What’s the return on government funding for research?

There are a few places in the academic literature you can look to for insight. Jones and Summers (2021) uses a hypothetical thought experiment to make the case that, on average, every dollar of R&D spent probably generates several dollars in benefits via its long-run impact on economic growth (see What are the returns to R&D? for more discussion). But that result applies only to R&D in general, government and non-government, bundled together. Is government funding above or below this average? This approach can’t say. Moreover, while I find it a compelling thought experiment, at some point we probably want to check the results against data. Fortunately, a set of recent papers help us do that.

This podcast is an audio read through of the (initial version of the) article Government funding for R&D and productivity growth, originally published on New Things Under the Sun.

Articles mentioned

Jones, Benjamin F., and Lawrence H. Summers. 2020. A calculation of the social returns to innovation. NBER Working Paper 27863. https://doi.org/10.3386/w27863

Fieldhouse, Andrew, and Karel Mertens. 2023. The Returns to Government R&D: Evidence from U.S. Appropriations Shocks. Federal Reserve Bank of Dallas Working Paper 2305. https://doi.org/10.24149/wp2305r2

Dyevre, Arnaud. 2024. Public R&D Spillovers and Productivity Growth. Working paper.

Moretti, Enrico, Claudia Steinwender, and John Van Reenen. 2025. The Intellectual Spoils of War? Defense R&D, Productivity, and International Spillovers. The Review of Economics and Statistics 107(1): 14-27. https://doi.org/10.1162/rest_a_01293

Which kind of inventor (or scientist) is going to benefit more from artificial intelligence: novices or experts? In theory, it can go either way.

This podcast is an audio read through of the (initial version of the) article Do prediction technologies help novices or experts more?, originally published on New Things Under the Sun.

Articles Cited
Nagaraj, Abhishek. 2021. The private impact of public data: Landsat satellite maps increased gold discoveries and encouraged entry. Management Science 68(1): 1-808. https://doi.org/10.1287/mnsc.2020.3878

Kao, Jennifer L. 2023. Mapping the cancer genome and R&D decisions in the pharmaceutical industry. SSRN Working Paper 3883041. https://doi.org/10.2139/ssrn.3883041

Tranchero, Matteo. 2024. Finding diamonds in the rough: data-driven opportunities and pharmaceutical innovation. Working paper.

Toner-Rodgers, Aidan. 2024. Artificial intelligence, scientific discovery, and product innovation. Working paper.

Some inventions and discoveries make the inventive process itself more efficient. One such class of invention is the prediction technology. These can take a lot of forms. AI is one example of a technology that can help scientists and inventors make better predictions about what is worth trying as a candidate solution to a problem, but as we’ll see, there are many other kinds of prediction technology as well.

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

Articles mentioned:
Hoelzemann, Johannes, Gustavo Manso, Abhishek Nagaraj, and Matteo Tranchero. 2024. The streetlight effect in data-driven exploration. NBER Working Paper 32401. https://doi.org/10.3386/w32401

Kim, Soomi. 2023. Shortcuts to innovation: the use of analogies in knowledge production. Working paper.

Tranchero, Matteo. 2024. Finding diamonds in the rough: data-driven opportunities and pharmaceutical innovation. Working paper.

Kao, Jennifer L. 2023. Mapping the cancer genome and R&D decisions in the pharmaceutical industry. SSRN Working Paper 3883041. https://doi.org/10.2139/ssrn.3883041

Toner-Rodgers, Aidan. 2024. Artificial intelligence, scientific discovery, and product innovation. Working paper.

New Things Under the Sun is once again putting together a list of dissertation papers related to innovation. If you want your paper to be included, email the title, an abstract, and a link to the paper, to [email protected] by the end of November.

In this post, coauthored with Caroline Fry, we look at the evidence on the effects of training programs for scientists in lower and middle income countries (LMICs).

This podcast is an audio read through of the (initial version of the) article Training scientists in low and middle income countries, originally published on New Things Under the Sun.

Articles mentioned:
Schreiber, Kelsey L., Christopher B. Barrett, Elizabeth R. Bageant, Abebe Shimeles, Joanna B. Upton, and Maria DiGiovanni. 2022. Building research capacity in an under-represented group: The STAARS program experience. Applied Economic Perspectives and Policy 44(4):1925-1941. https://doi.org/10.1002/aepp.13310

Fry, Caroline V., and Michael Blomfield. 2023. If you build it, they will come: The impact of clinical trial experience on African science. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.4629654

Fry, Caroline, and Ina Ganguli. 2023. Return on returns: Building scientific capacity in AIDS endemic countries. NBER Working Paper 31374. https://doi.org/10.3386/w31374

Fry, Caroline Viola. 2023. Bridging the gap: Evidence from the return migration of African scientists. Organization Science 34(1). https://doi.org/10.1287/orsc.2022.1580

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

Kahn, Shulamit, and Megan MacGarvie. 2016. Do return requirements increase international knowledge diffusion? Evidence from the Fulbright program. Research Policy 45(6):1304-1322. https://doi.org/10.1016/j.respol.2016.02.002

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.

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

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

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?