Research

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, of which 3% were migrants. The results could be explained by two channels, the cost and signaling 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 by KGB and the role of reputation, language, and network barriers. 

CESifo Working Paper 

👩🏽‍🏫 Doctoral Workshop (UCLouvain), Doctorissimes Conference (PSE), Globalization, Political Economy and Trade Thesis Research Seminar (PSE), CESifo Junior Workshop on Big Data

This paper presents a content analysis of gender stereotypes in popular song lyrics using word embeddings. We begin by explaining how we curated a novel data set comprising lyrics from popular songs in the US over the past 70 years. We then explain word embeddings, detailing both their nature and their application to our lyric corpus. Subsequently, we present a case study that examines the prevalence of gender stereotypes across various music genres. Our findings showed that while all genres exhibited stereotyping of men and women, the specific content of these stereotypes varied significantly by genre, often in surprising ways, such as that gender stereotypes in hip-hop, often perceived as being distinctly sexist, were rarely stronger in hip-hop than in other genres. Finally, we reflect on the strengths and limitations of using word embeddings to study music lyrics and provide suggestions for their best application to sociological questions.

Submitted to the Bulletin of Sociological Methodology

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This project aims to study the "sociology of innovation"; an adaptation of the sociology of industry by Granovetter (1998) by focusing on Germans who arrived in the US post the failed German revolution of 1848. The German failed revolution of 1848 marks the dividing line between early industrialization and the industrial revolution. In the Second Industrial Revolution, Germany was a pioneer in chemistry, steel, and machinery. Thus, observing Germans arriving from 1848 onwards, we can study the role of know-how, its transmission mechanisms, what matters for inventors, what happens to occupations of immigrants, what's the role of different skill composition, and quantify the impact on US innovation. 

We are using the full sample US historical census, ship lists containing 4 million Germans that arrived in the US from 1850 to 1897 (containing information on occupations at home), yearbooks of R&D labs, and historical patent data (1790-2010). 

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