The purpose of this paper is to evaluate popular academic theories believed to cause corruption through quantitative dataset proxies. In undertaking the exercise, the author examines various (and often competing) schools of thought on the topic, while showcasing the challenges that burden the objective study of corruption in a global context. The paper obtains a list of sixteen (16) variables extrapolated from academic literature; each (independent) variable is tied to a proxy dataset. The variables are first analysed through univariate statistics, before being subjected to bivariate correlation analysis against the (dependent) variable of corruption (itself tied to a proxy dataset, the Corruption Perception Index). The methodology employed in the analysis involves a standard mixture of statistical techniques—descriptive statistics  charts, logarithmic normalisation, Q-Q plott...