We argue that identification problems bedevil most applied spatial research. Spatial econometrics solves these problems by deriving estimators assuming that functional forms are known and by using model comparison techniques to let the data choose between competing specifications. We argue that in most situations of interest this, at best, achieves only very weak identification. Worse, in most cases, such an approach will simply be uninformative about the economic processes at work rendering much applied spatial econometric research ‘pointless’, unless the main aim is simply description of the data. We advocate an alternative approach based on the ‘experimental paradigm’ which puts issues of identification and causality at centre stage
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