In the last decade special attention has been focused on estimating a firm’s efficiency and productivity; Stochastic Frontier Analysis (SFA) has been one of the most used techniques that allows the separation of inefficiency from stochastic noise, assuming homogeneous technology is available to all producers and that there is independence between observations. However, this second assumption is violated data are spatial auto-correlated, thus biasing statistical inference. Attention has, therefore, shifted to models that allow the controlling of heterogeneity introducing, in the model or in the error term, contextual variables correlated with inefficiency. In our paper we propose viewing the spatial external factors (natural or artificial) in a new way: instead of identifying ex-ante a multitude of determinants, often statistically and economically difficult to detect, we suggested using an original methodology that, following a classical SFA approach, splits efficiency into three components: the first one is linked to the spatial lag, the second one to the DMU’s specificities, and the third to the error term. Finally, we tested our method using simulated data and examined the Italian wine sector, testing the ability to control spatial, global and local heterogeneity.

Spatially heterogeneous weights and a non‐compensatory aggregation scheme, are two important properties needed to construct a composite indicator capable of summarizing properly the multidimensional phenomenon of local spatial units. Such a composite indicator takes into account, on the one hand, the latent characteristics of the specific units related to their location in the territory, and on the other hand, the relative importance of sub‐indicators highlighting both positive and negative aspects of the studied phenomena. Under these premises, this article proposes a new method called Ordered Geographically Weighted Averaging (OGWA), which can consider different degrees of non‐compensability between sub‐indicators and, at the same time, the spatial heterogeneity for continuous, ordinal, and mixed data. The properties of the method are evaluated through a simulation study. Finally, the method is applied to construct a composite indicator to map the urban public infrastructure of São Sebastião do Paraíso, a city located in the southeastern region of Brazil.