Firm size and productivity. Evidence from the electricity distribution industry in Brazil

Tovar, Beatriz; Ramos–Real, Francisco Javier; Almeida, Edmar Luiz Fagundes de

doi:10.1016/j.enpol.2010.11.001

Cited by 73 publications

(33 citation statements)

References 22 publications

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“…Taking into account the results of the pioneering works in the sector that are summarised in [12], certain classic drivers of efficiency were analysed, such as load factor or density in consumption [13,14] while other less-frequent factors such as transmission losses [15] and corruption were also considered. The utilities' technical efficiency in the period 1998-2009 was evaluated following [16] within a fixed effect model (FEM) framework [17,18].…”

Section: Methodsmentioning

confidence: 99%

“…This is especially suitable for regulated industries. In fact, it has been used to measure efficiency and/or productivity changes in electricity utilities [13,14,15] and in other regulated infrastructure services: among others this includes: gas distribution [21], railways [25], airports [26], ports [27]. Moreover, another advantage of distance functions is that input and output prices are not necessary.…”

Section: Methodsmentioning

confidence: 99%

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Revisiting electric utilities’ efficiency in the Southern African Power Pool, 1998–2009

Ramos–Real¹,

Tovar²

2020

J. energy South. Afr.

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This study analyses the determinants of the technical efficiency performance for Southern African Power Pool (SAPP) power utilities in the period 1998-2009, excluding South Africa’s Eskom. The study formulated an explicit model for technical inefficiency by considering the vertical structure of the utilities and the definition of the product, considering the specific characteristics of this sample. It was found that the most significant improvement in the average efficiency of the sample occurred from 2000 to 2002, coinciding with the first SAPP Energy Plan of 2001. Density in consumption, control of corruption and load factor also contributed to the different levels of efficiency. The results provided a new empirical evidence that can be useful for the design of energy policy and incentive regulation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Revisiting electric utilities’ efficiency in the Southern African Power Pool, 1998–2009

Ramos–Real¹,

Tovar²

2020

J. energy South. Afr.

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“…Among the different indices for measuring productivity changes of decision making units (DMUs) over time, Malmquist indices have commonly been used by researchers and practitioners in various environments. Examples include the health sector (e.g., see Kirigia et al 2007;Chowdhury et al 2011), the electricity industry (e.g., see Tovar et al 2011;Aghdam 2011), telecommunications (e.g., see Lam and Shiu 2010;Hisali and Yawe 2011), the water industry (e.g., see Corton and Berg 2009;Portela et al 2011), agriculture (e.g., see Kao 2010;Xu 2012), transportation (e.g., see Gitto and Mancuso 2012;Pires and Fernandes 2012), the banking industry (e.g., see Asmild et al 2004; Portela and Thanassoulis 2010) and others. Caves et al (1982) introduced the earliest type of the Malmquist index and showed how the change in productivity experienced by an operating unit can be measured over time.…”

Section: Introductionmentioning

confidence: 99%

The Luenberger indicator and directions of measurement: a bottoms-up approach with an empirical illustration to German savings banks

Afsharchi

Ahn

2014

International Journal of Production Research

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The Luenberger productivity indicator applies directional distance functions which allow to specifying in what direction (i.e. direction of measurement) the operating units will be evaluated.In the presence of a change in the direction of measurement, the standard components of the existing Luenberger productivity indicator may provide values which are not compatible with reality. In order to eliminate this pitfall, the so-called bottoms-up approach is used to revisit the definition of the indicator and its components. We start with a list of selected sources of productivity change, namely efficiency change, technical change and direction change, then examine the best possible way of measuring each of the sources and combine them to derive a new measure of productivity change. The proposed indicator will be illustrated by means of an empirical application to a panel of 417 German saving banks over the time period 2006-2012. The example explains how the proposed approach is able to properly measure efficiency change, technical change and direction change. The results also provide conclusive evidence about the effect of the change in direction of measurement on the results of the productivity over time in a centralized management scenario.

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“…Input distance functions have been used in empirical studies for efficiency and productivity analysis of industrial units as in Abrate and Erbetta (2010) and Das and Kumbhakar (2012) as well as those of electricity networks such as Tovar et al (2011), Hess and Cullmann (2007), and Growitsch et al (2012). The output of electricity networks is determined exogenously by demand for energy and connections.…”

Section: Modelling a Stochastic Efficient Frontiermentioning

confidence: 99%

A New Perspective: Investment and Efficiency under Incentive Regulation

Poudineh¹,

Jamasb²

2016

The Energy Journal

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractFollowing the liberalisation of the electricity industry since the early 1990s, many sector regulators have recognised the potential for cost efficiency improvement in the networks through incentive regulation aided by benchmarking and productivity analysis. This approach has often resulted in cost efficiency and quality of service improvement. However, there remains a growing concern as to whether the utilities invest sufficiently and efficiently in maintaining and modernising the networks to ensure long term reliability and to meet their future challenges. This paper studies the relationship between investments and cost efficiency in the context of incentive regulation with ex-post regulatory treatment of investments using a panel dataset of 129 Norwegian distribution companies from 2004 to 2010. We introduce the concept of "no impact efficiency" as a revenue-neutral efficiency effect of investment under incentive regulation that makes a firm "investment efficient" in cost benchmarking. Also, we estimate the observed efficiency effect of investments and compare these with the no impact efficiency. Finally, we discuss some implications of incentive regulation and cost benchmarking for investment behaviour of network companies.

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Firm size and productivity. Evidence from the electricity distribution industry in Brazil

Cited by 73 publications

References 22 publications

Revisiting electric utilities’ efficiency in the Southern African Power Pool, 1998–2009

Revisiting electric utilities’ efficiency in the Southern African Power Pool, 1998–2009

The Luenberger indicator and directions of measurement: a bottoms-up approach with an empirical illustration to German savings banks

A New Perspective: Investment and Efficiency under Incentive Regulation

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