Some cautions on the use of instrumental variables estimators in outcomes research: how bias in instrumental variables estimators is affected by instrument strength, instrument contamination, and sample size.

OBJECTIVES: To examine the performance of instrumental variables (IV) and ordinary least squares (OLS) regression under a range of conditions likely to be encountered in empirical research.

METHODS: A series of simulation analyses are carried out to compare estimation error between OLS and IV when the independent variable of interest is endogenous. The simulations account for a range of situations that may be encountered by researchers in actual practice-varying degrees of endogeneity, instrument strength, instrument contamination, and sample size. The intent of this article is to provide researchers with more intuition with respect to how important these factors are from an empirical standpoint.

RESULTS: Notably, the simulations indicate a greater potential for inferential error when using IV than OLS in all but the most ideal circumstances.

CONCLUSIONS: Researchers should be cautious when using IV methods. These methods are valuable in testing for the presence of endogeneity but only under the most ideal circumstances are they likely to produce estimates with less estimation error than OLS.