Episodit
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The frequency of words associated with "progress" in English, German, and French books rose during the era of industrialization, but is down since the 1950s, at least according to google. Is this a signal of declining cultural interest in progress, as a concept? Or just an artifact of how google constructed its text corpus?
This podcast is an audio read through of the (initial version of the) article The Decline in Writing About Progress, originally published on New Things Under the Sun. -
Prior to the 2000s, many European countries practiced something called “the professor’s privilege” wherein university professors retained patent rights to inventions they made while employed at the university. This was a “privilege” because the norm is for patent ownership to be assigned to the organization that employs an inventor; professors were an exception to this norm. American universities, in contrast, had long followed a different approach, where patent rights were typically assigned to the university, who managed commercialization efforts. Professors then split the proceeds of commercializing their inventions with the university.
There had long been a sense that commercialization of university research worked better in America, and in the 2000s a number of European countries reformed their laws to move them closer in spirit to the American system. Professors lost their privilege and universities got more into the commercialization game.
If the goal of this reform was to encourage more professors to invent things that could be commercialized, several papers indicate this policy was a mistake.
This podcast is an audio read through of the (initial version of the) article Incentives to Invent at Universities, originally published on New Things Under the Sun.
Articles mentioned
Hvide, Hans K., and Benjamin F. Jones. 2018. University innovation and the professor's privilege. American Economic Review, 108 (7): 1860–98. https://doi.org/10.1257/aer.20160284Ejermo, Olof, and Hannes Toivanen. 2018. University invention and the abolishment of the professor's privilege in Finland. Research Policy 47 (4): 814-825. https://doi.org/10.1016/j.respol.2018.03.001.
Czarnitzki, Dirk, Thorsten Doherr, Katrin Hussinger, Paula Schliessler, and Andrew A Toole. 2017. Individual versus institutional ownership of university-discovered inventions. USPTO Economic Working Paper No. 2017-07. http://dx.doi.org/10.2139/ssrn.2995672
Valentin, F., and R.L. Jensen. 2007. Effects on academia-industry collaboration of extending university property rights. J Technol Transfer 32: 251–276. https://doi.org/10.1007/s10961-006-9015-x
Ouellette, Lisa Larrimore, and Andrew Tutt. 2020. How do patent incentives affect university researchers? International Review of Law and Economics 61. https://doi.org/10.1016/j.irle.2019.105883. -
Puuttuva jakso?
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A classic topic in the study of innovation is the link between physical proximity and the exchange of ideas. But I’ve long been interested in a relatively new kind of serendipity engine, which isn’t constrained by physical proximity: Twitter. Lots of academics use twitter to talk about new discoveries and research. Today I want to look at whether twitter serves as a novel kind of knowledge diffusion platform.
This podcast is an audio read through of the (initial version of the) article Twitter and the Spread of Academic Knowledge, originally published on New Things Under the Sun.
Articles mentioned
de Winter, J.C.F. 2015. The relationship between tweets, citations, and article views for PLOS ONE articles. Scientometrics 102: 1773-1779. https://doi.org/10.1007/s11192-014-1445-xJeong, J.W., M.J. Kim, H.-K. Oh, S. Jeong, M.H. Kim, J.R. Cho, D.-W. Kim and S.-B Kang. 2019. The impact of social media on citation rates in coloproctology. Colorectal Disease (10):1175-1182. https://doi.org/10.1111/codi.14719
Peoples, Brandon K., Stephen R. Midway, Dana Sackett, Abigail Lynch, and Patrick B. Cooney. 2016. Twitter predicts citation rates of ecological research. PLoS ONE 11(11): e0166570. https://doi.org/10.1371/journal.pone.0166570
Lamb, Clayton T., Sophie L. Gilbert, and Adam T. Ford. 2018. Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation. PeerJ 6:e4564. https://doi.org/10.7717/peerj.4564
Chan, Ho Fai, Ali Sina Önder, Sascha Schweitzer, and Benno Torgler. 2023. Twitter and citations. Economics Letters 231: 111270. https://doi.org/10.1016/j.econlet.2023.111270Finch, Tom, Nina O’Hanlon, and Steve P. Dudley. 2017. Tweeting birds: online mentions predict future citations in ornithology. Royal Society Open Science 4171371. http://doi.org/10.1098/rsos.171371
Tonia, Thomy, Herman Van Oyen, Anke Berger, Christian Schindler, and Nino Künzli. 2020. If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations. International Journal of Public Health 65: 1797–1802. https://doi.org/10.1007/s00038-020-01519-8
Branch, Trevor A., Isabelle M. Cȏté, Solomon R. David, Joshua A. Drew, Michelle LaRue, Melissa C. Márquez, E. C. M. Parsons, D. Rabaiotti, David Shiffman, David A. Steen, Alexander L. Wild. 2024. Controlled experiment finds no detectable citation bump from Twitter promotion. PLoS ONE 19(3): e0292201. https://doi.org/10.1371/journal.pone.0292201
Qiu, Jingyi, Yan Chen, Alain Cohn, and Alvin E. Roth. 2024. Social Media and Job Market Success: A Field Experiment on Twitter. SSRN Working Paper. https://doi.org/10.2139/ssrn.4778120
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Note:
Economists typically think that labor and capital are complementary - more of the one makes the other more productive. But there’s a flourishing literature that looks at the consequences of capital that replaces, rather than augments, human workers. In this post, I want to talk about a very simple equation that is inspired by the ideas in these papers, and which I think is a useful thinking tool.
This podcast is an audio read through of the (initial version of the) article When the Robots Take Your Job, originally published on New Things Under the Sun.
Articles Mentioned:
Acemoglu, Daron, and Pascual Restrepo. 2018. The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review 108(6): 1488-1542. https://doi.org/10.1257/aer.20160696Acemoglu, Daron, and Pascual Restrepo. 2022. Tasks, Automation, and the Rise in U.S. Wage Inequality. Econometrica 90(5): 1973-2016. https://doi.org/10.3982/ECTA19815
Korinek, Anton, and Donghyun Suh. 2024. Scenarios for the Transition to AGI. NBER Working Paper 32255. https://doi.org/10.3386/w32255
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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?
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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?
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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
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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?
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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 mentioned
Comin, 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.20221633
Giorcelli, Michela. 2019. The Long-Term Effect of Management and Technology Transfers. American Economic Review109(1): 121-152. https://doi.org/10.1257/aer.20170619
Giorcelli, 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
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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 mentioned
Martin, 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.2021.100541
Souitaris, 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.2006.05.002
Eesley, 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.3246
Lyons, 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.2704
Oster, Emily. 2016. Unobservable selection and coefficient stability: Theory and evidence. Journal of Business & Economic Statistics 37(2): 187-204. https://doi.org/10.1080/07350015.2016.1227711
Wallskog, Melanie. 2022. Entrepreneurial Spillovers Across Coworkers. PhD job market paper.
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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.undefined
What 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.
Articles discussed
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.
Borowiecki, 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/10.1086/689890
Biasi, Barbara, and Song Ma. 2023. The Education-Innovation Gap. NBER Working Paper 29853. https://doi.org/10.3386/w29853
Waldinger, 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
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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 mentioned
Comin, 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.3886019
Wilson, Mary Elizabeth. 2017. The geography of infectious diseases. Infectious Diseases: 938–947.e1. https://doi.org/10.1016%2FB978-0-7020-6285-8.00106-4
Wang, 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/s41586-022-04601-8
Hotez, 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/NEJMra064142
Henrich, 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/S0140525X0999152X
Apicella, 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.2020.07.015
Klein 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/10.1177/2515245918810225
Schimmelpfennig, 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/jvaa019
Vivalt, Eva, Aidan Coville, and K. C. Sampada. 2023. Tacit versus Formal Knowledge in Policy Decisions.
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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
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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.4368609
Aschenbrenner, 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
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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 mentioned
Wagner, 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.3012169
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_00839
Teplitskiy, 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
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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 mentioned
Li, 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_00839
Siler, Kyle, Kirby Lee, and Lisa Bero. 2014. Measuring the effectiveness of scientific gatekeeping. PNAS 112(2): 360-365. https://doi.org/10.1073/pnas.1418218112
Teplitskiy, 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
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