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In this paper we use simulations to examine how endogeneity biases the results reported by ordinary least squares (OLS) regression. In addition, we examine how instrumental variable techniques help to alleviate such bias. Our results demonstrate severe bias even at low levels of endogeneity. Our results also illustrate how instrumental variables produce unbiased coefficient estimates, but instrumental variables are associated with extremely low levels of statistical power. Finally, our simulations highlight how stronger instruments improve statistical power and that endogenous instruments can report results that are inferior to those reported by OLS regression. Based on our results, we provide a series of recommendations for scholars dealing with endogeneity
