In this paper, two nonparametric frontier models are applied to a sample of country level data to examine corruption's impact of economic growth. The primary data from the year 2014 are drawn from Transparency International and the Penn World Tables, including 137 countries in the analysis. In the first approach, corruption is modeled as an input in countries' production technologies and a test is conducted to determine whether corruption congests output. Including corruption in the technology implies that it affects the efficient frontier; however, it may only affect the distribution of effciency scores. In the second approach, the conditional order-m model is a partial frontier approach. Corruption is treated as a conditioning variable that could affect either the frontier or the distribution of efficiency scores relative to the frontier. Using these two appoaches with a variety of corruption measures (for robustness), an analysis of corruption's effects on national production technologies and the distribution of efficiency scores is carried out. Results provide insight into whether the appropriate policy is a crack-down on corruption or an overhaul of institutions.