Campaign funding strategies : how political parties and private donors choose which legislators to invest in
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This paper employs a series of hierarchical Bayesian regression models to contribute to the academic discourse on the funding strategies that political parties and private donors employ when deciding which political campaigns to support, either directly by way of campaign contributions or through independent expenditures. After giving a brief overview of the recent campaign finance trends in the United States Senate from 1991 through 2016 and the previous scholarly literature covering this specific subject, several hierarchical Bayesian regression models are estimated to explain campaign finance strategies over the past several decades. The main explanatory variables include electoral competitiveness, party loyalty and legislator ideology and while these particular variables are not new introductions to the campaign finance literature, the way they are implemented is. Due to the changing nature of campaign finance laws and the increasing cost of running campaigns, a hierarchical model is essential for looking at funding strategies during each individual election cycle. To my knowledge, this article is the first, within the campaign finance literature, to leverage Markov Chain Monte Carlo techniques to help attain robust findings despite limited sample size and its findings are invaluable to the campaign finance literature for the United States and internationally.