Using Mixed Logit in Land Use Models : Can Expectation-Maximization (EM) Algorithms Facilitate Estimation

The loss of native grassland habitat in the Northern Plains of the United States is prompting concern about the effect of farm programs on rangeland-to-cropland conversion. In theory, a mixed logit model can capture any pattern of response to economic or policy change. In reality, mixed logit likelihood functions can be time-consuming and difficult to maximize using conventional methods, even when only a small number of parameters are random. Expectation-Maximization (EM) algorithms dramatically reduce solution time while allowing a relatively large number of random parameters. Although our analysis is exploratory, it does indicate that the latent class model is a unique alternative for land use research. Results differ considerably from conditional logit, where the largest cross-marginal effect of crop revenue is on rangeland probability. We see little advantage in a random parameters (mixed logit) model specified using parametric distributions. Our results – that classes form around different types of land based on the propensity to change (or not change) – are intuitively plausible and may offer greater insight on land use and land use change than can be gleaned from models built around parametric distributions