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.1 For example, one of us (Matt), did a PhD in Iowa, and ended up writing a paper on the innovation impact of ethanol-style policies (ethanol is a big business in Iowa). One of us (Caroline), was leaving Sierra Leone after two years there, just as the Ebola epidemic was starting. She became interested in understanding why science capacity is so low in some countries and not others, and what that means for the development of drugs and vaccines to combat local problems. (Indeed, we’ll talk about two of the papers that emerged from that research program in just a minute.)
Testing the relationship between location and research choice
Both of us made research decisions that were, in part, influenced by exposure to local problems. Are we atypical, or is this path of exposure to research choice a common one? The role of exposure to local problems in determining research choice is difficult to test. People might locate themselves in places precisely because they are interested in the problems in those places. The ideal way to test this would be to randomly assign researchers to different locations and see if they work on local problems that they are exposed to. However, randomly assigning researchers usually isn’t particularly feasible. Alternatively, we could randomly “assign” problems to different locations and see if local researchers begin working on those problems after exposure.
One candidate for a problem that all-but randomly arises in some locations but not others is a novel disease outbreak. So one way to assess how strong is the local problems to local research link is to see how scientists respond to local disease outbreaks. Fry (2022) takes this strategy and evaluates the impact of the 2014 West African Ebola epidemic on the publication output of endemic country scientists: did scientists working in areas hit harder by Ebola begin to disproportionately work on it? To see, Fry starts with a dataset of 57 endemic country biomedical scientists (those affiliated with institutions in Sierra Leone, Guinea and Liberia, the three hardest hit countries, at the time of the epidemic). She then matches these endemic country scientists to 532 control scientists who are from non-endemic countries in West or Central Africa, but who are at similar points in their career, work in similar areas, publish at similar rates, have similar rates of international collaboration, and reside in countries with similar GDP per capitas. She pulls out the publication record for each sample scientist for the four years before and six years after the epidemic from the Elsevier Scopus publication database, and creates counts of annual publications. Finally, she separates these counts into Ebola and non-Ebola publications through a key word search of the title, abstract and key words of the publications.
Fry compares the changes in publication output of endemic country scientists to that of the control scientists, adjusting for persistent differences between individual scientists, typical career age trends, and variation in publication trends over time for all scientists. As illustrated in the figure below, prior to 2014 none of the scientists in her sample really focused on Ebola. Beginning in 2014, endemic country scientists experience a large and fairly sustained increase in their publication output of Ebola related publications, as compared to non-endemic country scientists. That implies exposure to a new problem in a researcher’s location can shift their attention towards that problem. (It could be about something besides exposure too – we’ll talk about that later)
We noted above that our ideal experiment would randomly allocate scientists to different locations. While we may not be able to do that, scientists do change locations of their own accord and insofar as local problems drive research choice, then we might expect to see similar patterns when they do.
Fry (2023) tests exactly this. The working paper builds a dataset of 32,113 biomedical scientists affiliated with an African institution between 2000 and 2020, their publication output in different disease areas (by extracting key words from the title and abstract of their publication), and uses the affiliation listed in these publications to infer their country affiliation in each year. She then compares the research choices of these African scientists (proxied by the number of publications on each diseases) with the disease burden in their country of residence. The idea is to compare the disease focus of mobile researchers before and after their move to that of matched control researchers who don’t migrate. She finds, indeed, that researchers are more likely to publish papers on diseases that are more prevalent in their host country after they move there. This trend is particularly salient for researchers moving into Africa from outside the continent. And note, this is relative to matched scientists who did not move, but prior to the move were publishing at similar rates, on the same diseases, as the scientists who move.
We can see similar dynamics beyond the specific context of neglected tropical diseases. Moscona and Sastry (2022) provide some additional data from global agriculture, where there is substantial international variation in crop pests and pathogens. Moscona and Sastry search for the names of specific pests and pathogens in the titles, abstracts, and descriptions of agricultural patents across the world (using a dataset on international crop pests and pathogens from the Centre for Agriculture and Bioscience International). For example, there might be a patent for a pesticide to control a specific kind of pest, or a patent for a gene that confers resistance to some kind of pathogen. Since inventors list their country of residence on patents, Moscona and Sastry can see if inventors disproportionately invent technologies that mitigate pests and pathogens present in their country of residence.
That seems to be the case. In the figure left (or above on mobile), they show that for any given crop pest or pathogen (which they call a CPP), the number of patents by inventors in the same country where those pests and pathogens are found is much higher than the share of patents by inventors from other countries. Moscona and Sastry also statistically estimates the relationship between patents on a given pest or pathogen by country inventors and the presence of those pests/pathogens in that country, holding country and pest/pathogen differences fixed. That analysis also finds local presence is a strong predictor of local patenting related to a given pest or pathogen.
Nagaraj and Yao (2023) provide additional evidence on the focus of management research. Their study focuses on the complete set of articles published in six top management science journals and, among other things, they’re interested in seeing how where the authors work affects what they choose to study. They can infer where the authors work from the location of their employer; to estimate the place(s) under study, they look for the city, state, country, and nationality words in the title and abstract. By this method, about 15-25% of articles have some kind of regional focus. That lets us ask - do researchers tend to study where they are?
Yes and no. In the figure below, Nagaraj and Yao (2023) focus on the 13 countries that are either among the top ten for researcher locations or research focus. On the vertical axis we have the researcher’s location; on the horizontal axis, the country under study (note articles can have more than one researcher and research topic). For each cell, they take all the authors from a given country and compute the share of their regions mentioned in their articles that go to a particular country. The darker the shading, the larger the share. If the diagonal line is darkest, that would tell us researchers are most likely to study their own countries.
We do see a dark diagonal line, consistent with researchers disproportionately studying their home country. But the most striking pattern on this chart is probably the dark vertical line on the right: everyone studies the United States! But setting aside the USA, Nagaraj and Yao’s work does find management researchers tend to be more likely to study their own countries.
Why would location affect research choice?
Taking this cluster of papers as providing at least preliminary evidence that location influences research choice, the next question is: why? We’ve suggested it could be due to researchers being exposed to local problems, and that’s certainly one likely channel. It would be consistent, for example, with research finding that women scientists are more likely to work on issues that disproportionately affect women (suggesting that different researchers find different problems more salient and important to investigate). But a researcher’s location could influence their choice of topics in a number of other ways too. Researchers around the world might be equally interested in a topic, but local researchers could have an advantage in studying a particular topic because of better access to local data, for example, samples of viruses, pests, pathogens, or infected people. It may also be that local funders of research, rather than researchers themselves, are more likely to know and care about local problems. (That said, at least in the case of the 2014 Ebola epidemic, Fry 2022 finds no correlation between domestic funding for Ebola research and the shift towards it)
Beyond these direct effects of location on research choice, one secondary effect could be social contagion from other researchers: even if researchers are not initially motivated to study local problems, they may want to locally collaborate, and if local collaborators are more likely to be working on local problems, they are more likely to begin working on the topic too. We do have some evidence that researchers more readily transfer ideas to each other locally. That may ultimately lead them to continue to focus on the topic even after they are no longer collaborating.
We can see some evidence for the importance of these geographically mediated social influence in Bell et al. (2019), in this case for inventions that go well beyond pests and pathogens in humans and crops. Bell and coauthors link data on 1.2 million inventors born between 1980-1984 who patent before they are 28-32 years old to the tax records of these inventors and their parents. Bell and coauthors have tax data for 1996-2012, and since tax records show where the filer lives, this allows them to see where these young patentees lived from age 12-16 (depending on birth year) forward.
When we’re broadening our scope to include all kinds of US patents, there’s no longer an obvious choice of a local “problem”, analogous to the regional pests and pathogens covered in the earlier papers. But there is still a lot of local variation in the things people invent locally, and Bell and coauthors do document this local social exposure to different kinds of inventive problems matters a lot. For example, families that move to a high-innovation area are not only more likely to become inventors, but on becoming an inventor, they also disproportionately patent in technologies that are strongly represented among the local community where they grew up, irrespective of current location. Whether by social exposure or network formation, local transmission of research focus seems to be persistent. If research is disproportionately likely to focus on local problems, at least initially, these kinds of dynamics could insure ongoing regional specialization, even after the local problem has abated. Indeed, Fry (2022) finds that scientists in Ebola-endemic countries in 2014 continue to publish disproportionately on neglected tropical diseases, especially Ebola, for years after the epidemic ended in 2016.
Another amplifier of this dynamic could be that expertise in a local problem can attract non-local people and resources. As we noted at the outset, rather than local problems shifting the research priorities of local scientists, people might locate themselves in places precisely because they are interested in the problems in those places. This seems to have been the case for high-income country scientists during the 2014 Ebola epidemic. Fry (2022)’s headline finding is actually that the Ebola epidemic attracted a large number of high-income country researchers to endemic countries, most of them seeking local collaborators to assist with access to specimens and navigating the local context. This resulted in a large increase in the rate of international collaboration (and hence, publications) for endemic country scientists (those who were already living and working in affected countries) working on Ebola. In contrast to a local funding story, this implies that an advantage in studying local problems can become a global comparative advantage, when the global community takes a sudden interest in the topic (as it did with Ebola).
Geography as destiny
So there seems to be a strong correlation between local problems and the direction of research. Local scientists (and probably local research funders in some cases) may initially be motivated to study local problems because they are more salient, or because they are easier to study. As that initial expertise is developed, it can become self-perpetuating, as collaborators pass on interest and expertise in the topic to new researchers who were not perhaps intrinsically interested in the topic initially, and as non-local researchers and resources who are interested in the local problem flow to the region.
That said, geography might not necessarily be destiny. Andrews and Smith (2023) explore the impact of location on research direction in the context of the placement of land grant colleges and agricultural research in the USA.2 These colleges were founded, in part, for the purpose of conducting agricultural research of interest to their state. Sometimes colleges get dropped in parts of the state where the local agriculture differs from the state norm though – do these colleges focus on local agricultural needs, even if they are unrepresentative of the state’s broader research needs? Consistent with the results we’ve seen before, this is indeed the case: land-grant colleges established in agriculturally anomalous counties (relative to the rest of the state) produce publications on a similarly anomalous mix of crops.
But this doesn’t necessarily prove exposure to local problems (in this case, related to agriculture) drive research priorities. Just as researchers may locate close to the problems they want to study, maybe colleges were placed near the crops that legislatures wanted researchers to focus on.
To see if this is the case, the paper uses data on the set of finalist locations for land grant colleges, and exploits the fact that amongst these finalist locations, the actual site was determined by vote or by drawing lots. For a set of ‘runner up’ locations with bids that were very similar to the winners, or where the winner was literally decided by lots, they actually do have a version of the ideal experiment we proposed at the outset of this post: (all but) random assignment of researchers to different locations! They then evaluate the extent to which the land grant colleges end up producing more research on agricultural conditions (specifically, the distribution of crops grown in that location) that are present in the winning location as compared to those in the runner up locations.
And contrary to what papers discussed so far find, they find no significant relationship between the volume of research into local conditions and the location of land grant colleges! What’s going on?
Andrews and Smith dig into the mechanism and propose that one reason there is no impact of the local mix of crops on a college’s research is that land grant colleges located in places with less representative crop distributions are more likely to build “extension stations” in other parts of the state that are more representative. In other words, these colleges “overcome” their geography by investments to obtain non-local information (though we should be cautious because the sample sizes here are small and so estimates relatively imprecise). This echoes Fry (2022), which found that high-income country researchers interested in Ebola can travel to Ebola endemic locations to do the research. If Andrews and Smith’s theory is true, it suggests we are not entirely subject to the whims of geography; we can certainly become interested in things that are not local to us, though all else equal we’re more influenced by local issues than distant ones. Moreover, geography still matters in the sense that when we become interested in non-local problems, we may have to travel to where the problem resides to study it well.
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Fry, Caroline Viola. 2023. Crisis and the Trajectory of Science: Evidence from the 2014 Ebola Outbreak. The Review of Economics and Statistics 105 (4): 1028–1038. doi: https://doi.org/10.1162/rest_a_01096
Nagaraj, Abhishek and Hongyu Yao. 2023. Geographic Bias in Management Scholarship: Data-Driven Estimates and Trends. Working paper.
Bell, Alex, Raj Chetty, Xavier Jaravel, Neviana Petkova, and John Van Reenen. 2019. Who Becomes an Inventor in America? The Importance of Exposure to Innovation. The Quarterly Journal of Economics Volume 134 (2): 647–713. https://doi.org/10.1093/qje/qjy028
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