اقدامات بهره وری در برنامه های نظارتی

The last decade has witnessed a change to more powerful incentive schemes and the adoption by a large number of regulators of some form of price cap regimes. The efficiency frontiers literature tackles the problem of measuring the X factor in a price cap regime with an RPI–X rule. However, that literature has by large focused solely on the theoretical aspects involved in the estimation of an efficient frontier. The empirical application of the theoretical concepts (which is the main interest of regulators) has not yet received equal attention. In this paper we address this issue and try to elaborate upon the applied aspects of efficiency measurement.

For decades, rate-of-return regulation has been the dominant practice in the regulation of utilities. This method, although allowing the firm to recover its costs and resulting in a lower cost of capital (due to the lower risk borne by the firms), provided little incentives for cost minimization among regulated firms. The last decade has witnessed a change to more powerful incentive schemes and the adoption by a large number of regulators of some form of price cap regimes1. The main purposes of a switch from rate-of-return regulation to price cap regulation have been to increase the incentives for firms to minimize their costs, and to ensure that, eventually, users benefit from these cost reductions — typically within 3–5 years after a regulatory price review. This objective requires the measurement of the expected efficiency gains that would lead to cost reductions at the firm level. The renewed attention given to productive efficiency is one of the main reasons for the increase in efforts to measure efficiency in regulated sectors. Efficiency measures are no longer a side show as they were under rate-of-return regulation. Efficiency gains of a firm can come from two main sources: shifts in the frontier reflecting efficiency gains at the sectoral level, and efficiency gains at the firm level reflecting a catching up effect. The latter are the gains to be made by firms not yet on the frontier. These firms should be able to achieve not only the industry gain (the shift of the frontier) but also specific gains offsetting firm specific inefficiencies. A regulator should bear in mind this decomposition when carrying out an efficiency analysis. The efficiency frontiers literature tackles the problem of measuring both components of the X factor in a price cap regime with an RPI–X rule. However, that literature has by large focused solely on the theoretical aspects involved in the estimation of an efficient frontier. The empirical application of the theoretical concepts (which is the main interest of regulators) has not yet received equal attention. In this paper we address this issue and try to elaborate upon the applied aspects of efficiency measurement in a regulatory context. The paper outline is as follows. Section 2 deals with the choices faced by a regulator willing to evaluate regulated firms' performances. Section 3 presents the consistency conditions that should be met by the efficiency measures to be useful to regulators, and discusses how to apply them in a regulatory setting. Finally, in Section 4, conclusions to this work are made.

In this paper we have dealt with the empirical application of the theoretical concepts developed by the efficiency measurement literature. We have considered a number of choices the regulator has to face when performing an efficiency analysis, and we have thoroughly discussed the regulatory implications of each particular choice. We are now able to propose an efficiency measurement procedure that takes into account every applied consideration made in this work. This procedure involves the following steps: 1. Identify a set of comparable firms. 2. Construct the theoretical core of the model: this step involves the selection of the kind of relationship that will be estimated (cost or production function), which has an implicit choice about the relevant efficiency concept; it also involves the definition of which variables are outputs and which are inputs. 3. Select all the environmental variables that could potentially affect performance. 4. Regress the initial model and follow a stepwise procedure to ensure that all the non-significant environmental variables are dropped from the final model. 5. Run a DEA model with the inputs, outputs and environmental variables selected in previous steps (final model), to identify efficient and inefficient firms32. 6. Regress the final model, including a dummy variable which takes a value of one if the firm is found efficient in step (5), and zero otherwise33. 7. Apply the consistency condition analysis. Once the regulator has completed this procedure, and is confident about his/her results, he/she can send the efficiency evaluation to each regulated firm, and invite responses from them. In this way, regulators can seek the involvement of the firms in the benchmarking process to ensure that the data on which the analysis is based is reliable and that the results are comprehensible and justifiable. Yardstick competition would then result in a “learning by doing” iterative process in which both firms and regulators learn while playing the game.