Accounting for Spatial Heterogeneity of Preferences in Discrete Choice Models

Abstract

There are reasons researchers may be interested in accounting for spatial
heterogeneity of preferences, including avoiding model misspecification and the
resulting bias, and deriving spatial maps of willingness-to-pay (WTP), which
are relevant for policy-making and environmental management. We employ
a Monte Carlo simulation of three econometric approaches to account for
spatial preference heterogeneity in discrete choice models. The first is based
on the analysis of individual-specific estimates of the mixed logit model. The
second extends this model to explicitly account for spatial autocorrelation of
random parameters, instead of simply conditioning individual-specific estimates
on population-level distributions and individuals’ choices. The third is the
geographically weighted multinomial logit model, which incorporates spatial
dimensions using geographical weights to estimate location-specific choice
models. We analyze the performance of these methods in recovering population-,
region- and individual-level preference parameter estimates and implied WTP
in the case of spatial preference heterogeneity. We find that, although ignoring
spatial preference heterogeneity did not significantly bias population-level
results of the simple mixed logit model, neither individual-specific estimates
nor the geographically weighted multinomial logit model was able to reliably
recover the true region- and individual-specific parameters. We show that the
spatial mixed logit proposed in this study is promising and outline possibilities
for future development.