The growing popularity of anti-establishment parties and politicians among voters in Western democracies since the mid-2010s has led to a number of explanations. One widely accepted approach, both in journalistic and academic circles, is the theory that the "left behind" voters are primarily responsible for this development. Despite the popularity of this theory, it has two central problems: firstly, a clear definition of what “left behind” means and secondly, a lack of empirical testing. This dissertation addresses these problems using the 2016 U.S. presidential election as an example. Based on an examination of media and academic discourse, an empirically testable definition of the theory is distilled, operationalized, and then empirically tested at the county level through two regression models. The results of the analysis show that the chosen operationalization of the theory can explain approximately two-thirds of the variance in the vote share for Donald Trump. In order to understand the proportion of unexplained variance, counties in which Trump's share of the vote as predicted by the statistical models deviates significantly from the actual vote are then examined in descriptive analysis in exploratory form. It becomes clear that the reasons for the strong deviations lie less in the selection of the chosen variables, but probably in their lack of weighting, as well as in other factors such as voter turnout and historical loyalty to a party. The dissertation places this empirical research in a broader context by viewing Trump's election as a symptom of a transitional phase between two political paradigms. Both by adopting this perspective and through the empirical aspects of the work, this dissertation contributes to a better understanding of the origins of Trump's electoral success and opens up new ways to address them.
