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Electricity Markets Regulation - Lesson 6 - Efficiency Assessments


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Regulators use efficiency assessment to set the efficiency targets of the regulated service providers. This session explains the role of the efficiency assessment, the methods to measure efficiency and the incorporation of efficiency results in the price control.

* Why measure efficiency?
* Methods for efficiency assessments : Uni-dimensional ratio analysis / Statistical and econometric methods / Linear programming methods / Virtual network models
* Application of efficiency results o TOTEX versus OPEX benchmarking : Building block approach / Cost controllability (short- and long-term) / Efficiency convergence speed / Capping efficiency scores / Using efficiency bands

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Electricity Markets Regulation - Lesson 6 - Efficiency Assessments

  1. 1. Training on Regulation A webinar for the European Copper Institute Webinar 6: Efficiency Assessments Dr. Konstantin Petrov / Dr. Daniel Grote 11.1.2009
  2. 2. Agenda 11/01/2010 a) TOTEX versus OPEX benchmarking 3. Application of efficiency results a) Overview 2. Methods for efficiency assessments c) Data Envelopment Analysis b) Performance indicators b) Efficiency convergence speed 1. Why measure efficiency? c) Supporting schemes e) Virtual network models d) Parametric Approaches
  3. 3. 1. Why measure efficiency? 11/01/2010 <ul><li>Regulation is needed in areas where competition does not work (e.g. natural monopolies - transmission, distribution networks) to limit excessive pricing and to set incentives for efficient performance </li></ul><ul><li>Regulators apply benchmarking to assess efficiency of regulated companies for the purposes of incentive regulation </li></ul>Major Reasons Cap regulation Actual Cost Current price level Current price + Inflation Current price + Inflation – productivity growth Efficiency gains time Influenced by company Influenced by company Set by regulator
  4. 4. 1. Why measure efficiency? 11/01/2010 Definition of efficiency Efficiency = Outputs Inputs + “ Correction for Environment” Distribution Company e.g. # employees, fuel, operational costs, Input Factors e.g. # customers, delivered energy (kWh), peak load (kW) Output Factors e.g. firm size, network topology, climate, topography, terrain, task complexity Environmental Factors
  5. 5. 1. Why measure efficiency? 11/01/2010 <ul><li>Technological change (frontier shift): change in production technology within the sector </li></ul><ul><li>Efficiency change (catch-up): change in efficiency of production </li></ul><ul><ul><li>Change in the scale of production (scale efficiency) </li></ul></ul><ul><ul><li>Pure technical efficiency change </li></ul></ul><ul><li>Allocative efficiency </li></ul><ul><ul><li>Input mix allocative efficiency: producing same outputs with different mix of inputs </li></ul></ul><ul><ul><li>Output mix allocative efficiency: producing different level of outputs with same mix of inputs </li></ul></ul><ul><li>Changes in operating environment </li></ul>Reasons for efficiency changes
  6. 6. 1. Why measure efficiency? 11/01/2010 Efficiency assessment and price control Efficiency Assessment Efficiency Scores Efficiency Improvement Targets Integration in Price Control Allowed Revenue (Tariffs) Efficiency Interface Benchmarking <ul><li>Approach </li></ul><ul><li>Sample </li></ul><ul><li>Model Orientation </li></ul><ul><li>Data Collection </li></ul><ul><li>Data Validation </li></ul>Conversion <ul><li>Convergence Time </li></ul><ul><li>Convergence Profile </li></ul><ul><li>Inefficiency Caps </li></ul><ul><li>Efficiency Bands </li></ul>Integration <ul><li>Chargeable Basis </li></ul><ul><li>Capex Treatment </li></ul><ul><li>Revenue Requirements </li></ul><ul><li>Regulatory Formula </li></ul>
  7. 7. 1. Why measure efficiency? 11/01/2010 Practical Relevance of Benchmarking and the X-factor <ul><li>Reflects the regulatory view for anticipated efficiency improvement </li></ul><ul><li>Ensures ex-ante sharing of the anticipated efficiency gains between customers and regulated companies </li></ul><ul><li>The X-factor is not a confirmation but rather indication of the anticipated efficiency improvement </li></ul><ul><li>In some regulatory regimes the X-factor has a dual function: </li></ul><ul><ul><li>Efficiency improvement </li></ul></ul><ul><ul><li>Revenue profiling </li></ul></ul>
  8. 8. 2. Methods for efficiency assessments 11/01/2010 Overview (1) Benchmarking Methods Partial Methods Total Methods Non-parametric Parametric Reference Networks (Virtual Networks) Index Methods Data Envelopment Analysis (DEA) Stochastic Frontier Analysis (SFA) Ordinary Least Squares (OLS) Corrected Ordinary Least Squares (COLS) Total Factor Productivity (TFP) Uni-dimensional ratios Performance Indicators Linear programming Econometrics Engineering Models Total methods can be based on the average performance or the efficient frontier of comparable companies
  9. 9. 2. Methods for efficiency assessments 11/01/2010 Overview (2) <ul><li>Efficiency performance assessment (benchmarking) applied in various forms </li></ul><ul><li>Methods differ in the standard of comparison </li></ul><ul><li>No consensus among regulators as to which methodology is best </li></ul><ul><li>Sometimes different methods applied simultaneously for cross-checks </li></ul><ul><li>Frontier methods preferred by regulators, in particular DEA and SFA </li></ul><ul><ul><li>Parametric (econometric) models (Germany, UK) </li></ul></ul><ul><ul><li>DEA analysis (Norway, the Netherlands, Germany, several countries in CEE) </li></ul></ul><ul><ul><li>Reference network models (Spain, Sweden, Chile, Argentina) </li></ul></ul>
  10. 10. 2. Methods for efficiency assessments 11/01/2010 Overview (3) Efficiency Score A B C D E Measures of relative inefficiencies towards best performance Conversion (definition of efficiency increase targets) Companies
  11. 11. 2. Methods for efficiency assessments 11/01/2010 Performance Indicators <ul><li>Uni-dimensional ratios: </li></ul><ul><ul><li>Comparison of single performance indicators between firms </li></ul></ul><ul><ul><li>Fails to account for the relationships between different input and output factors </li></ul></ul><ul><li>Productivity (Managerial) Indicators </li></ul><ul><ul><li>GWh/Employee </li></ul></ul><ul><ul><li>OPEX/GWh </li></ul></ul><ul><ul><li>OPEX/Employee </li></ul></ul><ul><ul><li>GWh/Line Length </li></ul></ul><ul><li>Financial indicators </li></ul><ul><ul><li>Debt/Equity Ratio </li></ul></ul><ul><ul><li>Return on Investment (ROI) </li></ul></ul><ul><ul><li>Return on Capital Employed (ROCE) </li></ul></ul><ul><li>Partial methods produce simple, easy to calculate straightforward indicators of performance </li></ul><ul><ul><li>… but do not recognize trade-offs between different improvement possibilities or areas </li></ul></ul><ul><ul><li>Can only be used as a rough indication </li></ul></ul>
  12. 12. 2. Methods for efficiency assessments 11/01/2010 Index methods – Total Factor Productivity (TFP) <ul><li>Total factor productivity (TFP) is a measure of the physical output of a regulated company produced by a given quantity of inputs </li></ul><ul><li>With multiple inputs (Y) and outputs (X), outputs are usually weighted by their revenue shares (s R ) and inputs are weighted by their cost shares (s C ) </li></ul><ul><li>Weights can be either static or dynamic (different weights used for each period) </li></ul><ul><li>Extensively used in the US for utility regulation (both energy and telecoms) </li></ul><ul><li>Data requirements can be harsh </li></ul><ul><li>TFP does not provide any information about ‘infra-marginal’ efficiency improvement possibilities; for this we need more articulated benchmarking techniques (frontier-based methodologies) </li></ul><ul><li>More suitable for an assessment of company performance over time than comparisons between regulated companies </li></ul>Input factors Output factors
  13. 13. 2. Methods for efficiency assessments 11/01/2010 Frontier methods <ul><li>Frontier methods are based on the concept that all companies should be able to operate at an optimal efficiency level that is determined by other efficient companies in the same sample </li></ul><ul><li>These efficient companies are usually referred to as the “peer firms” and determine the “efficiency frontier” </li></ul><ul><li>The “efficiency frontier” is formed from the observed performance of the companies in the analyzed sample, as determined by the relationships between the inputs and outputs of the sampled units </li></ul><ul><li>The companies that form the efficiency frontier use the minimum quantity of inputs to produce the same quantity of outputs (input oriented model) </li></ul><ul><li>The “efficiency frontier” is used as a reference against which the comparative performance of all other companies (that do not lie on the frontier) is measured </li></ul><ul><li>The distance to the efficiency frontier provides a measure for the inefficiency </li></ul>
  14. 14. 2. Methods for efficiency assessments 11/01/2010 Data Envelopment Analysis (DEA) (1) Output 1 Input 1 Input 2 Data Envelope A B C D E most efficient companies F F’ Inefficiency Input minimisation Inefficiency Output 2 Data Envelope A B C D E F most efficient companies F’ Output maximisation G G’
  15. 15. 2. Methods for efficiency assessments 11/01/2010 Data Envelopment Analysis (DEA) (2) <ul><li>Variable returns to scale account for short-run scale inefficiencies </li></ul><ul><li>In the long run, firms should optimally adjust their size so that constant returns to scale are </li></ul><ul><li>achieved </li></ul>Outputs Inputs A B C constant returns to scale frontier variable returns to scale frontier F F’
  16. 16. 2. Methods for efficiency assessments 11/01/2010 Data Envelopment Analysis (DEA) (3) <ul><li>DEA is a non-parametric approach to calculate the relative Input-Output efficiency of a regulated company </li></ul><ul><li>DEA benchmarks an individual company in relation to the best-practice (most efficient) companies </li></ul><ul><li>Companies that are able to produce a given output at minimum cost or a maximum output with a given input define the best-practice frontier that envelops all data points </li></ul><ul><li>Inefficiency is determined by the distance between the observed company and the best-practice frontier </li></ul><ul><li>Calculation of inefficiency is done via a series of linear programming (mathematical software needed) </li></ul><ul><li>The programs will output a series of efficiency scores, which may be normalized, ranked, and split according to a number of components (scale, purely technical, allocative etc.) </li></ul>
  17. 17. 2. Methods for efficiency assessments 11/01/2010 Data Envelopment Analysis (DEA) (4) <ul><li>Advantages: </li></ul><ul><ul><li>Multi-dimensional method covering multiple inputs and outputs </li></ul></ul><ul><ul><li>Establishes peer companies </li></ul></ul><ul><ul><li>It does not require functional relationships between input and output factors </li></ul></ul><ul><ul><li>Distinguishes between different types of inefficiency (scale, productive, allocative, purely technical) in the presence of input (or output) price data </li></ul></ul><ul><li>Disadvantages: </li></ul><ul><ul><li>The results could be influenced by random errors, measurement error or extreme events </li></ul></ul><ul><ul><li>Results depend on the selection of input and output factors </li></ul></ul><ul><ul><li>Companies exhibiting “extreme” parameters will be classified as efficient “by default” </li></ul></ul><ul><ul><li>Provides no information about statistical significance of the results </li></ul></ul><ul><ul><li>Small samples and a high number of input or/and output variables can result in an over-specification of the model and “made-up” results for efficiency scores ( number of efficient firms increases with the number of input and output variables) </li></ul></ul>
  18. 18. 2. Methods for efficiency assessments 11/01/2010 Parametric / Econometric approaches – Regression analysis Corrected OLS (COLS) Ordinary Least Square (OLS) Most efficient observation Input (Costs) Output Stochastic Frontier Analysis (SFA)
  19. 19. 2. Methods for efficiency assessments 11/01/2010 Ordinary Least Squares (OLS) <ul><li>Regression analysis: Mathematical relationship (functional form) that describes the relationship between a dependent variable and one or more independent variables </li></ul><ul><li>Used to determine the values of parameters that cause the function to best fit a set of data observations </li></ul><ul><li>The OLS regression line cuts across the observations by minimising the sum of the squares of the distance (residual) between the line itself and each of the observations </li></ul><ul><li>Fit a line so that, at each point, the (regression) line is close to the corresponding observed values, while minimising the sum of squared deviations from the line over all the observable values in the sample </li></ul><ul><li>Efficiency frontier is based on the average cost function </li></ul><ul><li>OLS compares the (in)efficiency of an individual company with the average efficiency level </li></ul>
  20. 20. 2. Methods for efficiency assessments 11/01/2010 Corrected Ordinary Least Squares (COLS) <ul><li>Estimation of production or cost functions via Ordinary Least Squares </li></ul><ul><li>Use of regression residuals to characterise relative distances between observations in the sample </li></ul><ul><li>Corrects the regression line by subtracting the largest negative residual (for a cost function) from the OLS fit (shift the regression line to (unique) best-practice observation ) </li></ul><ul><li>Measures the relative inefficiency of all other companies (points) from the line passing through the largest negative residual (the most efficient company) </li></ul><ul><ul><li>Allows to assess the significance of each network cost driver </li></ul></ul><ul><li>No measurement of stochastic errors </li></ul><ul><li>Requires large data volume in order to create a robust regression relationship </li></ul><ul><li>Very dependent on data quality and, in particular, sensitive to outliers (the company defining the frontier could just be an outlier!) </li></ul>
  21. 21. 2. Methods for efficiency assessments 11/01/2010 Stochastic Frontier Analysis (SFA) <ul><li>Uses same premises as COLS, but treats best practice as a “stochastic” process (a mix of true efficiency and “random noise” effects) </li></ul><ul><li>Several statistical assumptions behind the errors </li></ul><ul><li>SFA requires a large sample size to be statistically relevant </li></ul><ul><li>In the presence of patchy and/or too small samples, COLS is relatively more reliable than SFA (SFA cannot be drawn as a “frontier” line as COLS) </li></ul><ul><li>Less sensitive to inputs and/or outputs as DEA / COLS </li></ul><ul><li>Allows to assess the significance of each network cost driver </li></ul><ul><li>Considers stochastic errors explicitly </li></ul><ul><li>Complex and statistically demanding </li></ul><ul><li>Requires large data sets in order to create a robust regression relationship </li></ul><ul><li>Genuine inefficiency could be allocated to stochastic elements: scores might be too generous (too high) </li></ul>
  22. 22. 2. Methods for efficiency assessments 11/01/2010 Virtual network models <ul><li>Artificially construct an efficient (engineering-designed) reference network according to commonly accepted planning principles and taking into account technical and geographical constraints </li></ul><ul><li>The regulated firm’s relative (in)efficiency is estimated by the firm’s performance in relation to the virtual network </li></ul><ul><li>Virtual network models are not dependent on obtaining and analyzing data of “real” companies </li></ul><ul><li>Does not require a significant set of comparable companies as benchmarks </li></ul><ul><li>Very complicated and difficult to specify </li></ul><ul><li>Model sensitive to changes in inputs </li></ul><ul><li>Reasons for the deviation from reference network might be beyond control of the company </li></ul>
  23. 23. 3. Application of efficiency results 11/01/2010 TOTEX versus OPEX benchmarking <ul><ul><li>Building Block Approach </li></ul></ul><ul><ul><ul><li>Implemented as linked (coupled) cap regulation </li></ul></ul></ul><ul><ul><ul><li>Explicit projection of capex for the upcoming regulatory period </li></ul></ul></ul><ul><ul><ul><li>Separate checks and inclusion of investments </li></ul></ul></ul><ul><ul><ul><li>Sometimes formalised efficiency analysis based on controllable opex </li></ul></ul></ul><ul><ul><li>TOTEX Approach </li></ul></ul><ul><ul><ul><li>Implemented as unlinked (decoupled) cap or yardstick regulation </li></ul></ul></ul><ul><ul><ul><li>Inclusion of (historic) capital cost into efficiency assessment modelling (total cost analysis) </li></ul></ul></ul><ul><ul><ul><li>Standardisation of capital costs for benchmarking purposes </li></ul></ul></ul><ul><ul><ul><li>(Planned) investment for the regulatory period not taken into account for the annual allowed revenue </li></ul></ul></ul>
  24. 24. 3. Application of efficiency results <ul><li>Building blocks (UK, Australia, Central and Eastern Europe) supported by: </li></ul><ul><ul><li>Efficiency carry over schemes </li></ul></ul><ul><ul><li>Sliding scale schemes </li></ul></ul><ul><li>Total cost approach (Germany, Norway, the Netherlands, Austria) supported by: </li></ul><ul><ul><li>Quantity terms (pre-specified cost drivers) incorporated in price control formulas </li></ul></ul><ul><ul><li>Explicit investment allowances </li></ul></ul><ul><ul><li>Inefficiency caps </li></ul></ul>TOTEX versus OPEX benchmarking
  25. 25. 3. Application of efficiency results 11/01/2010 Efficiency convergence speed <ul><li>The X-factor prescribes the rate of change in the company’s prices or revenues, reflecting the expected transition from the existing price level towards the efficient price level </li></ul><ul><li>Regulator to decide whether existing price level serve as starting point for the regulatory formula or whether one-off cut of the initial price </li></ul><ul><li>Advantage of initial one-off cut, prices can be brought to more realistic levels at once </li></ul><ul><li>Large one-off adjustments quickly eliminate inefficiencies at the beginning, but decrease incentives for further efficiency improvements by the company </li></ul>Allowed revenue Initial level Initial one-off cut 1 2 3 4 5 Regulatory period Proportional decrease
  26. 26. 3. Application of efficiency results 11/01/2010 Supporting Schemes <ul><li>Inefficiency caps (Austria, Germany) </li></ul><ul><ul><li>Germany: Minimum (individual) efficiency score – 60 % </li></ul></ul><ul><ul><li>Austria: Max. (individual) efficiency increase – 5.45 % </li></ul></ul><ul><li>Sliding scale (Norway, 1997-2001 and 2002-2006) </li></ul><ul><ul><li>Base return with dead band plus caps/ collars </li></ul></ul><ul><li>Efficiency bands (Norway, 1997-2001) </li></ul>Germany (2009-2013) Norway (1997-2001) KA b,0 Year 1 Year 10 KA dnb,t Permanently Controllable costs (base year) KA vnb,0 Temporary non - controllable costs Max. 60% of total costs after deducting of (permanently) non-controllable cost (proportionally over 10 years) - non-controllable costs - 15% Profit floor level (tariff increase) Profit cap level (tariff reduction)) Dead band 8,3% 2 % Profit floor level (tariff increase) Profit cap level (tariff reduction)) Dead band
  27. 27. Summary <ul><li>There are several benchmarking techniques and no consensus amongst regulators as to which methodology is best </li></ul><ul><li>Data quality and model specification are fundamental for successful and defensible outcomes </li></ul><ul><li>Benchmarking is an indication and not a confirmation of efficiency position </li></ul><ul><li>Integration of benchmarking results should take into account its imperfections and the specifics of the price control design </li></ul>
  28. 28. End of webinar 6 <ul><ul><ul><li>KEMA Consulting GmbH </li></ul></ul></ul><ul><ul><ul><li>Kurt-Schumacher-Str. 8, 53113 Bonn </li></ul></ul></ul><ul><ul><ul><li>Tel. +49 (228) 44 690 00 Fax +49 (228) 44 690 99 </li></ul></ul></ul><ul><ul><ul><li>Dr. Konstantin Petrov </li></ul></ul></ul><ul><ul><ul><li>Managing Consultant </li></ul></ul></ul><ul><ul><ul><li>Mobil +49 173 515 1946 E-mail: </li></ul></ul></ul>