The recent surge in studies analysing spatial dependence in political science has gone hand-in-hand with increased attention paid to the choice of estimation technique. In comparison, specification choice has been relatively neglected, even though it leads to equally, if not more, serious inference problems. In this article four specification issues are analysed. It is argued that to avoid biased estimates of the spatial effects, researchers need to consider carefully how to model temporal dynamics, common trends and common shocks, as well as how to account for spatial clustering and unobserved spatial heterogeneity. The remaining two specification issues relate to the weighting matrix employed for the creation of spatial effects: whether it should be row-standardised and what functional form to choose for this matrix. The importance of these specification issues is demonstrated by replicating Hays' model of spatial dependence in international capital tax rate competition. Seemingly small changes to model specification have major impacts on the spatial effect estimates. It is recommended that spatial analysts develop their theories of spatial dependencies further to provide more guidance on the specification of the estimation model. In the absence of sufficiently developed theories, the robustness of results to specification changes needs to be demonstrated.