Abstract
Over the past 30 years considerable work scrutiny has been undertaken in the area of corruption and its effect on many facets of society. In business, efforts to measure corruption have been frequently debated and models have been proposed to reflect different firm characteristics in the presence of corruption. Based on these measures, research usually considers single variable measures over time, generally cross-nationally. This study constructs a new model incorporating multiple different variables working in concert over the period from 2000-2015, to postulate a variety of different relationships and firm characteristics at the state level in the United States. In doing so, the model is constructed to limit biases that a single variable can have on the data. The model analyzes state level firm financial performance by utilizing ROA and Tobin’s Q as well as comparing high corruption state data to low corruption state data. The study finds that the presence of corruption increases the firm’s financial performance at the state level. These data are then used to conduct univariate testing with Ordinary Least Squares modelling to examine fixed firm effects as well as time lagged data. Significance is found to hold for these constraints and that firm financial performance is enhanced in high corruption states for most of the sample constructs. Supplementary models are subsequently constructed to test the robustness against significant economic events and legislative changes. The model is found to provide additional evidence when these tests are applied, thereby maintaining significance. The evidence from these tests are discussed and the conclusion reached is that corruption provides the opportunity for firms to enhance their financial performance, particularly for large firms, value firms, and firms with low leverage. It is also concluded that the benefits in performance from corruption are more beneficial in the short term.
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