The many applications of soil survey and GIS based data to agricultural development and agri-environmental research necessitates the development of econometric approaches that allow researchers to combine these fine scale data with larger scale—aggregated—dependent variable choice data while minimizing the loss of information caused by the aggregation process. The study presents an intuitive, new approach to estimation of a logit model with group–average choice data that are subject to a measurement error. We illustrate the approach in a Monte Carlo analysis and in an application to a dataset on farmer choice of tillage.
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