The multinomial logit model is perhaps the most commonly used regression model for nominal outcomes in the social sciences. A concern raised by many researchers, however, is the assumption of the independence of irrelevant alternatives (IIA) that is implicit in the model. In this article, the authors undertake a series of Monte Carlo simulations to evaluate the three most commonly discussed tests of IIA. Results suggest that the size properties of the most common IIA tests depend on the data structure for the independent variables. These findings are consistent with an earlier impression that, even in well-specified models, IIA tests often reject the assumption when the alternatives seem distinct and often fail to reject IIA when the alternatives can reasonably be viewed as close substitutes. The authors conclude that tests of the IIA assumption that are based on the estimation of a restricted choice set are unsatisfactory for applied work.
