Modelling Endogenous Spatial Regimes in Farms Technologies

Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can produce model misspecification and preclude causal inference. In this article, we adopt a two-step procedure to deal with unobserved spatial heterogeneity while accounting for spatial dependence in a cross-sectional setting. The first step of the procedure takes explicitly unobserved spatial heterogeneity into account to endogenously identify subsets of farms that follow a similar local production econometric model, i.e. spatial production regimes. The second step consists in the specification of a spatial autoregressive model with autoregressive disturbances and spatial regimes. The entire method is applied to two regional samples of olive growing farms in Italy. The main finding is that spatial autoregressive models with regimes fit the data best, proving that explicitly accounting for unobserved spatial heterogeneity is of crucial importance when modelling the production function of farms.