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Spatial microeconometric studies typically suffer from various forms ofinaccuracies that are not present when dealing with the classical regionalspatial econometrics models. Among those, missing data, locational errors,sampling without a formal sample design, measurement errors and misalignmentare the typical sources of inaccuracy that can affects the results in a spatialmicroeconometric analysis. In this paper, we have examined the effects ofmeasurement error introduced in a logistic model by random geo-masking, whendistances are used as predictors. Extending the classical results on themeasurement error in a linear regression model, our MC experiment on hospitalchoices showed that the higher the distortion produced by the geo-masking, thehigher is the downward bias in absolute value towards zero of the coefficientassociated to the distance in a regression model.

Measurement error induced by locational uncertainty when estimating discrete choice models with a distance as a regressor