Politics of Representation and Money Flows: the Political Economy of Globalisation
Dec 8, 2023 — 12 p.m. to 1:30 p.m.
Event organised by the Centre on Governance and the Konrad Adenauer Research Chair in Empirical Democracy Studies
Representative Democracy in Times of Globalization: The Political Preferences of Non-citizen residents by Ekaterina R. Rashkova
Living in a globalized world, where millions of people no longer live in their countries of birth, we ought to be asking ourselves whether and to what extent the traditional model of representative democracy is changing or needs to change. In particular, to what extent do citizens who live abroad participate in the democratic processes of their home country, and, conversely, what is the relationship with the electoral options in their new homelands? This research note explores the latter aspect by focusing on the Dutch national election held in March,2021. Based on a small sample of survey data, this exploratory analysis shows that non-citizen residents largely support less-established parties that have positioned themselves as parties that want to innovate and bring about new politics. This finding suggests that allowing immigrants to vote at national elections could have a visible impact on electoral outcomes.
Ekaterina R. Rashkova is Associate Professor of Comparative Politics at the Utrecht University School of Governance. Her research interests lie in the study electoral and party systems, the strategic behavior of political actors, and representation. Her current research interests include the political integration of non-citizen residents in host countries, as well as the connection of national political parties to their diaspora abroad. Her work compares new and established democracies and has appeared among others in Comparative European Politics, West European Politics, European Political Science Review, International Political Science Review, Party Politics, and Political Studies. In Fall 2023, she is a Visiting Research Scholar at SUNY Binghamton in NY.
Link Prediction in Money Laundering Networks: an Empirical Approach from The Netherlands by Peter Gerbrands
Despite the limited and mostly obfuscated data on money laundering networks, this paper demonstrates how high-quality administrative data, converted into a multi-layer network, can be used to predict the location of potential money laundering activities using a gradient boosting model. The model performs well, given the complexity of finding consciously well-hidden links. The model misses only 17.4% of all observed suspicious transactions and almost half of the predicted transactions are predicted correctly. The results emphasize the importance of a “personal layer” that connects people to each other. Networks of employment and corporate ownership are less important in money laundering networks. Especially the preferential attachment index, edge betweenness, and the length of the shortest path are critical.
Peter Gerbrands is a Post-Doctoral Researcher at the of Utrecht University School of Economics, where is develops the data infrastructure for . He teaches data science courses: “Applied Data Analysis and Visualization” and “Introduction to R”. His research interests are agent-based simulations, social network analysis, complex systems, big data analysis, statistical learning, and computational social science. He applies his skills primarily for policy analysis, especially related to illicit financial flows, i.e. tax evasion, tax avoidance and money laundering and has published in Regulation & Governance, and EPJ Data Science. Prior to becoming an academic, Peter had a long career in IT consulting. In Fall 2023, he is a Visiting Research Scholar at SUNY Binghamton in NY.