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
This paper introduces a new composite indicator method integrating the spatial dependence into the robust directional model in the case of undesirable outputs. The proposed approach is advantageous compared to the traditional and conditional robust BoD models in that it allows to compare the performance of individual units with local cluster of peers. The methodology has been tested on a very detailed database of Italian municipalities for the year 2015 in the municipal solid waste collection and processing sector and confirms the existence of strong local constraints linked to the disposal facilities planned by higher level Authorities.
This paper follows the research mainstream aimed to link the efficiency frontier approaches and the composite indicators (CI) methods. More in detail, the main drawbacks of the CI methods based on Benefit of the Doubt (BoD) approach are the sensitivity to the outliers, the perfect compensability and the lack of consideration about the marginal rate of substitution between simple indicators. Following Simar and Vanhems (J Econ 166(2):342–354, 2012) results, we propose a weighting method that bypassing all previous shortcomings suggests a comprehensive approach to construct robust and non-compensatory composite indicators. This approach is based on the integration of BoD by a directional distance function. In order to better highlight the advantages and limitations of our approach we present two applications: in the first one we test our approach on simulated data, while in the second one we consider the supply level of the Italian national health service with the aim to analyse the regional differences and verify the robustness of the results
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