New Things Under the Sun is a living literature review; as the state of the academic literature evolves, so do we. Here are a few recent updates.
Importing Knowledge in the Age of Mass Migration
The article Importing Knowledge looks at what happens when scientists and inventors immigrate. As might be expected, after receiving migrant inventors, a receiving country tends to do better in the technology fields where the migrant inventors have comparative strength. But what’s perhaps less expected is that the impact on the field exceeds the extra output brought by these talented inventors and seems to spill over to domestic inventors. This article has now been updated to include a discussion of a new paper by Diodato, Morrison, and Petralia, which investigates the same question using a new dataset for the period 1870-1950.
Diodato, Morrison, and Petralia (2021) looks across the United States over the period 1870-1950 to see what happens when different US cities receive more migrant scientists and inventors. In particular, they want to know what happens to the inventive activities of US-born inventors when foreign inventors move to town. With quite a lot of creative and tedious work, they are able to construct year-by-year, city-by-city, field-by-field, inventor-country-of-origin data for the USA over 1870-1950. They document several facts that are consistent with the above case studies.
First, when a city receives more migrant inventors with expertise in a given technology field (as indicated by how the patents of these migrant-inventors are classified), this is associated with increased patenting by US-born residents of the same city. For their second fact, they restrict their attention to cities and years where US-born inventors have no patents in a given technology field. They then show that when a migrant inventor working in that field shows up, the city is more likely to have patents, in that field, by US-born inventors, in subsequent years. Third, analogous to Bahar, Choudhury, and Rapoport (2020) [note: discussed previously in the article], they show all this holds even when you use some statistical techniques to try and tease out just the migrants who moved to various cities for reasons uncorrelated with that city’s technological opportunity. This should strip out cases, for example, where a town gets a new college or national lab that attracts a lot of inventors, from all backgrounds, who work in a particular technology field.
Why does this happen? “Importing Knowledge” argues that’s because the inventors bring more than just their own brainpower - they also bring new knowledge and ideas that spreads through local inventor networks. From the same piece, but later:
Diodato, Morrison, and Petralia’s study of migration to different US cities in the first half of the twentieth century provide three additional strands of suggestive evidence. First, they show that most of the “oomph” of having migrant inventors comes from having only a small number of them. Stated more precisely, the increase in patents from US-born inventors, in a given technology, that arises when migrant inventors skilled in that technology move to the city is only slightly larger when many migrant inventors move in, as compared to fewer. Second and closely related, they show the impact of migrant scientists moving to your city fades over time. Both are consistent with the notion that it only takes that first “seed” to get a “garden of knowledge” going - though more seeds can help it grow faster.
Finally, Diodata, Morrison, and Petralia provide some evidence that migrant inventors may help connect US born inventors with foreign knowledge, even if the migrant inventor doesn’t personally have that knowledge. To illustrate the idea, suppose Nikola is an inventor who emigrates from France to the USA and takes up residence in New York. Let’s suppose Nikola is an active inventor of technologies related to electricity. Meanwhile, suppose France is renowned for its food processing technologies, even though this is not an area in which Nikola is active. Diodato, Morrison, and Petralia show that having Nikola show up in New York increases the patenting of US-born New Yorkers both in the technologies in which he is directly involved (electricity in this example), as well as the technologies he is not directly involved in, but which are associated with his country of origin (food processing, in this example). The first effect is larger and more robust, but both are there.
Read the whole thing for a lot more evidence on these points.
The article Gender and What Gets Researched argues that one of the factors that affects researcher’s choice of research topic is what they find personally meaningful. This, in turn, can be affected by different people’s life experiences. One simplistic but well documented place you can see evidence of this is in the different research choices of men and women. “Gender and What Gets Researched” looked at some good evidence on differing research choices related to biomedical science, but a new 2022 paper by Risi and coauthors provides some evidence that this isn’t restricted to just that context.
Risi et al. (2022) look at the influence of gender on research topics in history by analyzing a sample of 10,000+ articles from major US history journals over 1951-2014. They use natural language processing algorithms to extract from this sample 90 different “topics”, where topics are defined as sets of words that are usually found together. Once topics are assigned to different papers, it becomes possible to tally up the genders of the authors of each paper to see if topics differ in how much they are studied by men and women. As indicated in the table below, there are some considerable differences across topics.
Over 1951-2014, women substantially outnumber men in the study of not just the “women and gender” topic, but also “family”, “body history”, and even “consumption and consumerism.”
As “Gender and What Gets Researched” points out, we have to be careful in how we analyze data like this; it could also be that differences in the topics favored by men and women does not stem from different preferences, but that various barriers prevent women from working on preferred topics. But “Gender and What Gets Researched” also argues that as the share of women in a field rises, the field more broadly begins to reflect their (initially distinctive) concerns. For example:
The left figure below tracks the Jensen-Shannon distance between the topics covered by men and women in history articles. This is an index that measures the difference between two statistical distributions; in this case, the distribution of men and women among the 90 different topics that were identified by Risi and coauthors’ natural language processing algorithms. As this index falls, the difference between these distributions is narrowing; knowing someone’s gender is increasingly less useful for predicting what topics they work on. Meanwhile, at right below, we can see the rise of women in the field of history. As with patents, as more women enter the field, the dissimilarity of the topics studied appears to be dropping.
That said, while this is consistent with the idea that the ideas and perspectives of women have become mainstreamed, it is also possible that the Jensen-Shannon Distance fell merely because women came to study the same subjects as men, not because men began to do research in topics that used to be distinctive to women. However, Rishi et al. show the share of articles mentioning words like “women” or “gender” has grown substantially over the 1951-2014, whether the authors are men or women, and that these terms are less and less confined to a small niche subset. That suggests the gender-difference between topics is falling at least partially because men are taking up the topics that used to be distinctive to women.
Adjacent Knowledge is Useful looks at three different setting that let us say something about what kind of knowledge is most useful - knowledge that’s really close, really far, or somewhere in between. One of those setting was an experiment where life scientists attended a symposium where they were divided up into rooms and then talked about research with a random subset of people. Among other things, the experiment monitored which of these randomized conversations resulted in subsequent collaborations. The authors found collaborations were most likely to emerge among life scientists who worked on some overlapping topics, but not a lot. The updated article discusses a subsequent article that largely confirmed that finding, in a broader non-experimental study, again using natural language techniques to extract topics from text.
A 2021 paper by Smith, Vacca, Krenz, and McCarty largely confirms Lane and coauthors work in a broader non-experimental context. Smith and coauthors look at what factors are correlated with researchers choosing to begin collaborating with each other during 2015-2018 for a sample of 3400 researchers at the University of Florida (all the faculty they could match to enough data to run their analysis). Specifically, they are interested in seeing whether faculty are more likely to begin collaborating if they work on more similar or dissimilar topics.
Doing so requires a measure of how similar is the research expertise of different faculty at the University of Florida. They use a text analysis approach, based on the abstracts of the 14,000+ articles authored by faculty at the university in the three years prior to 2015. Specifically, they use an algorithm to create 5,000 different topics, each of which is defined by a cluster of words that are commonly used together (where more unusual words count for more than very common words). Smith and coauthors can then say different papers relate to different topics, based on the overlap of the words in the paper’s abstract and the words commonly associated with the topic. They can then use the topics assigned to each paper to figure out which topics are associated with each faculty. At last, they can compare any pair of faculty by seeing how similar are the topics they worked on over 2012-2014, on a scale ranging from 0 (no topics in common) to 1 (working on the exact same set of topics with the exact same weight on each topic).
Finally, they can do their analysis and see how the similarity of topics two faculty worked on over 2012-2014 is associated with their propensity to initiate a collaboration in 2015-2018. As with Lane and coauthors, they find an inverted U-shape. Faculty are less likely to begin collaborating if they have a very close topic overlap or very distant. Again, the highest probability of working together is among scholars whose topic overlap is in the intermediate range. This association is quite robust, and is not notably weakened by adding a host of additional factors to the analysis (for example, being in the same department, details of the network structure of coauthorship networks, field specific effects, etc.).
Blue line corresponds to probability individuals collaborate. From Smith et al. (2021).
In other news, this month I finally finished uploading all the pre-existing audio of me reading New Things Under the Sun to all major podcast platforms: Apple, Spotify, Google, Amazon, Stitcher. It’s now possible to listen to 28 episodes of New Things Under the Sun, one right after the other, the equivalent of a medium-length audiobook.
Henceforth, I’ll plan to release all original articles as both a newsletter and in podcast format. I won’t be recording these updates as podcasts though, unless there is a strong demand for them.
Finally, I also was also interviewed on a podcast called The Future Of… with Azam Farooqi. The topic, naturally enough, was the future of innovation. Check it out!
