Dynamics of productivity change in the Australian electricity industry: Assessing the impacts of electricity reform

Energy Policy 39 (2011) 3281–3295

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Dynamics of productivity change in the Australian electricity industry: Assessing the impacts of electricity reform Reza Fathollahzadeh Aghdam n Department of Finance and Economics, College of Industrial Management, King Fahd University of Petroleum and Minerals, PO Box 257, Dhahran 31261, Saudi Arabia

a r t i c l e i n f o

abstract

Article history: Received 12 June 2010 Accepted 3 March 2011 Available online 2 April 2011

The Australian electricity industry has undergone a significant reform, since the mid-1990s. Key changes comprised functional unbundling, market restructuring, regulatory reform, public corporatisation and privatisation. Technological development has been another indisputable constituent of these changes, in the wake of ICT revolution. The principle rationale behind these changes has been that they would improve productivity of the industry and social well-being of people. This paper examines the dynamics of productivity changes in the Australian electricity industry and conducts several hypotheses-testings to identify whether industry’s efficiency measures are truly improved as a result of the reform-driven changes. Malmquist Total Factor Productivity Index approach and ANOVA are used for this purpose. The results reveal that the productivity gains in the industry have been largely driven by technological improvements and, to a lesser extent, by reform-induced comparative efficiency gains. On average at national level and for the entire industry, there are efficiency gains that, to large extents, can be attributed to functional unbundling and public corporatisation and, to a lesser extent, to market restructuring and privatisation. The results, however, reveal that the reform-driven changes have made insignificant contribution to comparative efficiency, at the level of thermal generation. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Australian electricity reform Malmquist TFP index ANOVA hypothesis-testing

1. Introduction The Australian electricity industry has experienced several reforms in its history. The industry – like most developed countries – emerged in the 1880s (see, Casazza and Delea, 2003, p. 1; EANSW, 1986, p. 1) and has evolved ever since. In its evolution, there have been certain periods, when a pace of change has become significant and distinctive. Such periods are often associated with a reform – a substantial change in industry’s institutional facet. In Australia, five such periods are generally identifiable (Fathollahzadeh, 2006, pp. 59–62): (i) early years (1888–1913), there was free and fierce competition among numerous private-sector players and the infant industry lacked transmission lines and standards; (ii) wars and depression years (1914–1944), in spite of progressive inventions during this period, the industry developed slowly due to complicated socio-political turmoils caused by the two world wars and the Great Depression; (iii) industry consolidation (1945–1985), there was significant expansion of electricity industries in each State, independent from one another, as the governments realized significant development potentials behind electricity; (iv) internal reforms (1986–1993), a set of State-wide reforms that was launched by

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State governments in response to emerging concerns about the inefficiencies in the industry, essentially originated from the energy crises of the 1970s (see for example, Beardow 2002, p. 8); and (v) nation-wide market reform (1994-present), the industry started to undergo a radical and nation-wide reform till present time. This reform is often called the market reform attributable to its underlying free market philosophy, emphasising on promotion of competition through a bid-based market structure (see also, Quiggin, 2001; Sharma, 2003; Sharma & Bartels, 1997). The market reform has changed almost every institutional facet of the industry, including its organisational setting, market structure, regulatory framework, and ownership arrangement. The traditionally vertically integrated organisation of the industry has been functionally unbundled, essentially into four segments of generation, transmission, distribution and retail. The market structure of the industry for electricity trade and pricing has been drastically moved away from its traditional order-of-merit mechanism to a mandatory bid-based system. A nation-wide regulatory framework has been formed for overseeing and harmonising activities of Statebased regulatory bodies. Further, considerable parts of the industry have been either corporatised (yet remaining under public ownership) or privatised. While much of these changes took place by the year 2002, some changes have accomplished in recent years. The process of institutional change in the industry continues evolving as challenges keep emerging.

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Although the market reform became the first comprehensive national approach towards electricity in Australia, each State tailored the reform programme to its own political interest. Hence, a divergence became observable in States’ approaches to electricity reform during this period. One aspect of such divergence was observed in the type of ownership arrangement adopted by various States. For example, Victoria decided to privatise its industry, almost entirely. Other States found the privatisation politically more challenging and, in some instances, totally unpopular. South Australia, for instance, privatised its industry in a different way, by leasing out key parts of its industry to private sector for a long period of time. New South Wales (NSW) chose to rather corporatise its industry, but kept the ownership under public control. Qeensland, Western Australia, Tasmania, and Northern Territory did not do much for changing their industries’ ownership arrangement. Another critical divergence was seen in industry’s regulatory framework that was sighted to being a multiplicity of institutional involvement and jurisdictional contrasts that contributed to unaccountability, overlap, ambiguity, and inconsistency in the system (Sharma, 2003). By 2002, this issue became a major challenge, needing to be urgently addressed. A major impetus to this issue was provided by the findings of the Council of Australian Governments Review (COAG, 2002), recommending – alongside other things – a national regulatory framework that could oversee and harmonise federal, State, and territory regulatory frameworks. The Ministerial Council of Energy (MCE, 2003), in recognition of COAG Review’s recommendations, agreed on establishment of a new national regulatory framework. The Australian Energy Market Commission (AEMC) and the Australian Energy Regulator (AER), each with well-defined tasks and responsibilities, were formed as key institutional components of the new regulatory framework. Due to the changes that started in 2002 and almost completed by 2006, some analysts may wish to break down the market reform period into two separate periods: between 1994–2002 and 2003–present. Nonetheless, one should note that the essence of reform philosophy (i.e., promotion of competition through a newly introduced bid-based market structure) and main organisational setting of the industry have remained unchanged since the mid-1990s. Therefore, recent changes in the industry can best be considered as an error-elimination stage for the entire process of the market reform, rather than a new reform. Such stages will continue happening in the years to come because this is how human organisations evolve institutionally. The principle rational behind the market reform has been that this reform, through competition, would improve the productivity of the industry and ultimately will enhance social well-being of people. Several studies have been conducted to substantiate this rationale. Analyses of these studies, this paper argues, have not adequately accomplished this task. These studies, each using either a scenario approach (applying ex-ante or ex-post counterfactuals) or a trend analysis, mostly assumed how the impacts of electricity reform on productivity of the industry would look like. Perhaps, the use of scenario approach was inevitable in the early stages of implementation of the reform. However, nearly two decades to the reform, no alternative approach has been employed in the context of Australia. A typical alternative approach could be ‘hypothesis-testing’ that allows for examining the significance of the impacts of reform-driven changes on productivity measures of the industry. This would be possible by developing an appropriate panel dataset, across the Australian States over a suitable time span. Against this background, the objective of this paper is to examine the dynamics of productivity changes in the Australian electricity industry on the basis of a panel dataset for the period 1969–2007 across Australian States and territories. The paper estimates various productivity measures, using Malmquist Total

Factor Productivity (TFP) index and Distance Function approach. Several models are configured at two levels of aggregation, namely: the entire electricity industry; and thermal generation. The paper also conducts several hypotheses-testings to examine whether or not the industry’s comparative efficiency measures are truly improved as a result of the aforementioned reformdriven institutional changes (i.e., changes in organisational setting, market structure, regulatory framework, and ownership arrangement). Organisation of the rest of the paper is as follows. Section 2 is devoted to literature review, with a view to identify a research gap and justify this paper’s methodology. Section 3 specifies the structure of the models. Section 4 describes the dataset that is developed for this paper. Section 5 discusses the empirical results and Section 6 provides conclusions.

2. Literature review As mentioned in the previous section, in order to substantiate the soundness of the rationale behind electricity reform, several studies have been undertaken by various research groups. These studies have covered a broad range of specific objectives. However, they all – whether explicitly or implicitly; fully, or partially – aimed to assess the impacts of electricity reform on the productivity of the electricity industry and the wider economy. Prior to the inception of the market reform, a series of studies were carried out by a group of individuals, think tanks and governmental bodies, in the early 1990s. Among the studies that were conducted by individuals, one should refer to the works of Lawrence et al. (1990, 1991) (Swan Consultants, 1991, 1992; Zeitsch and Lawrence, 1993; Zeitsch et al., 1992). The most important studies that were conducted by think tanks and governmental bodies included: Industry Commission (IC, 1991); London Economics (1993, Part 1); Bureau of Industry Economics (BIE, 1992, 1994); and Electricity Supply Association of Australia (ESAA, 1992; 1994 (also known as: London Economics, Part 2)). These studies – whether using a simple or sophisticated approach; measuring partial or total factor productivity indices – mostly remarked that introduction of competition and privatisation would improve the productivity of electricity industry. Intertwined with the results of the studies of the early 1990s, a series of anecdotal debates were going on at that time about how to reform the electricity industry. Such debates became more intense by the release of the report that was conducted by independent committee of enquiry into competition policy in Australia (NCPRC, 1993). This report essentially argued that competition should be promoted, not only for electricity industry, but also for most infrastructure industries and Government Business Enterprises (GBEs) such as gas, water, telecom, post, aviation, transport, and port authorities. In this way, the electricity reform was considered as an integral element of a wider economic reform programme of Australian governments under the title of National Competition Policy (NPC). NCP called for a set of institutional changes (more or less) like those that as noted above, eventually, imposed on electricity industry. Accordingly, National Electricity Market (NEM) was designed on the basis of a UK-style model of mandatory bid-base system1 . 1 The United Kingdom became one of the most leading pioneers of the global wave of electricity reforms of the 1990s (Sioshansi & Pfaffenberger, 2006). Australia embraced this model regardless of existing oppositions. According to Booth (2003), this was done while the UK was having so much trouble with its reform model in the 1990s. Booth argued that Australia could have adopted Scandinavian model instead (p. 53). Booth, however. comments that this choice can be explained by Australia’s ‘old cultural cringe’ and ‘strong ties to Britain’ (pp. v-2).

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The main steps towards operationlisation of the NEM were: operation of Victoria Pool, which was known as Victorian Power Exchange, in January 1995; formation of National Electricity Code Administration (NECA) as regulatory administrator and National Electricity Market Management Company (NEMMCO) as market operator, in March 1996; operation of the New South Wales separate State electricity market, in May 1996; harmonisation of these two State pools (known as NEM1), in May 1997; Queensland’s intention to join the NEM, in December 1998 (Booth, 2003, p. 209); completion of Queensland and South Australia connections to the NEM by 2002; and completion of the undersea High Voltage Direct Current (HVDC) transmission line and, hence, Tasmania’s operational connection to NEM, in May 2006. Western Australia and Northern Territory, due to geographical distance, have remained unconnected. However, their individual State electricity reforms have been very much coordinated with general policy directions of NCP in the NEM area (i.e., New South Wales, Victoria, Queensland, and South Australia). As NCP started to be implemented and NEM was forming, debates for or against the soundness of rationales behind electricity reform not only did not end, but also became more intense. A key turning point in such debates was the release of Industry Commission report (IC, 1995). This study was undertaken by IC at the behest of the COAG. Its objective was to comprehensively assess the economy-wide benefits (reform-induced growth and revenue) of implementing a set of reforms in Australia under NCP. These were a set of sectoral reforms for GBEs, including electricity industry. IC (1995) estimated that GBEs would achieve significant productivity gains as a result of implementation of NCP and its related reforms and that Australian economy on average would gain an extra 1.39% annual growth in real GDP in comparison to an alternative (reference) scenario where no reform is implemented at all (SAIIR, 2002, p. 29). Quiggin critically argued that IC’s ‘estimated productivity gains are overoptimistic’ (Quiggin, 1997). As a result of Quiggin’s criticism, more empirical studies were engaged in this debate. Key studies included: Whiteman (1999); Coelli et al. (1998, pp. 221–242); Productivity Commission (PC, 1999); Short et al. (2001); and Abbot (2006). In the rest of this section, previous studies (mostly those of which focused on Australia context) are analysed in terms of their methodology for addressing such concerns and with a view to identify a research gap. Their results are not discussed as they have been mixed. Two methodological frameworks are distinguished in previous studies: (i) a framework for measuring economic performances (either at micro- or macro-level); and (ii) a framework for assessing to what extent change in performance is due to the market reform and to what extent, to other factors. These frameworks, in the context of this paper, are called measurement- and assessment-frameworks, respectively. This distinction has not been made explicit in the reviewed studies, perhaps because these frameworks are often tightly intertwined with each other. This paper, however, contends that this distinction is useful because it allows a more comprehensive approach for assessing industry’s performance, and for ascertaining the institutional impacts of electricity reform on industry performance. The following sub-sections help to further substantiate this matter. 2.1. Measurement-frameworks Analysis of previous studies can be divided into two groups: (a) analyses of the impacts of electricity reform on economic performance of the electricity industry that is essentially expressed in terms of productivity gains; (b) analyses – relying largely on the results of the first set of studies (either exogenously given or carried out independently) – of the impacts of electricity

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reform on economic performance of the wider economy that is essentially expressed in terms of GDP growth. In the context of this paper, the first group of analyses is called micro-impact studies (e.g., Lawrence, Swan & Zeitsch et al., 1991; London Economics, 1993; Whiteman, 1999), while the second group is called macroimpact studies (including IC, 1995; PC, 1999; Quiggin, 1997; Short et al., 2001; Whiteman, 1999). In turn, the impacts measured on the basis of the former are called micro-impacts, while the impacts measured on the basis of the latter are called macro-impacts. It should also be noted that macro-impact studies generally had their own micro-impact analysis as well (e.g., Whiteman, 1999) or, occasionally, results of micro-impact analysis was taken from another study as exogenously given (e.g., PC, 1999). This paper, obviously, remains focused only on micro-impacts. Table 1 provides more detailed classification for the approaches that are used in the micro-impact studies. Each study involves with some kind of productivity analyses. Such analyses typically employ either an Index Approach (IA) or a Frontier Approach (FA) which measure productivity in terms of various partial or overall productivity indices. As can be seen in Table 1, there are some variations, across various studies, in the selection of a specific approach. For example, Lawrence et al. (1991) apply IA for measuring various Partial Factor Productivities (PFPs) and Total Factor Productivity (TFPs) (a sophisticated multilateral TFP); Whiteman (1999) uses FA with advanced Data Envelopment Analysis (DEA) and Stochastic Frontier Approach (SFA); Short et al. (2001) – simple PFPs; and Coelli et al. (1998, pp. 221–242) – Malmquist TFP index, using Distance Function. In preparation of Table 1, while utmost care is made to keep it as self-explanatory as possible, it is assumed that readers are familiar with these approaches. It is beyond the scope of this paper to elaborate these approaches but, for interested readers, Coelli et al. (1998) would be a suitable introductory text. 2.2. Assessment-frameworks Review of previous studies reveals that scenario analysis is the most dominant and explicitly mentioned assessment-framework, especially after the release of Industry Commission report (IC, 1995). SAIIR (2002) (p. 32) also points this out and mentions that ‘the most common approach to estimate benefits of reform has been to estimate a best practise outcome for the electricity sector, and then assume that reform will achieve that outcome’. This implies that impacts of electricity reform have been assessed by the gap between two scenarios, namely no-reform and reform. The no-reform scenario consists of the continuation of the prereform trends of productivities, whereas the reform scenario assumes that these productivities will be improved to the bestpractise benchmark. The appropriateness of scenario analysis as a methodological framework has however been subject of critical debate, since 1995, when the IC report was released. It is argued by critics that scenario analysis suffers from a methodological weakness in assessing the impacts of electricity reform. Such a weakness, according to the critics, relates mainly to the fact that this framework is not able to specify ‘what changes in practices need to take place, that reform will induce the Australian y electricity industry y to emulate the performance of the benchmark enterprise’ (Quiggin, 1997, p. 258, italics added). While the assessment-framework should be able to address this issue, the scenario analysis assumes these gains as somewhat granted as a result of reform. This assumption, critics argue, ‘involves a substantial leap of faith’ (SAIIR, 2002, p. 32) that has inflated the estimated benefits of electricity reform (see also Johnson and Rix, 1991, pp. 130–134). Although the methodological weaknesses of scenario analysis are revealed by some of the critics, the recommendations provided by these critics have not given any significant guidance.

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Benchmarking Scope

Table 1 Classification of existing studies in terms of their measurement- and assessment-frameworksa. Source: adopted from Fathollahzadeh (2006). Measurement-frameworks Index Approach (IA) Partial Index Number

Overall Index Number

Sophisticated PFP

PFP

TFP

Sophisticated TFP

Partial Index

e.g. Cost Minimisation

Overall Index

Abbot (2006)

Lawrence et al. (1991)

Lawrence et al. (1991)

Zeitch et al. (1992) ESAA (1992)

ESAA (1992)

Coelli (1998)

International

Domestic

International

Murtough et al. (2001)

a

Distance Function

Lawrence et al. (1990)

Lawrence et al. (1990) Domestic

Frontier Approach (FA) Behavioural Framework Axiomatic Framework

Swan Constultant (1991) BIE (1992)

London Economics (1993) Part 1

BIE (1992)

Coelli (1998) Murtough et al. (2001)

Swan Constultant (1991) BIE (1992)

London Economics (1993) London Economics (1993) Part 1 Part 1

ESAA (1994), London Economics Part 2

Short et al. (2001) This Paper (hypothesis-testing)

IC (1995)

IC (1995) Whiteman (1999)

All studies, except this paper, adopted either ‘scenario analysis’ or ‘trend analysis’ as their assessment-framework.

Essentially, this is because the recommendations have focused on highlighting the weaknesses in the measurement-frameworks of the previous studies and the sensitivity of the measured productivity gains to the assumptions behind counterfactuals (reform/ no-reform scenarios) which are often subjective. For example, with regard to IC (1995), Quiggin (1997, p. 256) argues that the ‘estimated productivity gains are overoptimistic, representing upper bounds to possible achievement rather than likely outcomes’. Consequently, for instance, Quiggin (1997, p. 258) urges ‘the choice of an appropriate benchmark’ and SAIIR (2002, p. 32) similarly recommends the adoption of ‘more reliable estimates of a best practise benchmark’. These recommendations, while useful, do not adequately reflect the methodological weaknesses of scenario analysis as a distinct assessment-framework. As a result, responses to the criticisms have all focused on the adoption of an alternative measurement-framework, but almost all of the studies continued using scenario analysis as their assessment-framework. For instance, Whiteman (1999), in responding to Quiggin’s criticism, carried out another study applying two alternative measurementframeworks (i.e., DEA and SFA), but using the same assessmentframework (i.e., scenario analysis). Although Whiteman’s use of SFA was due to its advantage in making allowances for data noise and errors, the results became even more paradoxical. In fact, theoretically the inefficiency measured by SFA is supposed to be less than that from DEA (SAIIR, 2002, p. 21), whereas the results from SFA in Whiteman’s study, in contrast, were almost double the results from DEA. Perhaps, application of scenario analysis, using ext-ante counterfactuals, is known as inevitable for assessing reform-impacts, prior to, or in the early stages of implementation of a reform. For instance, IC (1995), Quiggin (1997) and Whiteman (1999) were among such analyses. In these studies, potential impacts of the market reform were subject of assessment, while there were no historical data available (at least, inside Australia) to conduct such assessment. However, more studies continued applying similar

scenario analysis, years after implementation of the market reform. A minor breakthrough could be found in Short et al. (2001) that developed ex-post counterfactuals, while still using a similar scenario analysis. In this study, reform scenario was the actual trend, while no-reform scenario was estimated on the basis of some (subjective) assumptions. Abbott (2006), which is the most recently cited article on this topic using a trend analysis as its assessment-framework, has also made no significant breakthrough. In fact, despite valid criticisms, none of the studies have adopted any alternative assessment-framework. To sum up this discussion on methodology, one can note that, in the literature, attentions have been paid to conceptual and methodological weaknesses of conventional productivity analyses. This triggered some improvements. These improvements however, in the context of the reviewed studies (Australia), appear to be highly imbalanced, inadequate and skewed towards measurement-frameworks. Application of more advanced measurement-framework does not necessarily imply a better assessment. Their theoretical underpinnings – intertwined with free market philosophy and economic rationalism – render most of these studies normative and subjective rather than positive and objective. With regard to assessment-frameworks, improvements have been trivial. Observations revealed that only trend analysis and scenario approach were employed. A robust assessment-framework should indeed allow for testing hypotheses against a suitable panel dataset. Panel should include both reformed and no-reformed firms/industries. In this way, dataset would contain actual counterfactuals rather than those of which are developed on the basis of subjective assumptions. In panel, a productivity measure is a quantitative variable, whereas reform/no-reform state of a firm/industry is a qualitative variable—in some texts called environment variable (Coelli et al., 1998, pp. 166–171). Each qualitative variable can be associated with a reform-driven institutional change as described earlier. Such variables can be captured by well-defined dummy (binary) variables. Consequently, examination of the causality between

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quantitative variables (productivity measures) and qualitative variables (reform-driven institutional change) can be reduced to development of an appropriate set of ‘hypothesis-testing’. ¨ et al. Such a methodology can be found in, for instance, Fare (1985), Atkinson and Halvorsen (1986). These studies employed useful assessment-frameworks that allowed for testing significance of ownership type (private vs. public) on productivity measures. Pollitt (1995) also adopted similar assessment-framework and evaluated the impacts of privatisation, restructuring and management-reform on productivity of electricity industry. These studies used several parametric and non-parametric techniques for this purpose, including ANOVA. Steiner (2000) and Hattori and Tsutsui (2004) are also among studies which employed a useful parametric approach, as assessment-framework. Hattori and Tsutsui (2004), for instance, focused on the impacts of electricity reform on electricity prices, as the most important constituent of productivity gains, in the context of OECD countries. The study tested the impacts of functional unbundling, private ownership, retail access (i.e., third party access to the network which is one of key elements of recent regulatory reform) and establishment of wholesale market on electricity price. Following studies of this sort, this paper adopts ‘hypothesistesting’ as its main assessment-framework. In addition, ‘conventional trend analysis’ also accompanies ‘hypothesis-testing’ for analysing dynamics of TFP changes by its decompositions.

3. Model structure As noted in Section 2, methodological framework of this paper has two folds. This section, hence, describes the structure of the models in two sub-sections, as follows: 3.1. The measurement-framework This paper follows Coelli et al. (1998, pp. 221–242) for its measurement-framework. It measures various TFPs of the Australian electricity industry on the basis of (input-oriented) Malmquist index. This index, using the concept of Distance Function (Coelli et al., 1998, pp. 67–67 & 222–232), is modelled as follows: " #1=2 dt ðyt ,xt Þ dt1 ðyt ,xt Þ dt1 ðyt1 ,xt1 Þ i i mi ðyt1 ,xt1 ; yt ,xt Þ ¼ t1 i  dti ðyt1 ,xt1 Þ di ðyt1 ,xt1 Þ dti ðyt ,xt Þ ð1Þ where x is the vector of inputs; y is the vector of outputs; mi represents TFP change; and dt1 ðyt ,xt Þ, for example, represents the i (input-oriented) distance of a representative firm at year t from the Production Possibility Frontier (PPF) at year t  1. PPF estimates the best practiced productivities in a certain year, given the available technology. Each input–output datapoint ðyt ,xt Þ, located on PPF, refer to firms that are performing at highest efficiency. Data-points that are falling away from PPF are belonged to firms that are performing inefficient. Distance Function of dt(yt, xt), therefore, estimates comparative efficiency (or inefficiency) of firms, which range between zero and one. Distance Functions of d  1(yt, xt) and dt(yt  1, xt  1) have also applications for capturing the dynamics of productivity changes. In Eq. (1), mi measures TFP changes. A value of mi greater than one indicates positive TFP growth from year t 1 to year t, while a value of mi less than one indicates a negative TFP growth. The ratio outside the square brackets in Eq. (1) measures the comparative efficiency change between years t and t 1—relative proximity to PPF. The remaining part of Eq. (1) is a measure of technological change—shifts in the PPF.

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The Constant Return to Scale (CRS) technology is assumed for all models. This assumption, according to Grifell-Tatje´ and Lovell (1995), avoids the interpretational problem that is encountered with TFP changes, when the Variable Return to Scale (VRS) technology is assumed. In the case of VRS, the estimated TFP changes using the approach used in this paper may not properly reflect the TFP gains or losses (see also, Coelli et al., 1998, p. 224). It should be mentioned that, in this paper, the following criteria are imposed on the process of assigning inputs and outputs in every model: (i) An input is a factor for which, if other factors remain constant, using less of it would be more desirable for the firm/industry and (ii) An output is a factor for which, if other factors remain constant, making more of it would be more desirable for the firm/industry. In this way, inputs and outputs are treated slightly different from what conventionally considered as inputs and outputs of a system. On the basis of these criteria, physical inputs and outputs (e.g., labour, capital and electricity generated) are assigned as they have traditionally been assigned in most studies. This is while, financial data (expenditure, revenue, or price/cost related data) are assigned differently from conventional ways2. For example, ‘average price’ is considered as an input, because the lower prices would be reflecting more productive industry and, hence, more desirable. Along these lines, data requirement would be less cumbersome than conventional TFP analysis. The key point behind these criteria is that they classify inputs and outputs in a way that is consistent with the axioms of feasible output set as described in Coelli et al. (p. 62). Throughout this process of modelling, there is no need to adopt explicit behavioural assumption for firms such as profit maximisation or cost minimisation (1998, p. 221). Further, these criteria imply that, unlike conventional modelling processes, one can measure technical and non-technical (i.e., allocative or economic) efficiencies using the same process of modelling. In this way, it is the interpretation of efficiency measures that varies from model to model. While one model may measure ‘pure technical efficiency’, one may measure ‘economic (price) efficiency’. Table 2, describes the input–output configuration of eight (8) models that are developed for this paper. In this Table, the interpretations of efficiency measures for each model are indicated. As can be noted from Table 2, models are configured at two (2) levels of aggregation, namely entire industry (Models 1–4); and thermal generation (Models 5–8). Due to low quality and quantity of data for network segment of the industry, no model is configured for transmission and distribution sub-sectors. The Malmquist TFP index holds three important properties that make it useful for the purpose of this paper. More specifically, this index is: (i) based on a dynamic framework; (ii) more data-friendly; (iii) free from conventional microeconomics behavioural assumptions (i.e., profit maximisation or cost minimisation) as imposed on conventional modelling; and (iv) decomposable into efficiency change and technological change (see Eq. (1), above). The forth property, in particular, allows for decomposing, say, TFP growth into those of which are due to comparative efficiency gains (moving towards the PPF) and those of which are due to technological improvements (shifts in PPF). In such decomposition, it is logical to think that comparative efficiency gains would be the most desirable outcome of introducing ‘competition’. This allows for 2 See Zeitsch et al. (1992), ESAA (1994), Coelli et al. (1998, pp. 161–165 & 209–112) for models that included financial data in conventional ways.

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Table 2 Input–output configuration of models. Model

Time period

Level of aggregation

Interpretation of comparative efficiency

No. inputs

Inputs

1 2 3 4 5 6 7 8

1969–2007 1969–2002 1969–2007 1969–2002 1969–2007 1969–2002 1969–2007 1969–2002

ESI ESI ESI ESI TG TG TG TG

Pure technical Pure technical Economic (price) Economic (price) Pure technical Pure technical Economic (price) Economic (price)

4 5 5 6 2 3 3 4

X1, X1, X1, X1, X2, X2, X2, X2,

X3, X3, X3, X3, X4 X4, X4, X4,

X5, X5, X5, X5,

X6 X6, X7 X6, X8 X6, X7, X8

X7 X8 X7, X8

No. outputs

Outputs

1 1 1 1 1 1 1 1

Y1 Y1 Y1 Y1 Y2 Y2 Y2 Y2

Notes: ESI: the entire electricity industry, TG: the thermal generation segment, T&D: the transmission and distribution, X1: Energy input—ESI (terajoules), X2: fuel input—TG (terajoules), X3: installed capacity—ESI (MW), X4: installed capacity—TG (MW), X5: network capacity—T&D (MVA), X6: network length—T&D (Km), X7: labour—ESI (persons), X8: average price ($/MWh), Y1: final electricity supply/consumption (GWh), Y2: thermal electricity generation (GWh).

examining the extents of which competition policies (NCP) have proven success. This will be possible by developing appropriate hypotheses against ‘efficiency gains’ that could possibility be associated to an institutional change, which is captured by a dummy variable (see Section 3.2, below). 3.2. The assessment-framework Five dummy variables are introduced by this paper to capture key institutional changes that have taken place since the early 1990s: (1) Organisational structure, S1: the value is 1 if functionally unbundled, and 0 if vertically integrated; (2) Market structure, S2: the value is 1 if mandatory bid-base, and 0 if traditional order-of-merit; (3) Regulatory framework, R1: the value is 1 if national, and 0 if State-based; (4) Public corporate ownership, O1: the value is 1 if ownership type is Public Corporate (PC), and 0 if otherwise (including public (Pu) and private (Pr) ownership); (5) Private ownership, O2: the value is 1 if private ownership, and 0 if otherwise (i.e., Pu and PC ownership). Table 3 shows the periods of which these variables score the value 1. Using these dummy variables, the measured efficiencies (symbolized by Z) are classified in different groups over time and space. This allows for testing whether the mean value of comparative efficiency measures of the industry have significantly improved as a result of the institutional changes that are captured by the above-mentioned dummy variables. All tests are one-tailed. The following set of hypotheses is designed for this purpose: ( ( H0 : ZS1 ¼ 0 ¼ ZS1 ¼ 1 H0 : ZS2 ¼ 0 ¼ ZS2 ¼ 1 ðiÞ ðiiÞ H1 : ZS1 ¼ 0 r ZS1 ¼ 1 H1 : ZS2 ¼ 0 r ZS2 ¼ 1 ( ( H0 : ZR1 ¼ 0 ¼ ZR1 ¼ 1 H0 : ZO1 ¼ 0 ¼ ZO1 ¼ 1 ðiiiÞ ðivÞ H1 : ZR1 ¼ 0 r ZR1 ¼ 1 H1 : ZO1 ¼ 0 r ZO1 ¼ 1 ( H0 : ZO2 ¼ 0 ¼ ZO2 ¼ 1 ðvÞ ð2Þ H1 : ZO2 ¼ 0 r ZO2 ¼ 1 where, for instance, hypothesis (2 i) is designed to assess the impacts of ‘organisational restructuring’ (captured by dummy variable S1) on efficiency measures. In this hypothesis, ZS1 ¼ 0 and ZS1 ¼ 1 refer to mean value of efficiency within ‘vertically integrated’ and ‘functionally unbundled’ firms, respectively. Null hypothesis (H0), hence, refers to this statement that ‘there is no significant difference between mean of efficiency measures within firms with different organisational structures (i.e., ZS1 ¼ 0 and ZS1 ¼ 1 )’. These tests are easily doable using the Analysis of Variance (ANOVA) technique for unequal numbers of observations as described in Spiegel (1988,

Table 3 Periods of which dummy variables score the value 1 in each State.

NSW VIC QLD SA WA TAS NT Australia

S1

S2

R1

O1

O2

1994–2007 1993–2007 1995–2007 1997–2007 2003–2007 1998–2007 – 1993–2007

1996–2007 1995–2007 1998–2007 1998–2007 2006–2007 2006–2007 – 1995–2007

2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007 2006–2007

1991–2007 1990–1995 – 1995–1998 – – – 1990–1995

– 1996–2007 – 1999–2007 – – – 1996–2007

pp. 310, 321–322). This is a non-parametric ANOVA test, based on F-Stat. F¨are et al. (1985) employs a number of alternative nonparametric tests, namely Kruskal–Wallis, median scores, Van der Waerden and Savage score. Such tests are based on w2-Stat. This paper relies only on the ANOVA test.

4. Dataset In this paper, a panel dataset for eight States/territories – for simplicity, hereafter, are called States – over the period 1969– 2007 is developed. The data for inputs (Xs) and outputs (Ys) of various State electricity industries, corresponding to those listed in Table 2, are obtained mostly from the Energy Supply Association of Australia3 (ESAA various-a; ESAA, various-b) publications in various years. In this section, key considerations about the dataset are explained. The inputs and outputs of the Snowy Mountain Scheme – on the basis of EANSW (1986) and also according to the assumption made by Whiteman (1999) – are divided between the two States (NSW and Victoria) in the ratios 23 and 13, respectively. The Australian Capital Territory (ACT) is merged with NSW and collectively is regarded as one State, named NSW hereafter. Therefore, the cross-section of the panel data includes: NSW, Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA), Tasmania (TAS), and the Northern Territory (NT). Fuel inputs data (X2) refers to energy content of fossil fuels (e.g., oil products and gas) that are consumed by thermal electricity plants. This data is readily available in ESAA publications. Energy input data (X1), in contrast, are not provided by ESAA. This, according to a definition often used in ‘energy balances’, refer to total energy content that are required to produce gross electricity if entirely produced by fossil fuels. In 3 Prior to 2004, ESAA was the name of Electricity Supply Association of Australia.

R.F. Aghdam / Energy Policy 39 (2011) 3281–3295

this definition, average energy efficiency of thermal plants is used to calculate equivalent energy content of primary energy for producing non-fossil electricity (e.g., hydro, solar and wind). Labour force data (X7) is no longer produced by ESAA in a consistent way, since 2003. This has caused a significant loss of observation (2003–2007) for the models that include labour variable as an input (Models 2, 4, 6 and 8). This variable, hence, is not included in some models of this paper (Models 1, 3, 5 and 7), in order to utilise non-labour data for 2003–2007. This issue

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seems to be not problematic because, according to most of the previous studies, labour productivities have shown continuous improvement over time and across States. What is more worrisome in this respect is the fact that the existing labour data, especially in recent years (after 1998), does not include outsourced contractors. This implies that many measured labour productivities in previous studies entail overestimation. With regards to financial data (e.g., expenditure, revenue), ESAA no longer provides consistent time series. This, just like labour data,

Table 4 Summary of statistics of efficiency measures and the results of hypothesis tests—State efficiency measures are averaged for Australia. Type of efficiency level of aggregation model

Technical-ESI Model 1

Number of observations Total 39 Organisational structure (S1) Vertically integrated 24 functionally unbundled 15 Market structure (S2) Order-of-merit Mechanism 29 Bid-base system 10 Ownership arrangement (O1 and O2) Public 9 Public Corporate 18 Private 12 Regulatory framework (R1) State-base 37 National 2 Mean of efficiencies Total 0.9547 Organisational structure (S1) Vertically integrated 0.9413 functionally unbundled 0.9763 Market structure (S2) Order-of-merit mechanism 0.9481 Bid-base system 0.9739 Ownership arrangement (O1 and O2) Public 0.8870 Public corporate 0.9761 Private 0.9736 Regulatory framework (R1) State-base 0.9536 national 0.9755 Standard deviation Total 0.0248 Organisational structure (S1) Vertically integrated 0.0210 Functionally unbundled 0.0118 Market structure (S2) Order-of-merit mechanism 0.0247 Bid-base system 0.0124 Ownership arrangement (O1 and O2) Public 0.0020 Public corporate 0.0108 Private 0.0117 Regulatory framework (R1) State-base 0.0245 national 0.0304

Technical-ESI Model 2

Cost-ESI Model 3

Cost-ESI Model 4

Technical-TG Model 5

Technical-TG Model 6

Cost-TG Model 7

Cost-TG Model 8

34

39

34

39

34

39

34

24 10

24 15

24 10

24 15

24 10

24 15

24 10

29 5

29 10

29 5

29 10

29 5

29 10

29 5

14 13 7

9 18 12

14 13 7

9 18 12

14 13 7

9 18 12

14 13 7

34 0

37 2

34 0

37 2

34 0

37 2

34 0

0.9528

0.9641

0.9619

0.9336

0.9416

0.9354

0.9434

0.9419 0.9788

0.9535 0.9812

0.9540 0.9810

0.9353 0.9311

0.9437 0.9365

0.9380 0.9314

0.9461 0.9370

0.9490 0.9748

0.9591 0.9788

0.9595 0.9760

0.9367 0.9249

0.9437 0.9294

0.9391 0.9249

0.9458 0.9294

0.9181 0.9780 0.9753

0.9109 0.9804 0.9797

0.9371 0.9799 0.9783

0.9454 0.9307 0.9292

0.9509 0.9348 0.9356

0.9509 0.9319 0.9292

0.9539 0.9364 0.9356

0.9528 –

0.9632 0.9805

0.9619 –

0.9350 0.9080

0.9416 –

0.9369 0.9080

0.9434 –

0.0247

0.0211

0.0208

0.0215

0.0207

0.0213

0.0195

0.0210 0.0066

0.0195 0.0088

0.0195 0.0067

0.0237 0.0179

0.0221 0.0165

0.0232 0.0179

0.0204 0.0163

0.0248 0.0043

0.0217 0.0097

0.0216 0.0051

0.0221 0.0179

0.0205 0.0193

0.0215 0.0179

0.0188 0.0193

0.0151 0.0061 0.0039

0.0037 0.0082 0.0091

0.0086 0.0062 0.0060

0.0138 0.0165 0.0190

0.0131 0.0151 0.0190

0.0139 0.0166 0.0190

0.0138 0.0148 0.0190

0.0247 –

0.0209 0.0233

0.0208 –

0.0211 0.0099

0.0207 –

0.0208 0.0099

0.0195 –

Test 1 (S1)—Ho: no significant difference between mean of efficiencies within firms with different organisational structure. F-value 34.5682 29.1171 25.3516 17.1812 0.3436 0.8555 0.8703 Pr(F) 0.0000 0.0000 0.0000 0.0002 0.5613 0.3619 0.3569 Test 2 (S2)—Ho: no significant difference between mean of efficiencies within firms with different market structure. F-value 9.9010 5.2620 7.2268 2.6481 2.3000 2.1086 3.4997 Pr(F) 0.0033 0.0285 0.0107 0.1135 0.1379 0.1562 0.0693 Test 3 (R1)—Ho: no significant difference between mean of efficiencies within firms with different regulatory framework. F-value 1.4956 – 1.2320 – 3.1712 – 3.7424 Pr(F) 0.2291 – 0.2742 – 0.0832 – 0.0607 Test 4 (O1)—Ho: no significant difference between mean of efficiencies within firms with different ownership (public corporate vs. otherwise). F-value 68.5584 63.5185 37.5596 27.2384 0.6101 2.3893 0.9232 Pr(F) 0.0000 0.0000 0.0000 0.0000 0.4397 0.1320 0.3429 Test 5 (O2)—Ho: no significant difference between mean of efficiencies within firms with different ownership (private vs. otherwise) F-value 13.2151 9.1211 11.5267 5.9931 0.7460 0.7417 1.5190 Pr(F) 0.0008 0.0049 0.0017 0.0200 0.3933 0.3955 0.2255

1.5549 0.2215 3.2257 0.0819 – – 2.8849 0.0991 1.4418 0.2387

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Table 5 Summary of statistics of efficiency measures and the results of hypothesis tests—efficiency measures, at State levels. Type of efficiency level of aggregation Model Number of observations Total Organisational structure (S1) Vertically integrated Functionally unbundled Market structure (S2) Order-of-merit mechanism Bid-base system Ownership arrangement (O1 and Public Public corporate Private Regulatory framework (R1) State-base National Mean of efficiencies Total Organisational structure (S1) Vertically integrated Functionally unbundled Market structure (S2) Order-of-merit mechanism Bid-base system Ownership arrangement (O1 and Public Public corporate Private Regulatory framework (R1) State-base National Standard deviation Total Organisational structure (S1) Vertically integrated Functionally unbundled Market structure (S2) Order-of-merit mechanism Bid-base system Ownership arrangement (O1 and Public Public corporate Private Regulatory framework (R1) State-base National

Technical-ESI Model 1

Technical-ESI Model 2

Cost-ESI Model 3

Cost-ESI Model 4

Technical-TG Model 5

Technical-TG Model 6

Cost-TG Model 7

Cost-TG Model 8

273

238

273

238

273

238

273

238

205 68

200 38

205 68

200 38

205 68

200 38

205 68

200 38

224 49 O 2) 225 27 21

213 25

224 49

213 25

224 49

213 25

224 49

213 25

205 22 11

225 27 21

205 22 11

225 27 21

205 22 11

225 27 21

205 22 11

259 14

238 0

259 14

238 0

259 14

238 0

259 14

238 0

0.9548

0.9527

0.9642

0.9619

0.9336

0.9416

0.9353

0.9434

0.9446 0.9855

0.9445 0.9961

0.9546 0.9929

0.9547 1.0000

0.9271 0.9533

0.9367 0.9672

0.9293 0.9535

0.9388 0.9676

0.9480 0.9858

0.9479 0.9940

0.9572 0.9959

0.9574 1.0000

0.9261 0.9678

0.9364 0.9861

0.9282 0.9679

0.9384 0.9862

0.9465 0.9943 0.9930

0.9458 0.9939 1.0000

0.9566 1.0000 0.9987

0.9558 1.0000 1.0000

0.9248 0.9829 0.9647

0.9355 0.9817 0.9745

0.9265 0.9866 0.9647

0.9371 0.9862 0.9745

0.9536 0.9756

0.9527 –

0.9633 0.9804

0.9619 –

0.9350 0.9079

0.9416 –

0.9368 0.9079

0.9434 –

0.0645

0.0668

0.0527

0.0539

0.0756

0.0723

0.0766

0.0732

0.0690 0.0336

0.0697 0.0137

0.0556 0.0275

0.0560 0.0000

0.0768 0.0688

0.0727 0.0655

0.0782 0.0689

0.0738 0.0657

0.0678 0.0324

0.0688 0.0166

0.0549 0.0225

0.0553 0.0000

0.0775 0.0551

0.0740 0.0305

0.0789 0.0552

0.0751 0.0305

0.0305 0.0166 0.0164

0.0502 0.0162 0.0000

0.0470 0.0000 0.0059

0.0543 0.0000 0.0000

0.0004 0.0278 0.0452

0.0002 0.0297 0.0397

0.0002 0.0283 0.0452

0.0004 0.0304 0.0397

0.0652 0.0480

0.0668 –

0.0531 0.0438

0.0539 –

0.0747 0.0905

0.0723 –

0.0757 0.0905

0.0732 –

O 2)

O 2)

Test 1 (S1)—Ho: no significant difference between mean of efficiencies within firms with different organisational structure. F-value 22.1683 20.6314 29.8039 24.8353 6.2771 5.8111 5.1715 Pr(F) 0.0000 0.0000 0.0000 0.0000 0.0128 0.0167 0.0237 Test 2 (S2)—Ho: no significant difference between mean of efficiencies within firms with different market structure. F-value 14.4638 11.1467 23.3832 14.7485 12.7490 11.0304 11.1578 Pr(F) 0.0002 0.0010 0.0000 0.0002 0.0004 0.0010 0.0010 Test 3 (R1)—Ho: no significant difference between mean of efficiencies within firms with different regulatory framework. F-value 1.5357 1.4072 1.7144 1.9070 Pr(F) 0.2163 – 0.2366 – 0.1915 – 0.1684 Test 4 (O1)—Ho: no significant difference between mean of efficiencies within firms with different ownership (public corporate vs. otherwise). F-value 13.2151 9.1211 11.5267 5.9931 0.7460 0.7417 1.5190 Pr(F) 0.0008 0.0049 0.0017 0.0200 0.3933 0.3955 0.2255 Test 5 (O2)—Ho: no significant difference between mean of efficiencies within firms with different ownership (private vs. otherwise) F-value 8.1932 5.8974 10.0950 5.8736 3.8834 2.4121 3.3617 Pr(F) 0.0045 0.0159 0.0017 0.0161 0.0498 0.1217 0.0678

has caused unbalanced data-availability for the models that include financial data. Real average electricity price (X8)4 seems to be the most reliable variable that is available and, hence, can be included in the models. There are, however, some considerations that must be mentioned here. From 1969 to 2002, nominal average price was calculated as ‘total revenue’ divided by ‘total consumption’. From

4 This is calculated as nominal prices divided by average annual State-CPIs (1990 ¼100). State-CPIs are extracted from ABS (2010).

5.0297 0.0258 9.9093 0.0019 – 1.4418 0.2387 2.0978 0.1488

2003 to 2007, for NEM States (i.e., NSW, VIC, QLD, SA, and TAS), this variable is replaced with volume-weighted average prices of the wholesale electricity market (i.e., NEM). It should be noted that proportionate volume-shares of Snowy are not included in this variable for NSW and VIC. In order to have a consistent time series for the Northern Territory during 2003–2007, data from annual reports of Power and Water Corporation (PWC various years) are used. These reports provide original figures of ‘total revenue’ and ‘total consumption’ for NT between 2003 and 2007. As for WA, ESAA (2004, 2005) has provided the average price data for 2003 and 2004.

R.F. Aghdam / Energy Policy 39 (2011) 3281–3295

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Table 6 Summary of test results about impact of reform-driven institutional changes on efficiency measures of the Australian electricity industry. Industry

State-based measures (State levels)

Averaged measures at national level

segment

M.

efficiency/change

S1

S2

R1

O1

O2

S1

S2

R1

O1

O2

ESI

1 2 3 4 5 6 7 8

Pure technical efficiency Pure technical efficiency Economic (price) efficiency Economic (price) efficiency Pure technical efficiency Pure technical efficiency Economic (price) efficiency Economic (price) efficiency

nnnn nnnn nnnn

nnn nnn nnnn

nnn nn nnn

nn n nn

nnnn nnnn nnnn

nn n nn

nnn nn nn

nnn nnn nn nnn nn

nnn nnn nn nnn nn

n n

nnn

nl nl nl nl nl

nl – nl – nl – nl –

nnnn nnnn nnnn

nnnn n n n n

nl – nl – nl – nl –

TG

nl nl nl

nl nl nl nl

nnnn

n

nl nl nl nl

nl nl nl nl

Notes: nnnn, nnn, nn, n imply that null hypothesis is rejected at 0.0001, 0.001, 0.01, and 0.5 level of significance, respectively.‘nl’ implies that null hypothesis is not rejected.‘-’ implies that test is not applicable due to lack of data.

For 2006 and 2007, the volume-weighted average of MCAP5 and STEM6 price is calculated for this purpose using data provided by WA Independent Market Operator (IOM, 2008). Finally, dummy variables (Table 3) are gathered from various sources and the author’s personal communications with institutions and industry professionals. At the national level, dummy variables are generally implying the same interpretation as described for States. However, as States are not synchronized, the variable-values are assigned 1, if at least one State-base dummy variable has taken the value 1. For example, variable S1 is taking value 1 for 1993–2007 and value 0 otherwise (see the last row of Table 3). 5. Empirical results All models of this paper are run by the DEAP programme developed by Professor Coelli of University of Queensland (Coelli, 1996). The results of ANOVA are calculated using Microsoft Excel programme. This section discusses the results. Findings of hypothesis-testings are presented in Section 5.1. This is followed by a discussion, in Section 5.2, on main findings about dynamics of TFP changes, where TFP trends are analysed by their decompositions, using trend analysis. The discussion of this section is limited to the most intriguing findings at national and State levels. The results are selective to conserve space. Full results in tabular form are available from the author on request. 5.1. Findings of hypotheses-testings Table 4 provides a summary of statistics for various comparative efficiency measures (averaged for the whole Australia), for the sample, as well as being aggregated over data-groups, where different types of organisational structure, market structure, regulatory framework, and ownership arrangement are observed. Each efficiency measure at the whole national level is calculated on the basis of geometrical mean of efficiency measures of Statelevel data-points in each year. The number of observations, means, and standard deviations of the comparative efficiencies are presented in Table 4. Further, the results of the five hypotheses tests (as described in Section 3.2) are presented by F-value and F-probability in Table 4. Table 5 shows similar summary for comparative efficiency measures at State levels. This is a summary of entire State-level data-points that includes total of 273 observations in Models 1, 3, 5 and 7; and 238 observations in Models 2, 4, 6 and 8. Table 6 further summarises tests-results of Tables 4 and 5, regarding the impacts of reform-driven institutional changes 5 6

Marginal cost of administrative price. Short term energy market.

(captured by S1, S2, R1, O1, and O2 variables) on various efficiency measures of the Australian electricity industry. As can be noted the impacts are mixed. For efficiency measures which are averaged for the whole Australia, the results reveal that the efficiency gains at the level of entire electricity industry, to large extents, are driven by functional unbundling and public corporatisation, and to a lesser extent, by market restructuring and privatisation. In these, statement and similar statements to come in this section of the paper, the extents of each impact are made quantitative, by the level of significance.7 In Table 6, this is shown by the number of ‘n’ regarding the result of each test. The results, further, reveal that regulatory reform has made insignificant contribution to efficiency gains and that none of the reform-driven changes has made any significant contribution to country-wide (average) efficiency measures, at the levels of generation. For efficiency measures at their (original) State-level datapoints (Table 5), the results reveal that the efficiency gains at the level of entire electricity industry are driven by functional unbundling, to a large extent; market restructuring and public corporatisation, to a lesser extent; and privatisation, to a far lower extent. Regulatory reform has shown insignificant contribution to efficiency gains. At the level of thermal generation, efficiency gains are relatively more attributable to market restructuring and public corporatisation and, to a lesser extent, functional unbundling. At this level, regulatory reform and privatisation have made insignificant contribution to efficiency gains. 5.2. Dynamics of TFP changes The findings in Section 5.1 were exclusive about comparative efficiency gains of the market reform (1994-present) because, as noted in Section 3, such efficiency gains should be a logical outcome of implementation of competition policy (NCP). Thus, it deserved such a careful and detailed assessment. However, apart from comparative efficiency gains, the impacts of reform can also be realised in terms of improvements in TFP measures. Improvements of TFP measures may be caused by, e.g., technological developments (shifts in PPF). 8The models of this paper allow analysis of the dynamics of TFP changes by two components, namely comparative 7 Statistically speaking, level of significance refers to probability of Type-I error – probability of rejecting a hypothesis, when it should be accepted – which is quantified by Pr(F) as indicated in Tables 4 and 5. The smaller the amount of such probability is regarded as the larger the extent of the reform impact, because it implies that the differences between mean variables, before and after a reform change, are more meaningful, in statistical sense. 8 There is yet another source of TFP changes, such as changes in scale efficiency that may occur as a result of economies of scale. In this paper, as the CRS technology is assumed (See Section 3), such efficiency, in this paper’s models, is embedded in CRC efficiency measures and is not decomposed.

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Fig. 1 shows trends of the Australian electricity industry TFP by States, corresponding to Model 3, where economic (price) efficiency is incorporated in measuring the TFP at the level of entire electricity industry. This figure generally reveals that the magnitude and direction of productivity changes across States have been mixed. State TFPs are ranked differently over the past years (1969–2007). Average national TFP (thicker line —scaled on the right) generally shows an increasing trend in productivity of the

efficiency changes and technological changes. This analysis is made over a time period that not only covers the market reform period (1994–2007), but also includes the internal reforms period (1986– 1993) and considerable portion of the industry consolidation period (1969–1985). Understanding the dynamics of TFP changes is useful for truly assessing the impact of the market reform on productivity of the industry. This section is devoted to discuss the most intriguing findings of this analysis.

5.6 2.1

4.6

1.9

1.7

3.6

1.5 2.6 1.3 1.6 1.1

0.9

NSW

VIC

QLD

SA

wa

tas

NT

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.6

AUS

Fig. 1. TFP by States in Australia, corresponding to the Model 3. (Note: Australia average (AUS) is scaled on the right).

4.6

3.6

1.85

2.6 1.35 1.6

0.85

NSW

VIC

QLD

SA

wa

tas

NT

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.6

AUS

Fig. 2. TFP by States in Australia, corresponding to the Model 4. (Note: Australia average (AUS) is scaled on the right).

R.F. Aghdam / Energy Policy 39 (2011) 3281–3295

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Figs. 2 and 3 show trends of the overall TFPs by States, corresponding to Models 4 and 8, respectively. In these models, economic (price) efficiency is used in measuring the TFP at the levels of entire electricity industry and thermal generation, respectively. Tasmania TFP is excluded from Fig. 3 because it predominantly generates its electricity from hydro-powers. There is, at least, one common finding in Figs. 1–3 and that is that TFP measures show a faster increasing trend from 1985 to 1998. This is intriguing because no one can conclude that TFP gains are exclusively attributable to the market reform (started in 1994). In other words, it is clear that much of the TFP gains are already realised during the State-wide internal reforms of the 1980s. As noted in Section 3, changes in Malmquist TFP index are decomposable into efficiency change and technological change. Figs. 4–9 shows some of such decompositions, corresponding to some of the models of this paper. Figs. 4–6 are associated with

Australian electricity industry. Some declines in average national TFP are notable during the 1970s and the early 1980s. These, according to many analysts, can be associated to external international events, particularly the oil shocks of the 1970s (see, e.g., Beardow 2002). The sudden increase of TFPs in 2003–2004, which is followed by a sharp decline soon after, is difficult to interpret. Similar pulsations are observed in the models that labour input (X7) is excluded (due to lack of data) but a longer observation-period (1969–2007) is included. This pulsation can be mainly related to the changes that have taken place in the basis of ESAA data, since 2003. This can be associated to data inconsistencies which were discussed in Section 4—predominantly, State electricity prices. Models 2, 4, 6, and 8 are relatively free from such inconsistencies. These models use labour input (X7) but lose considerable number of observations (2003–2007).

3.4 2 3 1.8 2.6 1.6

2.2

1.4

1.8

1.2

1.4

1

1

0.8

NSW

VIC

QLD

SA

wa

NT

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.6

AUS(excl. TAS)

Fig. 3. TFP by States in Australia, corresponding to the Model 8. (Note: Australia average (AUS) is scaled on the right).

1.5

1.4

1.3

1.2

1.1

1

technical efficiency

technology shifts

TFP

Fig. 4. Technical efficiency and corresponding TFP index (Model 1) in entire electricity industry.

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.9

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1.7 1.6 1.5 1.4 1.3 1.2 1.1 1

technical efficiency

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.9

TFP

technology shifts

Fig. 5. Technical efficiency and corresponding TFP index (Model 2) in entire electricity industry.

2.2 2.1 2 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1

economic (price) efficiency

technology shifts

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.9

TFP

Fig. 6. Economic efficiency and corresponding TFP index (Model 4) in entire electricity industry.

Models 1, 2 and 4, respectively, capturing TFP measures at the level of entire electricity industry. In Model 1, only physical inputs are used, whereas in Models 2 and 4, economic (price) inputs are also incorporated. Fig. 7 shows the decomposition of TFP in Queensland electricity industry. Fig. 8 shows the decomposition of TFP index, associated with Model 8, where economic (price) efficiency is incorporated in measuring TFP at the level of thermal generation segment of the industry. Finally, Fig. 9 shows the trend of TFP (of Model 8) by its decomposition in Queensland’s thermal generation. These figures clearly confirm that productivity gains of the Australian electricity industry are largely driven by technological changes (shifts in PPFs) and that the impacts of efficiency gains have been either relatively small or insignificant, in many instances. These figures further reveal that technological shifts mainly stem from the internal reform period (1986–1993) and not necessarily from the market reform.

Such technology driven productivity gains, then, continued during the course of the market reform. This is plausible as upgrading industry’s technology is naturally a gradual process due to sunk-cost effects. This also confirms that technological innovations of the 1980s have been really influential on industry’s productivity improvements. This makes sense because innovations and diffusion of technologies in Telecommunication and Internet (or, generally speaking, ICT revolution) began in the 1980s. Distributed Electricity Generation and Combined Cycle Gas Turbine (CCGT) technologies also emerged during the 1980s (Hunt and Shuttleworth, 1996, pp. 1–8; Sharma and Bartels, 1997). Together, these became the technological breakthrough that played a vital role in the reforms of the 1980s and onwards. Reasons for this include an end to a 100 years old argument of ‘natural monopoly’ behind vertical integrated structure of the electricity industry; and a possibility of separating generation from transmission in the industry. Feasibility of functional

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2.1

1.8

1.5

1.2

economic (price) efficiency

technology shifts

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.9

TFP

Fig. 7. Economic efficiency and corresponding TFP index (Model 4) in QLD electricity industry.

2.1 2 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1

economic (price) efficiency

technology shifts

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

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1973

1971

1969

0.9

TFP

Fig. 8. Economic efficiency and corresponding TFP index (Model 8) in thermal electricity generation.

unbundling of the industry was therefore an integral element of such technological breakthrough. One should also note that the ICT has worked as an ‘add-on’ component of generation and transmission technologies. It is true that generation and transmission technologies have not been changed as fast as ICT. However, ICT revolution has contributed to more efficient use of existing technologies (see, Booth 2003, p.22). That is what a shift in PPF literally implies.

6. Conclusions Analysis of TFP dynamics reveal that the productivity gains in the Australian electricity industry have shown a strong and steady growth over the last 22 years. Such productivity growth, the results of this paper reveal, has been largely driven by technological improvements and to a lesser extents by comparative efficiency

gains. This is consistent with the nature of the electricity industry which, since its inception, has been a highly technology-driven. The results also reveal that much of these technology-induced productivity gains were already realised during State-wide internal reforms between 1986 and 1993 and prior to the inception of the market reform of the 1990s and onwards. This, too, is plausible as ICT was well revolutionised by the mid-1980s and consequently paved a road for substantial technological upgrades in electricity industry. The results of hypothesis-testing reveal that, at the level of entire electricity industry, efficiency measures (averaged for the whole country) have shown improvements that are, to large extents, attributable to functional unbundling and public corporatisation, and to a lesser extent, market restructuring and privatisation. The results further reveal that the reformdriven institutional changes have made insignificant contribution to efficiency gains at the level of thermal generation of the industry.

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2.8 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1

economic (price) efficiency

technology shifts

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

0.8

TFP

Fig. 9. Economic efficiency and corresponding TFP index (Model 8) in QLD thermal electricity generation.

For State-level efficiency measures, the results reveal that there are efficiency gains at the level of entire electricity industry, attributable to functional unbundling, to a large extent; market restructuring and public corporatisation, to a lesser extent; and privatisation, to a far lower extent. At the level of thermal generation, efficiency gains are more attributable to market restructuring and public corporatisation and, to a lesser extent, functional unbundling. At this level, regulatory reform and privatisation have relatively made insignificant contribution to efficiency gains.

Acknowledgment The author would like to express his profound gratitude to Professor Deepak Sharma of University of Technology, Sydney for reviewing various drafts of this paper and providing valuable inputs. He would like to thank Professor John Quiggin of University of Queensland for providing constructive feedback, too. Thanks should go to Deanship of Scientific Research of King Fahd University of Petroleum and Minerals for providing onemonth summer grant, which helped towards completion of this paper. Sincere thanks also go to anonymous reviewers for their valuable comments. The author is, of course, responsible for the contents.

Appendix: list of Acronyms and Abbreviations AEMC AER ANOVA BIE COAG CPI CRS DEA ESAA ESI FA GBE

Australian Energy Market Commission Australian Energy Regulator Analysis of Variance Bureau of Industry Economics Council of Australian Governments Review Consumer Price Index Constant Return to Scale Data Envelope Analysis Electricity (or Energy) Supply Association of Australia Electricity Supply Industry Frontier Approach Government Business Enterprise

GDP Gross Domestic Product HVDC High Voltage Direct Current IA Index Approach IC Industry Commission ICT Information and Communication Technologies MCAP Marginal Cost of Administration Price MCE Ministerial Council of Energy NCP National Competition Policy NCPRC National Competition Policy Review Committee NECA National Electricity Code Administration NEM National Electricity Market NEMMCO National Electricity Market Management Company NSW New South Wales NT Northern Territory OECD Organisation of Economic Cooperation and Development PFP Partial Factor Productivity PPF Production Possibility Frontier QLD Queensland SA South Australia SAIIR South Australian Independent Industry Regulator SFA Stochastic Frontier Approach STEM Short Term Energy Market T&D Transmission and Distribution TAS Tasmania TFP Total Factor Productivity TG Thermal Generation VIC Victoria VRS Variable Return to Scale WA Western Australia

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