New Things Under the Sun

Welcome to patents week! I set out to write a post about using patents to measure innovation, but it turned into four. I'm releasing podcasts of each episode, one per day, but if you're too excited to wait, you can read all four here, on New Things Under the Sun.How many inventions are patented? Less than half, more than zeroPatents (weakly) predict innovation: Correlations between patents and other proxies for innovationDo studies based on patents get different results? For the sample on New Things Under the Sun, not reallyCan we learn about innovation from patent data? The definitive New Things Under the Sun PostThis podcast covers #4: Can We Learn About Innovation From Patent Data?

Welcome to patents week! I set out to write a post about using patents to measure innovation, but it turned into four. I'm releasing podcasts of each episode, one per day, but if you're too excited to wait, you can read all four here, on New Things Under the Sun.How many inventions are patented? Less than half, more than zeroPatents (weakly) predict innovation: Correlations between patents and other proxies for innovationDo studies based on patents get different results? For the sample on New Things Under the Sun, not reallyCan we learn about innovation from patent data? The definitive New Things Under the Sun PostThis podcast covers #3: Do studies based on patents get different results?

Welcome to patents week! I set out to write a post about using patents to measure innovation, but it turned into four. I'm releasing podcasts of each episode, one per day, but if you're too excited to wait, you can read all four here, on New Things Under the Sun.How many inventions are patented? Less than half, more than zeroPatents (weakly) predict innovation: Correlations between patents and other proxies for innovationDo studies based on patents get different results? For the sample on New Things Under the Sun, not reallyCan we learn about innovation from patent data? The definitive New Things Under the Sun PostThis podcast covers #2: Patents (weakly) predict innovation

Welcome to patents week! I set out to write a post about using patents to measure innovation, but it turned into four. I'm releasing podcasts of each episode, one per day, but if you're too excited to wait, you can read all four here, on New Things Under the Sun.How many inventions are patented? Less than half, more than zeroPatents (weakly) predict innovation: Correlations between patents and other proxies for innovationDo studies based on patents get different results? For the sample on New Things Under the Sun, not reallyCan we learn about innovation from patent data? The definitive New Things Under the Sun PostThis podcast covers #1: How many inventions are patented?

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

Correction: In this podcast, I misspoke towards the end and referred to Eesley and Lee (2020) as Eesley and Wang (a 2017 paper I wrote about earlier here). Apologies to the authors.A lot of particularly interesting innovation happens at startups. Suppose we want more of this. One way we could try to get more is by giving entrepreneurship training to people who are likely to found innovative startups. Does that work? This post takes a look at some meta-analyses on the effects of entrepreneurship education, then zeroes in on a few studies focusing on entrepreneurship training for science and engineering students or which is focused on tech entrepreneurship.This podcast is an audio read through of the (initial draft) of Teaching Innovative Entrepreneurship, published on New Things Under the Sun.Articles mentionedMartin, Bruce C., Jeffrey J. McNally, and Michael J. Kay. 2013. Examining the formation of human capital in entrepreneurship: A meta-analysis of entrepreneurship education outcomes. Journal of Business Venturing 28(2): 211-224. https://doi.org/10.1016/j.jbusvent.2012.03.002Carpenter, Alex, and Rachel Wilson. 2022. A systematic review looking at the effect of entrepreneurship education on higher education students. The International Journal of Management Education 20(2): 100541. https://doi.org/10.1016/j.ijme.2021.100541Souitaris, Vangelis, Stefania Zerbinati, and Andreas Al-Laham. 2007. Do entrepreneurship programs raise entrepreneurial intention of science and engineering students? The effect of learning, inspiration and resources. Journal of Business Venturing 22(4): 566-591. https://doi.org/10.1016/j.jbusvent.2006.05.002Eesley, Charles E., and Yong Suk Lee. 2020. Do university entrepreneurship programs promote entrepreneurship? Strategic Management Journal 42(4): 833-861. https://doi.org/10.1002/smj.3246Lyons, Elizabeth, and Lauren Zhang. 2017. Who does (not) benefit from entrepreneurship programs? Strategic Management Journal 39(1): 85-112. https://doi.org/10.1002/smj.2704Oster, Emily. 2016. Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics 37(2): 187-204. https://doi.org/10.1080/07350015.2016.1227711Wallskog, Melanie. 2022. Entrepreneurial Spillovers Across Coworkers. PhD job market paper.

Here’s a striking fact: through 2022, one in two Nobel prize winners in physics, chemistry, and medicine also had a Nobel prize winner as their academic advisor.undefinedWhat accounts for this extraordinary transmission rate of scientific excellence? In this podcast I’ll focus one potential explanation: what do we know about how innovative teachers influence their students, and their students’ subsequent innovative career? I’ll focus on two strands of literatures: roughly speaking, how teachers influence what their students are interested in and the impact of their work. This podcast is an audio read through of the (initial version of the) article "Teacher Influence and Innovation," originally published on New Things Under the Sun.Articles discussedBorowiecki, Karol Jan. 2022. Good Reverberations? Teacher Influence in Music Composition since 1450. Journal of Political Economy 130(4): 991-1090. https://doi.org/10.1086/718370Koschnick, Julius. 2023. Teacher-directed scientific change: The case of the English Scientific Revolution. PhD job market paper.Azoulay, Pierre, Christopher C. Liu, and Toby E. Stuart. 2017. Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs. American Journal of Sociology 122(4): 1223-1271. https://doi.org/10.1086/689890Biasi, Barbara, and Song Ma. 2023. The Education-Innovation Gap. NBER Working Paper 29853. https://doi.org/10.3386/w29853Waldinger, Fabian. 2010. Quality Matters: The Expulsion of Professors and the Consequences for PhD Student Outcomes in Nazi Germany. Journal of Political Economy 118(4): 787-831. https://doi.org/10.1086/655976

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.

This week, Arnaud Dyèvre (@ArnaudDyevre) and I follow up on a previous podcast, where we documented a puzzle: larger firms conduct R&D at the same rate as smaller firms, despite getting fewer (and more incremental) innovations per R&D dollar. Why wouldn’t firms decelerate their research spending as the return on R&D apparently declines? In this follow-up podcast, we look at one explanation: firms of different sizes face different incentives when it comes to innovation.This podcast is an audio read through of the (initial version of the) article "Big firms have different incentives", originally published on New Things Under the Sun.

How do academic researchers decide what to work on?  Part of it comes down to what you judge to be important and valuable; and that can come from exposure to problems in your local community. This podcast is an audio read through of the (initial version of the) article "Geography and What Gets Researched", originally published on New Things Under the Sun.

Most of the time, we think of innovation policy as a problem of how to accelerate desirable forms of technological progress. But there are other times when we may wish to actively slow technological progress. The AI pause letter is a recent example, but less controversial examples abound. A lot of energy policy acts as a brake on the rate of technological advance in conventional fossil fuel innovation. Geopolitical rivals often seek to impede the advance of rivals’ military technology.Today I want to look at policy levers that actively slow technological advance, sometimes (but not always) as an explicit goal.This podcast is an audio read through of the (initial version of the) article "How to impede technological progress", originally published on New Things Under the Sun.

This is not the usual podcast on New Things Under the Sun. For the third issue of Asterisk Magazine, Tamay Besiroglu and I were asked to write an article on how likely it is that artificial intelligence will lead to not just faster economic growth, but explosive economic growth. (Tamay will introduce himself in a minute here). Since we wrote that article as a literal dialogue, we thought it would be fun to also record ourselves performing the parts we wrote for ourselves and that is what we bring to you on this very special edition of New Things Under the Sun. During this podcast, you’ll hear two voices - mine and Tamay’s - as we perform our debate about the potential for explosive economic growth after we develop sufficiently advanced artificial intelligence.Then, after about an hour, our performance of the article will wrap up, but we keep talking. For another forty minutes, we talk a bit about policy implications of artificial intelligence, the prospects for spooky smart AI, and how our own views have evolved on this topic.If you want to read our article instead of listening, head over to here. If you’ve already read that and just want to hear some of our extra commentary, jump to about one hour into this podcast. Special thanks to Clara Collier, Asterisk’s Editor-in-Chief, for reaching out to us and giving us this opportunity.

We’ve got something new this week! This is post, which is on how the size of firms is related to the kind of innovation they do, is the first ever collaboration published on New Things Under the Sun. My coauthor is Arnaud Dyèvre (@ArnaudDyevre), a PhD student at the London School of Economics working on growth and the economic returns to publicly funded R&D. Going into this post, Arnaud knew this literature better than me and drew up an initial reading plan. We iterated on that for awhile, jointly discovering important papers, and eventually settled on a set of core papers, which we’ll talk about in this post. I think this turned out great and so I wanted to extend an invitation to the rest of you - if you want to coauthor a post with me, go to newthingsunderthesun.com/collaborations to learn more. One last thing; I want to assure listeners that, as in all my posts, I read all the papers that we talk about in detail in the following podcast. There is no division of labor between coauthors on that topic, because I view part of my job as making connections between papers, and I think that works better if all the papers covered on this site are bouncing around in my brain, rather than split across different heads. So what you are about to hear is not half Arnaud and half me, it’s all him and all me, all the time.Articles mentioned

Innovation has, historically, been pretty good for humanity. But technology is just a tool, and tools can be used for good or evil purposes. So far, technology has skewed towards “good” rather than evil but there are some reasons to worry things may differ in the future. What does science and technology policy look like in a world where we can no longer assume that more innovation generally leads to more human flourishing? It’s hard to say too much about such an abstract question, but a number of economic growth models have grappled with this idea.This podcast is an audio read through of the (initial version of the) article "When technology goes bad", originally published on New Things Under the Sun.Articles Mentioned:Jones, Charles. 2016. Life and Growth. Journal of Political Economy, 124 (2): 539 - 578. http://dx.doi.org/10.1086/684750Jones, Charles. 2023. The A.I. Dilemma: Growth versus Existential Risk. Working paper.Singla, Shikhar. 2023. Regulatory Costs and Market Power. LawFin WP 47. http://dx.doi.org/10.2139/ssrn.4368609Aschenbrenner, Leopold. 2020. Existential risk and growth. Global Priorities Institute Working Paper 6-2020. Link.Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous. 2012. The Environment and Directed Technical Change. American Economic Review 102 (1): 131-66. http://dx.doi.org/10.1257/aer.102.1.131

Scientific peer review is widely used as a way to distribute scarce resources in academic science, whether those are scarce research dollars or scarce journal pages. At the same time, peer review has several potential short-comings. One alternative is to empower individuals to make decisions about how to allocate scientific resources. Indeed, we do this with journal editors and grant makers, though generally in consultation with peer review. Under what conditions might we expect individuals empowered to exercise independent judgement to outperform peer review?This podcast is an audio read through of the (initial version of the) article "Can taste beat peer review?", originally published on New Things Under the Sun.Articles mentionedWagner, Caroline S., and Jeffrey Alexander. 2013. Evaluating transformative research programmes: A case study of the NSF Small Grants for Exploratory Research programme. Research Evaluation 22 (3): 187–197. https://doi.org/10.1093/reseval/rvt006Goldstein, Anna, and Michael Kearney. 2017. Uncertainty and Individual Discretion in Allocating Research Funds. Available at SSRN. https://ssrn.com/abstract=3012169 or http://dx.doi.org/10.2139/ssrn.3012169Card, David, and Stefano DellaVigna. 2020. What Do Editors Maximize? Evidence from Four Economics Journals. The Review of Economics and Statistics 102 (1): 195–217. https://doi.org/10.1162/rest_a_00839Teplitskiy, Misha, Hao Peng, Andrea Blasco, and Karim R. Lakhani. 2022. Is novel research worth doing? Evidence from peer review at 49 journals. Proceedings of the National Academy of Sciences 119 (47): e2118046119. https://doi.org/10.1073/pnas.2118046119

People rag on peer review a lot (including, occasionally, New Things Under the Sun). Yet it remains one of the most common ways to allocate scientific resources, whether those be R&D dollars or slots in journals. Is this all a mistake? Or does peer review help in its purported goal to identify the science most likely to have an impact and hence, perhaps most deserving of some of those limited scientific resources?A simple way to check is to compare peer review scores to other metrics of subsequent scientific impact; does peer review predict eventual impact?A number of studies find it does. This podcast is an audio read through of the (initial version of the) article What does peer review know?, originally published on New Things Under the Sun.Articles mentionedLi, Danielle, and Leila Agha. 2015. Big names or big ideas: Do peer-review panels select the best science proposals? Science 348(6233): 434-438. https://doi.org/10.1126/science.aaa0185Park, Hyunwoo, Jeongsik (Jay) Lee, and Byung-Cheol Kim. 2015. Project selection in NIH: A natural experiment from ARRA. Research Policy 44(6): 1145-1159. https://doi.org/10.1016/j.respol.2015.03.004.Card, David, and Stefano DellaVigna. 2020. What do Editors Maximize? Evidence from Four Economics Journals. The Review of Economics and Statistics 102(1): 195-217. https://doi.org/10.1162/rest_a_00839Siler, Kyle, Kirby Lee, and Lisa Bero. 2014. Measuring the effectiveness of scientific gatekeeping. PNAS 112(2): 360-365. https://doi.org/10.1073/pnas.1418218112Teplitskiy, Misha, and Von Bakanic. 2016. Do Peer Reviews Predict Impact? Evidence from the American Sociological Review, 1978 to 1982. Socius, 2. https://doi.org/10.1177/2378023116640278

A frequent worry is that our scientific institutions are risk-averse and shy away from funding transformative research projects that are high risk, in favor of relatively safe and incremental science. Why might that be?Let’s start with the assumption that high-risk, high-reward research proposals are polarizing: some people love them, some hate them. If this is true, and if our scientific institutions pay closer attention to bad reviews than good reviews, then that could be a driver of risk aversion. In this podcast, I look at three channels through which negative assessments may have outsized weight in decision-making, and how this might bias science away from transformative research.This podcast is an audio read through of the (initial version of the) article Biases Against Risky Research, originally published on New Things Under the Sun.Articles mentionedGross, Kevin, and Carl T. Bergstrom. 2021. Why ex post peer review encourages high-risk research while ex ante review discourages it. PNAS 118(51) e2111615118. https://doi.org/10.1073/pnas.2111615118Krieger, Joshua, and Ramana Nanda. 2022. Are Transformational Ideas Harder to Fund? Resource Allocation to R&D Projects at a Global Pharmaceutical Firm. Harvard Business School Working Paper 21-014. Jerrim, John, and Robert Vries. 2020. Are peer reviews of grant proposals reliable? An analysis of Economic and Social Research Council (ESRC) funding applications. The Social Science Journal 60(1): 91-109. https://doi.org/10.1080/03623319.2020.1728506Lane, Jacqueline N., Misha Teplitskiy, Gary Gray, Harder Ranu, Michael Menietti, Eva C. Guinan, and Karim R. Lakhani. 2022. Conservatism Gets Funded? A Field Experiment on the Role of Negative Information in Novel Project Evaluation. Management Science 68(6): 3975-4753. https://doi.org/10.1287/mnsc.2021.4107

Talent is spread equally over the planet, but opportunity is not. Today I want to look at some papers that try to quantify the costs to science and innovation from barriers to immigration. Specifically, let’s look at a set of papers on what happens to individuals with the potential to innovate when they immigrate versus when they do not. (See my post Importing Knowledge for some discussion on the impact of immigration on native scientists and inventors)This podcast is an audio read through of the (initial version of the) article Innovators Who Immigrate, originally published on New Things Under the Sun.Articles Mentioned:Agarwal, Ruchir and Patrick Gaule. 2020. Invisible Geniuses: Could the Knowledge Frontier Advance Faster? American Economic Review: Insights 2(4): 409-24. https://doi.org/10.1257/aeri.20190457Agarwal, Ruchir, Ina Ganguli, Patrick Gaule, and Geoff Smith. 2023. Why U.S. immigration matters for the global advancement of science. Research Policy 52(1): 104659. https://doi.org/10.1016/j.respol.2022.104659Gibson, John and David McKenzie. 2014. Scientific mobility and knowledge networks in high emigration countries: Evidence from the Pacific. Research Policy 43(9): 1486-1495. https://doi.org/10.1016/j.respol.2014.04.005Kahn, Shulamit, and Megan J. MacGarvie. 2016. How Important is U.S. Location for Research in Science? The Review of Economics and Statistics 98(2): 397-414. https://doi.org/10.1162/REST_a_00490Shi, Dongbo, Weichen Liu, and Yanbo Wang. 2023. Has China’s Young Thousand Talents Program been successful in recruiting and nurturing top-caliber scientists? Science 379(6627): 62-65. https://doi.org/10.1126/science.abq1218Prato, Marta. 2022. The Global Race for Talent: Brain Drain, Knowledge Transfer, and Growth. Job market paper. https://dx.doi.org/10.2139/ssrn.4287268

Are there some kinds of discoveries that are easier to make when young, and some that are easier to make when older?This podcast is an audio read through of the (initial version of the) article Age and the Nature of Innovation, originally published on New Things Under the Sun.Articles Mentioned:Yu, Huifeng, Gerald Marschke, Matthew B. Ross, Joseph Staudt and Bruce Weinberg. 2022. Publish or Perish: Selective Attrition as a Unifying Explanation for Patterns in Innovation over the Career. Journal of Human Resources 1219-10630R1. https://doi.org/10.3368/jhr.59.2.1219-10630R1Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044Kalyani, Aakash. 2022. The Creativity Decline: Evidence from US Patents. Dissertation paper. https://www.aakashkalyani.comGalenson, David W. 2007. Old Masters and Young Geniuses: The Two Life Cycles of Artistic Creativity. Princeton University Press.Weinberg, Bruce A. and David W. Galenson. 2019. Creative Careers: The Life Cycles of Nobel laureates in Economics. De Economist 167: 221-239. https://doi.org/10.1007/s10645-019-09339-9Jones, Benjamin F., and Bruce A. Weinberg. 2011. Age Dynamics in Scientific Creativity. PNAS 108(47): 18910-18914. https://doi.org/10.1073/pnas.1102895108Jones, Benjamin F., E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866Kaltenberg, Mary, Adam B. Jaffe, and Margie E. Lachman. 2021. Invention and the Life Course: Age Differences in Patenting. NBER Working Paper 28769. https://doi.org/10.3386/w28769

Scientists are getting older. Is this a problem? What’s the relationship between age and innovation?This podcast is an audio read through of the (initial version of the) article Age and the Impact of Innovations, originally published on New Things Under the Sun.Articles MentionedCui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044Jones, Benjamin, E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866Yu, Huifeng, Gerald Marschke, Matthew B. Ross, Joseph Staudt and Bruce Weinberg. 2022. Publish or Perish: Selective Attrition as a Unifying Explanation for Patterns in Innovation over the Career. Journal of Human Resources 1219-10630R1. https://doi.org/10.3368/jhr.59.2.1219-10630R1Wu, L., Wang, D. & Evans, J.A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019). https://doi.org/10.1038/s41586-019-0941-9Kaltenberg, Mary, Adam B. Jaffe, and Margie E. Lachman. 2021. Invention and the Life Course: Age Differences in Patenting. NBER Working Paper 28769. https://doi.org/10.3386/w28769Liu, Lu, Yang Wang, Roberta Sinatra, C. Lee Giles, Chaoming Song, and Dan Wang. 2018. Hot streaks in artistic, cultural, and scientific careers. Nature 559: 396-399. https://doi.org/10.1038/s41586-018-0315-8