Replication Data for: The Sensitivity of Sensitivity Analysis

This article evaluates the reliability of sensitivity tests (Leamer 1978). Using Monte Carlo methods we show that, first, the definition of robustness exerts a large influence on the robustness of var¬iables. Second and more importantly, our results also demonstrate that inferences based on sen¬sitivity tests are most likely to be valid if determinants and confounders are almost uncorrelated and if the variables included in the true model exert a strong influence on outcomes. Third, no definition of robustness reliably avoids both false positives and false negatives. We find that for a wide variety of data-generating processes, rarely used definitions of robustness perform better than the frequently used model averaging rule suggested by Sala-i-Martin. Fourth, our results also suggest that Leamer’s extreme bounds analysis and Bayesian model averaging are extremely un¬likely to generate false positives. Thus, if based on these inferential criteria a variable is robust, it is almost certain to belong into the empirical model. Fifth and finally, we also show that research¬ers should avoid drawing inferences based on lack of robustness