IUE e chapter 7: measuring the efficiency of utilities


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Chapter 7 describes the main ways used to measure the efficiency of utilities, and to compare the efficiency of one utility against its peers. Where simple unit cost measure can be used they should be, but because the output of each stage of a utility’s value chain (see Chapter 1) is hard to categorize (see the problems of exogenous cost-drivers described in Chapter 6) more complex efficiency measures are needed. The most commonly used measures are Ordinary Least Squares (OLS) or Corrected Ordinary Least Squares (COLS) regressions of some measure of costs against proper output measures, so the text explains the simple one-output and two-output cases diagrammatically. However, the data can lie in such a fashion that perverse or ‘counter-intuitive’ results are obtained – coefficiencts that clearly should be negative, for example, can turn out positive. In such cases a non-parametric technique called Data Envelope Analysis (DEA) has distinct advantages over the regression approach; it is explained and recommended. The text debates the general pros and cons of each which leads into a deeper philosophical discussion of what Bayesian statisticians call ‘prior probabilities’. The chapter then reviews possible distributions of inefficiency within a production set, explains the Bayesian approach from its philosophical origins, examines Stochastic Frontiers Estimation in this light, and how it needs to be modified for industries which are natural monopolies, explains the main measures of Total Factor Productivity, and finally how to calculate Malmquist indicators of efficiency relative to the production set’s probable boundary.¬†Three kinds of production set boundary are examined, all of which are relevant when comparing the efficiency of utilities from different parts of the world.

The chapter begins by distinguishing the core concepts of productivity and efficiency, and technical or productive efficiency (X-inefficiency) from allocative efficiency, and noting which are the most useful concepts in practice. We next cover measuring the efficiency of utilities in practice when the outputs are very simple, using the simplest method possible Рunit costs. Drawing on the findings of Chapter 6 when utility outputs are more complex Рe.g. making a service available 24/7 to millions of customers across a wide variety of terrain, rather than simply producing bulk energy or water Рthe technique of making OLS regressions of costs against exogenous cost-drivers is explained in simple diagrammatic terms. Problems with the OLS approach that may emerge out of the data are then examined and the alternative non-parametric approach of DEA is explained. However, DEA also has problems, so the philosophy behind the Bayesian approach is thoroughly explained with a minimum of equations. The Bayesian approach is then made real by explaining the rationale for Stochastic Frontiers Analysis (SFA), and how this should be modified in industries which are natural monopolies such as energy and water distribution or collection grids. The assumption of fixed technologies is then relaxed and the rationale for Total Factor Productivity is explained, as are its central weaknesses in this context. Finally Malmquist indicators of efficiency relative to a hypothesised efficiency frontier are explained, and what this means when technology is remembered, and even when it is forgotten. An Appendix deals with panel data approaches.

This chapter is essential prior reading before the serious student embarks on understanding the main ways natural monopolies are regulated in Chapter 8. Alternatively the serious student can skim chapter 7, read Chapter 8 in detail, and then return to study Chapter 7 properly.

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