Relaxing the IIA Assumption in Locational Choice Models: A Comparison Between Conditional Logit, Mixed Logit, and Multinomial Probit Models

This paper estimates a locational choice model to assess the demand for local public services, using a data set where individuals chooses between 26 municipalities within a local labor market. We assess the importance of the IIA assumption by comparing the predictions of three difference models; the conditional logit (CL) model, the mixed logit (MXL) model, and the multinomial probit (MNP) model. Our main finding is that a MXL or a MNP estimator leads to exactly the same conclusions as the traditional CL estimator. That is, given the data used here, the IIA assumption, and hence the use of a CL estimator, seems to be valid when estimating Tiebout-related migration. The only instance when we get somewhat different results when using the MXL or MNP estimator compared with the CL estimator is when we have a too parsimonious model. One possible hypothesis explaining this result is that omitted variables are captured by the distribution parameters of the coeffcients of the included variables, leading to the false conclusion that the coefficients are not fixed. This hypothesis is supported by the results from a Monte Carlo investigation.