New Things Under the Sun is a living literature review; as the state of the academic literature evolves, so do we. This post highlights some recent updates.
Local Learning
Last year I wrote a post called Remote Breakthroughs about the changing nature of innovation among remote collaborators. Part of that post discussed evidence that local interactions do a better job of exposing us to new ideas than remote interactions. I’ve now spun that discussion out into it’s own expanded article, specifically on that topic. This was mostly prompted by a new paper, van der Wouden and Youn (2023). Here’s an excerpt from that new article, titled Local Learning:
In my experience, the internet can’t be beat for encountering a diversity of ideas. But often, that encounter is at a pretty surface level. You read a tweet; a headline; a blog post synthesizing some studies, etc. Nothing wrong with surface level engagement - you can’t engage in everything deeply. But pushing the innovation frontier increasingly requires deep engagement with at least some domain of knowledge. And there are reasons to think that offline/in-person interaction might be better for forging that kind of deep engagement with new ideas.
To start, let’s look at van der Wouden and Youn (2023), which wants to see if in-person collaboration on academic projects more reliably leads to the transfer of knowledge between coauthors than remote collaboration. To answer that question, the authors gather data on 1.7mn academics who, at some point over the period 1975-2015, produce a sequence of three papers that exhibit a very specific pattern. In reverse order, they need:
The last paper in the sequence to be solo-authored
The second-to-last paper to be coauthored with at least one other author
At least one more prior paper.
They’re going to pull all that information from the Microsoft Academic Graph.
Next, they want an estimate of what knowledge domains the academic is fluent enough in to publish an original research paper in. To get those, they leverage the 292 subdisciplines that the Microsoft Academic Graph tags papers with. By looking at the subdisciplines tagged to your work, they can get an idea about what you are an expert in, and also how your areas of expertise grow over time. Moreover, by focusing specifically on solo-authored work, they can be most sure that it’s really you who is the expert, and not one of your coauthors.
The main idea of the paper is to figure out an academic’s areas of expertise based on all papers they’ve published, up to and including the first one in the sequence of three alluded to above. Next, they look to see if the second paper in the above sequence was conducted with local or remote collaborators. Finally, they look at the final paper in the sequence, which was solo-authored, and see if it is tagged with any new subdisciplines, relative to all your papers up-to-and-including the first one in the sequence. If so, they take that as evidence that the author gained expertise in a new subject in between the first and third paper, possibly via their interaction with their collaborators on the second paper. Lastly, they can see if this “learning” effect is more common when you work with local or remote coauthors.
In the following figure, we can see how the probability of writing a solo-authored paper tagged with a new subdiscipline changes when you work with increasingly distant colleagues on your previous paper. van der Wouden and Youn call this the “learning rate.” If your collaborators were local (under 700m away, a 10 minute walk), then about 7.5% of the time, your next paper is on something you haven’t written about before. If your collaborators are out of town, say more than 25km, the probability drops to more like 4.5%.
This pattern is consistent across fields, though stronger in some fields than others. For example, the relative probability of pivoting to a new topic after a local collaboration compared to a distant one is generally higher in STEM fields than in non-STEM fields. Moreover, while the figure above is raw data, you get a similar effects when you toss in a bunch of additional control variables: the number of coauthors, the career stage of the academic, the ranking of the institution they are affiliated with, and so on.
The article Planes, Trains, Automobiles, and Innovation is about a similar theme: how changing technology affects the ability to collaborate over a distance. The article originally covered three studies, each about how the expansion of transit options - new air routes, new train routes, or more local roads - facilitated more remote collaboration among scientists and inventors. I’ve added to this article a discussion of Koh, Li, and Xu (2022), which looks at the expansion of the Beijing subway system:
Koh, Li, and Xu (2022) studies the impact of the dramatic expansion of the Beijing subway on private sector innovation. The subway system in Beijing grew pretty slowly until the 2000s, when the pace of expansion dramatically ramped up ahead of the 2008 Summer Olympics in Beijing and as part of the government’s stimulus response to the 2007-2008 financial crisis. The number of subway stations went from 41 to 379 between 2000 and 2018, while the total track length grew from 54.1km to 655km over the same time frame. Koh, Li, and Xu cut Beijing up into 0.5km squares and look at what happens to the number of patents by distant collaborators residing in different 0.5km blocks. Across a lot of different approaches,1 they find a subway connection that reduces travel time between blocks by at least an hour leads to a 15-38% increase in patent applications filed.
Change in number of patents between blocks after travel time is reduced by an hour or more. From Koh, Li, and Xu (2022)
Now that this article also discusses subways, I could have changed the title to “Planes, Trains, Subways, Automobiles, and Innovation”, but since that is quite a mouthful I instead changed the title to Transportation and Innovation.
The article The “idea” of being an entrepreneur tries to argue that one important factor about whether people choose to become entrepreneurs or not is if they even conceive of entrepreneurship as an option. The piece argues this idea - that yes, even people like you can be an entrepreneur - is often spread by social contagion from people who are like ourselves but are also entrepreneurs.
I’ve now added a new section to this article about the transmission of the “idea” of entrepreneurship via mass media.
If transmitting the “idea” of entrepreneurship matters, then countries with mass media celebrating entrepreneurship might get more entrepreneurs, because people consuing this media diet are more likely to consider entrepreneurship a viable option. This is a tough hypothesis to test, since mass media tends to reflect the society it is targeting. In a society with lots of entrepreneurship and lots of mass media celebrating entrepreneurship, which caused which? Likely it’s a bit of both! Another reason it’s hard to test this hypothesis is because, ideally, you want to compare people exposed to one mass media diet to people exposed to another one, but who are otherwise identical. But most people have access to the same mass media (that’s what makes it mass!), and so if one group chooses not to consume it, it’s likely because they differ in some way.
Slavtchev and Wyrwich (2023) identifies one peculiar instance in history that does permit testing this hypothesis. When Germany split into East and West, following the Second World War, most forms of entrepreneurship were banned in East Germany. From the 1960s on though, West Germany consciously crafted and broadcast TV programming into East Germany, as a matter of policy. Compared to East German television, West German television tended to celebrate individualism, business, entrepreneurship, and the like. This programming was popular, if you could get it: surveys indicate over 90% of people who could access the broadcasts tuned in at least several times per week.
But not everyone could get it. A few regions that were far from the broadcast towers, or where signals were blocked by hills and mountains, could not easily access this programming, and surveys indicate many fewer people in these regions regularly watched West German programming: just 15% several times a week, and 68% never.
Yet besides their geographic distance and different topography, the regions of East Germany with access to West German television don’t seem to have been much different from the (small number of) regions of East Germany without. Slavtchev and Wyrwich argue this is the kind of natural experiment we’re looking for: mass media promoting entrepreneurship in a society that is not already celebrating it (it was mostly outlawed!), and different levels of exposure to this mass media among groups that were otherwise similar. Lastly, after the collapse of the USSR, many forms of entrepreneurship became legal once again in East Germany, so Slavtchev and Wyrwich can actually see if this differential mass media exposure mattered: do parts of formerly East Germany with greater exposure to West German television end up with more entrepreneurship than those without?2
Yes. The figure below tracks the per capita number of new businesses and new self-employed individuals over time, across three different regions: in solid blue, the rate of entrepreneurship among regions with access to West German TV; in solid red, the same for regions without access to West German TV; and in dashed blue, the rate of entrepreneurship among regions with access to West German TV, but adjacent to places without. For every group, the rate of entrepreneurship falls over time (maybe as everyone who wants to start a business starts one?), but the blue is always above the red: East German regions who watched West German television typically had around 10% more entrepreneurs per capita than East German regions who could not.
From Slavtchev and Wyrwich (2023)
If Slavtchev and Wyrwich are correct that regions isolated from West German television programming really are no different, in terms of their economic opportunities and capabilities, then the above again underscores the impact of ideas. Perhaps if people internally weighed up the expected costs and benefits of starting a business, they would come to the same conclusion at roughly the same rate, throughout formerly East Germany. In that case, there would be no difference between the rates of entrepreneurship across East German regions, or at least no difference related to access to West German airwaves.
The rest of the piece looks at evidence related to social transmission, but I think this is complementary.
To close out the theme of innovation over different distances, let’s return to the article we started with, Remote Breakthroughs. Among other things, that article argues one way to maximize the power of local learning is to build a team of people who are learning from different local environments, and collaborating remotely! I’ve added discussion of another article, Prato (2022) to Remote Breakthroughs, which provides some evidence of this dynamic.
Prato is 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. We might think it’s bad for your inventive output if one of your co-inventors moves abroad, but in fact Prato finds the opposite is true: co-inventors of migrating inventors produce nearly 20% more patents per year, after their colleague migrates, as compared to the co-inventors of non-migrating inventors. Prato argues identifies a similar channel to Frey and Presidente: if you can’t move and get exposed to new people and new ideas, perhaps the next best thing is to work remotely with people who can?
Those are all the updates related to the geography of innovation, but this month I also made an update to another article, One question, many answers. That post looks at the many-analyst literature, wherein multiple teams are asked to use the same data to resolve the same question. My post previously contained the following figure from Breznau et al. (2022), which illustrates the large span of answers different teams got when answering the question, “does immigration lower public support for social policies?” It seems that if you ask different researchers the same question, using the same data, you get completely different answers!
Engzell (2023) is a comment on Breznau et al. (2022) that argues you shouldn’t really interpret this figure as telling you that if you ask different teams the same question you can get all sorts of different answers. It’s more like, if you ask researchers a set of different but related questions, the answers to those different questions can differ a lot. But the post is called “One question, many answers” not “Many questions, many answers.” So the above figure isn’t really appropriate for the post.
Fortunately, Breznau et al. (2022) make it very easy to play with their data to generate figures that are more relevant to the post. Here is the updated text:
Breznau et al. (2022) get 73 teams, comprising 162 researchers to answer the question “does immigration lower public support for social policies?” Again, each team was given the same data. In this case, they were given responses to surveys about support for six different government social policies, and various ways to measure “immigration:” as a stock (how many immigrants are in the country?), as a flow (how many new immigrants are coming into the country?), as a change in the flow (how is the annual inflow of immigrants changing?), etc. They were also given other country-level explanatory variables such as GDP per capita and the Gini coefficient.
It’s not surprising that we might get different results when we look at how different measures of immigration (stock vs flow, for example) affect attitudes towards different social policies (jobs vs healthcare, for example).3 But even when we restrict our attention to teams using the same measure of immigration, and looking at the impact on attitudes about the same category of social policy, we still get a lot of variation across different teams. The paper has a nice web-app that lets you filter the results along lots of different dimensions, which I used to create the following charts. They show how different teams (using different modeling strategies and sets of control variables), obtained different results about the impact of the flow if immigration on social support for six different kinds of government social policy.
Charts created from the web app associated with Breznau et al. (2022). I used the effect sizes tab, with the test variable set to flow.
… most results are small, but there are some major outlier results, and a lot of variation in whether results are deemed statistically distinguishable from zero or not.
A few other New Things Under the Sun related updates:
If you missed it, the site now has a better index. Claim articles are grouped into topics, and each topic gives a short description of what’s in an article, to help you decide if reading it will help you find what you’re looking for.
I did an AMA on the progress forum. Check it out for lots of short questions that I invariably answered with three paragraphs.
I did a podcast (with transcript) with Sarah Gulliford-Kearns for Commonplace, the digital publication of Knowledge Futures. Knowledge Futures is the org behind the fantastic digital infrastructure for New Things Under the Sun. The interview was about living literature reviews among other things.
I was a guest on Innovation Metrics, a podcast by Elijah Eilert. We talked a lot about the social transmission of the idea of entrepreneurship, and about libraries and wikipedia!
Until Next Time
Thanks for reading! As always, if you want to chat about this post or innovation in generally, let’s grab a virtual coffee. Send me an email at matt.clancy@openphilanthropy.org and we’ll put something in the calendar.
To reiterate: this is about comparing entrepreneurship rates across different parts of formerly East Germany; it’s not about comparing rates of entrepreneurship in West Germany and East Germany!
This is a point I missed in an earlier version of this post. I became aware of this issue after Engzell (2023), a comment on Breznau et al. (2022), pointed out this issue.
