Physical proximity is more important for meeting new people (with new knowledge) than for collaboration
Innovation disproportionately happens in cities. What is it about packing people together that makes them so innovative?
In Urban social infrastructure and innovation, we looked at a few papers that showed denser neighborhoods with lots of restaurants, cafes, and bars facilitated more innovation, and that the kind of innovation that happens in cities tends to reflect the social milieu of the surroundings. In particular, the residents of dense parts of cities tend to work on a more diverse collection of technologies, and that this is reflected in the kinds of patents they create. In Innovation at the office, we looked at papers that show bringing workers together in the office can help them form new connections.
In this article, I want to look at some evidence that one of the most important functions of cities is to introduce us to new people. I’ll go on to argue being close seems to be very important for initiating and consolidating new relationships, but that once they’re formed it’s no longer so important that you stay physically close - at least from the perspective of facilitating innovation.
Consider a 2018 paper by Christian Catalini. Catalini exploits a natural experiment related to a French university’s 17-year quest to rid its campus of asbestos. Asbestos removal is a disruptive process and whenever a university lab’s turn for renovation came up, it required relocating them to whatever space was available, with little ability for the lab to petition about its location. This meant that labs across campus suddenly found themselves with new neighbors, or separated from old neighbors.
Catalini finds that labs are more likely to collaborate after they are moved into the same building. In the diagram below, year 0 represents the time when two previously separated labs come to reside in the same building.
However, when labs that used to be in the same building are separated, it doesn’t seem to have any impact on their probability of collaborating.
That suggests being neighbors is important for meeting new people, but close proximity isn’t really important after you know about each other. Another line of evidence from Catalini comes from the likelihood two labs would know about each other’s work regardless of their proximity. Catalini uses the publications the labs put out to measure the degree to which different labs work on different topics. It turns out being located in the same building has a big impact on the probability two labs collaborate, when they normally work on different things. For labs that work on similar scientific topics, it doesn’t seem to matter much if they are near or far. In this case, it seems likely the labs don’t need to be close to meet; they were probably always going to meet since they go to the same conferences, seminar series, and so on.
But in Catalini’s study the distances between labs are never that great. The worst case scenario is they get moved across campus; it’s still probably pretty easy to collaborate at those distances (former neighbors can just arrange to meet in the quad or something). But other work finds similar results for much larger moves.
Agrawal, Cockburn and McHale (2006) look at the citations patents receive when an inventor moves. For example, suppose Ada is an inventor in Ames, Iowa who moves to New York City. Once in New York Ada comes up with a new patented invention. Agrawal, Cockburn, and McHale show inventors in Ames are more likely to cite Ada’s new patent then are inventors of technologically similar patents from cities equally far from New York (for example, Birmingham, Alabama). It’s like the Ames inventors have kept in touch with Ada and know what she’s working on. Indeed, 80% of the increased citations Ada receives from Ames can be attributed to people who either (1) worked on a patent with Ada back when she was in Ames or (2) worked at the same organization as Ada back when she was in Ames. These are precisely the kind of people we would expect to know Ada personally.
Agrawal, Cockburn, and McHale also show this effect is stronger for citations across different technologies. For example, suppose Ada works on lithium battery technology. When she moves to New York, she receives about 135% as many citations from other lithium battery inventors who are located in Ames, than she receives from lithium battery inventors who are located from some other equally distant city (for example, Birmingham Alabama). But she receives nearly 175% as many citations from inventors who don’t work on batteries but reside in Ames, than she does from inventors who don’t work on batteries but reside in somewhere like Birmingham. Again; this is consistent with the idea that being located together in the same city was especially important for Ada to meet people who she wouldn’t normally meet - in this case, people who do not work on the same kind of thing as her.
While professional connections are probably the most likely to be useful for inventing, they are not the only kind of connection people have. If I make friends with people at a party, these friendships might also be a vehicle for the transmission of useful information. Diemer and Regan (2022) begins to address this gap with a novel measure of friendships: Facebook data. They have an index based on the number of friendships between Facebook users in different US counties, over a one-month snapshot in April 2016. Unfortunately, this measure of informal ties isn’t as granular as what Agrawal, Cockburn and McHale were able to come up with. If you’re an inventor with a patent, this Facebook dataset doesn’t tell the authors who your friends are and where they live; instead, it tells them something like, on average, how strong are friendship linkages between people in your county and other counties. Still, its one of the first large-scale datasets that lets us look at these kinds of social ties.
Diemer and Regan want to see if these informal ties facilitate the transfer of ideas and knowledge by once again looking at patent citations. But this is challenging, because there are a whole host of possible confounding variables. To take one example, suppose:
you’re more likely to be friends with people in your industry
everyone in your industry lives in the same set of counties
you’re also more likely to cite patents that belong to your industry
That would create a correlation between friendly counties and citations, but it would be driven by the fact that these counties share a common industry, not informal knowledge exchange between friends.
Diemer and Regan approach this by leveraging the massive scale of patenting data to really tighten down the comparison groups. Their main idea (which they borrowed from a 2006 paper by Peter Thompson) is to take advantage of the fact that about 40% of patent citations are added by the patent examiner, not the inventor. Instead of using cross-county friendships to predict whether patent x cites patent y, which would suffer from the kinds of problems discussed above, they use cross-county friendships to predict whether a given citation was added by the inventor, instead of the examiner.
The idea is that both the patent examiner and the inventor will want to add relevant patent citations (for example, if both patents belong to the same industry, as discussed above). But a key difference is that only the inventor can add citations that the inventor knows about, and one way the inventor learns about patents is through their informal ties. So if patent x cites patent y, no matter who added the citation, we know x and y are probably technologically related, or there wouldn’t be a citation between them. But that doesn’t mean the inventor learned anything from patent y (or was even aware of it). But if patents from friendly counties are systematically more likely to be added by inventors, instead of otherwise equally relevant citations added by examiners, that’s evidence that friendship is facilitating knowledge transfer.
Diemer and Regan actually look at three predictors of who added the citation: cross-county friendships, geographic distance between counties, and the presence of a professional network tie between the cited and citing patent (for example, is the patent by a former co-inventor or once-removed co-inventor). And at first glance, it does look like geographic distance matters: it turns out that if there is a citation crossing two counties, the citation is more likely to have been added by the inventor if the counties are close to each other.
But when you combine all three measures, it turns out the effect of distance is entirely mediated by the other two factors. In other words, once you take into account who you know, distance doesn’t matter.1 Distance only appears to matter (in isolation) because we have more nearby professional ties and friendships, and we are more likely to cite patents linked to us by professional ties and friendships. Consistent with Agrawal, Cockburn, and McHale’s finding that 80% of excess citations from movers comes from people who are professionally connected, Diemer and Regan find professional network connections are a much stronger predictor of who added the citation than friendliness of counties, though both matter. Lastly, as with Agrawal, Cockburn, and McHale, when patent citations flow between more technologically dissimilar patents, the predictive power of how friendly two counties are looms larger. That’s consistent with friendships being especially useful for learning about things outside your normal professional network. But the bottom line is this - distance only matters, in this paper, because it affects who you know.
We can go one step further. Miguelez and Noumedem Temgoua (2020) adopt an approach similar to Agrawal, Cockburn, and McHale, looking at inventors who move. But they look at citations between patents in different countries, when inventors migrate. They find patents from country A are more likely to cite patents from country B when more inventors have migrated from A to B (this is possible thanks to a nice new dataset that actually tracks migrant inventors). As with Agrawal, Cockburn, and McHale, this effect is actually stronger for countries that are otherwise technologically less similar. This is again consistent with the notion that proximity - here, merely residing in the same country - is especially helpful for forming social ties with people who work on different technologies than is typical for the country.
The preceding three studies show that personal connections can transcend distance. But they’re all based on patent citations, which are a problematic measure of knowledge flows. As discussed here, patent citations are a noisy indicator of knowledge transfer, and after the year 2000 are increasingly skewed by a small number of super-citing patents that make tons of low-quality citations. This isn’t as much of an issue for Agrawal, Cockburn, and McHale, whose dataset comes from the 1990s, nor for Diemer and Regan who perform some extra analysis to mitigate this issue.2 But given that Miguelez and Noumedem Temgoua’s data runs through 2010, it would be nice to have some extra validation that these patent citation signals are picking up long-distance knowledge transfers among people who know each other.
Prato (2022) provides circumstantial evidence this is the case, in my view. The reason we care about long-distance knowledge transfer is that we assume the knowledge is useful; it enables people to invent things they would not otherwise be able to. If that’s the case, then having a long-distance colleague who is learning lots of new things (either through their local environment or their own inventive efforts) should impact your own research. Prato (2022) provides some evidence this is the case, by studying inventors who immigrate from the US to the EU and vice-versa. For each of these migrating inventors, she finds another inventor from the same country with a similar patenting career up to the year the migrant starts patenting in the other region, but who did not immigrate. That is, she has two groups: migrating inventors and non-migrating inventors. The two groups are roughly comparable, at least in terms of their patenting productivity, up until the year the migrating inventor leaves (as discussed in more detail here, migrating inventors seem to benefit a lot from immigration).
She then identifies all of the co-inventors of these groups. Now she has two more groups: inventors who collaborate with inventors who migrate, and inventors who collaborate with similar inventors who do not migrate. She then compares what happens to patent productivity of those who work with migrants to the patent productivity of those who work with non-migrants. Because we’re interested in the possibility that simply being exposed to new ideas, from a colleague who moved abroad, might make you more productive, I’ll focus on some results from Prato’s appendix, which exclude patents jointly invented with the migrating inventor, or their non-migrating match. Prato finds co-inventors of migrating inventors produce more patents per year, after their colleague migrates, as compared to the co-inventors of non-migrating inventors. Though various interpretations are possible, when paired with the citation evidence, that’s consistent with productive new colleagues abroad serving as a conduit to new ideas.
We can also go beyond patents entirely. Another major source of “paper trails” for knowledge are academic papers.
Head, Li, and Minondo (2019) look at citations between mathematics papers as evidence of how knowledge moves through a community of researchers. Specifically, they want to know what kinds of things predict whether paper x cites paper y. For our purposes, the variable of interest is their measure of social ties between mathematicians. They measure this in a lot of different ways: advisor-advisee relationships, whether two mathematicians worked in the same place at the same time, or went to the same graduate school around the same time, etc. Note - by definition - most of these relationships are defined in terms of physical proximity at some point in time. Head, Li, and Minondo find a couple of things that are consistent with what we’ve talked about so far.
First, if you do not include any data on social ties, mathematicians are less likely to cite each others’ work if they live far away from each other. But, when you do include social ties, the strength of this relationship gets cut in half. And, looking only at data from the early 2000s onward, the impact of distance disappears completely once you account for social ties. In layman’s terms, what’s going on is something like this: mathematicians are more likely to cite mathematicians they know, and more likely to know mathematicians who live nearby. But, since the year 2000, the only thing that matters (in the data) is the existence of a social tie. If two mathematicians work in the same department and then one moves away this doesn’t really impact the likelihood that they cite each other’s work. It’s the same kind of finding as we had for patents.
Moreover, as with the patents, the importance of social ties is stronger for mathematicians who work in different fields. Again - proximity helps forge relationships, especially relationships that would not normally form in the course of keeping up to date on the field. And those relationships remain pretty durable to moves.
Freeman, Ganguli, and Murciano-Goroff (2015) have some descriptive data on distance and academic collaboration that’s also consistent with this. Looking at 126,000 papers in the fields of particle and field physics, nanoscience and nanotechnology, and biotechnology and applied microbiology, they don’t find any consistent evidence about the impact of having geographically distant coauthors. When all the authors are based in the USA, the citations received by papers authored by geographically distant coauthors are no different than those received by geographically proximate ones in two of the three fields (international collaboration did tend to reduce citations in all cases). But even if it’s possible to productively collaborate at a distance, a strong majority of coauthors first met while they were geographically close (either as colleagues or advisors and advisees).
So, across a lot of contexts we find evidence consistent with this story: innovators meet other innovators who live nearby, whether they work in the same field or not. Once a relationship is formed, it remains pretty productive even after you get subsequently separated, in the sense that you can still collaborate well or at least learn from each other.
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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
Diemer, Andreas, and Tanner Regan. 2022. No inventor is an island: Social connectedness and the geography of knowledge flows in the US. Research Policy 51: 104416. https://doi.org/10.1016/j.respol.2021.104416
Thompson, Peter. 2006. Patent Citations and the Geography of Knowledge Spillovers: Evidence from Inventor- and Examiner-added Citations. The Review of Economics and Statistics 88 (2): 383–388. https://doi.org/10.1162/rest.88.2.383
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
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
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