A generalization of the multinomial logit (MNL) model is developed for cases in which discrete alternatives are ordered so as to induce stochastic correlation among alternatives in close proximity. The model belongs to the Generalized Extreme Value class introduced by McFadden, and is therefore consistent with random utility maximization. If the true model is nearly MNL, iterative estimation on an ordinary MNL computer package provides approximate parameter estimates and a test for the hypothesized failure of the MNL'S "independence from irrelevant alternatives" assumption. A straightforward extension can handle cases where observations have been selected on the basis of a truncated choice set. The model's properties are investigated through a numerical example, and through two empirical applications whose rather unsatisfactory results are very briefly described.

Economists have been paying increasing attention to the study of situations in which csumers face a discrete rather than a continous set of choices.Such models are potentially very important in evaluating the impact of government programs upon consi.mterwelfare.But very little has been said in general regarding the tools of applied welfare economics in discrete choice situations.This paper shows how the conventional methods of applied welfare economics can be modified to handle such cases.It focuses on the cornputation of the excess burden of taxation, and the evaluation of gua].itychange.The results are applied to stochastic utility models, including the popular cases of prohit and logit analysis.Throughout, the ernp)-asis is on providing rigorous guidelines for carrying out applied work.

We apply recent econometric advances to study the distribution of commuters' preferences for speedy and reliable highway travel. Our analysis applies mixed logit to combined revealed and stated preference data on commuter choices of whether to pay a toll for congestion-free express travel. We find that motorists exhibit high values of travel time and reliability, and substantial heterogeneity in those values. We suggest that road pricing policies designed to cater to such varying preferences can improve efficiency and reduce the disparity of welfare impacts compared with recent pricing experiments.