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?

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?

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 #2: Patents (weakly) predict innovation

Welcome to patents week! I set out to write a post about using patents to measure innovation, but it turned into four. I'm releasing podcasts of each episode, one per day, but if you're too excited to wait, you can read all four here, on New Things Under the Sun.

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 #1: How many inventions are patented?

Technology has advanced by leaps and bounds in the past few centuries, but much of that progress is still limited to the richest countries. Why don't new technologies spread quickly throughout the world, benefiting billions of people? In this podcast, we’ll focus on one particular answer: new technologies improve productivity, but they improve productivity more when paired with knowledge on how to use them. If this is true, new technologies will be less beneficial to recipients who don’t have the knowledge to use them effectively - and thus, they may not spread as much as we expected.

This podcast is an audio read through of the (initial draft) of Training enhances the value of new technology, published on New Things Under the Sun. This is a collaboration with Karthik Tadepalli, an economics PhD student at the University of California, Berkeley. See here for more on New Things Under the Sun's collaboration policy.

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

Verhoogen, 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

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.2012.03.002

Carpenter, 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.

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.