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Article Excerpt
1. INTRODUCTION
The deregulation of electricity markets in many countries has led to the establishment of regulatory authorities to oversee the pricing practices of electricity distributors, as they are natural monopolies. Typically regulators focus on establishing the maximum price an electricity distributor can charge, the maximum revenue a distributor can earn, or a hybrid of the two. However a limitation of regulating only price or revenue is that distributors will have little incentive to improve quality of supply. Indeed the absence of quality from the pricing process can create an adverse incentive for distributors to reduce capital and operating expenditure to increase profits, thus reducing the quality of supply.
Various methods have been used by regulatory authorities to address the problem of how to include quality of supply in pricing. In New South Wales, Australia, the Independent Pricing and Regulatory Tribunal (IPART) provides regulatory oversight of electricity distributors. IPART is currently addressing this problem through the use of an S factor (PB Associates, 2004). An S Factor is a variable that measures aspects of quality of supply, which is in turn linked to the revenue Distribution Network Service Providers (DNSP) receive as regulated income. However, customers have had little involvement in this process. In developing the S factor, PB Associates (2004) relied on previous studies that define the average cost to DNSPs of reliability improvements, rather than asking the customers how much they value these improvements.
The use of the S factor is a part of an emerging trend in Australia to incorporate quality when setting regulated prices. For instance, in Victoria the S factor has been used as part of the distribution price control formula to provide a financial incentive to DNSPs to meet (or exceed) their service performance targets. South Australia has adopted a reward/penalty system, where an index of quality of supply measures is established and a benchmark is set. The South Australian utility will report annually on its performance relative to this index and appropriate rewards/penalties will be implemented in future years (South Australian Independent Industry Regulator, 2000a; 2000b). This trend is also evident in other countries. In the United Kingdom, the Office of Gas and Electricity Markets has been working towards including incentives for increased quality of supply within the existing regulatory framework as part of the Information Incentives Project (Office of Gas and Electricity Markets, 2001). In the United States emphasis is also being placed on enhancing quality of service with the use of a financial rewards/ penalties system for changes in supply quality above or below predetermined benchmarks (Allen Consulting Group, 2001).
Despite this greater interest in the quality of electricity supply, it is apparent that customers' preferences have played a small role in determining standards and setting incentives (South Australian Independent Industry Regulator, 2000a; 2000b; Office of the Regulator General, 2000; Allen Consulting Group, 2001). In part this is because few studies have examined the willingness to pay of businesses for improved quality of electricity supply, even though several studies have examined the preferences of households for improved quality of supply (e.g. Doane, Hartman and Woo, 1988, Goett, Hudson and Train, 2000, Carlsson and Martinsson 2008). This is significant given that businesses make up a larger proportion of electricity demand (e.g. Ministerial Council on Energy, 2004).
From the perspective of economic theory, one way to analyse whether the quality of electricity supply should be improved is through the use of cost-benefit analysis (Mishan, 1972). This requires estimation of the costs of improving electricity supply and the benefits to customers from doing so. It is relatively straight Willingness to Pay for Improved Quality of Electricity Supply / 119 forward to quantify the costs of improving the quality of supply, as DNSPs will know the costs associated with installing or upgrading assets to different levels of quality (PB Associates, 2004). But, it is equally important to know what customers will pay for this increased quality in order to quantify the benefits to customers. Market data can be used to provide some information about businesses' willingness to pay for improved quality of supply, however it only produces lower bound estimates for some but not all of the aspects of quality of supply (see for example Caves, Herriges and Windle 1992, Beenstock and Ephrain 1997). Because of the limitations associated with market based or revealed preference approaches, stated preference techniques have been used for determining businesses' willingness to pay for improved quality of supply. Two stated preference techniques have predominantly been used to value quality of supply either for businesses or households: the contingent valuation method (eg Doane, Hartman and Woo, 1988) and choice modelling (eg Goett, Hudson and Train, 2000). Contingent valuation generally involves respondents indicating their willingness to pay for a single alternative through use of payment cards, voting in a referendum or other elicitation formats whereas choice modelling applications involve respondents evaluating multiple alternatives that are defined using a set of attributes (Boyle, 2003). Of the two techniques, choice modelling is now used more frequently than contingent valuation as it provides a richer information set, producing estimates of the value of each of the attributes that make up the good of interest (Bennett and Blamey 2001). There have however been no previous choice modelling studies that have sought to understand businesses' willingness to pay to improve quality of supply. Two previous studies by Woo and Train (1988) and Moeltner and Layton (2002) used a survey based approach to examine how the costs of outages are influenced by various attributes, including the duration of the outage, its frequency, when it occurs, and whether advance notice was given. However, neither sought to estimate willingness to pay for improved quality of supply. Rather both focus on understanding how costs change with different quality of supply options, which only provides a lower bound estimate of willingness to pay.
Given the lack of analysis of customer WTP for improved quality of electricity supply, especially by business consumers, in this paper we evaluate how customers in the manufacturing and service industries value improvements in electricity supply quality using choice modelling. We also explore differences in the preferences of businesses located in urban and rural/regional areas.
2. CHOICE MODELLING
Choice modelling is used in a range of applications...
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