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.20160284

Ejermo, 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.

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.

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/10.1086/718370

Koschnick, 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

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/B978-0-444-53540-5.00002-1

Verhoogen, 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.

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/10.1086/684750

Jones, 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

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/rvt006

Goldstein, 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

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.aaa0185

Park, 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

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-x

Jeong, 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.2023.111270

Finch, 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

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 mentioned
Gross, 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.2111615118

Krieger, 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/10.1080/03623319.2020.1728506

Lane, 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.20190457

Agarwal, 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.2022.104659

Gibson, 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.2014.04.005

Kahn, 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_00490

Shi, 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.abq1218

Prato, 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.59.2.1219-10630R1

Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044

Kalyani, Aakash. 2022. The Creativity Decline: Evidence from US Patents. Dissertation paper. https://www.aakashkalyani.com

Galenson, 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/s10645-019-09339-9

Jones, Benjamin F., and Bruce A. Weinberg. 2011. Age Dynamics in Scientific Creativity. PNAS 108(47): 18910-18914. https://doi.org/10.1073/pnas.1102895108

Jones, Benjamin F., E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866

Kaltenberg, 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 Mentioned
Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging Scientists and Slowed Advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044

Jones, Benjamin, E.J. Reedy, and Bruce A. Weinberg. 2014. Age and Scientific Genius. NBER Working Paper 19866. https://doi.org/10.3386/w19866

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.59.2.1219-10630R1

Wu, 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/s41586-019-0941-9

Kaltenberg, 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

Liu, 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

Suppose in some parallel universe history proceeded down a quite different path from our own, shortly after Homo sapiens evolved. If we fast forward to 2022 of that universe, how different would the technological stratum of that parallel universe be from our own? Would they have invented the wheel? Steam engines? Railroads? Cars? Computers? Internet? Social media? Or would their technologies rely on principles entirely alien to us? In other words, once humans find themselves in a place where technological improvement is the rule (hardly a given!), is the form of the technology they create inevitable? Or is it the stuff of contingency and accident?

In academic lingo, this is a question about path dependency. How much path dependency is there in technology?

This week's podcast is a bit unusual. I designed New Things Under the Sun to feature two kinds of articles: claims and arguments. Almost everything I write (and podcast) is a claim article. Today’s podcast is the other kind of thing, an argument. 

The usual goal of a claim article is to synthesize several academic papers in service of assessing a specific narrow claim about innovation. Argument articles live one level up the chain of abstraction: the goal is to synthesize many claim articles (referenced mostly in footnotes) in service of presenting a bigger picture argument. That means in this podcast you won’t hear me talk much about specific papers; instead, I’ll talk about various literatures and how I think they interact with each other.

This podcast is an audio read through of the (initial version of the) article Are technologies inevitable?, originally published on New Things Under the Sun.

Remote work seems to be well suited for some kinds of knowledge work, but it’s less clear that it’s well suited for the kind of collaborative creativity that results in breakthrough innovations. A series of new papers suggests breakthrough innovation by distributed teams has traditionally been quite difficult, but also that things have changed, possibly dramatically, as remote collaboration technology has improved.

This podcast is an audio read through of the (initial draft of the) post Remote Breakthroughs, originally published on New Things Under the Sun.

Articles Mentioned
Van der Wouden, Frank. 2020. A history of collaboration in US invention: changing patters of co-invention, complexity and geography. Industrial and Corporate Change 29(3): 599-619. https://doi.org/10.1093/icc/dtz058

Lin, Yiling, Carl Benedikt Frey, and Lingfei Wu. 2022. Remote collaboration fuses fewer breakthrough ideas. arXiv:2206.01878. https://doi.org/10.48550/arXiv.2206.01878

Lin, Yiling, James A. Evans, and Lingfei Wu. 2022. New directions in science emerge from disconnection and discord. Journal of Informetrics 16(1): 101234. https://doi.org/10.1016/j.joi.2021.101234

Berkes, Enrico, and Ruben Gaetani. 2021. The Geography of Unconventional Innovation. The Economic Journal131(636): 1466-1514. https://doi.org/10.1093/ej/ueaa111

Duede, Eamon, Misha Teplitskiy, Karim Lakhani, and James Evans. 2021. Being Together in Place as a Catalyst for Scientific Advance. arXiv:2107.04165. https://doi.org/10.48550/arXiv.2107.04165

Frey, Carl Benedikt, and Giorgio Presidente. 2022. Disrupting Science. Working Paper.

Esposito, Christopher. 2021. The Geography of Breakthrough Innovation in the United States over the 20th Century. Papers in Evolutionary Economic Geography 2126. Working paper.

These are weird times. On the one hand, scientific and technological progress seem to be getting harder. Add to that slowing population growth, and it’s possible economic growth over the next century or two might slow to a halt. On the other hand, one area where we seem to be observing rapid technological progress is in artificial intelligence. If that goes far enough, it’s easy to imagine machines being able to do all the things human inventors and scientists do, possibly better than us. That would seem to pull in the opposite direction, leading to accelerating and possibly unbounded growth; a singularity.

Are those the only options? Is there a middle way? Under what conditions? This is an area where some economic theory can be illuminating. This article is bit unusual for New Things Under the Sun in that I am going to focus on a small but I think important part of a single 2019 article: “Artificial Intelligence and Economic Growth” by Aghion, Jones, and Jones. There are other papers on what happens to growth if we can automate parts of economic activity,undefined but Aghion, Jones, and Jones (2019) is useful because (among other things) it focuses on what happens in economic growth models if we automate the process of invention itself.

This podcast is an audio read through of the (initial draft of the) post What if we could automate invention?, originally published on New Things Under the Sun.

Articles Mentioned
Aghion, Philippe, Benjamin F. Jones, and Charles I. Jones. 2019. Artificial Intelligence and Economic Growth. In The Economics of Artificial Intelligence: An Agenda, ed. Ajay Agrawal, Joshua Gans, and Avi Goldfarb. National Bureau of Economic Research. ISBN 978-0-226-61333-8

For decades, the office was the default way to organize workers, but that default is being re-examined. Many workers (including me) prefer working remotely, and seem to be at least as productive working remotely as they are in the office. Remote capable organizations can hire from a bigger pool of workers than is available locally. All in all, remote work seems to have been underrated, relative to just a few years ago.

But there are tradeoffs. I’ve written before that physical proximity seems to be important for building new relationships, even though those relationships seem to remain productive as people move away from each other. This podcast narrows the focus down to the office. Does bringing people together in the office actually facilitate meeting new people? (spoiler: yes) But I’ll try and get more specific about how, when, and why this happens too.

This podcast is an audio read through of the (initial draft of the) post Innovation at the Office, originally published on New Things Under the Sun.

Articles Mentioned:
Allen, Thomas and Gunter Henn. 2007. The Organization and Architecture of Innovation. Routledge Publishing. Link.

Miranda, Arianna Salazar and Matthew Claudel. 2021. Spatial proximity matters: A study on collaboration. PLoS ONE 16(12): e0259965. https://doi.org/10.1371/journal.pone.0259965

Catalini, Christian. 2017. Microgeography and the Direction of Inventive Activity. Management Science 64(9) https://doi.org/10.1287/mnsc.2017.2798

Roche, Maria P., Alexander Oettl, and Christian Catalini. 2022. (Co-)Working in Close Proximity: Knowledge Spillovers and Social Interactions. NBER Working Paper 30120. https://doi.org/10.3386/w30120

Hasan, Sharique, and Rembrand Koning. 2019. Prior ties and the limits of peer effects on startup team performance. Strategic Management Journal 40(9): 1394-1416. https://doi.org/10.1002/smj.3032

Appel-Meulenbroek, Rianne, Bauke de Vries, and Mathieu Weggeman. 2017. Knowledge Sharing Behavior: The Role of Spatial Design in Buildings. Environment and Behavior 49(8): 874-903. https://doi.org/10.1177/0013916516673405

Kabo, Felichism W., Natalie Cotton-Nessler, Yongha Hwang, Margaret C. Levenstein, and Jason Owen-Smith. 2014. Proximity effects on the dynamics and outcomes of scientific collaborations. Research Policy 43(9): 1469-1485. https://doi.org/10.1016/j.respol.2014.04.007

A huge quantity of academic research that seeks to understand how science works relies on citation counts to measure the value of knowledge created by scientists. This measure of scientific impact is so deeply embedded in the literature that it's absolutely crucial to know if it’s reliable. So today I want to look at a few recent articles that look into this foundational question: are citation counts a good measure of the value of scientific contributions?

This podcast is an audio read through of the (initial draft of the) post Do Academic Citations Measure the Impact of New Ideas?, originally published on New Things Under the Sun.

Articles Mentioned:
Teplitsky, Misha, Eamon Duede, Michael Menietti, and Karim R. Lakhani. 2022. How Status of Research Papers Affects the Way They are Read and Cited. Research Policy 51(4): 104484. https://doi.org/10.1016/j.respol.2022.104484

Gerrish, Sean M., and David M. Blei. 2010. A Language-based Approach to Measuring Scholarly Impact. Proceedings of the 26th International Conference on Machine Learning: 375-382. http://www.cs.columbia.edu/~blei/papers/GerrishBlei2010.pdf

Gerow, Aaron, Yuenig Hu, Jordan Boyd-Graber, and James Evans. 2018. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academy of Science 115(13): 3308-3313. https://doi.org/10.1073/pnas.1719792115

Poege, Felix, Dietmar Harhoff, Fabian Guesser, and Stefano Baruffaldi. 2019. Science Quality and the Value of Inventions. Science Advances 5(12). https://doi.org/10.1126/sciadv.aay7323

Yin, Yian, Yuxiao Dong, Kuansan Wang, Dashun Wang, and Benjamin Jones. 2021. Science as a Public Good: Public Use and Funding of Science. NBER Working Paper 28748. https://doi.org/10.3386/w28748

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

Tahamtan, Iman, and Lutz Bornmann. 2019. What do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published Between 2006 and 2018. Scientometrics 121: 1635-1684. https://doi.org/10.1007/s11192-019-03243-4

Kousha, Kayvan, and Mike Thelwell. 2016. Are Wikipedia Citations Important Evidence of the Impact of Scholarly Articles and Books? Journal of the Association for Information Science and Technology 68(3): 762-779. https://doi.org/10.1002/asi.23694

An old divide in the study of innovation is whether ideas come primarily from individual/group creativity, or whether they are “in the air”, so that anyone with the right set of background knowledge will be able to see them. In this episode, I look at how much redundancy there is in innovation: if the discoverer of some idea had failed to find it, would someone else have figured it out later?

This podcast is an audio read through of the (initial draft of the) post How common is independent discovery?, originally published on New Things Under the Sun.

Articles Mentioned:
Ogburn, William F., and Dorothy Thomas. 1922. Are Inventions Inevitable? A Note on Social Evolution. Political Science Quarterly 37(1): 83-98. https://www.jstor.org/stable/2142320

Haagstrom, Warren O. 1974. Competition in Science. American Sociological Review 39(1): 1-18. https://doi.org/10.2307/2094272

Hill, Ryan, and Carolyn Stein. 2020. Scooped! Estimating Rewards for Priority in Science. Working Paper.

Painter, Deryc T., Frank van der Wouden, Manfred D. Laubichler, and Hyejin Youn. 2020. Quantifying simultaneous innovations in evolutionary medicine. Theory in Biosciences 139: 319-335. https://doi.org/10.1007/s12064-020-00333-3

Bikard, Michaël. 2020. Idea Twins: Simultaneous discoveries as a research tool. Strategic Management Journal 41(8): 1528-1543. https://doi.org/10.1002/smj.3162

Ganguli, Ina, Jeffrey Lin, and Nicholas Reynolds. 2020. The Paper Trail of Knowledge Spillovers: Evidence from Patent Interferences. American Economic Journal: Applied Economics 12(2): 278-302. https://doi.org/10.1257/app.20180017

Lück, Sonja, Benjamin Balmier, Florian Seliger, and Lee Fleming. 2020. Early Disclosure of Invention and Reduced Duplication: An Empirical Test. Management Science 66(6): 2677-2685.  https://doi.org/10.1287/mnsc.2019.3521

Iaria, Alessandro, Carlo Schwarz, and Fabian Waldinger. 2018. Frontier Knowledge and Scientific Production: Evidence from the Collapse of International Science. Quarterly Journal of Economics: 927-991. https://doi.org/10.1093/qje/qjx046

Borjas, George J., and Kirk B. Doran. 2012. The Collapse of the Soviet Union and the Productivity of American Mathematicians. The Quarterly Journal of Economics 127(3): 1143-1203. https://doi.org/10.1093/qje/qjs015

Hill, Ryan, and Carolyn Stein. 2021. Race to the bottom: competition and quality in science. Working paper.

Cotropia, Christopher Anthony, and David L. Schwartz. 2018. Patents Used in Patent Office Rejections as Indicators of Value. SSRN Working Paper https://dx.doi.org/10.2139/ssrn.3274995

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.20160696

Acemoglu, 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

A basket of indicators all seem to document a similar trend. Even as the number of scientists and publications rises substantially, we do not appear to be seeing a concomitant rise in new discoveries that supplant older ones. Science is getting harder.

This podcast is an audio read through of the (initial draft of the) post Science is getting harder, published on New Things Under the Sun.

Articles mentioned:
Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. Are Ideas Getting Harder to Find? American Economics Review 110(4): 1104-1144. https://doi.org/10.1257/aer.20180338

Wang, Dashun and Albert-László Barabási. 2021. The Science of Science. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108610834

Li, Jichao, Yian Yin, Santo Fortunato, and Dashun Wang. 2019. A dataset of publication records for Nobel Laureates. Scientific Data 6: 33. https://doi.org/10.1038/s41597-019-0033-6

Collison, Patrick and Michael Nielsen. 2018. Science is Getting Less Bang for Its Buck. The Atlantic. 

Chu, Johan S.G. and James A. Evans. 2021. Slowed canonical progress in large fields of science. PNAS 118(41): e2021636118. https://doi.org/10.1073/pnas.2021636118

Milojević, Staša. 2015. Quantifying the cognitive extent of science. Journal of Informetrics 9(4): 962-973. https://doi.org/10.1016/j.joi.2015.10.005

Carayol, Nicolas, Agenor Lahatte, and Oscar Llopis. 2019. The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science. SSRN working paper. http://dx.doi.org/10.2139/ssrn.3347326

Larivière, Vincent, Éric Archambault, & Yves Gingras. 2007. Long-term patterns in the aging of the scientific literature, 1900–2004. Proceedings of ISSI 2007, ed. Daniel Torres-Salinas and Henk F. Moed. https://www.issi-society.org/publications/issi-conference-proceedings/proceedings-of-issi-2007/

Cui, Haochuan, Lingfei Wu, and James A. Evans. 2022. Aging scientists and slowed advance. arXiv 2202.04044. https://doi.org/10.48550/arXiv.2202.04044

Marx, Matt, and Aaron Fuegi. Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686

We all know the proverb “Necessity is the mother of invention.” This proverb is overly simplistic, but it gets at something true. One place you can see this really clearly is in global crises, which vividly illustrate the linkage between need and innovation, without the need for any fancy statistical techniques.

Let’s look at three examples.

This is an audio read through of the (initial version of) When Extreme Necessity is the Mother of Invention, published on New Things Under the Sun.

Articles Mentioned:
Agarwal, Richer, and Patrick Gaule. 2022. What Drives Innovation? Lessons from COVID-19 R&D. Journal of Health Economics 82: 102591. https://doi.org/10.1016/j.jhealeco.2022.102591

Bloom, Nicholas, Steven J. Davis, and Yulia Zhestkova. 2021. COVID-19 Shifted Patent Applications towards Technologies That Support Working from Home. AEA Papers and Proceedings 111: 263-266. https://doi.org/10.1257/pandp.20211057

Hassler, John, Per Krusell, and Conny Olovsson. 2021. Directed Technical Change as a Response to Natural Resource Scarcity. Journal of Political Economy 129(11): 3039-3072. https://doi.org/10.1086/715849

Ilzetzki, Ethan. 2022. Learning by Necessity: Government Demand, Capacity Constraints, and Productivity Growth. Working paper.

Gross, Daniel P., and Bhaven N. Sampat. 2020. Organizing Crisis Innovation: Lessons from World War II. NBER Working Paper 27909. http://doi.org/10.3386/w27909

New scientific research topics can sometimes face a chicken-and-egg problem. Professional success requires a critical mass of scholars to be active in a field, so that they can serve as open-minded peer reviewers and can validate (or at least cite!) new discoveries. Without that critical mass,undefined working on a new topic topic might be professionally risky. But if everyone thinks this way, then how do new research topics emerge; how do groups of people pick which topic to focus on?

One way is via coordinating mechanisms; a small number of universally recognized markers of promising research topics. This podcast looks at some evidence about how well prizes and other honors work at helping steer researchers towards specific research topics.

This is an audio read through of the (initial version of) "Steering Science with Prizes", published on New Things Under the Sun.

Articles mentioned:
Azoulay, Pierre, Toby Stuart, and Yanbo Wang. 2014. Matthew: Effect or Fable? Management Science 60(1): 92-109. https://doi.org/10.1287/mnsc.2013.1755

Reschke, Brian P., Pierre Azoulay, and Toby E. Stuart. 2018. Status Spillovers: The Effect of Status-conferring Prizes on the Allocation of Attention. Administrative Science Quarterly 63(4): 819-847. https://doi.org/10.1177/0001839217731997

Jin, Ching, Yifang Ma and Brian Uzzi. 2021. Scientific prizes and the extraordinary growth of scientific topics. Nature Communications 12: 5619. https://doi.org/10.1038/s41467-021-25712-2

Azoulay, Pierre J., Michael Wahlen, and Ezra W. Zuckerman Sivan. 2019. Death of the Salesman but Not the Sales Force: How Interested Promotion Skews Scientific Valuation. American Journal of Sociology 125(3): 786-845. https://doi.org/10.1086/706800

Azoulay, Pierre, Christian Fons-Rosen, and Joshua S. Graff Zivin. 2019. Does Science Advance One Funeral at a Time? American Economic Review 109(8): 2889-2920. https://doi.org/10.1257/aer.20161574

Evolution via natural selection is a really good explanation for how we gradually got successively more complex biological organisms. Perhaps unsurprisingly, there have long been efforts to apply the same general mechanism to the development of ever more complex technologies. One domain where this has been studied a bit is in computer programming. Let’s take a look at that literature to see how well the framework of biological evolution maps to (one form of) technological progress.

This podcast is an audio read through of the (initial version of the) article "Progress in Programming as Evolution", published on New Things Under the Sun.

Articles mentioned:

Arthur, W. Brian, and Wolfgang Polak. 2006. The evolution of technology within a simple computer model. Complexity11(5): 23-31. https://doi.org/10.1002/cplx.20130

Miu, Elena, Ned Gulley, Kevin N. Laland, and Luke Rendell. 2018. Innovation and cumulative culture through tweaks and leaps in online programming contests. Nature Communications 9: 2321. https://doi.org/10.1038/s41467-018-04494-0

Miu, Elena, Ned Gulley, Kevin N. Laland, and Luke Rendell. 2020. Flexible learning, rather than inveterate innovation or copying, drives cumulative knowledge gain. Science Advances 6(23): eaaz0286. DOI: 10.1126/sciadv.aaz0286

Valverde, Sergi and Ricard V. Solé. 2015. Punctuated equilibrium in the large-scale evolution of programming languages. Journal of the Royal Society Interface 12: 20150249. http://doi.org/10.1098/rsif.2015.0249

If you want to shape the direction of technology, you can try to pull the kinds of technology you want into existence by shaping how markets will receive different kinds of technology.

One specific context where we have some really nice evidence about the efficacy of pull policies is the automobile market. Making fuel more expensive or just flat out mandating carmakers meet certain emissions standards seems to pretty reliably nudge automakers into developing cleaner and more fuel efficient vehicles. We’ve got two complementary lines of evidence here: patents and measures of progress in fuel economy.

This podcast is an audio read through of the (initial version of the) article "Pulling more fuel efficient cars into existence," published on New Things Under the Sun.

Articles mentioned:

Aghion, Philippe, Antoine Dechezleprêtre, David Hémous, Ralf Martin, and John Van Reenen. 2016. Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry. Journal of Political Economy 124(1): 1-51. https://doi.org/10.1086/684581

Rozendaal, Rik, and Herman R.J. Vollebergh. 2021. Policy-Induced Innovation in Clean Technologies: Evidence from the Car Market. CESifo working paper no. 9422. http://dx.doi.org/10.2139/ssrn.3969578

Knittel, Christopher R. 2012. Automobiles on Steroids: Product Attribute Trade-Offs and Technological Progress in the Automobile Sector. American Economic Review 101: 3368-3399. http://doi.org/10.1257/aer.101.7.3368

Klier, Thomas, and Joshua Linn. 2016. The effect of vehicle economy standards on technology adoption. Journal of Public Economics 133: 41-63. https://doi.org/10.1016/j.jpubeco.2015.11.002

Kiso, Takahiko. 2019. Environmental Policy and Induced Technological Change: Evidence from Automobile Fuel Economy Regulations. Environmental and Resource Economics 74: 785-810. https://doi.org/10.1007/s10640-019-00347-6

Reynaert, Mathias. 2021. Abatement Strategies and the Cost of Environmental Regulations: Emission Standards on the European Car Market. The Review of Economic Studies 88(1): 454-488. https://doi.org/10.1093/restud/rdaa058

Ahmadpoor, Mohammad, and Benjamin F. Jones. 2017. The Dual Frontier: Patented inventions and prior scientific advance. Science 357(6351): 583-587. https://doi.org/10.1126/science.aam9527

Roach, Michael, and Wesley M. Cohen. 2013. Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research. Management Science 59(2): 504-525. https://doi.org/10.1287/mnsc.1120.1644

There’s this idea that technology is characterized by path dependency: once you start going down one technology trajectory, you kind of get locked in and it’s hard to switch to another, possibly better trajectory. That can happen for lots of reasons, but one possibility is that it’s something about the nature of knowledge itself. The more you know, the more you can learn: knowledge begets more knowledge. So whichever technology trajectory we start on becomes the one we know the most about, and therefore the one it makes most sense to stick with. 

One line of evidence about this comes from dynamics of patenting.

This podcast is an audio read through of the (initial version of the) article "Patent Stocks" and Technological Inertia, published on New Things Under the Sun.

Articles Mentioned:
Aghion, Philippe, Antoine Dechezleprêtre, David Hemous, Ralf Martin, and John Van Reenen. 2016. Carbon taxes, path dependency, and directed technical change: Evidence from the auto industry. Journal of Political Economy 124(1): 1-51. https://doi.org/10.1086/684581

Rozendaal, Rik, and Herman R.J. Vollebergh. 2021. Policy-Induced Innovation in Clean Technologies: Evidence from the Car Market. CESifo working paper no. 9422. http://dx.doi.org/10.2139/ssrn.3969578

Noailly, Joëlle and Roger Smeets. 2015. Directing technical change from fossil-fuel to renewable energy innovation: An application using firm-level data. Journal of Environmental Economics and Management 72: 15-37. https://doi.org/10.1016/j.jeem.2015.03.004

Popp, David. 2002. Induced Innovation and Energy Prices. American Economic Review 92(1): 160-180. https://doi.org/10.1257/000282802760015658

Porter, Michael E., and Scott Stern. 2000. Measuring the “ideas” production function: evidence from international patent output. NBER Working Paper 7891. https://doi.org/10.3386/w7891

Lazkano, Itziar, Linda Nøstbakken, and Martino Pelli. 2017. From fossil fuels to renewables: the role of electricity storage. European Economic Review 99: 113-129. https://doi.org/10.1016/j.euroecorev.2017.03.013

Park, Gwangman, and Yongtae Park. 2006. On the measurement of patent stock as knowledge indicators. Technological Forecasting and Social Change 73(7): 793-812. https://doi.org/10.1016/j.techfore.2005.09.006

Clancy, Matthew S. 2017. Combinations of technology in US patents, 1926-2009: a weakening base for future innovation? Economics of Innovation and New Technology 27(8): 770-785. https://doi.org/10.1080/10438599.2017.1410007

Suppose we think there should be more research on some topic: asteroid deflection, the efficacy of social distancing, building safe artificial intelligence, etc. How do we get scientists to work more on the topic?

This podcast is an audio read through of the (initial version of the) article , published on New Things Under the Sun. 

Articles mentioned:
Myers, Kyle. 2020. The Elasticity of Science. American Economic Journal: Applied Economics 12(4): 103-34. https://doi.org/10.1257/app.20180518

Hill, Ryan, Yian Yin, Carolyn Stein, Dashun Wang, and Benjamin F. Jones. 2021. Adaptability and the Pivot Penalty in Science. SSRN Working Paper. https://dx.doi.org/10.2139/ssrn.3886142

Bhattacharya, Jay, and Mikko Packalen. 2011. Opportunities and benefits as determinants of the direction of scientific research. Journal of Health Economics 30(4): 603-615. https://doi.org/10.1016/j.jhealeco.2011.05.007

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.1816454115

Arts, Sam, and Lee Fleming. 2018. Paradise of Novelty - or Loss of Human Capital? Exploring New Fields and Inventive Output. Organization Science 29(6): 1074-1092. https://doi.org/10.1287/orsc.2018.1216

Azoulay, Pierre, Joshua S. Graff Zivin, and Gustavo Manso. 2011. Incentives and creativity: evidence from the academic life sciences. The RAND Journal of Economics 42(3): 527-554. https://doi.org/10.1111/j.1756-2171.2011.00140.x

Brogaard, Jonathan, Joseph Engelberg, and Edward Van Wesep. 2018. Do Economists Swing for the Fences after Tenure? Journal of Economic Perspectives 32(1): 179-94. https://doi.org/10.1257/jep.32.1.179

We can say very little about the long-run outlook of technological change, and even less about the exact form such change might take. But a certain class of models of innovation - models of combinatorial innovation - does provide some insight about how technological progress may look over very long time frames. Let’s have a look.

This podcast is an audio read through of the (initial version of the) article Combinatorial Innovation and Progress in the Very Long Run, published on New Things Under the Sun. 

Articles mentioned:
Weitzman, Martin L. 1998. Recombinant Growth. Quarterly Journal of Economics 113(2): 331-360. https://doi.org/10.1162/003355398555595

Koppl, Roger, Abigail Devereaux, James Herriot, and Stuart Kauffman. 2019. The Industrial Revolution as a Combinatorial Explosion. Working paper. (Earlier version - arXiv:1811.04502)

Jones, Charles. 2021. Recipes and Economic Growth: A Combinatorial March Down an Exponential Tail. NBER Working Paper 28340. https://doi.org/10.3386/w28340

Poincaré, Henri. 1910. Mathematical Creation. The Monist 321-335. https://doi.org/10.1093/monist/20.3.321

Agrawal, Ajay, John McHale, and Alex Oettl. 2019. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth. Chapter in The Economics of Artificial Intelligence, eds. Ajay Agrawal, Joshua Gans, and Avi Goldfarb. Chicago: University of Chicago Press, pgs. 149-174. https://doi.org/10.7208/9780226613475-007

It might seem obvious that we want bold new ideas in science. But in fact, really novel work poses a tradeoff. While novel ideas might sometimes be much better than the status quo, they might usually be much worse. Moreover, it is hard to assess the quality of novel ideas because they’re so, well, novel. Existing knowledge is not as applicable to sizing them up. For those reasons, it might be better to actually discourage novel ideas, and to instead encourage slow and incremental expansion of the knowledge frontier. Or maybe not.

For better or worse, the scientific community has settled on a set of norms that appear to encourage safe and creeping science, rather than risky and leaping science.

This podcast is an audio read through of the (initial version of the) article Conservatism in Science, published on New Things Under the Sun. 

Articles mentioned:
Azoulay, Pierre, Christian Fons-Rosen, and Joshua S. Graff Zivin. 2019. Does Science Advance One Funeral at a Time? American Economic Review 109(8): 2889-2920. https://doi.org/10.1257/aer.20161574

Wang, Jian, Reinhilde Veugelers, and Paula Stephan. 2017. Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy 46(8): 1416-1436. https://doi.org/10.1016/j.respol.2017.06.006

Li, Danielle. 2017. Expertise versus bias in evaluation: evidence from the NIH. American Economic Journal: Applied Economics 9(2): 60-92. https://doi.org/10.1257/app.20150421

Ayoubi, Charles, Michele Pezzoni, and Fabiana Visentin. 2021. Does i pay to do novel science? The selectivity patterns in science funding. Science and Public Policy 48(5): 635-648. https://doi.org/10.1093/scipol/scab031

Boudreau, Kevin J., Eva C. Guinan, Karim R. Lakhani, Christoph Riedl. 2016. Looking across and looking beyond the knowledge frontier: intellectual distance, novelty, and resource allocation in science. Management Science 62(10): 2765-2783. https://doi.org/10.1287/mnsc.2015.2285

Publication bias can distort our picture of scientific evidence. One plausible solution to publication bias is to create a home for work that for, whatever reason, struggles to find a home in a good journal. Would that work? One place to get some evidence on this is to look at our experience with preprint servers.

This podcast is a read through of the (initial version of the) article Publication bias without editors? The case of preprint servers, published on New Things Under the Sun.

Articles mentioned:
Frankel, Alexander, and Maximilian Kasy. Forthcoming. Which Findings Should be Published? American Economic Journal: Microeconomics

Baumann, Alexandra, and Klaus Wohlrabe. 2020. Where have all the working papers gone? Evidence from four major economics working paper series. Scientometrics 124: 2433-2441. https://doi.org/10.1007/s11192-020-03570-x

Larivière, Vincent, Cassidy R. Sugimoto, Benoit Macaluso, Staša Milojević, Blaise Cronin, and Mike Thelwall. 2014. arXiv E-prings and the journal of record: An analysis of roles and relationships. Journal of the Association for Information Science and Technology 65(6): 1157-1169. https://doi.org/10.1002/asi.23044

Tsunoda, Hiroyuki, Yuan Sun, Masaki Nishizawa, Xiaomin Liu, and Kou Amano. 2020. The influence of bioRxiv on PLOS ONE’s peer-review and acceptance time. Proceedings of the Association for Information Science and Technology 57(1) e398. https://doi.org/10.1002/pra2.398

Fanelli, Daniele, Rodrigo Costas, and John P. A. Ioannidis. 2017. Meta-assessment of bias in science. PNAS 114(14): 3714-3719. https://doi.org/10.1073/pnas.1618569114

Franco, Annie, Neil Malhotra, and Gabor Simonovits. 2014. Publication bias in the social sciences: Unlocking the file drawer. Science 345(6203): 1502-1505. https://doi.org/10.1126/science.1255484

Brodeur, Abel, Nikolai Cook, and Anthony Heyes. 2020. Methods Matter: p-hacking and publication bias in causal analysis in economics. American Economic Review 110(11): 3634-60. https://doi.org/10.1257/aer.20190687

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.

1. How many inventions are patented? Less than half, more than zero
2. Patents (weakly) predict innovation: Correlations between patents and other proxies for innovation
3. Do studies based on patents get different results? For the sample on New Things Under the Sun, not really
4. Can we learn about innovation from patent data? The definitive New Things Under the Sun Post

This podcast covers #4: Can We Learn About Innovation From Patent Data?

One of the most influential economics of innovation papers from the last decade is “Are Ideas Getting Harder to Find” by Bloom, Jones, Van Reenen, and Webb, ultimately published in 2020 but in earlier draft circulation for years. While the paper is ostensibly concerned with testing a prediction of some economic growth models, it’s broader fame is attributable to it’s documentation of a striking fact: across varied domains, the R&D efforts necessary to eke out technological improvement keep getting higher. Let’s take a look at their evidence, as well as some complementary evidence from other papers.

This podcast is a read through of the (initial version of the) article Innovation (mostly) gets harder, published on New Things Under the Sun.

Articles mentioned:
Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. Are Ideas Getting Harder to Find? American Economics Review 110(4): 1104-1144. https://doi.org/10.1257/aer.20180338

Besiroglu, Tamay. 2020. Are models getting harder to find? Masters Thesis, University of Cambridge. https://www.tamaybesiroglu.com/projects

Boeing, Philipp, and Paul Hünermund. 2020. A global decline in research productivity? Evidence from China and Germany. Economics Letters 197: 109646. https://doi.org/10.1016/j.econlet.2020.109646

Miyagawa, Tsutomu and Ishikawa Takayuki. 2019. On the Decline of R&D Efficiency. Research Institute of Economy, Trade and Industry discussion paper 19052. https://ideas.repec.org/p/eti/dpaper/19052.html

Two studies suggest the social sciences have bigger problems with publication bias than do the biological sciences, which tend to have more problems than the hard sciences. Why?

This podcast is a read through of the (initial version of the) article Why is publication bias worse in some fields than others?, published on New Things Under the Sun.

Articles mentioned:
Fanelli, Daniele, Rodrigo Costas, and John P. A. Ioannidis. 2017. Meta-assessment of bias in science. Proceedings of the National Academy of Sciences of the United States of America 114(14): 3714-3719. https://doi.org/10.1073/pnas.1618569114

Fanelli, Daniele. 2010. “Positive” Results Increase Down the Hierarchy of the Sciences. PLoS ONE 5(4): e100688. https://doi.org/10.1371/journal.pone.0010068

Doucouliagos, Chris, and T.D. Stanley. 2013. Are all economic facts greatly exaggerated? Theory competition and selectivity. Journal of Economic Surveys 27(2): 316-339. https://doi.org/10.1111/j.1467-6419.2011.00706.x

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=f

Breznau, 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.0003081

Franco, 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.1255484

Andrews, 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.20180310

Camerer, 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.aaf0918

Open Science Collaboration. 2015. Estimating the reproducibility of psychological science. Science 349(6251) aac4716. https://doi.org/10.1126/science.aac4716

Christensen, 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.20171350

Wolfson, 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.

Suppose you set loose a bunch of scientists on the same question, letting each use their best judgment on the method to answer a question. Would you expect them to come to the same conclusions?

Unfortunately, the truth is the state of our “methodological technology” just isn’t there yet. There remains a core of unresolvable uncertainty and randomness in the best of circumstances. Science isn’t certain.

This podcast is an audio read through of (initial version of the) article One question, many answers, published on New Things Under the Sun.

Articles mentioned
Huntington-Klein, Nick, Andreu Arenas, Emily Beam, Marco Bertoni, Jeffrey R. Bloem, Pralhad Burli, et al. 2021. The influence of hidden researcher decisions in applied microeconomics. Economic Inquiry, 59: 944– 960. https://doi.org/10.1111/ecin.12992

Silberzahn R, Uhlmann EL, Martin DP, et al. 2018. Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results. Advances in Methods and Practices in Psychological Science: 337-356. https://doi.org/10.1177/2515245917747646

Breznau, 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. https://doi.org/10.31222/osf.io/cd5j9

Jojanneke A. Bastiaansen, Yoram K. Kunkels, Frank J. Blaauw, Steven M. Boker, Eva Ceulemans, Meng Chen, Sy-Miin Chow, et al. 2020. Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology. Journal of Psychosomatic Research 137(110211). https://doi.org/10.1016/j.jpsychores.2020.110211

Swenson, Isaac, Jason M. Lindo, and Krishna Regmi. 2020. Stable Income, Stable Family. NBER Working Paper 27753. https://doi.org/10.3386/w27753

As a source of data for studying innovation, patents are really seductive. There’s nothing else quite like them. And at first glance, one of the most appealing things patents is that they cite each other. That means, patents might help us understand how knowledge spills over from one application to another, which is one of the most distinctive things about innovation, as compared to other economic activities.

But there are dangers. A citation might not mean quite what you think. This podcast looks at their shortcomings, while ultimately concluding they can still provide value, especially when they can be complemented with other sources of data.

This podcast is an audio read through of the (initial version of the) post Measuring Knowledge Spillovers: The Trouble with Patent Citations, published on New Things Under the Sun.

Articles mentioned:
Moser, Petra, Joerg Ohmstedt, and Paul W. Rhode. 2016. Patent Citations—An Analysis of Quality Differences and Citing Practices in Hybrid Corn. Management Science 64 (4) 1926-1940. https://doi.org/10.1287/mnsc.2016.2688

Jaffe, Adam B., Manuel Trajtenberg, and Michael S. Fogarty. 2000. The Meaning of Patent Citations: Report on the NBER/Case-Western Reserve Survey of Patentees. NBER Working Paper 7631. https://doi.org/10.3386/w7631

Kuhn, J., Younge, K. and Marco, A. 2020. Patent citations reexamined. The RAND Journal of Economics 51: 109-132. https://doi.org/10.1111/1756-2171.12307

Lampe, Ryan. 2012. Strategic Citation. The Review of Economics and Statistics, 94(1), 320-333. https://www.jstor.org/stable/41349178

Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson. 1993. Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations. The Quarterly Journal of Economics 108, no. 3: 577-98. https://doi:10.2307/2118401

Michael Roach, and Wesley M. Cohen. 2013. Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research. Management Science 59 (2) 504-525. https://doi.org/10.1287/mnsc.1120.1644

Younge, Kenneth A. and Jeffrey M. Kuhn. 2016. Patent-to-Patent Similarity: A Vector Space Model. SSRN Working paper. http://dx.doi.org/10.2139/ssrn.2709238

Feng Sijie. 2020. The proximity of ideas: An analysis of patent text using machine learning. PLoS ONE 15(7): e0234880. https://doi.org/10.1371/journal.pone.0234880

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/1803924

Hamermesh, 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.51.1.162

Backhouse, 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/10.1215/00182702-4166239

Angrist, 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.24.2.3

Angrist, 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.20181508

Bédécarrats, 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.1816454115

Kuhn, 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/cm5v3

Heckman, 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.20191574

Maher, 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.0230104

Panhas, 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/10.1215/00182702-4166299

de 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

The universe of knowledge is vast. Is there any rhyme or reason to searching through it? What kind of knowledge is most likely to be useful for an innovator?

This is a big literature, but today I want to look at three papers that use different metrics to suggest knowledge which is distinct but close to your existing knowledge tends to be most useful.

This is an audio read through of the (initial version of the) article Adjacent Knowledge is Useful, published on New Things Under the Sun.

Articles mentioned
Lane, Jacqueline N., Ina Ganguli, Patrick Gaule, Eva Guinan, and Karim R. Lakhani. 2020. Engineering Serendipity: When does knowledge sharing lead to knowledge production? Strategic Management Journal: https://doi.org/10.1002/smj.3256

Clancy, Matthew, Paul Heisey, Yongjie Ji, and GianCarlo Moschini. 2020. The Roots of Agricultural Innovation: Patent Evidence of Knowledge Spillovers. NBER Working Paper 27011. https://doi.org/10.3386/w27011

Cornelius, Philpp B., Bilal Gokpinar, and Fabian J. Sting. 2020. Sparking Manufacturing Innovation: How Temporary Interplant Assignments Increase Employee Idea Values. Management Science. https://doi.org/10.1287/mnsc.2020.3673

 It’s long been assumed that the best sorts of innovation happen when smart people work in an environment where spontaneous face-to-face interaction is the norm. Importantly, if that’s true, it implies the widespread transition to more remote work - where spontaneous face-to-face interaction is not possible - poses a threat to innovation. In this podcast, I want to look at a case study for a sector that:

• engages in frontier knowledge work
• has strong incentives to adopt practices that produce better outcomes
• has been well studied
• has increasingly moved to a model of remote collaboration

I am talking, of course, about academia.

This podcast is an audio read through of the (initial version of the) article An example of successful innovation by distributed teams: academia, published on New Things Under the Sun.

Articles Mentioned:
Agrawal, Ajay, John McHale, and Alexander Oettl. 2015. Collaboration, Stars, and the Changing Organization of Science: Evidence from Evolutionary Biology. In The Changing Frontier: Rethinking Science and Innovation Policy, eds. Adam B. Jaffe and Benjamin F. Jones, pgs. 75-102. http://www.nber.org/chapters/c13038

Freeman, Richard B., Ina Ganguli, Raviv Murciano-Goroff. 2015. Why and Wherefore of Increased Scientific Collaboration. In The Changing Frontier: Rethinking Science and Innovation Policy, eds. Adam B. Jaffe and Benjamin F. Jones, pgs. 17-48. http://www.nber.org/chapters/c13040

Clancy, Matthew. 2020. The Case for Remote Work. The Entrepreneurs Network Briefing Paper. 

Agrawal, Ajay, John McHale, and Alexander Oettl. 2017. How stars matter: Recruiting and peer effects in evolutionary biology. Research Policy 46(4): 853-867. https://doi.org/10.1016/j.respol.2017.02.007

Dubois, Pierre, Jean-Charles Rochet, and Jean-Marc Schlenker. 2014. Productivity and mobility in academic research: evidence from mathematicians. Scientometrics 98: 1669-1701. https://doi.org/10.1007/s11192-013-1112-7

Waldinger, Fabian. 2012. Peer Effects in Science: Evidence from the Dismissal of Scientists in Nazi Germany. The Review of Economic Studies 79(2): 838-861. https://doi.org/10.1093/restud/rdr029

Waldinger, Fabian. 2016. Bombs, Brains, and Science: The Role of Human and Physical Capital for the Creation of Scientific Knowledge. The Review of Economics and Statistics 98(5): 811-831. https://doi.org/10.1162/REST_a_00565

Azoulay, Pierre, Joshua S. Graff Zivin, and Jialan Wang. 2010. Superstar Extinction. The Quarterly Journal of Economics 125(2): 549-589. https://doi.org/10.1162/qjec.2010.125.2.549

Kim, E. Han, Adair Morse, and Luigi Zingales. 2009. Are elite universities losing their competitive edge? Journal of Financial Economics 93(3): 353-381. https://doi.org/10.1016/j.jfineco.2008.09.007

Head, Keith, Yao Amber Li, and Asier Minondo. 2019. Geography, Ties, and Knowledge Flows: Evidence from Citations in Mathematics. The Review of Economics and Statistics 101(4): 713-727. https://doi.org/10.1162/rest_a_00771

Hellmanzik, Christiane, and Lukas Kuld. 2021. No place like ho

How, exactly, should you increase your R&D spending? One kind of program seems to work and would be an excellent candidate for more funds: the US’ Small Business Innovation Research (SBIR) program and the European Union’s SME instrument (which was modeled on the SBIR).

This podcast is an audio read through of the (initial version of the) article An Example of High Returns to Publicly Funded R&D, published on New Things Under the Sun.

Articles mentioned:
Howell, Sabrina T. 2017. Financing Innovation: Evidence from R&D grants. American Economics Review 107(4): 1136-1164. DOI: 10.1257/aer.20150808

Santoleri, Pietro and Mina, Andrea and Di Minin, Alberto and Martelli, Irene. 2020. The Causal Effects of R&D Grants: Evidence from a Regression Discontinuity. SSRN working paper: http://dx.doi.org/10.2139/ssrn.3637867

Wang, Yanbo, Jizhen Li, and Jeffrey L. Furman. 2017. Firm performance and state innovation funding: Evidence from China’s Innofund program. Research Policy 46(6): 1142-1161. https://doi.org/10.1016/j.respol.2017.05.001

Myers, Kyle, and Lauren Lanahan. 2021. Estimating spillovers from publicly funded R&D: Evidence from the US Department of Energy. Working paper. 

Maybe one of the most important functions of cities is to introduce us to new people. Being close seems to be very important for initiating and consolidating new relationships, but once those relationships are formed it’s no longer so important that you stay physically close - at least from the perspective of facilitating innovation.

This podcast is an audio read through of the (initial version of the) article Why Proximity Matters: Who You Know, published on New Things Under the Sun.

Articles mentioned:
Catalini, Christian. 2018. Microgeography and the Direction of Inventive Activity. Management Science 64(9): 4348-4364. https://doi.org/10.1287/mnsc.2017.2798

Agrawal, Ajay, Iain Cockburn and John McHale. 2006. Gone but not forgotten: knowledge flows, labor mobility, and enduring social relationships. Journal of Economic Geography 6: 571-591. https://doi:10.1093/jeg/1b1016

Miguelez, Ernest, and Claudia Noumedem Temgoua. 2020. Inventor migration and knowledge flows: A two-way communication channel? Research Policy 49(9): 103914. https://doi.org/10.1016/j.respol.2019.103914

Head, Keith, Yao Amber Li, Asier Minondo. 2019. Geography, Ties, and Knowledge Flows: Evidence from Citations in Mathematics. Review of Economic Studies 104(4): 713-727. https://doi.org/10.1162/rest_a_00771

Freeman, Richard B., Ina Ganguli, and Raviv Murciano-Goroff. 2015. Why and Wherefore of Increased Scientific Collaboration. Chapter in The Changing Frontier: Rethinking Science and Innovation Policy, eds. Adam B. Jaffe and Benjamin F. Jones: 17-48. https://doi.org/10.7208/chicago/9780226286860.003.0002

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.

1. How many inventions are patented? Less than half, more than zero
2. Patents (weakly) predict innovation: Correlations between patents and other proxies for innovation
3. Do studies based on patents get different results? For the sample on New Things Under the Sun, not really
4. Can we learn about innovation from patent data? The definitive New Things Under the Sun Post

This podcast covers #3: Do studies based on patents get different results?

Innovation disproportionately happens in cities. What is it about packing people together that makes them so innovative?

This is an audio read through of the (initial version of the) article Urban Social Infrastructure and Innovation, published on New Things Under the Sun.

Articles mentioned:
Carlino, Gerald A., Satyajit Chatterjee, and Robert M. Hunt. 2007. Urban density and the rate of invention. Journal of Urban Economics 61(3): 389-419. https://doi.org/10.1016/j.jue.2006.08.003

Berkes, Enrico, and Ruben Gaetani. 2020. The Geography of Unconventional Innovation. The Economic Journalueaa111. https://doi.org/10.1093/ej/ueaa111

Roche, Maria P. 2020. Taking Innovation to the Streets: Microgeography, Physical Structure, and Innovation. The Review of Economics and Statistics 102(5): 912-928. https://doi.org/10.1162/rest_a_00866

Andrews, Michael. 2019. Bar Talk: Informal Social Interactions, Alcohol Prohibition, and Invention. Available at SSRN. http://dx.doi.org/10.2139/ssrn.3489466

When a scientist or inventor migrates, they take their knowledge with them. And in the right environment, that knowledge can act as the seed of something much larger than an individual can accomplish.

This is an audio read through of the (initial draft of the) article Importing Knowledge, published on New Things Under the Sun.

Articles Mentioned:
Moser, Petra, Alessandra Voena, and Fabian Waldinger. 2014. German Jewish Émigrés and US Invention. American Economic Review 104(10): 3222-55. https://doi.org/10.1257/aer.104.10.3222

Ferrucci, Edoardo. 2020. Migration, innovation and technological diversion: German patenting after the collapse of the Soviet Union. Research Policy 49(9): 104057. https://doi.org/10.1016/j.respol.2020.104057

Choudhury, Prithwiraj, and Do Yoon Kim. 2018. The ethnic migrant inventor effect: Codification and recombination of knowledge across borders. Strategic Management Journal 40(2): 203-229. https://doi.org/10.1002/smj.2977

Bahar, Dany, Prithwiraj Choudhury, and Hillel Rapoport. 2020. Migrant inventors and the technological advantage of nations. Research Policy 49(9): 103947. https://doi.org/10.1016/j.respol.2020.103947

Bernstein, Shai, Rebecca Diamond, Timothy McQuade and Beatriz Pousada. 2019. The contribution of high-skilled immigrants to innovation in the United States. Working Paper. 

Ganguli, Ina. 2015. Immigration and Ideas: What did Russian scientists “bring” to the United States? Journal of Labor Economics 33(S1P2). https://doi.org/10.1086/679741

Sometimes obvious ideas work. If you want to encourage more innovation, give people better access to knowledge: libraries.

This is an audio read through of the (initial version of the) article Free Knowledge and Innovation, published on New Things Under the Sun.

Articles mentioned:
Berkes, Enrico, and Peter Nencka. 2020. Knowledge Access: The Effects of Carnegie Libraries on Innovation. Working Paper.

Furman, Jeffrey L., Markus Nagler, and Martin Watzinger. 2018. Disclosure and Subsequent Innovation: Evidence from the Patent Depository Library Program. NBER Working Paper No 24660

Thompson, Neil C., and Douglas Hanley. 2020. Science is Shaped by Wikipedia: Evidence from a Randomized Control Trial. MIT Sloan Research Paper No. 5238-17

Two different lines of evidence suggest 20 years is a good rule of thumb for how long it takes to go from science to technology: statistical correlations between R&D and productivity, and citations between patents and scientific articles.

This podcast is an audio read through of the (initial version of the) article How long does it take to go from science to technology?, published in New Things Under the Sun.

Articles mentioned:
Adams, James D. 1990. Fundamental stocks of knowledge and productivity growth. Journal of Political Economy 98(4): 673-702. https://www.jstor.org/stable/2937764

Baldos, Uris Lantz, Frederi G. Viens, Thomas W. Hertel, and Keith O. Fuglie. 2018. R&D spending, knowledge capital, and agricultural productivity growth: a Bayesian approach. American Journal of Agricultural Economics 101(1): 291-310. https://doi.org/10.1093/ajae/aay039

Marx, Matt, and Aaron Fuegi. 2020. Reliance on science: Worldwide front-page patent citations to scientific articles. Strategic Management Journal 41(9): 1572-1594. https://doi.org/10.1002/smj.3145

Marx, Matt, and Aaron Fuegi. 2020. Reliance on science by inventors: hybrid extraction of in-text patent-to-article citations. NBER Working Paper 27987. https://ssrn.com/abstract=3718899

Arora, Ashish, Sharon Belenzon, and Lia Sheer. 2017. Back to basics: why do firms invest in research? NBER Working Paper 23187. https://ssrn.com/abstract=2920404

Watzinger, Martin, and Monika Schnitzer. 2019. Standing on the Shoulders of Science. CEPR Discussion Paper No. DP13766. https://ssrn.com/abstract=3401853

Ahmadpoor, Mohammad, and Benjamin F. Jones. 2017. The Dual Frontier: Patented inventions and prior scientific advance. Science357(6351): 583-587. https://doi.org/10.1126/science.aam9527