The academic literature provides excellent methodologies to identify best practices and to calculate inefficiencies by stochastic frontier analysis. However, these methodologies are regarded as a black box by policy makers and managers and therefore results are hard to accept. This paper proposes an alternative class of stochastic frontier estimators, based on the notion that some observations contain more information than others about the true frontier. If an observation is likely to contain much information, it is assigned a large weight in the regression analysis. In order to establish the weights, we propose an iterative procedure. The advantages of this more intuitive approach are its transparency and its easy application. The method is applied to Dutch local administrative services (LAS) in municipalities. The method converges quickly and produces reliable estimates. About 25% of the LAS are designated as efficient. The average efficiency score is 93%. For the average sized LAS no economies of scale exist.