How do innovators decide what are interesting problems to work on? In other posts we’ve looked at how research priorities may be influenced by your life experiences (as reflected in gender or local geography). In this post, let’s look at the influence of the interests of a student’s mentors.

To start, we’ll establish some correlations between the interests of students and their teachers. Borowiecki (2022) focuses on teacher to student transmission of interests among musical composers from 1450-1975; Koschnick (2023) among undergraduates and faculty at Oxford and Cambridge over 1600-1800; Azoulay, Liu, and Stuart (2017) on modern post-docs and their advisors in the life sciences. In the next section, we’ll try to go further and show that these correlations are likely to be in large part about the teacher’s influence on student interests, rather than students sorting themselves to work with teachers who share their interests.

All three papers involve heroic data construction efforts. Borowiecki’s core analysis relies on data about 341 composers, where they lived, what music they wrote, and how impactful their music is (measured by either modern Spotify follows, length of their biographies in a major musical dictionary, or rankings by Charles Murray). Borowiecki also identifies 221 student-teacher connections among this group, when the one taught the other at a music conservatory. Lastly, because Borowiecki has detailed information on the musical themes of his composers, he can algorithmically assess how similar are the musical themes of any two composers.

Borowiecki’s main analysis shows that composers write music with themes that are more similar to the themes of their teachers, than to other composers. This effect holds when you restrict the comparisons to other composers living in the same country and alive at the same time as the teacher. He finds this similarity persists for around 20 years, and even across generations: composers write music more similar to the teacher of their teacher than to other composers who might have taught their teacher but didn’t.

Let’s turn to interests in science, which are studied by Koschnick (2023). Koschnick’s analysis builds on a dataset that matches students and faculty at Cambridge and Oxford (over 1600-1800) to a database of publications in England, based on names and birth and death dates (where available). He wants to use these matched publications to infer student and faculty’s interest in different areas of science (or other topics): for example, students/faculty with more publications about astronomy are probably more interested in astronomy. To do so, Koschnick trains a large language model to classify publications into topics - he’s helped here by the era’s propensity to write very long and descriptive titles of their works.1 Finally, he wants to match students to teachers, to see if being around teachers more interested in a specific area of science makes the student more likely to work on that area. For that, he relies on the college system employed by these universities. Students at these universities belong to one of dozens of colleges, where they live with their college peers and are primarily taught by faculty from their college. Since Koschnick knows which college each faculty belongs to, he knows with a high degree of certainty which faculty are teaching which students.

Koschnick documents that after they graduate, students tend to publish more on scientific topics which were more common among the publications of the faculty at the college they attended. If the share of faculty publications at your college in one scientific field doubles, then the share of publications in that field written by its students rises by 1-3%. That doesn’t sound like much, but note the average college share of science in any field is tiny - only 0.6%. So doubling the share is quite easy. In fact, the variation across colleges can vary by much more than double. One standard deviation in this data is more like a 6x increase over the average.

Finally, Azoulay, Liu, and Stuart (2017) build a dataset on 489 elite life scientist post-doctoral students and their 333 advisors. These post-docs are Pew or Searle Scholars, which is useful because the Pew Scholar Oral History and Archives Project provides extensive documentation on the biography of Pew scholars, which Azoulay, Liu, and Stuart will draw on in the analysis discussed in the next section. For now, suffice it to say Azoulay and coauthors show that post-docs who work with advisors that have previously held patents are more likely to seek patents of their own in the future.

Birds of a Feather?

These three papers establish that students appear to share interests with their teachers, whether that interest be a particular style of music, a field of science, or commercializing research. But we haven’t done anything to establish this correlation is down to teacher influence. It might just as easily be that young composers seek out teachers whose music they like, that students go to colleges strong in the subject area they are interested in, and that budding entrepreneurial scientists seek out mentors with experience commercializing their research. All three papers present evidence that these kinds of explanations are probably not the main story.

To begin with, both Borowiecki and Koschnick’s papers involve students making decisions at a relatively young age, before we might imagine they have deeply developed personal preferences. In Borowiecki (2022), 75% of students begin their training at a music conservatory, with their advisor, before the age of 22. Koschnick’s paper focuses on undergraduates. Both papers also primarily take place in eras that predate the information technology revolutions, when information about potential teachers was less readily available.

Koschnick’s paper goes on to argue that, instead, undergraduates to Oxford often selected their college based on geographical affinities. For example, in his data, students from Devon and Cornwall are more likely to go to Exeter college and students from Pembroke more likely to go to Jesus college. In one analytical exercise, he shows that students are more likely to write about a given scientific topic if the faculty of the college people in their region usually go to happen to be stronger in that field, during the years the student is at uni. In that particular exercise, he doesn’t even need to know where students actually ended up going to school, just where they would be predicted to go based on where they live.

For Azoulay, Liu, and Stuart’s study of postdocs and their advisors, they have access to an unusually rich source of information about the decision-making process of their subjects: the oral histories of Pew scholars. The authors read a sample of 62 such histories (each is long; 100-400 pages) to see what kinds of factors Pew scholars self report as being important in their decision of which postdoc mentor to work with. The overwhelmingly most important factor cited was the scientific topic being investigated, followed by geography (where the lab was), the advisor’s prestige in the field, and interpersonal rapport. None mentioned the commercial orientation of the advisor, or their interest in patenting. And this wasn’t simply because they were shy to talk about non-academic goals; when asked about their own patents, interviewees were apparently quite candid.

Azoulay, Liu, and Stuart use this qualitative analysis to form the basis of some additional quantitative exercises. They come up with measures of scientific similarity, geographical proximity, and prestige, which they use to derive statistical models of the matching process between postdocs and mentors. They can then see if matches that are poorly explained by these stated factors seem to be unusually correlated with the decision to patent, which would be evidence that people left their true motivations - a desire to work with a scientist who patents - unstated. But they don’t really find any evidence of this. The statistics back up what the scholars say: recent graduates don’t really think about patenting when deciding who to work with for their postdocs. But if they “accidentally” end up working with an advisor with a history of patenting, they’re more likely to patent themselves, later in their career.

Both Borowiecki and Koschnick also perform an exercise based on teacher composition at conservatories and colleges. In one exercise, Borowiecki looks at how similar are the musical styles of a student and teacher, as compared to teachers at the same conservatory who either left shortly before the student joined or arrived shortly after the student left. The idea here is that if students had started at conservatory at a slightly different time they might well have ended up working with this alternative teacher. Koschnick’s study exploits an even more abrupt change in the faculty: the ouster of roughly half the fellows of the University of Oxford following the English civil war (they didn’t support the winning side) and their replacement, which he argues was random at least as regards to scientific field interest. He then looks to see if student interests in specific scientific fields are also correlated with interests of the replacement faculty (who students could not have anticipated would be their teachers). Both these exercises support the notion that teacher influence is the main reason for the correlation between student and teacher interests. Musical composers are more similar to the teachers they actually had, as compared to teachers at the same conservatory who weren’t available, and student interests in different scientific topics are correlated with the interests of the replacement faculty who unexpectedly ended up teaching them. The size of the effects is comparable to the main analysis, though estimated with less precision (because these analyses are necessarily based on smaller samples).

Lastly, a final reason to believe that these correlations arise from teacher influence, rather than sorting, is that we see similar effects in other domains. The New Things Under the Sun posts Entrepreneurship is contagious and The “idea” of being an entrepreneur cover a related literature that argues role models play an important part in building an interest in becoming an entrepreneur. That literature is pretty big, and pulls together observational, quasi-experimental, and experimental data. So we already had evidence that preferences could be powerfully shaped by role models.

How Big?

So across a few contexts, we have evidence that students pick up the interests of their teachers. How big is this effect? Each paper frames effect sizes in different ways that are hard to compare against each other. Koschnick finds that across colleges, if faculty writing on scientific topics rose by about 1 standard deviation - or about 650% - then students would increase their share of writing on these topics by 5-15% (from a low base). Borowiecki finds that composers are 10-30% of one standard deviation closer to the music of their teachers than the comparison group. And Azoulay, Liu, and Stuart find having a patenting mentor has an effect on patenting comparable to the gender divide on patent rates.

My interpretation of this is that teachers can transmit something about their “style” of innovation to their students. But at the same time, these numbers suggest to me teacher influence isn’t the dominant determinant of interests (and I think it would be surprising if it was!).

New articles and updates to existing articles are typically added to this site every three weeks. To learn what’s new on New Things Under the Sun, subscribe to the newsletter.

Cited in the above

Gender and what gets researched

Geography and what gets researched

Entrepreneurship is contagious

The “idea” of being an entrepreneur

Cites the Above

Teachers and the transmission of excellence

Indexed at

Traits of innovative agents

Articles cited

Borowiecki, Karol Jan. 2022. Good Reverberations? Teacher Influence in Music Composition since 1450. Journal of Political Economy 130(4): 991-1090. https://doi.org/10.1086/718370

Koschnick, Julius. 2023. Teacher-directed scientific change: The case of the English Scientific Revolution. PhD job market paper.

Azoulay, Pierre, Christopher C. Liu, and Toby E. Stuart. 2017. Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs. American Journal of Sociology 122(4): 1223-1271. https://doi.org/10.1086/689890