Carsten Schwemmer, M.A. Computational Methods for the Social Sciences: Applications to the Study of Ethnic Minorities Cumulative Dissertation for obtaining the academic grade Dr. rer. pol. submitted to University of Bamberg Faculty for Social Sciences, Economics, and Business Administration First advisor Prof. Dr. Marc Helbling, University of Bamberg Second advisor Prof. Dr. Thomas Saalfeld, University of Bamberg Additional member of the promotion committee Prof. Dr. Kai Fischbach, University of Bamberg Submitted in Bamberg on June 17, 2019 Successfully defended in Bamberg on September 20, 2019 URN: urn:nbn:de:bvb:473-irb-464634 DOI: https://doi.org/10.20378/irb-46463 Danksagung (Acknowledgments in German Language) Diese Dissertationsschrift ist das Resultat meiner ersten Schritte auf der Reise durch die akademische Welt. Es war nicht immer klar, ob “Forscher zu werden” der richtige Weg für mich ist. Zu jeder Zeit, aber insbesondere in Phasen der Orientierungslosigkeit, hatte und habe ich das Glück von zahlreichen Men- schen unterstützt zu werden. Bei allen möchte ich mich hiermit ganz herzlich bedanken. Ohne euch wäre diese Dissertationsschrift nie zu Ende geschrieben worden. Zunächst möchte ich meinen Betreuern Marc Helbling und Thomas Saalfeld danken. Wenn ich durch die Tätigkeit als wissenschaftlicher Mitarbeiter eines gelernt habe, dann dass Zeit eine der wertvollsten Ressourcen ist. In dieser Hinsicht wart ihr nicht nur ausgezeichnete Betreuer, sondern auch die denkbar besten Chefs: Ihr habt mir ausreichend Zeit für meine Forschung gegeben und hattet gleichzeitig stets ein offenes Ohr wenn ich euren Rat gebraucht habe. Zudem habt ihr mich immer dabei unterstützt die nächsten Schritte meiner akademischen Reise vorzubereiten. Ebenso bin ich dankbar für die Unterstützung zahlreicher Kolleginnen und Kollegen, die entweder selbst als Koautor/-innen an Teilen dieser Schrift be- teiligt waren, oder wichtiges Feedback zu meinen Forschungsprojekten gege- ben haben: Danke Michael Eberhardt, Jorge Fernandes, Kai Fischbach, Diana Fischer-Preßler, Lucas Geese, Sebastian Jungkunz, Menusch Khadjavi, Caro- line Schultz, Stephan Simon, Jasper Tjaden, Oliver Wieczorek und Sandra Ziewiecki. Ich danke auch den Teilnehmer/-innen mehrerer Forschungskollo- quien in Bamberg und der Graduiertenschule BAGSS für all die hilfreichen wissenschaftlichen Diskussionen. Aus einigen Arbeitsbeziehungen sind über die Jahre hinweg Freundschaften entstanden. Ich möchte mich jedoch auch bei meinen Freund/-innen außer- halb der Wissenschaft, bei meiner Freundin Mareike und bei meiner Familie bedanken. Ihr habt mir auf unzählige Arten geholfen und mir emotionalen Rückhalt gegeben. Zuletzt gilt mein besonderer Dank meinen Eltern Manfred und Monika, die es mir ermöglicht haben, meinen Lebensweg nach eigenen Wünschen und Vorstellungen zu gestalten. Contents 1 Preface ..................................... 1 1.1 Substantive contributions to the study of ethnic minorities . 2 1.2 About the application of computational methods . 16 1.3 Concluding remarks . 39 2 First Article: Ride with Me - Ethnic Discrimination, Social Mar- kets, and the Sharing Economy ...................... 51 3 Second Article: MPs’ principals and the substantive representa- tion of disadvantaged immigrant groups ................ 94 4 Third Article: Social Media Strategies of Right-Wing Movements - The Radicalization of Pegida ...................... 140 1 Preface In this manuscript I introduce my contributions to the emerging academic discipline Computational Social Science. At the time of writing in 2019, scholars have already used this term for over a decade (Lazer et al. 2009), but the development of this field is still ongoing. At its core, computational social scientists, including myself, seek to provide new answers to important social science research questions. They draw on computational methods at the intersection of computer science and statistics. This interdisciplinary approach comes with many potential benefits, but also with challenges, both of which I try to address in this cumulative dissertation. Naturally, the focus of computational social science research will lean stronger to- wards either of the involved disciplines. Trained as a sociologist, my research pre- dominantly focuses on the application of computational methods for social science aspects rather than on the development of computational methods on its own merit. Or, to put it in the words of Andreas Jungherr, I am “taking the social in Com- putational Social Science seriously” (Jungherr 2018, p. 29). This dissertation deals with the study of ethnic minorities, a social science research field about the interac- tions between mainstream societies and minorities such as refugees. These dynamic interactions lead to the emergence of many societal problems, such as political mobi- lization with the aim to maintain power of majority members and exclude members of immigrant origin. The overarching question for this dissertation is: how can computational methods be applied to provide new insights for the study of ethnic minorities? The articles for this dissertation include findings from research across 1 three related and interconnected domains: ethnic discrimination in the sharing econ- omy, political representation of ethnic minorities and collective action driven by xenophobia. In the first subsection of this preface, I will provide a summary of the substantial contributions to the study of ethnic minorities across these domains. All of the included articles were submitted to international, peer-reviewed social science journals. At the time of writing, two of the three articles have already been published and one article is under review. Unsurprisingly, the corresponding journals predominantly focus on social science aspects rather than computational methods. This is strongly reflected in the content of all articles: details about many of the computational aspects either had to be moved to appendices or did not find a place at all. This makes it difficult to highlight the importance of my computational contributions, as topics like the development of research software or algorithms for working with textual data could not be discussed in depth. For this reason, I will use the second part of this preface to provide more insights into the computational methods which served as the backbone for this dissertation. At last, in the third part of this preface I will close with some concluding remarks about the present and the future of computational methods for social science research. 1.1 Substantive contributions to the study of ethnic minori- ties This section provides an overview of the most important contributions to the study of ethnic minorities. The first article of this dissertation is related to discrimination of ethnic minorities (Tjaden, Schwemmer, and Khadjavi 2018). The second article 2 examines the political representation of ethnic minorities (Geese and Schwemmer 2019). The last article deals with xenophobic collective action affecting ethnic mi- norities (Schwemmer 2019b). These topics are connected to each other in several ways. To provide only one example, a stronger representation of ethnic minorities by political actors who act to fulfill their needs will make it harder for xenophobic movements to gain power and to lead the way for right-wing forces in the correspond- ing political system. Moreover, these topics are also connected in a methodological way: they share a lot of problems that make it difficult to conduct social science research.´Analyzing phenomena such as ethnic discrimination, substantive represen- tation and collective action requires the measurement of corresponding indicators in ways that fulfill standards of modern social science research. For instance, ex- perimental research designs are often used to study ethnic discrimination. However, this approach tends to suffer from low external validity, that is the generalization of experimental research findings to real world scenarios. Likewise, using survey data to analyze attitudes towards ethnic minorities introduces other methodolog- ical issues, such as social desirability bias (Edwards 1957). In addition, studying (ethnic) minorities is difficult by definition, as it often comes with a low number of observations that can be analyzed. As demonstrated in this dissertation, us- ing computational methods can help to overcome such methodological problems. I show that extracting and analyzing real world data, using computational models for working with unstructured data such as large text corpora and creating research software are efficient approaches for answering fundamental research questions for the study of ethnic minorities. In what follows, I will first discuss the substantive contributions of each article. 3 Discrimination of ethnic minorities A large body of literature has consistently shown that discrimination of ethnic mi- norities is a persistent driver of inequalities across a multitude of domains (Bertrand and Mullainathan 2004; Pager and Shepherd 2008; Pager, Bonikowski, and West- ern 2009; Ahmed, Andersson, and Hammarstedt 2010; Lin and Lundquist 2013; Pedulla 2018). To name a few, ethnic minorities suffer from inequalities related to wages, education and employment. Many of these inequalities emerge from unequal treatment of minority groups in comparison to majority groups on markets like the housing market. Multiple studies have already been conducted to assess the role of ethnic discrimination in such markets (e.g. Pager and Shepherd 2008). One