  This paper derives identification, estimation, and inference results using
spatial differencing in sample selection models with unobserved heterogeneity.
We show that under the assumption of smooth changes across space of the
unobserved sub-location specific heterogeneities and inverse Mills ratio, key
parameters of a sample selection model are identified. The smoothness of the
sub-location specific heterogeneities implies a correlation in the outcomes. We
assume that the correlation is restricted within a location or cluster and
derive asymptotic results showing that as the number of independent clusters
increases, the estimators are consistent and asymptotically normal. We also
propose a formula for standard error estimation. A Monte-Carlo experiment
illustrates the small sample properties of our estimator. The application of
our procedure to estimate the determinants of the municipality tax rate in
Finland shows the importance of accounting for unobserved heterogeneity.
