We investigate the relationship between economic freedom and corruption using data from U.S. states covering almost a quarter of a century. Our study advances the existing literature on several fronts. First, instead of using subjective cross-country corruption indices assembled by various investment risk services, we use a more objective measure of corruption: the number of government officials convicted in a state for crimes related to corruption. Second, unlike previous studies, we exploit both time series and cross-sectional variation in the data in the estimation of a panel error correction model. The panel error correction model results show that in the long-run economic freedom, per capita income, and education have a negative and statistically significant impact on corruption whereas income inequality has a positive and statistically significant impact. The causality tests associated with the panel error correction model reveal bidirectional causality between economic freedom and corruption in both the short-run and long-run.