Academic Migration and Academic Networks: Evidence from Scholarly Big Data and the Iron Curtain

Iron Curtain and Big Data are two words usually used to denote completely two different eras. Yet, the context the former offers and the rich data source the latter provides, enable the causal identification of the effect of networks on migration. Academics in countries behind the Iron Curtain were strongly isolated from the rest of the world. This context poses the question of the importance of academic networks for migration post the fall of the Berlin Wall and Iron Curtain. Using Microsoft Academic Knowledge Graph, a scholarly big data source, mapping of academics’ networks is possible and information about the size and quality of their co-authorships, by location is achieved. Focusing on academics from Eastern Europe (henceforth EE) from 1980-1988 and their academic networks (1980-1988), We investigate the effect of academic network characteristics, by location, on the probability to migrate post the fall of the Berlin Wall in 1989 and up to 2003, marking the year many EE countries held referendums or signed treaties to join the EU. The unique context ensures that there was no anticipation of the fall of the Eastern Bloc and together with the data that offers unique rich information, identification is achieved. Approximately 30k academics from EE were identified, from which 3% were migrants. The results could be explained by two channels, the cost and signalling channel. The cost channel is how the network characteristic reduces or increases the cost of migration and thus acting as a facilitator or a de-facilitator of migration. The signal channel on the other hand in which the network characteristic serves as a signal for the academic himself and his quality and his potential contribution and addition to the new host institution, thus also serving as a facilitator or a de-facilitator of migration. We find that mostly network size and quality results could be explained by the cost channel and signalling channel, respectively. Size of the network tends to be more important than the quality, which is a context-specific result. We find heterogeneous effects by fields of study that align with previous lines of research. Heterogeneous effects are explained by two things: threat of attention and arrest from KGB and the role of reputation, language, and network barriers.