Alexander Subbotin and Samin Aref
This paper that uses Scopus data to analyze the migration of academics in and out of Russia. What’s interesting about this paper is the use of bibliometric data to track migration flows through changes in affiliation. To do this, the authors use a set of 2.4 million citations from 659,000 authors who at some point had an academic affiliation in Russia (5.2% of whom had affiliations in different countries at some point).
The authors define the migration patterns of researchers by looking at the trajectory of their publications. Authors who first publish with an American institution as their affiliation and then a Russian one are labelled as immigrants, and vice versa. This is a tricky way to define immigration because it could be the case that many “immigrants” are actually people born and raised in Russia who left to do their doctoral studies and returned afterwards. This purely affiliation-based definition of migration flows is certainly a bit problematic if the construct in question is “brain drain”.
A supplemental analysis could look throughout the data for the names of authors with a high probability of being Russian or of Eastern European origin. Of course, there would be a number of tricky ethical questions around this type of analysis, and it might lead to similar questions about whether differences in citations might be driven by racial prejudice. Still, it’s an idea, and the two measures in combination might ameliorate things.
There are perhaps not many surprises in this paper. The results show a hierarchy of citation success such that emigrants from Russia get the most citations, immigrants to Russia get the second-most, and academics who never leave Russia get the least. This probably exactly matches most readers’ expectations, since any time spent in a country with a stronger research community should result in some boost in exposure/citations. Further, there is likely some selection effect such that researchers with more ability might do their doctoral studies in a foreign country, or they might receive job offers at good universities in other countries later in their career.
This paper is a good example of how a very big dataset may not be enough to make a paper interesting - it clearly takes a lot of thought and work to uncover exactly what facet of a dataset tells the best story, and it seems like this paper may not quite be there yet. However, I can’t be too hard on it. The results make sense and it’s only a working paper. I’m excited to see where it goes from here!