The challenges of establishing causality using qualitative, archival data: Towards a comparative--and geographical-- historical analysis.

Authors: Monica Varsanyi*, CUNY - Graduate Center, Doris Marie Provine, Arizona State University
Topics: Qualitative Methods, Political Geography, Immigration/Transnationalism
Keywords: immigration, qualitative methods, archival research
Session Type: Paper
Day: 4/5/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Virginia B, Marriott, Lobby Level
Presentation File: No File Uploaded

If a central goal of social science is not only to describe what is, but to establish causality, how do we use qualitative, archival data in this pursuit? This paper uses a case study of state immigration policy to explore these questions and the challenges of establishing causality using qualitative, archival data. Over the past fifteen years, there has been an explosion of immigration policy-making at the state scale, with some states taking an anti-immigrant stance while others support immigrant integration. What explains this geographical variation? A number of recent studies have used quantitative approaches to identify causal effects, or the degree to which particular factors contribute to the emergence of state-level immigration policy (covariation). In contrast, a qualitative research design incorporating small-N, cross-case comparison and within-case process tracing can effectively and rigorously illuminate causal mechanisms that elucidate why and how these factors have mattered and the processes by which they have contributed to the outcome over time. To illustrate this approach, we present a case study of immigration policy focusing on Arizona and New Mexico. Despite their interconnected history and geography, Arizona has developed a draconian immigration enforcement stance, while New Mexico has become one of the most immigrant welcoming states in the nation. As we demonstrate in this paper, their different approaches have roots deeply anchored in each state’s history. A qualitative approach, drawing on archival data, can effectively identify the processes by which these divergent approaches have developed.

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