Clustering of auto supplier plants in the United States: Generalized method of moments spatial logit for large samples

A linearized logit version of Pinkse and Slade's spatial GMM estimator reduces estimation to two steps - standard logit followed by two-stage least squares. Linearization produces a model that can be estimated using large datasets. Monte Carlo experiments suggest that the linearized model accurately identifies the presence of spatial effects and is capable of producing accurate estimates of marginal effects. In an application to the location of supplier plants in the U.S. auto industry, the results imply no additional clustering of new plants beyond the level of clustering of existing plant locations.