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

Abstract

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.