is kind of what Susan piloting that we can, we can talk more about if you Scott Cunningham:Want. Yeah. I do wanna talk about that. I wanna talk about the, the decision though, you know, to, to be, because you, you sort of started off in college, you know, you said things like, oh, you can become an educator and then you've gone in this non-academic direction and you know, it, it, and that's like a, that's a more common story now, you know, right. Of, of top talent, very talented PhDs that you could have easily seen 20 years ago, would've been an academia. Their counterfactuals are, are following you. And so, you know, it's, it's a, it's a big part of our, you know, collective story as economists that this, this new labor market that didn't, that didn't exist historically now exists and draws in so much talent. And I was just curious in a way you're kind of like a, a first generation person like that, you know, when you think about it, right. Cause text's not very old, right. Facebook, Facebook, what it's like 2007. And so, you know, so you've got this, you, you, you've got this, this chance to kind of say like, it must have been, so I don't wanna put words in your mouth, but I guess I was just wondering, what were the feelings like as you considered not taking an academic track and when did it start to be something in your mind that you thought that's gonna be something I'm explore Kyle Kretschman:Probably pretty early, because if you wanna really trace the roots of like tech economists back, it starts obviously with Hal varying at Google and me and Nick, actually, we, we sent an email to Hal, probably 2008 saying, do you have any, have any use for some summer interns who can do some empirical IO? And he said, no, not, not at this time, but so, but he Scott Cunningham:Answered the email. Kyle Kretschman:He did answer the email. Yeah. It was nice, nice of him to answer. Cause we knew he was probably pretty busy, but so it, honestly, when Amazon started hiring economists, I was probably searching for about a year to move into tech. If you wanna move back to the decision point coming outta grad school, honestly it was a challenging labor or a challenging job market for me, somebody who is a lover variety, who is working on empirical IO problems with campaign, policy, campaign, finance reform, policy recognition. That's, that's not fitting a lot of the standard application process. Yeah. Once again, that's so that's probably a theme for me. And again, at the time it was hard. I was, I was in the running for jobs at VA wakes force that I thought would be really good fit because they're the EDU the emphasis would be on education with the research ability to do research and work on problems that were more widely probably policy oriented. Yeah. But neither neither of them came through. So I just always knew that I industry was gonna be an option. And so Scott Cunningham:What year is this? What, Kyle Kretschman:What, what this would've been in this would've been in Scott Cunningham:20 11, 20 11. Okay. Oh, so you moved through the, you moved through the program or kind of relatively quickly. Oh 7, 4, 4, 5 years. Okay. Kyle Kretschman:Five years. Yeah. Five years. Yeah. Oh six to 11. Okay. But so for about a year, about six. Yeah. Yeah. And so starting in 2013 is whenever I started applying to the first tech job as a data scientist and got it went great until I talked to the VP who was a business part, like pure business person. When I was talking to the hiring manager at the time, it was a company who was providing college counseling as a software service. And so they would do this at their, their clients were both for profit and not for profit companies. And we were talking like, we'd get into details about treatment effects models and how we could measure the impact of their intervention. It went great. But then I had the flyout scheduled, but then the interview with the VP, he said, well, how am I gonna monetize your algorithm? Right. And I was like, I'm not sure I know what algorithm means, but right. I, I wasn't prepared for that language and that application and how you turn econometric modeling and measurement into, into business impact at the time. Yes. Right. So spent another year looking around with different opportunities like that and honestly learning again. So, so whenever Amazon, so this would've been in 2014 and then Amazon was hiring its first big cohort with pat. So this was a cohort that was about, I think there was about 13 of us. It was a no brainer. Kyle Kretschman:Whenever, whenever we did the interview, it just was like, all right, this is exactly right for me. I was hop. I was hoping it was right on the other side. And I could probably tell you some funny stories about the interview process, but I was like, this is, this is what's meant to be. Yeah. So it, it, it was like a 10 year journey from 2004 when I switched outta computer science into 2014 being like this, just this fit. Scott Cunningham:Right. Right. Right. So outta curiosity, you know, is, is there, is there something that you think is supposed to be learned by the fact that when you were on the job market and you had that interview with that, that gig and the, and you get to the VP and he articulates questions that are not traditional econ questions, or even econometrics questions like business profitability to act, it's kind of ironic, isn't it like to everybody? That's not an economist. That's actually what we, they think we do, you know, is like, they think we do all that stuff. And then they don't know that we're like, like you said, you know, trying to set up a Lara and solve, solve it, like what's a Lara, but do you think your competition at that time did know how to answer questions like that? Like non-economists in those positions Kyle Kretschman:Probably at an inflection point. Yeah. Because this is the same time. Wherever machine learning is becoming more common toolkit with an industry. So there would be like machine learning algorithms that are designed for, you know, prediction, problem sequencing, anything like that that are specifically designed to be used in a business setting to monitor. Scott Cunningham:So they, they not only know machine learning, it's like, they also can kind of immediately articulate why this would be profitable. Kyle Kretschman:I think so. Yeah, because again, the computer, so it's like in learning the language and this is the language that would probably be more understood within a machine learning computer science version is okay, well, I'm gonna use this to change the recommendation engine right. Is very common one. Yeah. That's obviously gonna be, so how are you gonna monetize it? I'm gonna improve the match and the recommendation engine it's gonna have this. So I think at the time there was a little bit of it, but, you know, hopefully I think, I think I learned pretty quick that you can, you can use econometrics in a similar vein. As I said, it's a flavor of data science, Scott Cunningham:Have you had to become a blue collar machine learner? Kyle Kretschman:I've had to understand it, but not, I think you mean by blue collar, you mean like implementing it Scott Cunningham:And yeah, I just, when I, I usually say blue collar in the sense of like, you know, you, don't like, you know, you basically are picking up these skills, but you weren't like, you know, you didn't get a PhD in computer science. You know, Kyle Kretschman:The answer was then that answer is definitely yes. So like as we, as our cohort and as we grew, the economics discipline at Amazon, that was a big part of it is how one could we bring in some machine learning scientist help educate and teach us. Mm. And yeah. So, and even in, sometimes in lecture style, we would do that because it was so important, but then even more so learning to so that you can interact with different stakeholders specifically, like machine learning scientists. Mm. Then understanding when you can actually implement it and marry it within the econometric models was definitely a huge part of the education process. Scott Cunningham:So you go to Amazon, is that right? That's like your first entry into tech Kyle Kretschman:That's Scott Cunningham:Right. Is Amazon, what's your title? Kyle Kretschman:So Scott Scott Cunningham:A scientist or economist. Kyle Kretschman:I, it was something like business intelligence engineer. There wasn't an economist job family. There was, as you said, it was kinda the forefront. I think it was this. Yeah. I think that's what it was, but Scott Cunningham:Cause it is now right. Baja has a that's Kyle Kretschman:Right. Scott Cunningham:He created a job title called economist. Kyle Kretschman:That's right. Yeah. And that got set up about a year in, so like, and I was part of the group. So we would set these, we would set up like these people and process mechanisms that allow economists to be so influential and productive within Amazon. Scott Cunningham:Mm, okay. So how is he doing it? Why, why is Susan saying he performed a miracle by making economist productive? Can you kind of describe, like, if you had to just guess at like the counterfactual, if it hadn't been, you know, pat, it hadn't even been an economist that was hired into Pat's position. Like, what is it that he, what, what is it that he, or Amazon or whatever is making you go transform and become this new version of yourself? Kyle Kretschman:There's, there's a lot of factors and I could probably spend an hour on this, but I'll, I'll try to, I'll try to reduce it down to like some key mechanisms and ideas. The first is that Amazon is probably the most data driven company. I know. Mm. They are so focused on measurement, both of things you can directly measure. And, but they are. So they were very early interested in economic measurements that are UN observables either coming from like coming from econometric models. That, that was whenever pat demonstrated some of those that was like the light bulb went off the, so, because again, it, Amazon was run by and still generally is people with operation science background. And so this over index on measuring as, as coly and as precisely as possible, well that's that's economics. So that, that was part of it. Another part of it is culturally Amazon operates that makes decisions based on six page white papers, you wanna make some economists really productive, have them write a six page white paper instead of giving them a presentation, especially to people like who may be in the background with MBAs or other people who have a comparative advantage, we economists have a care advantage in writing. Kyle Kretschman:So it was little bit of like a surprise, but you might hear these anecdotes where it's true. Like whenever you go into a, a decision making meeting, you come in with your six page white paper that says here's the business decision to be made here is my recommendation. And here's why, and people sit there and it can be a room for five people can be a room of 25 executives. They sit and read the paper and they read the whole thing. Is there an append that can go on forever depending on how big the meeting is. Sure. But that structure of, of data driven decision making, combined with how you're presenting your argument is written seems like, seems like economists should be pretty good at that. Right? Scott Cunningham:Is that a pat thing? He came up with work, the work he made, Kyle Kretschman:What was the six page idea was from Jeff Bezos. And so that was, would Scott Cunningham:Those be circulated throughout the, throughout the, the, the firm, Kyle Kretschman:The stakeholders who needed to be part of the decision making they be circulated. But again, this is every, like everybody's writing six pages. PowerPoint is basically outlawed at, at Amazon. And again, that happened mid 2000. Sometimes people can Google it to find out, but that six page culture and decision making culture, just again, fit economists. Scott Cunningham:So how is a six page paper similar to the kinds of writing that, you know, you sort of associate with economists and how is it different? Kyle Kretschman:So its I'll start with the differences. So one with the six page versus like a 30 page academic, you are not going to be able to share the research process. You are not supposed to share the research process. You're supposed to share the clear recommendation and how you got to that recommendation. Right? So if you think about like a 30 page academic paper XT, be condensed down into those six pages. In my view, they're just, that's just not how the industry operates, but you probably would know better than me on that where, but so again, where it's the same is again, it's a data driven argument. The purpose of this paper, the abstract here is the hypothesis that I have that and here's how I tested it. And here's how I'm making my conclusion. So what I always found really honestly easy was I felt like I was doing the scientific process. Like I felt I, I was with business decision making it generally work within what is the hypothesis? How are we doing this? How are we testing it? What are we think some alternative conclusions could be, but what are we making towards it? So yeah, yeah. Again, it was closer to what I felt like would be a scientific paper in and that hold of day driven mindset is again, that's more, it's very common. Amazon have a common Spotify now Scott Cunningham:Has that been influential throughout, throughout industry? Has that, how have you noticed Amazon influencing Kyle Kretschman:Some Scott Cunningham:Yeah. Like most people don't understand. Kyle Kretschman:Yeah. There there's some companies who definitely have completely adopted it. There's some companies who haven't, but the, the six pager again, that's, this is not a, this isn't a concept just to economist and tech. This is the concept is, is held up as one of the key mechanisms for all of Amazon. Scott Cunningham:Mm mm Hmm. Kyle Kretschman:One other. Scott Cunningham:How often were you writing those? Kyle Kretschman:Depends on what level you were farther in my career. That's the only thing I did was write six page papers and it would be part of like, my team would help, but again, anytime you have a key business decision to be made or an update, like you're gonna be writing the six page. So yeah, it's again, the farther, the more seniority you have though, the more that becomes your job is to communicate side and guide through these business decisions. Scott Cunningham:Do they, to you, Kyle Kretschman:They belong to the team because it's always Scott Cunningham:Put 'em on a, you can't they're like proprietary though to Amazon. Kyle Kretschman:Oh, correct. Yeah. No, they, they're not publicly available. They're Scott Cunningham:Proprietary. Like it must is it what's that feel like to do something? What's it, what's it feel like to, to do something that creative in that kind of like scientific that's siloed within the firm? Does that feel strange? Kyle Kretschman:No, it didn't. Because what it enables is to be able to work on some of the hardest questions without having to worry about without having to worry about com communication strategies or right. For press release. So no, it felt like we were able, and this is going back to like some of the things that pat and we did at Amazon make successful. We worked on some of the hardest problems at Amazon from a very early stage because we said that it wouldn't be publicly available. Right. So that's gonna do that. And Scott Cunningham:That's been a key part. Yeah. Because okay. I get it. Okay. That, that makes a lot of sense. Yeah. So who did you discover? You were, go ahead. Sorry, Kyle. Kyle Kretschman:No, I was gonna say maybe the last me to highlight. Cause again, I, I, we could probably spend this whole interview on this, but the, the other key mechanism that pat pioneered was the proliferation of economists as a job family was not pat saying and us saying, go do this. And I can give through my own personal example. It was the other business executives, seeing the measurement, seeing the results on product, just saying, okay, I want that. So it really was a demand, AKA demand, internal demand for more economists, that was gonna say, I want this with my business decision making process and want these people who can do this and collaborate across the difference. It was not a, oh, we're gonna put economist in the siloed function that everybody's gonna come here. And that was, that was my story. But the very first year I worked on projects directly for the consumer CFO, basically the whole year. It wasn't necessarily by design, but it was what happened. And at the end of the year, year and a half, the, the VP of finance said, come over here and do this with me and come build, come build an economics team and an economics function here within my organization. And that's really is again, that's the real key was it was business decision makers, demanding the ability to understand this and demanding the skill set, just like they would data science, machine learning because of demonstrated value. Scott Cunningham:What were they witnessing with their own eyes that was so compelling that they would Inc that it would increase demand. Kyle Kretschman:So both I'll call it like ad hoc economic analysis on maybe big strategy projects, but also then the introduction of econometric systems into product. Scott Cunningham:Mm. What does that mean? Introduction of econometric systems into products. Kyle Kretschman:So say you have a product that is gonna, let's go back to the recommended system. And I use that again as an abstract, but within there you might make a change to it and you might make a change with the recommender system. That's gonna cause a treatment effect. Right. So, okay. So we can do that one off to estimate that, but you could also then build an economic system. That's gonna measure those treatment effects and changes like an AB platform or things like that. So maybe people might be more common and familiar with like experimental platforms. This would also be then econom. This would be sub out the AB part of it and sub in an economic model, that's going to be doing always on measurement sometimes at a, you know, service level. So sometimes within like individual pages, sometimes it's gonna be at a monthly level, but the integration of econometric models into the product. Scott Cunningham:Right, right. Wow. So how are you a different economist because of that experience at Amazon, if you had to guess, what was it the treatment effect? Kyle Kretschman:Oh, it mean it was, it was incredibly formative because it to tie like it put the fit together with the application to where I could understand and really to where it is, my job is to take a business question, turn it into a scientific process that can be solved with econometrics. And then also be thinking about, is this a problem that needs a scalable solution? Right. So, so Amazon taught me business integration taught me so many different languages, taught me leadership and management taught me how to work with stakeholders in collaborative ways, but then even more so how to deliver the value through econometric measurement, both again, as I said, not only, not only just in ad hoc research papers or one off analysis, but also then where does this fit directly within the products that we build in tech? Scott Cunningham:Yeah. So where'd you go, seems like people don't stay very long in tech. That's like normal. Whereas like, is, is that right? People kind of like, it, it's less normal to stay your whole career at Amazon unless is that wrong or, Kyle Kretschman:I mean, it's got it still do. So it's probably tough to say that because really the, the field started, like you said, really proliferated in 2012. So I stayed at Amazon for six years and I thought I'd be staying even longer. But Spotify came with the opportunity to one work on something I care very deeply about, which is the music industry. I'm a huge music fan. They also came with the idea to build again. So, you know, that was the part that really enticed me was Spotify did not have any PhD economists who were in an and, and economist roles. They had like one in a data science role, but they didn't have the structured economic discipline that they were seeing that Amazon was proliferating. And also then going into like Uber, Airbnb and the other tech companies. And so they said, can you build again? Kyle Kretschman:And I said, yeah, I'm, I'm excited to build. And then last one, all these there's definitely personal considerations here too. And Spotify just really did a great job showing how the company as a whole has Swedish cultures and values. And at the time I had a nine month old and they said, this is a great place to come be a father with the balance and that, and I said, all right, let's make the jump and come to Spotify. And so now I've been here about two years. So cuz I, I actually went to Spotify in may of 2020. Scott Cunningham:So remind me again, your job title at Spotify. Kyle Kretschman:So I'm head of economics. Scott Cunningham:Is, is that the, is that, is that like chief economist? I, I feel like I see different, different job titles and I don't know exactly what, what everything, Kyle Kretschman:Yeah. It, it it's on the path to it. So I'm, I'm the highest ranking PhD economist at Spotify. Scott Cunningham:I see. Okay. I've been there for two years. Okay, go ahead. Sorry. Kyle Kretschman:Yeah. Cause again, that's what I was brought into build was to build, like we did at Amazon was overall integration of PhD economists within the different business units. Scott Cunningham:So this is the part I'm, I'm having some hard time, like, you know, putting, visualizing or putting in my own words. What exactly will it look like if you have been successful in five years at that goal and what would it look like if you had been a complete, complete bust? What are the two things that are like empirical that I would be able to, to observe? Kyle Kretschman:Yeah. A complete bust is probably that an economics discipline is not, is not part of Spotify and there's not, there's not a job family. So a complete bus would've been, I, I moved to Spotify, an economics discipline. I either in, or I'm working data science job, what success looks like is actually what we put first from a, so I'll talk about the people in process, discipline success. We, I came into was Scott Cunningham:Real quick. So Kyle Kretschman:Foundation on basically. Yeah. Scott Cunningham:So, so failure actually would mean that the economist community within Spotify just never materialized, is that what you're saying? And that, and that means like this, having groups of economists that, that think and use the kinds of training we had in graduate school, but in a way that is actually productive in the firm is, is that, is that right? Kyle Kretschman:So, so yeah, and again, that's, Scott Cunningham:The job is successful if you're able to actually create internal demand for economists. Kyle Kretschman:Yep. That's right. And that's, that's what I would say against from the process side. And then from the product side, that's using econometric research in the ways that I've been talking about it's using it both not only for individual analysis, but also then building econometric measurement systems that improve the product to get towards Spotify's mission of, of billion listeners and fans who can connect with over a million creative artists who are making a living. So that's, so it's a combination, it's the combination people process. Do we have the people set up? Do we have this integrated system of economists working alongside all these different types of stakeholders along with the product side of, do we have these measurement techniques that we're applying in a way that is important to Spotify's not only Spotify's business, but all the stakeholders that have an interest in Bon life. Scott Cunningham:So I feel like, you know, I think to academics that, that, and, and maybe even to some degree students, maybe I'm, maybe I'm completely an outlier here and I'm wrong, but you know, I think there's this like really shallow is a negative word. It, I mean, shallow, literally more and just like, it's just the thinnest knowledge possible of what exactly, you know, the, the, the core skillset of a successful economist is in tech. You know, and for many people they think, I think they, they think it's such a primitive level. They're like, it needs to be somebody that can code, you know, it's a data scientist, but, but it, but it, but that's not what I associate with economics. Right. So what would you, what would you articulate? It is, Kyle Kretschman:So it's the ability to do econom applied econometric research. That's applied to business problems. Mm. So within that is coding. Yes. Scott Cunningham:Right, right. Within that is coding. Kyle Kretschman:I, the vast majority, I won't say everyone, but the vast majority of tech economists are gonna have some level of coding and maybe they're not coding anymore. Like I'm not doing any coding anymore, but like they, they have that ability. So that's just again, that's, that's a skillset, but the real ability is doing long-term economic research. Because the questions that we get asked are very hard and difficult, and they are maybe in the academic setting, maybe they are publication worthy, takes that take three years, four years to actually solve with the right model. Yeah. But it's the ability to take that three year research roadmap and make it progress. So when you're doing that, you need to have your summary statistics that the business ca

In this episode of Mixtape: the Podcast, I interviewed Petra Todd, professor of economics at University of Pennsylvania. Dr. Todd is a widely regarded and highly influential applied and theoretical econometrician who has written across many topics ranging from developing tests for evaluating racial discrimination in motor vehicle searches, to analysis of large conditional cash transfers (PROGRESA), to making seminal contributions to our understanding of program evaluation methodologies such as regression discontinuity design and matching. She is unique among many who write in the area of program evaluation for merging design based approaches to causal inference with approaches built on economic models, or "structural" methods. In this interview, we discussed her love of economics, her work with and mentorship from Jim Heckman, the early work she did studying the PROGRESA conditional cash transfer program and the value of structural econometrics more generally for applied researchers interested in causal inference and understanding programs. To learn more about the topics we discussed, see this new forthcoming article in the Journal of Economic Literature, coauthored with her former colleague Kenneth Wolpin, entitled “The Best of Both Worlds: Combining RCTs with Structural Modeling.” http://athena.sas.upenn.edu/petra/papers/surveywkenlatest.pdf

In this episode of Mixtape: the Podcast, I interviewed Petra Todd, professor of economics at University of Pennsylvania. Dr. Todd is a widely regarded and highly influential applied and theoretical econometrician who has written across many topics ranging from developing tests for evaluating racial discrimination in motor vehicle searches, to analysis of large conditional cash transfers (PROGRESA), to making seminal contributions to our understanding of program evaluation methodologies such as regression discontinuity design and matching. She is unique among many who write in the area of program evaluation for merging design based approaches to causal inference with approaches built on economic models, or "structural" methods. In this interview, we discussed her love of economics, her work with and mentorship from Jim Heckman, the early work she did studying the PROGRESA conditional cash transfer program and the value of structural econometrics more generally for applied researchers interested in causal inference and understanding programs. To learn more about the topics we discussed, see this new forthcoming article in the Journal of Economic Literature, coauthored with her former colleague Kenneth Wolpin, entitled “The Best of Both Worlds: Combining RCTs with Structural Modeling.” http://athena.sas.upenn.edu/petra/papers/surveywkenlatest.pdf Get full access to Scott's Substack at causalinf.substack.com/subscribe

The COVID-19 pandemic has already significantly widened wealth and income disparities around the world.  Poorer populations suffered higher rates of infection, and workers in low paying jobs were the most impacted by widespread shutdowns. But not all pandemics have had the same effect. In a paper in the Journal of Economic Literature, author Guido Alfani looks at the history of pandemics stretching back to the medieval Black Death to examine how factors like mortality rates and the response by wealthy elites affected gaps between the rich and poor. Alfani says that the lessons from previous pandemics like cholera in the 19th century offer hope for how public policy responses can actually have a meaningful impact on reducing inequality over the long run. Alfani spoke with Chris Fleisher about how the history of global pandemics can help inform responses to COVID-19 and efforts to address the root causes of poverty. Music in the audio by Podington Bear.

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/east-asian-studies

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/chinese-studies

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/public-policy

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/economics

As the glittering skyline in Shanghai seemingly attests, China has quickly transformed itself from a place of stark poverty into a modern, urban, technologically savvy economic powerhouse. But as Scott Rozelle and Natalie Hell show in Invisible China, the truth is much more complicated and might be a serious cause for concern. China's growth has relied heavily on unskilled labor. Most of the workers who have fueled the country's rise come from rural villages and have never been to high school. While this national growth strategy has been effective for three decades, the unskilled wage rate is finally rising, inducing companies inside China to automate at an unprecedented rate and triggering an exodus of companies seeking cheaper labor in other countries. Ten years ago, almost every product for sale in an American Walmart was made in China. Today, that is no longer the case. With the changing demand for labor, China seems to have no good back-up plan. For all of its investment in physical infrastructure, for decades China failed to invest enough in its people. Recent progress may come too late. Drawing on extensive surveys on the ground in China, Rozelle and Hell reveal that while China may be the second-largest economy in the world, its labor force has one of the lowest levels of education of any comparable country. Over half of China's population—as well as a vast majority of its children—are from rural areas. Their low levels of basic education may leave many unable to find work in the formal workplace as China's economy changes and manufacturing jobs move elsewhere. In Invisible China: How the Urban-Rural Divide Threatens China's Rise (U Chicago Press, 2020), Rozelle and Hell speak not only to an urgent humanitarian concern but also a potential economic crisis that could upend economies and foreign relations around the globe. If too many are left structurally unemployable, the implications both inside and outside of China could be serious. Understanding the situation in China today is essential if we are to avoid a potential crisis of international proportions. This book is an urgent and timely call to action that should be read by economists, policymakers, the business community, and general readers alike. Scott Rozelle is the Helen F. Farnsworth Senior Fellow and the co-director of the Stanford Center on China's Economy and Institutions in the Freeman Spogli Institute for International Studies and Stanford Institute for Economic Policy Research at Stanford University. His research focuses almost exclusively on China and is concerned with agricultural policy, the emergence and evolution of markets and other economic institutions in the transition process and inequality, with an emphasis on rural education, health and nutrition. Rozelle's papers have been published in top academic journals, including Science, Nature, American Economic Review, and the Journal of Economic Literature. He is fluent in Chinese and has established a research program in which he has close working ties with several Chinese collaborators and policymakers. For the past 20 years, Rozelle has been the chair of the International Advisory Board of the Center for Chinese Agricultural Policy; a co-director of the University of California's Agricultural Issues Center; and a member of Stanford's Walter H. Shorenstein Asia-Pacific Research Center and the Center on Food Security and the Environment. Host Peter Lorentzen is an Associate Professor in the Department of Economics at the University of San Francisco, where he leads a new Master's program in Applied Economics focused on the digital economy. His own research focuses on China's political economy and governance. Learn more about your ad choices. Visit megaphone.fm/adchoices

Publication bias is when academic journals make publication of a paper contingent on the results obtained. How big of an issue is this really?This podcast is an audio read through of the (initial version of the) article Publication Bias is Real, published on New Things Under the Sun.Articles mentioned:Frankel, Alexander and Maximilian Kasy. Forthcoming. Which findings should be published? American Economic Journal: Microeconomics. https://www.aeaweb.org/articles?id=10.1257/mic.20190133&&from=fBreznau, Nate, Eike Mark Rinke, Alexander Wuttke, Muna Adem, Jule Adriaans, Amalia Alvarez-Benjumea, Henrik K. Andersen, et al. 2021. Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty. MetaArXiv. March 24. doi:10.31222/osf.io/cd5j9.Dwan, Kerry, Douglas G. Altman, Juan A. Arnaiz, Jill Bloom, An-Wen Chan, Eugenia Cronin, et al. 2008. Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias. PLoS ONE 3(8): e3081. https://doi.org/10.1371/journal.pone.0003081Franco, Annie, Neil Malhotra, and Gabor Simonovits. 2014. Publication bias in the social sciences: Unlocking the file drawer. Science 345(6203): 1502-1505. DOI: 10.1126/science.1255484Andrews, Isaiah, and Maximilian Kasy. 2019. Identification of and Correction for Publication Bias. American Economic Review 109(8): 2766-94. https://doi.org/10.1257/aer.20180310Camerer, Colin F., Anna Deber, Eskil Forsell, Teck-Hua Ho, Jürgen Huber, Magnus Johanson et al. 2016. Evaluating replicability of laboratory experiments in economics. Science 351(6280): 1433-1436. https://doi.org/10.1126/science.aaf0918Open Science Collaboration. 2015. Estimating the reproducibility of psychological science. Science 349(6251) aac4716. https://doi.org/10.1126/science.aac4716Christensen, Garret, and Edward Miguel. 2018. Transparency, Reproducibility, and the Credibility of Economics Research. Journal of Economic Literature 56(3): 920-80. https://doi.org/10.1257/jel.20171350Wolfson, Paul J., and Dale Belman. 2015. 15 years of research on U.S. employment and the minimum wage. Tuck School of Business Working Paper No. 2705499. http://dx.doi.

Getting an academic field to change its ways is hard. But it does happen. And I think changes in the field of economics are a good illustration of some of the dynamics that make that possible.This podcast is an audio read through of the (initial version of the) article How a field fixes itself: the applied turn in economics, published on New Things Under the Sun.Articles mentioned:Leamer, Edward E. 1983. Let's Take the Con Out of Econometrics. American Economic Review 73(1): 31-43. https://www.jstor.org/stable/1803924Hamermesh, Daniel S. 2013. Six Decades of Top Economics Publishing: Who and How? Journal of Economic Literature 51(1): 162-72. https://doi.org/10.1257/jel.51.1.162Backhouse, Roger E., and Béatrice Cherrier. 2017. The age of the applied economist: the transformation of economics since the 1970s. History of Political Economy 49 (annual supplement): 1-33. https://doi.org/10.1215/00182702-4166239Angrist, Joshua D., and Jörn-Steffen Pischke. 2010. The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. Journal of Economic Perspectives 24(2): 3-30. https://doi.org/10.1257/jep.24.2.3Angrist, Josh, Pierre Azoulay, Glenn Ellison, Ryan Hill, and Susan Feng Lu. 2020. Inside job or deep impact? Extramural citations and the influence of economic scholarship. Journal of Economic Literature 58(1): 3-52. https://doi.org/10.1257/jel.20181508Bédécarrats, Florent, Isabelle Guérin, and François Roubaud. 2020. Randomized control trials in the field of development. Oxford University Press.Mercier, Hugo and Dan Sperber. 2017. The enigma of reason. Harvard University Press.Akerlof, George A., and Pascal Michaillat. 2018. Persistence of false paradigms in low-power sciences. PNAS 115(52): 13228-13233. https://doi.org/10.1073/pnas.1816454115Kuhn, Thomas. 1970. The Structure of Scientific Revolutions. University of Chicago Press.Smaldino, Paul E., and Cailin O'Connor. 2021. Interdisciplinarity can aid the spread of better methods between scientific communities. Preprint. https://doi.org/10.31222/osf.io/cm5v3Heckman, James J., and Sidharth Moktan. 2020. Publishing and promotion in economics: the tyranny of the top five. Journal of Economic Literature 58(2): 419-70. https://doi.org/10.1257/jel.20191574Maher, Thomas V., Charles Seguin, Yongjun Zhang, and Andrew P. Davis. 2020. Social scientists' testimony before Congress in the United States between 1946-2016, trends from a new dataset. PLOS ONE 15(3): e0230104. https://doi.org/10.1371/journal.pone.0230104Panhas, Matthew, and John D. Singleton. 2017. The empirical economist's toolkit: from models to methods. History of Political Economy 49(annual supplement): 127-157. https://doi.org/10.1215/00182702-4166299de Souza Leão, Luciana, and Gil Eyal. 2019. The rise of randomized controlled trials (RCTs) in international development in historical perspective. Theory and Society 48: 383-418. https://doi.org/10.1007/s11186-019-09352-6

Friedrich Hayek is one of the giants of 20th century economics. He did important work on everything from business cycles to psychology, earning a Nobel Prize in economics in 1974.  However, Hayek is perhaps best known for his book, The Road to Serfdom. Since its publication in 1944, many leaders and politicians have cited it as a proof that countries that experiment with socialism will inevitably end up as a totalitarian state. Duke economist Bruce Caldwell says that the book's message was much more nuanced and often misinterpreted by later generations.  He sets the record straight in the September issue of the Journal of Economic Literature by expanding on Hayek’s thinking and the intellectual climate in which Hayek was writing. Just as important, Caldwell’s research shows the importance of digging into the origins of economic debates. Caldwell recently spoke with the AEA’s Tyler Smith about the intellectual backdrop to The Road to Serfdom, the challenges of writing for a wider audience, and the history of economic ideas.   The edited highlights of that conversation are below, and the full interview can be heard using the podcast player below.

Michael & Jonathan are joined by George Selgin. The discussion focuses on what is money vs. currency, considerations for monetary policy, Bitcoin, and Central Bank Digital Currencies. ABOUT GEORGE SELGIN George Selgin is a senior fellow and director of the Center for Monetary and Financial Alternatives at the Cato Institute and Professor Emeritus of Economics at the University of Georgia. His research covers a broad range of topics within the field of monetary economics, including monetary history, macroeconomic theory, and the history of monetary thought. He is the author of The Theory of Free Banking (Rowman & Littlefield, 1988); Good Money: Birmingham Button Makers, the Royal Mint, and the Beginnings of Modern Coinage (University of Michigan Press, 2008); Money: Free & Unfree (The Cato Institute, 2017); Less Than Zero: The Case for a Falling Price Level in a Growing Economy (The Cato Institute, 2018) and, most recently, Floored! How a Misguided Fed Experiment Deepened and Prolonged the Great Recession (The Cato Institute, 2018). He also contributed a chapter to libertarianism.org's Visions of Liberty. Selgin is one of the founders, with Kevin Dowd and Lawrence H. White, of the Modern Free Banking School, which draws its inspiration from the writings of F. A. Hayek on denationalization of money and choice in currency. Selgin has written for numerous scholarly journals, including the British Numismatic Journal; the Economic Journal; the Economic History Review; the Journal of Economic Literature; and the Journal of Money, Credit, and Banking; and for popular outlets such as the Christian Science Monitor, the Financial Times, and the Wall Street Journal, among others. Selgin holds a BA in economics and zoology from Drew University, and a PhD in economics from New York University. Follow George on Twitter @GeorgeSelgin ABOUT DIGITAL DOLLAR SUBSCRIBE TO THE EMAIL INBOX UPDATES! https://digitaldollar.substack.com For more information about our sponsor, visit https://10xts.com for digital asset compliance solutions for financial services and capital markets. Follow us on Twitter @GoDigitalDollar --- Send in a voice message: https://anchor.fm/digitaldollar/message

Donald Kenkel's expertise is in areas of health economics and public sector economics. Broadly speaking, most of his research is on the economics of disease prevention and health promotion. He is the author of the chapter on "Prevention" in the Handbook of Health Economics (2000). With external funding from the National Institutes of Health, he has conducted a series of studies on the economics of public health policies, including: alcohol taxes and other policies to prevent alcohol problems (Journal of Applied Econometrics 2001, American Economic Review Papers & Proceedings 2005); cigarette taxes to prevent youth smoking (Journal of Political Economy 2002, Journal of Health Economics 2008); and advertising to promote smoking cessation (Journal of Political Economy 2007). His more recent research is on the economics of cigarette sales on Indian reservations (National Tax Journal 2015), the economics of tobacco regulation (Journal of Benefit-Cost Analysis 2015, Journal of Economic Literature forthcoming), and the market for e-cigarettes (Journal of Health Economics 2019, Journal of Risk & Uncertainty 2020). Another area of research and teaching interest is in cost-benefit analysis of public policies, especially policies that affect health. Kenkel is a former President of the Society for Benefit-Cost Analysis. From July 2018 through April 2020 he was a Senior Economist and then Chief Economist at the Council of Economic Advisers in the Executive Office of the President. Donald Kenkel received a grant from the Foundation for a Smoke-Free World in 2020.

De fleste gennemgange af økonomiens teorihistorie slutter med Keynes' død. Det gør vores også. Næsten. Vi tager også et temaafsnit om Chicago-økonomerne. Inden vi kommer så langt, vil jeg i dagens afsnit lave et lille eksperiment. Jeg vil komme med et meget kort overslag over nogle af de vigtige teoretiske udviklinger indenfor økonomi fra 1946 og frem til nu. Det er et eksperiment, fordi jeg udelukkende bruger min egen hukommelse med en forudsætning om, at jeg må have huske noget af det væsentligste. Der er sikkert meget, der er glemt, men i hvert fald kommer vi igennem as-if-economics, adfærdsøkonomi, eksperimenter, entreprenørens genfødsel og meget mere. Der er sikkert noget, som jeg har glemt, men så vil det med garanti blive dækket i næste sæson, hvor jeg og min nye medvært Otto Brøns vil tale om alle nobelprismodtagerne i rækkefølge. Glæd dig! Har du nogensinde tænkt over, hvad økonomi er for en videnskab? Hvordan opstod den, og hvem var dens grundlæggere? Eller har du interesseret dig for moderne diskussioner om samfundet, herunder ulighed, ressourceforbrug eller konkurrence? Hvis dette er tilfældet, er økonomiens teorihistorie vigtig og nyttig for dig. Den type af diskussioner er nemlig mindst lige så gammel som den økonomiske videnskab selv, og du vil i dens rødder også finde rødderne til de moderne argumenter. Til dagens afsnit har jeg læst:  Artinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. (2015). Heuristics as Adaptive Decision Strategies in Management. Journal of Organizational Behavior, s. 33-52. Becker, G. S. (1993). The Economic Way of Looking at Behavior. Journal of Political Economy, s. 385-409. Boettke, P. (2017). Don't Be a "Jibbering Idiot": Economic Principles and the Properly Trained Economist. The Journal of Private Enterprise, s. 9-15. Bruni, L., & Sugden, R. (2007). The Road Not Taken: How Psychology Was Removed From Economics, and How It Might Be Brought Back. The Economic Journal, s. 146-173. Camerer, C. (1999). Behavioral Economics: Reunifying Psychology and Economics. Proceedings of the National Academy of Sciences of the USA, s. 10575-10577. Coase, R. (1937). The Nature of the Firm. Economica, s. 386-405. Conlisk, J. (1996). Why Bounded Rationality? Journal of Economic Literature, s. 669-700. De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, Biases, and Rational Decision-Making in the Human Brain. Science, s. 684-687. Friedman, M. (1953). Essays in Positive Economics. Chicago: University of Chicago Press. Gul, F., & Pesendorfer, W. (2008). The Case for Mindless Economics. The Foundations of Positive and Normative Economics, s. 3-42. Hayek, F. A. (1948). Individualism and Economic Order. Chicago: University of Chicago Press. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decisions Under Risk. Econometrica, s. 263-291. Kirzner, I. M. (1973). Competition and Entrepreneurship. Chicago: The University of Chicago Press. Shane, S., & Venkataraman, S. (2000). The Promise of Entrepreneurship as a Field of Research. Academy of Management Review, s. 217-226. Smith, V. L. (2003). Constructivist and Ecological Rationality in Economics. The American Economic Review, s. 465-508. Todd, P. M., & Gigerenzer, G. (2007). Environments That Makes Us Smart: Ecological Rationality. Current Directions in Psychological Science, s. 167-171. Williamson, O. (1996). Economics and Organization: A Primer. California Management Review, s. 131-146. I like to dedicate this season to my teachers Ole Bruus and Bruce Caldwell. All mistakes and mispronunciations are mine alone and no fault of theirs.

Časovnica: [00:00:30] Aktualne teme: o nadaljevanju podcasta, projektu skupine evidance-based pristopa k prehrani in vadbi ter poskusu svetovnega rekorda v log liftu. [00:08:30] Kako je pot do naših ciljev lahko izvor sreče in zadovoljstva. Zakaj se moramo konstantno premikati proti nekemu cilju. O zanemarjanju prihodnosti in upiranju kratkoročnim ciljem ter zakaj je to pomembno pri delu na področju fitnesa in zdravja. [00:37:15] Ali je kava dober vir kofeina za športnike? O nevarnosti predoziranja s kofeinom z določenimi komercialno dostopnimi izdelki. [00:49:00] Omega-3 indeks. Kaj je in zakaj je pomemben? [01:22:15] Omega-3 indeks in športniki. Kako dosledni so športniki pri zagotavljanju potreb po omega-3? Zakaj športniki potrebujejo več omega-3 [01:37:15] Zaključek.   Omenjeni članki: Hipoteza rdeče kraljice: Brockhurst et al. (2014) ‘Running with the Red Queen: the role of biotic conflicts in evolution', Proceedings of the Royal Society B: Biological Sciences, 281(1797), p. 20141382. https://doi.org/10.1098/rspb.2014.1382 "Zanemarjanje prihodnosti": Frederick et al. (2002) ‘Time Discounting and Time Preference: A Critical Review', Journal of Economic Literature, 40(2), pp. 351–401.  https://doi.org/10.1257/002205102320161311 Vsebnost kofeina v kavi: McCusker et al. (2003). Caffeine Content of Specialty Coffees. Journal of Analytical Toxicology, 27(7), 520–522.  https://doi.org/10.1093/jat/27.7.520  Omega-3 index in zdravje: von Schacky, C. (2020) ‘Omega-3 index in 2018/19', Proceedings of the Nutrition Society, (October 2019), pp. 1–7. https://doi.org/10.1017/S0029665120006989Sarteret al. (2015) ‘Blood docosahexaenoic acid and eicosapentaenoic acid in vegans: Associations with age and gender and effects of an algal-derived omega-3 fatty acid supplement', Clinical Nutrition. Elsevier Ltd, 34(2), pp. 212–218. https://doi.org/10.1016/j.clnu.2014.03.003 Harris et al. (2018) ‘Erythrocyte long-chain omega-3 fatty acid levels are inversely associated with mortality and with incident cardiovascular disease: The Framingham Heart Study', Journal of Clinical Lipidology. Elsevier Inc, 12(3), pp. 718-727.e6. https://doi.org/10.1016/j.jacl.2018.02.010 Harris et al. (2016) ‘Red blood cell oleic acid levels reflect olive oil intake while omega-3 levels reflect fish intake and the use of omega-3 acid ethyl esters: The Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico–Heart Failure trial', Nutrition Research. Elsevier B.V., 36(9), pp. 989–994. https://doi.org/10.1016/j.nutres.2016.06.012 Harris et al. (2017) ‘The Omega-3 Index and relative risk for coronary heart disease mortality: Estimation from 10 cohort studies', Atherosclerosis. Elsevier Ltd, 262, pp. 51–54. https://doi.org/10.1016/j.atherosclerosis.2017.05.007 ALA nimajo enakih koristi kot EPA in DHA: Burdge et al. (2002) ‘Conversion of α-linolenic acid to eicosapentaenoic, docosapentaenoic and docosahexaenoic acids in young women', British Journal of Nutrition, 88(04), p. 411. https://doi.org/10.1079/BJN2002689 Wendland. (2006) ‘Effect of linolenic acid on cardiovascular risk markers: a systematic review', Heart, 92(2), pp. 166–169. https://doi.org/10.1136/hrt.2004.053538 Su et al. (2018) ‘Effect of dietary alpha-linolenic acid on blood inflammatory markers: a systematic review and meta-analysis of randomized controlled trials', European Journal of Nutrition. Springer Berlin Heidelberg, 57(3), pp. 877–891. https://doi.org/10.1007/s00394-017-1386-2 Jovanovski et al. (2017) ‘The effect of alpha-linolenic acid on glycemic control in individuals with type 2 diabetes', Medicine (United States), 96(21). https://doi.org/10.1097/MD.0000000000006531 Omega-3 index pri športnikih: Ritz et al. (2020). Dietary and Biological Assessment of the Omega-3 Status of Collegiate Athletes: A Cross-Sectional Analysis. PloS one, 15(4), e0228834. https://doi.org/10.1371/journal.pone.0228834 Davinelli et al. (2019). Relationship Between Distance Run Per Week, Omega-3 Index, and Arachidonic Acid (AA)/Eicosapentaenoic Acid (EPA) Ratio: An Observational Retrospective Study in Non-elite Runners. Frontiers in physiology, 10, 487. https://doi.org/10.3389/fphys.2019.00487   Sledite nam: Nenadov instagram: @nenad.feelgood Matjažev instagram: @matjaz.feelgood Feelgood Skupnost na Facebooku: https://bit.ly/feelgoodskupnost Spletna stran: https://www.feel-good.si   Poslušate nas lahko na: Podbean: https://bit.ly/podbean-zdp Stitcher: https://bit.ly/stitcher-zdp Pocket Casts: https://bit.ly/pocket-zdp Podcast Addict: https://bit.ly/addict-zdp Castbox: https://bit.ly/castbox-zdp iTunes: https://bit.ly/itunes-zdp  

Anyone who has ever taken Econ 101 will likely be familiar with our guest Gregory Mankiw.  The Harvard professor’s Principles of Economics is in its ninth edition and is perhaps the most widely used textbook in introductory courses throughout the country. Now, in an essay published in the Journal of Economic Literature, Mankiw reflects on some of the big questions he’s confronted during the three decades since his career as a textbook author began.  He says that textbook authors, especially introductory ones, are like “ambassadors” for the profession. They should strive to present the relevant issues in economics fairly, doing their best to keep their biases in check, even if they’re not always successful. The AEA's Chris Fleisher and Tyler Smith spoke with Mankiw in March via Skype, when he was at home working on the 11th edition of his intermediate macroeconomics textbook amid the COVID-19 outbreak. Theme music by Sound of Picture.

Our guest on the podcast is Annamaria Lusardi, an authority on financial literacy and financial education. Lusardi is the Denit Trust Distinguished Scholar and Professor of Economics and Accountancy at the George Washington University School of Business, where she also serves as the academic director of the Global Financial Literacy Excellence Center. Prior to joining George Washington University, she taught at Dartmouth College for 20 years. She has also taught at Princeton University, the University of Chicago Harris School of Public Policy, the University of Chicago Booth School of Business, and Columbia Business School. She received her doctorate from Princeton.BackgroundAnnamaria Lusardi bio Annamaria Lusardi curriculum vitae Annamaria Lusardi publications Financial Literacy"The Economic Importance of Financial Literacy: Theory and Evidence," by Annamaria Lusardi and Olivia S. Mitchell, Journal of Economic Literature, 2014. "A Financial Literacy Test That Works," by Annamaria Lusardi and Olivia S. Mitchell, Forbes, Dec. 14, 2017. The 2019 TIAA Institute-GFLEC Personal Finance Index "Financial Literacy and Wellness Among African-Americans," by Paul J. Yakoboski,  Annamaria Lusardi, and Andrea Hasler, Global Financial Literacy Excellence Center. "Financial Literacy and Retirement Planning in the United States," by Annamaria Lusardi and Olivia S. Mitchell, Journal of Pension Economics & Finance, October 2011. “Financial Literacy Around the World: An Overview," by Annamaria Lusardi and Olivia S. Mitchell, Journal of Pension Economics and Finance, October 2011. Implications of Financial Illiteracy"Optimal Financial Knowledge and Wealth Inequality," by Annamaria Lusardi, Pierre-Carl Michaud, and Olivia S. Mitchell, The National Bureau of Economic Research, January 2013. "Financial Literacy and Stock Market Participation," by Maarten van Rooij, Annamaria Lusardi, and Rob Alessie, The National Bureau of Economic Research, October 2007. "National Financial Capability Study," Finra Investor Education Foundation, December 2019. “Financially Fragile Households: Evidence and Implications,” by Annamaria Lusardi, Daniel J. Schneider, and Peter Tufano, The National Bureau of Economic Research, May 2011. "Financial Literacy and Planning: Implications for Retirement Wellbeing," The National Bureau of Economic Research, by Annamaria Lusardi and Olivia S. Mitchell, May 2011. Financial Education"Are States Providing Adequate Financial Literacy Education?" by Matt Kasman, Benjamin Heuberger, and Ross A. Hammond, Brookings, Oct. 3, 2018.  "Five Steps to Planning Success. Experimental Evidence From U.S. Households," by Aileen Heinberg, Angela A. Hung, Arie Kapteyn, Annamaria Lusardi, Anya Savikhin Samek, and Joanne Yoong, The National Bureau of Economic Research, June 2014. "John Lynch: Rethinking Financial Education," The Long View podcast, Morningstar.com, Dec. 11, 2019. "Financial Literacy, Financial Education, and Downstream Financial Behaviors," by Daniel Fernandes, John G. Lynch, and Richard G. Netemeyer, Management Science, Jan. 6, 2014. "Ariel Community Academy Students Are Investing on Wall Street by Fourth Grade," by Rodney Brooks, The Undefeated, Oct. 18, 2017.  

Most of us like to think that we use data to inform our decision-making process and path forward, but there's one challenge. It's possible and quite common that we seek out data to validate what we already believe. That's called confirmation bias. In speaking with Alex Edmans, a TED and Davos speaker, rigorous academic researcher and Professor of Finance at the London Business School, he argues that confirmation bias can lead us down the wrong path in business and in life, and provides ways to counteract this automatic human tendency. Alex’s research has been covered by the Wall Street Journal, New York Times, and The Economist, among others and he was interviewed by some of the most respected television channels like Bloomberg, BBC, CNBC, and CNN just to name a few. In addition, as the author of Grow The Pie: How Great Companies Deliver Both Purpose and Profit, Alex outlines actionable and evidenced-based ways for organizations to upgrade their leadership and drive the company into an empowering growth paradigm where everyone wins. Tune in to learn about: What is confirmation bias How you can effectively deal with confirmation bias as to elevate your leadership skills What the next era of business will look like (hint: all stakeholders win) The importance of learning soft skills in school and in business About the book Grow The Pie: How Great Companies Deliver Both Purpose and Profit   Connect with Alex Edmans: Linkedin Twitter Website TED Talk Alex Edmans' biography: Alex Edmans is Professor of Finance at London Business School and Academic Director of the Centre for Corporate Governance. Alex graduated from Oxford University and then worked for Morgan Stanley in investment banking (London) and fixed income sales and trading (New York). After a PhD in Finance from MIT Sloan as a Fulbright Scholar, he joined Wharton in 2007 and was tenured in 2013 shortly before moving to LBS. Alex’s research interests are in corporate finance (corporate governance, executive compensation, investment/growth/innovation, and M&A), behavioural finance, corporate social responsibility, and practical investment strategies. He has published in the American Economic Review, Journal of Finance, Journal of Financial Economics, Review of Financial Studies, and Journal of Economic Literature. He is Managing Editor of the Review of Finance, Associate Editor of the Journal of Financial Economics, a Research Fellow of the Centre for Economic Policy Research, and a Fellow of the European Corporate Governance Institute. He was previously Associate Editor of the Review of Financial Studies and a Faculty Research Fellow of the National Bureau of Economic Research. He won the Moskowitz Prize for Socially Responsible Investing, the FIR-PRI prize for Finance and Sustainability, the Investor Responsibility Research Centre prize, and the WRDS Award for Best Empirical Finance Paper at the WFA; was a finalist for the Smith-Breeden Prize for best paper in the Journal of Finance; and was named a Rising Star of Corporate Governance by Yale University and a Rising Star of Finance by NYU/Fordham/RPI. Alex’s research has been covered by the Wall Street Journal, Financial Times, New York Times, The Economist, and The Times; and interviewed by Bloomberg, BBC, CNBC, CNN, ESPN, Fox, ITV, NPR, Reuters, Sky News, and Sky Sports. Alex has spoken at the World Economic Forum in Davos, testified in the UK Parliament, presented to the World Bank Board of Directors as part of the Distinguished Speaker Series, and given the TED talk What to Trust in a Post-Truth World and the TEDx talk The Social Responsibility of Business. He has written op-eds for the Wall Street Journal and Financial Times, writes regularly for Harvard Business Review, Huffington Post, World Economic Forum, and CityAM, and runs a blog, Access to Finance, that aims to make complex finance topics accessible to a general audience.

What have we learned in the 18 years since 9/11? Chris, Melanie, and Bryan discuss whether counterterrorism policy takes account of academic research on the subject. Going forward, the goal should be to implement the most cost-effective policies — and over time, to calm public anxiety about terrorism. Bryan gives a shout out to a bipartisan duo of Net Assessment fans, Chris gripes about NFL officiating, and Melanie offers her appreciation of the Constitution via an unlikely source: former Vice President Joe Biden.   Links Khusrav Gaibulloev and Todd Sandler, "Six Things We've Learned About Terrorism Since 9/11," Washington Post, September 11, 2019 Khusrav Gaibulloev and Todd Sandler, "What We Have Learned about Terrorism since 9/11," Journal of Economic Literature, June, 2019 John Mueller and Mark Stewart, Terror, Security, and Money: Balancing the Risks, Benefits, and Costs of Homeland Security, (Oxford, 2011) John Mueller and Mark Stewart, Are We Safe Enough? Measuring and Assessing Aviation Security, (Elsevier, 2018) Trevor Thrall and Erik Goepner, "Step Back: Lessons for U.S. Foreign Policy from the Failed War on Terror," Cato, June 26, 2017 Scott Simon, "Edward Snowden Tells NPR: The Executive Branch Sort of Hacked the Constitution," NPR, September 12, 2019 Tom Schad, "As New Season Begins, NFL Coaches Still Trying to Sort Out Pass Interference Rule Changes," USA Today, September 5, 2019 Christopher Preble, “Covert Wars, to What End?" War on the Rocks, August 7, 2019 Austin Carson, "Recipient of the Georgetown University Lepgold Prize," Department of Political Science at the University of Chicago, September 4, 2019 Ari Cohn, Tweet, September 12, 2019 International Security Studies Section of the International Studies Association, Tweet, August 19, 2019

Russell Golman is an Assistant Professor of Behavioral Economics and Decision Sciences in the Social & Decision Sciences Department at CMU.  His pioneering, interdisciplinary work has been published in a wide range of academic journals, including Science Advances, Decision, the RAND Journal of Economics, the Journal of Economic Theory, the Journal of Economic Perspectives, and the Journal of Economic Literature.  In 2017 Professor Golman organized the Belief-Based Utility Conference at Carnegie Mellon with generous funding from the Russell Sloan Foundation and the Alfred P. Sloan Foundation. Professor Golman was trained as a game theorist with a Mathematics Ph.D. from the University of Michigan.  But whereas game theorists usually assume that people making strategic decisions are hyper-rational, Russell wanted to acknowledge that real people are influenced by each other and sometimes make mistakes. They often care deeply about their beliefs, not just about material outcomes. And they rarely settle into an equilibrium in which everybody is static and content.  Russell’s research interests expanded into behavioral economics and behavioral decision research as well as complex adaptive systems and social dynamics.  He took a postdoc in Social and Decision Sciences at CMU, where Herb Simon first conceived of the concept of bounded rationality 50 years earlier.  Professor Golman joined the faculty here in 2012. We talked to Russell about information avoidance and curiosity and to what lengths people will strive for both. In our grooving session, Kurt and Tim discuss information avoidance from a corporate perspective and wonder, “what impact does a manager have when he or she avoids a difficult conversation?” We also talked about ways to reduce information avoidance in the working world and how incentives may help managers through tough situations. We hope you enjoy this episode in our Carnegie Mellon series with Russell Golman. Links Russell Golman: https://www.cmu.edu/dietrich/sds/people/faculty/russell-golman.html CMU Social and Decision Sciences Department: https://www.cmu.edu/dietrich/ Carnegie Mellon University: https://www.cmu.edu/   Golman, Russell, David Hagmann, and George Loewenstein.  “Information Avoidance.” Journal of Economic Literature, 2017, 55: 96-135.Featured on The Academic Minute   Golman, Russell and George Loewenstein.  “Information Gaps: A Theory of Preferences Regarding the Presence and Absence of Information”  Decision, 2016, forthcoming.   Golman, Russell, George Loewenstein, Karl Ove Moene and Luca Zarri. “The Preference for Belief Consonance.” Journal of Economic Perspectives 2016, 30: 165-187.   GI Joe Fallacy: https://www.youtube.com/watch?v=GimHHAID_P0 Herb Simon: https://en.wikipedia.org/wiki/Bounded_rationality   Bluegrass music: https://en.wikipedia.org/wiki/Bluegrass_music Great Blue Heron Music Festival: https://greatblueheron.com/ Donna the Buffalo: https://donnathebuffalo.com/ Jam bands: https://en.wikipedia.org/wiki/Jam_band The Pines: https://www.youtube.com/watch?v=TuuFampLC6E The Cactus Blossoms: https://www.youtube.com/watch?v=Qj7jJk8TPZk   Kurt Nelson: @motivationguru and https://www.linkedin.com/in/kurtwnelson/ Tim Houlihan: @THoulihan and https://www.linkedin.com/in/tim-houlihan-b-e/ Check out the Behavioral Grooves website:https://behavioralgrooves.com/

Cliff Winston of the Brookings Institution talks with EconTalk host Russ Roberts about his recent article in the Journal of Economic Literature on the U.S. transportation system. Winston argues that the while the United States has a very good transportation system overall, it is extremely expensive and poorly organized. What is needed, Winston argues, is not more money, but to spend the money already allocated more wisely. He discusses the evolution of the U.S. transportation system, government's role in transportation, dramatic innovations that might transform aviation and driving, and the potential for privatizing airports and roads.

Cliff Winston of the Brookings Institution talks with EconTalk host Russ Roberts about his recent article in the Journal of Economic Literature on the U.S. transportation system. Winston argues that the while the United States has a very good transportation system overall, it is extremely expensive and poorly organized. What is needed, Winston argues, is not more money, but to spend the money already allocated more wisely. He discusses the evolution of the U.S. transportation system, government's role in transportation, dramatic innovations that might transform aviation and driving, and the potential for privatizing airports and roads.

Cliff Winston of the Brookings Institution talks with EconTalk host Russ Roberts about his recent article in the Journal of Economic Literature on the U.S. transportation system. Winston argues that the while the United States has a very good transportation system overall, it is extremely expensive and poorly organized. What is needed, Winston argues, is not more money, but to spend the money already allocated more wisely. He discusses the evolution of the U.S. transportation system, government's role in transportation, dramatic innovations that might transform aviation and driving, and the potential for privatizing airports and roads.