USING MULTIPLE NEIGHBORING INTERACTION EFFECTS IN SPATIAL REGRESSION SPECIFICATIONS TO REDUCE OMITTED VARIABLE BIAS
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A major challenge in the analysis of micro level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We consider a spatially lagged explanatory model (SLX) employing two spatial weighting matrices differentiating between local and regional neighborhoods. We empirically analyze spatial interaction between individual farms in Norway and additionally perform Monte Carlo simulations exploring the model’s performance under different data settings. Results show that including two spatial weighting matrices can indeed reduce the bias resulting from omitted variables. The empirical application identifies different local and regional spatial interdependencies of direct payments with opposite sign.