SODAS Lecture: Immigration and Social Distance - Evidence from Newspapers during the Age of Mass Migration

SODAS lecture
We are delighted to host Dr. Gloria Gennaro for this SODAS Lecture

Title: 
Immigration and Social Distance:  Evidence  from Newspapers during the Age of Mass Migration

Abstract: 
A constant of human history is the migration of peoples in search of a better future. In destination countries, these new arrivals come into contact with both the host population as well as already established immigrant communities. How does the arrival of new immigrants affect the perception of outgroup distance among the native majority group? And do new arrivals also change the perceived distance between the host population and existing immigrant groups? We address these questions in the context of the Age of Mass Migration (1860-1920), a period during which sizeable and diverse groups of migrants arrived on U.S. shores. Applying advanced computational linguistics techniques to a newly processed corpus of over 1.8 million newspaper issues (9 million pages) published by 3,675 local outlets in that period, we present a novel text-based measure of perceived socio-cultural distance between U.S.-born natives and 32 immigrant groups. For each mention of an immigrant group, we compute a  distance measure that captures whether the group's framing more closely resembles contexts used when portraying immigrants, rather than natives. We use this time- and county-varying outcome to analyse the short- and medium-term effects of immigration inflows on local perceptions of socio-cultural distance toward the arriving and existing immigrant groups. 

Bio: 
Gloria Gennaro is an Assistant Professor in Public Policy and Data Science at University College London. Previously she was a postdoctoral fellow at the Public Policy Group and Immigration Policy Lab in Zurich. She holds a Ph.D. in Social and Political Sciences from Bocconi University. Her research in comparative political economy explores political behaviour in democratic societies, using causal inference and computational social science.