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In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.
Can right‐wing terrorism increase support for far‐right populist parties and if so, why? Exploiting quasi‐random variation between successful and failed attacks across German municipalities, we find that successful attacks lead to significant increases in the vote share for the right‐wing, populist Alternative für Deutschland (AfD) party in state elections. Investigating channels, we find that successful attacks lead to differential increases in turnout which are mainly captured by the AfD. Using the German SOEP, a longitudinal panel of individuals, we investigate terror’s impact on individual political attitudes. We first document that people residing in municipalities that experience successful or failed attacks are indistinguishable. We then show that successful terror leads individuals to prefer the AfD, adopt more populist attitudes and report significantly greater political participation at the local level. Terror also leads voters to migrate away from (some) mainstream parties to the AfD. We also find differential media reporting: successful attacks receive more media coverage among local and regional publishers, coverage which makes significantly more use of words related to Islam and terror. Our results hold despite the fact that most attacks are motivated by right‐wing causes and targeted against migrants. Moreover, successful attacks that receive the most media coverage have nearly double the effect on the AfD vote share in state elections and they also increase the AfD vote share in Federal elections, highlighting media salience as a driver of our overall results.
Broad, long-term financial and economic datasets are a scarce resource, in particular in the European context. In this paper, we present an approach for an extensible, i.e. adaptable to future changes in technologies and sources, data model that may constitute a basis for digitized and structured long- term, historical datasets. The data model covers specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence achieving extensibility. Furthermore, based on historical German company and stock market data, we discuss a relational implementation of this approach.