1

Rectorate of Post graduation and Research

Postgraduate Programme Stricto Sensu in Economics

WHAT AFFECTS THE EFFICIENCY OF THE OPERATING

COSTS OF ELECTRICAL ENERGY DISTRIBUTORS IN

BRAZIL?

AN ANALYSIS USING STOCHASTIC FRONTIER

Author: Deisi Mara Ribeiro Silva

Supervisor: Dr. Tito Belchior Silva Moreira

Braslia - DF

2015

2

DEISI MARA RIBEIRO SILVA

WHAT AFFECTS THE EFFICIENCY OF THE OPERATING COSTS OF

ELECTRICAL ENERGY DISTRIBUTORS IN BRAZIL?

AN ANALSIS USING STOCHASTIC FRONTIER

Dissertation submitted to the Stricto Senso

Postgraduate Programme in Economics at the

Universidade Catlica de Braslia, as a partial

requisite for acquiring a Masters Degree in

Regional Economics.

Supervisor: Dr. Tito Belchior Silva Moreira

Braslia

2015

3

Dissertation by Deisi Mara Ribeiro Silva, entitled WHAT AFFECTS THE EFFICIENCY OF

THE OPERATING COSTS OF ELECTRICAL ENERGY DISTRIBUTORS IN BRAZIL? AN

ANALSIS USING STOCHASTIC FRONTIER, submitted as a partial requisite for acquiring a

Masters Degree in Regional Economics in the Stricto Sensu Postgraduate Programme in

Economics at the Universidade Catlica de Braslia, on , defended and

approved by the undersigned examination board.

________________________________________________________

Prof. Tito Belchior Silva Moreira

Supervisor

Economics UCB

________________________________________________________

Prof. Leonardo Monasterio

Economics UCB

________________________________________________________

Prof. Ricardo Silva Azevedo Arajo

Economics UnB

Braslia

2015

4

I dedicate this work to my family and my

supervisor Tito Belchior, to the Postgraduate

Department of Economics at the

Universidade Catlica de Braslia and to my

friends.

5

ACKNOWLEDGEMENTS

I am grateful to those who, in some way, assisted me throughout this journey. First and foremost

I thank my mother Ana Gabriela and father Hamilton Silva for their motivating words and their

assistance whenever I needed it. I am thankful to CNPq for the financial assistance, to Prof. Tito

Belchior, Prof. Leonardo Monastrio and to all the teachers of the Post graduation Department of

Economics at the Universidade Catlica de Braslia.

6

Were blind to our blindness. We have very little

idea of how little we know. Were not designed to

know how little we know.

(Daniel Kahneman)

7

ABSTRACT

The Brazilian electrical sector has faced many changes over the years. There have been

transformations especially in the activities that involve the distribution of energy. This study

measures the efficiency of the electricity distribution sector with expectations that this may help

to establish patterns of quality and service improvement thus helping in the regulatory process.

The objective of this study was to use benchmarking for panel comparisons, allowing us to

compare the performance of the distributors in Brazil in different time periods. The idea was to

estimate stochastic cost functions for 63 Brazilian distributors of electrical energy in the period

between 2001 and 2012 using data obtained from ANEEL. These functions were used to

calculate the efficiency of the firms and evaluate what causes inefficiency in operating costs. The

results show that the operating costs are highly sensitive to increases in payroll and third party

services. These two variables positively affected the dependent variable. The study also revealed

that quality variables such as energy not distributed and DEC (duration of interruptions per

consumer) can positively affect the efficiency of firms, though the results obtained for the latter

were not as expected. Other quality variables such as FEC (number of interruptions per

consumer) and technical losses seemed to negatively affect the efficiency of firms.

Keywords: Electrical Energy Distribution. Panel. Stochastic Frontier.

8

LIST OF TABLES

TABLE 1: Summary of the Empirical Study of Efficiency Analysis....30

TABLE 2: Statistical Description..34

TABLE 3: Variable Description for the Fixed Effects Model...34

TABLE 4: Variable Description for the Stochastic Frontier Model..35

TABLE 5: Distributing Firms divided by Groups According to the Dimension...36

TABLE 6: Composition of the CBO`s Ofcio 376/200938

TABLE 7: Distributing Firms Divided by Regions...39

TABLE 8: Results for the Stochastic Frontier Model...42

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LIST OF GRAPHS

GRAPH 1: Brazil DEC / FEC (2013)33

10

LIST OF FIGURES

FIGURE 1: Process for Establishing Regulation in the Electrical Sector..17

FIGURE 2: Process for Establishing Regulation in the Electrical Sector after changes18

FIGURE 3: Measurement and Decomposition of Cost Efficiency.26

FIGURE 4: Measurement and Decomposition of Cost Efficiency.26

11

LIST OF ABREVIATIONS

ANEEL National Agency of Electrical Energy

ABRADEE Brazilian Association of Electrical Energy Distributors

ONS National Operator of the Electrical System

MAE Wholesale Energy Market

CBO Brazilian Classification of Occupations

RESEB Restructuring Project for the Brazilian Electrical Sector

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INDEX

1. INTRODUCTION...13

2. BRAZILS ELECTRICAL SECTOR AND THE RESTRUCTURING

PROCESS.........................................................................................................................16

2.1.Preliminaries..16

3. METHODOLOGY..21

3.1.Fixed Effects Model..21

3.2.Parametric Models 21

3.2.1. Stochastic Frontier Models....23

3.2.2. Stochastic Cost Functions..24

3.3. Efficiency..26

3.4. A Guide to the Literature..29

3.5. Data Treatment .....33

4. RESULTS AND DISCUSSION41

4.1. Fixed Effects Model ....41

4.2. Stochastic Frontier Model42

5. CONCLUSIONS45

BIBLIOGRAPHY

APPENDICES

13

1. INTRODUCTION

The electricity sector in Brazil has undergone many transformations. Until the beginning of the

90s the Brazilian electricity sector had been a regulated state sector, with enterprises working

simultaneously in the energy generation, transmission and distribution sectors (Pires, 2008).

In the mid 90s Brazil was faced with an increase in demand that called for immediate

investments in the sector. Institutional and operational changes that culminated in the current

sector model were prepared based on the political-economic consensus and the regulator state,

whose objective was to direct development policies as well as regulate the sector. As a result,

many companies were privatized and some state agencies were created such as the regulatory

agency, ANEEL (ABRADEE).

Despite the new reforms, the state was unable to obtain resources that satisfied the need for new

investments and the need to maintain the public accounts balanced and thus could not expand

the supply of energy. The structure used at the time became unstable leading the process to be

restructured. A rationing scheme was created, and this made clear the great challenges the

country was facing in this area considering that a sustainable growth crucially depends on the

reliability and quality of the energy supply.

The growth of the economic viability of smaller electric generators, including the renewable

sources, has introduced a new concept of how electrical systems operate, specifically, how

energy is distributed. The growing decentralization of energy generated, which has been

increasing in various countries of the world, has caused the distribution networks to play a great

part on how the system operates by balancing the intermittent effects of these generators and

increasing the quality of energy supply (ABRADEE).

Unlike other network systems such as sanitation systems and gas, electricity cannot be stored in

an economically viable way, and thus the need for constant equilibrium between supply and

demand. By this we mean that the entire system can be affected or shutdown if there are

interruptions (ABRADEE).

Most of the research projects have been trying to improve the generation technology,

transmission and distribution of electricity. Without questioning the importance of these studies

14

which is are to the development of the sector, it is necessary to fully understand the economic

conditions that prevail in this market.

Measuring the efficiency of the electricity sector can help in the regulatory process because,

despite focusing on the electrical energy tariffs, it also permits us to establish patterns of quality

and service improvement (TANNURI-PIANTO; SOUZA, 2009).

In this work we use benchmarking to make panel comparisons, where we compare the

performance of the distributors in Brazil in different time periods. Such comparisons are of great

importance to economists and politicians considering that the development of productivity

constitutes an essential drive for welfare improvements (BOGETOFT; OTTO, 2011).

There are two main methods used in modern benchmarking. One is the non-parametric Data

Envelopment Analysis (DEA) which has its roots in mathematical programming whereas the

other is the parametric Stochastic Frontier Analysis (SFA). The latter approach allows for the

assumption that deviations from the frontier may reflect both the inefficiencies and noise in the

data (BOGETOFT; OTTO, 2011).

While the non-parametric models are by nature superior in terms of flexibility, the stochastic

frontier models have the ability to cope with noisy data. Unlike the robust estimation method, the

stochastic models are useful in producing results that are more sensitive to random variations in

data. They are the most flexible models when referring to the assumptions that can be made

about data quality.

The objective of this work is to estimate the stochastic cost functions for the Brazilian

distributors of electrical energy in the period between 2001 and 2012. We shall use these

functions to calculate the efficiency of these firms and evaluate the aspects of the operational

efficiencies.

Firstly we intend to make a preliminary exploratory analysis to observe how our input and output

variables affect the total operational costs. We need to ensure that the effects occur as expected.

In order to do this, we will use the panel fixed effects model to make the analysis.

This work is organized as follows. Section two provides a brief discussion of the restructuring

process of Brazils electrical sector. The third section describes the methodology used and other

15

methodologies proposed. Section four presents the results and makes a brief discussion of the

obtained results. In section five we conclude this work and suggest relevant points for the

efficiency analysis.

16

2. BRAZILS ELECTRICAL SECTOR AND THE RESTRUCTURING PROCESS

2.1. PRELIMINARIES

The world economy has been going since the 1970s through a great reform and deregulation

process, especially in the electrical sector.

Due to its territorial extension and the predomination of hydraulic sources in the electrical energy

generation, Brazil has always faced a great challenge in the industrialization process. This

challenge consisted in conciliating the supply of energy with the countrys economic growth

profile.

Firstly, to make decisions that concern the investments required for the generation, transmission

and distribution of energy, one must consider the size, composition and regional distribution of

the national GDP. One should consider GDP estimations made at least five years in advance. It is

the minimum time necessary to construct an electrical plant. Secondly, defining the tariff policy

requires a structure for energy production costs that becomes increasingly heterogeneous

throughout time. The reasons for this include:

- The fact that the lifespan of the installed productive capacity could remain active for

many decades after the amortization of investments

- The highest operation costs of the new plants, due to technological and environmental

reasons, and the location of the hydraulic sources.

The fiscal crisis of the 1980s, the depletion of external financial resources and the various

attempts by the government to use public service tariffs as the remedy to deal with the inflation

of that time, made it difficult to use the energy policy executed in the 70s. After the inflation

control of the Plano Real, Fernando Henrique Cardosos government initiated in 1995 a program

of reforms that included privatizing the sector and introducing a regulation model based on the

fact that, given the current stage of technological development, the generation and

commercialization of energy are potentially competitive, while the transmission and distribution

are natural monopolies (LIMA JR., 2005).

From the 1990s until the present days, three relevant facts have taken place in the electrical

energy market:

17

- In the second semester of 1994 there was an increase in the purchase power of the classes

of lower income, especially in the consumption of electronics and similar. As a

consequence there were also increases in consumption and the number of consumers of

electrical energy.

- The second fact was the restructuring process that began in 1995 allowing for

privatization. This generated great expectation on the final consumers, who questioned

more about the services and the fluctuation of tariffs that occurred after the government

stopped controlling the companies.

- The third fact happened in the second semester of 2001, when the electrical energy was

rationed across the country. The well-known Apago (or blackout) changed the

consumers consumption habits all over the country. New habits such as turning off

equipments that werent being used, maximizing the use of natural energy and replacing

bulbs and equipment with new models of low energy consumption technologies, were

incorporated in order to adapt the consumption to the energy supply.

In the beginning of the 90s there was an institutional reorganization in the sector in order to

reduce the states presence. The restructuring and regulatory process should have gone through

the following procedures:

Figure 1: Process for establishing regulation in the electrical sector.

Source; Adapted from the New Model Study of the Brazilian Electrical Sector PriceWaterHouse(2004).

The reformulation of the electrical sector formally started in 1995 with the creation of the

Restructuring Project of the Brazilian Electrical Sector, RESEB, by the Ministry of Mines and

Energy. In order to put the model into practice, two fundamental actions were adopted: a change

in the legal framework and the privatization of public enterprises of electrical energy.

Study and

definition

of the

model

Basic

Law

Detailed

Regulation

Market

Implementation

Privatization

18

One of the proposals was to segregate the large vertical companies (those that simultaneously

performed the activities of generation, transmission and distribution of electrical energy), and to

privatize the areas of generation and commercialization of electrical energy so as to establish at

first a competitive model of free competition as opposed to the eminent monopolistic market.

In 1996, Law no. 9.427 was implemented. The National Agency of Electrical Energy (ANEEL)

was created to serve as the regulating agent, the mediator and inspector of the sector and to be

responsible for the execution of concession auctions of the generation and transmission

enterprises.

In this same year, after the approval of the concessions law, the Brazilian government hired the

services of the American firm Coopers & Lybrant to help it develop a new model for the national

electrical company. The consultant suggested that the Brazilian case follow the same

restructuring model adopted by England and adapt it to the countrys reality.

And thus the new model known as the Restructuring Project for the Brazilian Electrical Sector,

the RESEB Project, was implemented on August 12th

1996, although the privatization process

had already begun since 1995 (PIRES, 2008).

The regulating process in Brazil suffered a few changes as seen in the figure below:

Figure 2 : Process for establishing regulation in the electrical sector after the changes

Source; Adapted from the New Model Study of the Brazilian Electrical Sector PriceWaterHouse(2004).

One of the failures of Fernando H. Cardosos government was to initiate privatization before

establishing a new regulating process. The first companies were privatized in 1995, but the

regulating agency, the National Agency of Electrical Energy (ANEEL), only began to operate in

1997, and in 1998, with the implementation of the two other central institutions of the new

model, the ONS and the Wholesale Energy Market (MAE), eighteen electrical companies had

Basic

Law

Study and

definition of

the model

Privatization Detailed

Regulating

Market

Implementation

19

already been auctioned. On the other hand, the government began to face strong opposition as a

result of the misconduct of the process, but especially because it wasnt trivial to defend the

advantages of the private initiative in a sector with a history of successful state enterprises. This

political obstacle, allied to the new government priorities after the 1998 election, resulted in the

paralysis of the privatization program in the subsequent years (LIMA JR., 2005).

The new regulatory process was granted the legal authority to create two new institutional agents

and a committee in MME Ministry of Mines and Energy:

- The EPE, Enterprise of Energy Research, is a public federal firm linked to the MME,

constituted by Law no. 10.847 and according to this law has the goal of providing

services on the study and research areas related to subsidizing the energy planning sector,

such as electrical energy, oil and natural gas and its derivatives, mineral coal, renewable

energy resources and energy efficiency, among others.

- The CMSE, Monitoring Committee of the Electrical Sector, was authorized by Law no.

10.848/04, and according to this law it has the principal function of monitoring and

evaluating the continuity and safety of the energy supply throughout the country. This

means that, among other activities, it monitors the development of generation,

transmission, distribution, marketing, import and export of electricity, natural gas and oil

derivatives.

Apart from these agents, there is also the important role of the CNPE, National Council of

Energy Policy and the System Expansion Planning Coordinator Committee, which are

responsible for the policies and industry planning, respectively.

According to ASMAE (1999) in order for the new implemented model and the new energy

market to work well, it is necessary to guarantee economic efficiency of the industrial park, the

expansion of the system by the self-sustaining industry, to ensure a system that operates on high

levels of reliability according to the quality requisites determined by society, and to guarantee

services that are available to all agents connected to the system without any discrimination.

The technicians working in firms that operate within these systems face a great challenge which

is to improve the quality of the electrical energy and its associated services performing the

20

generation, transmission and distribution services with the highest quality and reliability indexes

and the lowest possible costs (PIRES, 2008).

21

3. METHODOLOGY

3.1 FIXED EFFECTS MODEL

Panel data can be of great interest especially in microeconometric applications. The motivations

that allow for the use of panel data include the desire to explore this type of data in order to

control unobserved time invariant heterogeneity in cross sectional models and the desire to use

panel data to disentangle components of variance, to estimate transition probabilities among

states and to study the dynamics of cross-sectional populations, Arellano (2003). In order to

pursue these motivations, we can use the fixed effects model.

Considering the following equation

= + + +

where is a 1 x k vector of variables that vary over individual and time, is the k x 1 vector of

coefficients on x, is a 1 x p vector of time-invariant variables that vary only over individuals,

is the p x 1 vector of coefficients on z, is the individual-level effect, and is the

disturbance term. If the are correlated with the regressors, they are known as FE. If the are

correlated with some of the regressors in a model, we estimate them by treating them as

parameters or FE.

The FE modestly relaxes the assumption that considers the regression function constant over

time and space. In a one-way FE model we can allow each cross-sectional unit to have its own

constant term while the slope estimates () are constrained across units (Baum, 2006).

Using fixed effects models to analyze panel data permits us to explore the relationship between

predictor and outcome variables within the distributors of electrical energy. Each distributor has

its own individual characteristics that may or may not have the capacity to influence the

predictor variables.

3.2 PARAMETRIC MODELS

Suppose there is a production function f based on a technology set T. From this we have

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() = { | (, ) }

where x is a n dimensional input vector and y is the m = 1 dimensional output.

In this approach we assume a priori that the production function possesses a specific functional

form. We assume that

() = (; )

where the details of this function are defined by some unknown vector of parameters .

In the parametric approach we estimate these unknown parameters from the actual observations,

(xk, y

k), k = 1,,K. We assume the estimated values to be .

Unlike DEA, the stochastic frontier model is a regression model which uses the Maximum

Likelihood principle as the estimation principle. In other words we select the value of that

makes the actual observations as likely as possible (BOGETOFT; OTTO, 2011).

One more aspect needs to be specified in order to implement this idea. The data generation

process can explain the observations which are deviated from the production function. Three

main processes are suggested in the parametric approach. These include considering any

deviation as noise which corresponds to an ordinary regression model, considering any deviation

as an expression of inefficiency known as the deterministic frontier, and finally assuming that

deviations are caused by a combination of noise (the term v) and inefficiency (the term u). The

latter suggestion refers to the stochastic frontier approach (BOGETOFT; OTTO, 2011).

Table 1 is a summary of the parametric approaches analyzed in Bogetoft and Otto, 2011 where

is a noise and + is inefficiency.

23

Approach Additive Multiplicative

Regression y = f(x; ) + v y = f(x; ) exp(v)

Deterministic y = f(x; ) - u y = f(x; ) exp(u)

Stochastic y = f(x; ) + v - u y = f(x; ) exp(v) exp(-u)

Source: Bogetoft and Otto, 2011

If we look at the additive specifications we observe that the noise term v has a more increasing or

decreasing effect on output with respect to f(x; ). On the other hand the inefficiency term 0

will always make the observed output smaller than f(x; ).

As we look at the Farrell and Shephard efficiency measures, which show the multiplicative

impact, we can see that v can make the output larger or smaller, since exp() 1 when 0

and exp() 1 when 0.

After estimating the parametric functional form, it is possible to measure the output efficiencies

of each firm individually.

3.2.1. STOCHASTIC FRONTIER MODELS

The base SFA model looks as follows after a log transformation:

= ( ; ) + ,

~ (0, 2), ~ +(0,

2), k = 1,, K

The v term deals with the stochastic nature of the production process and captures the possible

measurement errors of the inputs and output while the term u refers to the firms inefficiency.

We consider these two terms independent from each other. The term v reflects the random

disturbances that are independently and identically distributed having a normal distribution with

a zero mean and a variance of 2. The term u reflects the random disturbances which are

normally distributed with a mean of 0 , then there is the possibility that is not very large, as () = 0 and 0. This

suggests relative efficiency of firm k. If < 0, then chances are that is large, meaning that

firm k is relatively inefficient.

Now given the cost function

(, ) = { |(, ) }

shows the minimum cost of producing the output combination y when input prices are w and the

technology set is T.

(, ) (, )

Therefore the observed cost will be higher than the minimum cost and the cost efficiency will be

the minimal cost compared to the actual costs

28

= (, )

1, (, )

We may wish to parameterize cost efficiency using the inefficiency term u such that

=

where 0 when 1, for (, ) .

A multiplicative error term v is introduced so that we obtain

= (, )

and therefore

= (, )

=

(, )

= (, )

We can use this equation to estimate CE. Taking the log we obtain

log() = log((, )) + +

Using a Cobb-Douglas function we illustrate the method where = =1 such that

= 011

where = + and = 1=1 to ensure homogeneity in prices.

In logarithmic form we have

log = 0 + 1 log 1 + + + = 0 + 1 log 1 + + log + +

where we have an ordinary term v, and a non-negative term u that reflects the level of

inefficiency.

We now have the cost efficiency measured as

29

= = 01

This equation can be rewritten as the statistically equivalent equation

log = 0 11 log + +

In order to deal with the homoneneity in input prices we can use one of the input prices as a

numeraire

log (

) = 0 1 log (

1

) 1 log (1

) +

which is equivalent to

log = 0 1 log 1 11 (1 1 + 1) log +

3.4. A GUIDE TO THE LITERATURE

The calculations of the efficiency measures of frontier production and cost functions were

introduced by Debreu (1951) and Farrell (1957), even though there are intellectual

antecedents, [such as Hicks (1935) suggestion that a monopolist would enjoy their position

through the attainment of a quiet life rather than through the pursuit of economic profits, a

conjecture formalized somewhat more by Leibenstein (1966, 1975)]. Farrell considered

analyzing technical efficiency in terms of realized deviations from an idealized frontier

isoquant. This approach fits into the econometric approach in which the inefficiency is

identified with disturbances in the regression model (Greene, 2007).

30

Table 1: Study of the Empirical Studies of Efficiency Analysis

Topics Work Title Description Author Year

Stochastic

Frontier Model

and DEA

Model

Estimating Technical

and Allocative

Inefficiency Relative

to Stochast

Production and Cost

Frontiers

This paper seeks to consider the

duality between the stochastic

frontier production and cost

functions assuming exact cost

minimization (technical

inefficiency only) and inexact

cost minimization (technical and

allocative inefficiency) The

inefficiency is measured using

data on steam electric generating

plants

C. A. Knox

Lovell and

Peter

Schmidt

1978

Fronteiras de

Eficincia

Estocsticas para as

Empresas

de Distribuio de

Energia Eltrica no

Brasil: Uma

Anlise de Dados de

Painel

This work suggests a

methodology for the efficiency

analysis of 22 distributors during

the period 1993 - 2001.

Tanuri-

Pianto,

Sampaio de

Souza,

Arcoverde

2007

A Model for

Technical

Inefficiency Effects

in a Stochastic

Frontier Production

Function for Panel

Data

An extension of recently

proposed models for inefficiency

effects in stochastic frontiers for

cross-sectional data.

Battese and

Coelli 1995

Benchmarking with

DEA, SFA, and R

This book presents recent

developments in effiiency

evaluation.

Bogetoft P.,

Otto L 2011

31

Avaliaao de

Eficincia de

Distribuidoras de

Energia Eltrica

Atravs da Anlise

Envoltria de Dados

com Restries aos

Pesos.

The analysis is carried out by

calculating and comparing the

efficiencies of 22 Brazilian

firms.

Sollero and

Lins 2004

Custos Operacionais

Eficientes das

Distribuidoras de

Energia Eletrica: um

Estudo Comparativo

dos Modelos DEA e

SFA

Efficiency measurements of 40

Brazilian electricity distribution

companies obtained using the

data envelopment analysis

(DEA) and stochastic frontier

analysis (SFA)

models

Souza e

Pessanha 2010

Choice of model and

availability of data

for efficiency

analysis of Dutch

network and supply

businesses in

electricity sector,

Netherlands

Eletricity Regulatory

Service

This work analyses various

models that can be used to

determine efficiency in the

electricity sector in Holland.

DTE 2000

Lovell and Schmidt (1978) attempted to demonstrate evidence on total inefficiency and its

technical and allocative components through a straightforward extension of the analysis of ALS

(1977) and Meeusen and Van den Broek (1977). In their work they make the behavioral

assumption that the firms objective is to minimize the cost of producing its targeted rate of

output, subject to a stochastic production function constraint. In the case of technical inefficiency

the firm operates beneath its stochastic production frontier, and in case of allocative inefficiency

it operates off its least cost production path. These authors provide empirical illustration of this

application. They estimate stochastic frontiers for a sample of US steam-electric generating

plants (LOVELL; SCHMIDT, 1978).

32

Battese and Coelli (2005) proposed a model for technical inefficiency effects in a stochastic

frontier production function using panel data. They applied the model to data from 14 Indian

paddy farmers observed over a ten-year period. They concluded that for the technical

inefficiency effects, involving a constant term, age and schooling of the farmers and year of

observation, can be a significant component in the stochastic frontier production function.

Furthermore, the application illustrated that the model specification allows for the estimation of

both technical change and time-varying technical inefficiency, given that inefficiency effects are

stochastic and have a known distribution.

A Study that specifically analyses efficiency in electrical energy distributors is the work done by

Tanuri Pianto and Souza (2009). In this paper they estimated production and cost stochastic

functions for 22 Brazilian electrical energy distributors, using the single-equation method for

panel data. From these functions they were able to obtain estimations for the efficiency levels of

the firms; the idea is that the results obtained could provide subsidies for the authorities

responsible for the energy regulation sector so that the factors which have impact over the sector

may be manipulated so as to reduce the negative impacts on the efficiency levels (TANURI

PIANTO; SOUZA, 2009).

Another paper by Sollero and Lins (2004) uses proportional restriction to the virtual inputs and

outputs in the efficiency analysis of 22 concessionaires of electrical energy in Brazil. The

application of DEA (Data Envelopment Analysis) in specific situations motivated the use of

weight restrictions so as to limit its complete freedom to variation permitted by the original DEA

model. Many studies with weight restrictions have been proposed since Thompson and others

(1986).

Souza and Pessanha (2010) present efficiency measures for 40 Brazilian distributors of electrical

energy. The measures are obtained by models of data envelopment (DEA) and stochastic frontier

models (SFA). These two techniques can mitigate the information asymmetry and improve the

capacity of the regulating agent to compare the performance of the distributors.

33

3.5. DATA TREATMENT

In this study, most of the information used to estimate the cost frontiers for the 63 distributing

firms of Brazilian electrical energy in the period of 2001 to 2012, were obtained from the

National Agency of Electrical Energy (ANEEL). The data collected is understood to affect the

efficiency of the firms. Some of the variables studied include operational cost, distributed energy

in MWh and some indicators of service quality such as technical losses, the average duration of

supply interruptions per consumer per year in hours (DEC) and the number of supply

interruptions per consumer per year (FEC), energy not distributed in MWh (Endist), and market

share (Share).

Graph 1: Brazil DEC/FEC (2013)

Brazil Supply Continuity

Reduction of 33% in the DEC in the period 1997 to 2013 and 52% in the FEC

SOURCE: ANEEL

The operational costs can be further explained by variables such as expenses with materials,

payroll, third-party services, insurance, taxes and other, as described in Nota Tcnica n

494/2013-SRE/ANEEL, and corresponding to the expense accounts registered in account

number 615 of the Electrical Sector Accounts Manual with a few adjustments in payroll and

DEC (hours)

FEC (no. of interruptions)

34

other. For payroll and third party service expenses, the price index IPCA was used while for the

remaining costs the IGPM was considered.

TABLE 2 Statistical Description

Variables Obs Mean Std. Dev. Min Max

Total Operational Cost 732 248533.9 365400.7 870.0345 2226883

Materials 732 12992.83 20353.76 28.63848 150791.3

Third-party Services 732 95399.47 127015.8 109.4355 820554.9

Insurance 732 875.9841 1384.881 0 12940.87

Taxes 732 4004.507 10509.47 -15614.53 194784.3

Staff 732 124896.5 210992.1 587.0554 1369676

Market (average) 610 2298928 3476958 5805.73 2.11E+07

Technical Losses 609 464324.6 682336.4 0 4184655

DEC 610 16.745 14.04277 0.37 102

TABLE 3 - Variable Description for Fixed Effects Model

Variables Description

CO

Operating Cost. Refers to the data of each distributor

concerning accounts that comprise the so-called Regulatory

operating cost, according to the Electric Sector Accounting

Manual - MCSE.

Materials Operational Expense classified as material costs according

to MCSE with historical values (R$ mil)

Third Party

Services Operation expense classified as third party costs according

to MCSE in historical values (R$ mil).

Insurance Insurance related to Distribution and Commercialization

activities.

Taxes Operation expense classified as tax costs according to

MCSE in historical values (R$ mil)

Other

Compensation for losses and damages, personal

consumption of energy, expenses with interns and the

Initiation Program of Labor, Consumer Council Expenses,

expenses with internal communication and reproduction,

collection tax, bank fees.

Market

(Average)

Energy delivered by the Distributor, in MWh, we have

High-Tension Markets (HT), Medium Tension (MT) and

Low Tension (LT).

35

DEC

Equivalent duration of Interruptions per consumer,

indicates the number of hours on average that a consumer is

without electric energy during a period, usually on a

monthly basis.

Technical Losses Technical losses, mainly caused by heating of the

conducting wires of energy (Joule Effect). Commercial

losses are linked to the theft and fraud of energy.

ENDIST Energy not distributed. ENDIST (DEC x market)/8760

Source: by author using data from ANEEL

TABLE 4- Variable Description for the Stochastic Frontier Model

Variables Description

logdo

Log of Operating Cost. Refers to the data of each

distributor concerning accounts that comprise the so-called

Regulatory operating cost, according to the Electric Sector

Accounting Manual - MCSE.

logm Operating Expense classified as material costs according to

MCSE with historical values (R$ mil)

logst Operation expense classified as third party costs according

to MCSE in historical vallues (R$ mil).

logseg Insurance related to Distribution and Comercialization

activities.

logw Payroll - Actuarial Surpluses or Deficits and retirement

program and or Resignation are not considered.

logt Operation expense classified as tax costs according to

MCSE in historical values (R$ mil)

logz

Compensation for losses and damages, personal

consumption of energy, expenses with interns and the

Initiation Program of Labor, Consumer Council Expenses,

expenses with internal communication and reproduction,

collection tax, bank fees.

logy (Market (average) - Energy delivered by the Distributor, in

MWh, we have High-Tension Markets (HT), Medium

Tension (MT) and Low Tension (LT).

Explanatory Variables for Technical Inefficiency

36

logdec

Equivalent duration of Interruptions per consumer,

indicates the number of hours on average that a consumer is

without electric energy during a period, usually on a

monthly.

logptec Technical losses, mainly caused by heating of the

conducting wires of energy (Joule Effect). Commercial

losses are linked to the theft and fraud of energy.

logendist Energy not distributed. ENDIST (DEC x market)/8760

logfec Equivalent frequency of Interruptions per consumer,

indicates how often, on average, there was no interruption

in the consumption unit (residence, business, industry, etc.)

logshare Market share calculated as the ratio between the amount of

energy (in MWh) distributed by the company and the total

amount of energy distributed.

Source: by author using data from ANEEL

In this study, the distributors were divided into two distinct groups, as specified in Nota Tcnica

No. 192/2014-SRE/ANEEL, in order to improve the comparison among them. One group

comprises of larger firms and the other of smaller firms. The smaller distributors face a reality

that is different from the others when we consider certain aspects such as the presence of the

subtransmission network and the fight against non-technical losses.

GROUP 1 (Large Concessionaires) GROUP 2 (Small concessionaires)

AES SUL COELBA BOA VISTA DME-PC

AME COELCE CAIUA EBO

AMPLA COPEL CEA EDEVP

BANDEIRANTE COSERN CERR BRAGANTINA

CEAL PIRATININGA CFLO JOAO CESA

CEB CPFL PAULISTA CHESP URUSSANGA

CEEE ELEKTRO JAGUARI ELETROCAR

CELESC ELETROACRE MOCOCA SANTA MARIA

CELG ELETROPAULO SANTA CRUZ ENF

CELPA EMG NACIONAL FORCEL

CELPE ENERSUL COCEL HIDROPAN

CELTINS EPB COOPERALIANA IGUAU

TABLE 5: Distributing Firms Divided by Groups According to their

Dimension

37

CEMAR ESCELSA CPEE MUXFELDT

CEMAT ESE CSPE SULGIPE

CEMIG LIGHT DEMEI NOVA PALMA

CEPISA RGE

CERON Source: ANEEL

Most of the smaller distributors receive energy at a distribution level from a supplier, thus

eliminating part of the transport and transformation costs. We classify these costs as network

utility expense and thus are not included in the operational costs (NOTA TCNICA No.

192/2014-SRE/ANEEL).

In Nota Tcnica No.192/2014-SRE/ANEEL the distributors are divided into these two groups

using algorithm clusterization to define the clusters.

Another important aspect to be considered is with respect to the regional differences determined

by the labor costs. The average salary can vary to a great extent among the Brazilian regions, and

consequently, those concessionaires operating in regions where the labor cost is higher will have

to adjust their costs to this reality. Without considering this effect we can obtain an incorrect

efficiency measure, favoring the concessionaires located in regions where the labor cost is lower.

For this reason, a salary variable was created and used in the efficiency calculations, adjusting

the regions to the differences according to RAIS (Source for the construction methodology of the

indicator). This indicator measures the position of the formed groups of concession areas in

relation to the average (NOTA TCNICA No.192/2014-SRE/ANEEL).

The concessionaires were grouped into eight salary groups with the three capitals, So Paulo, Rio

de Janeiro and Braslia being treated separately. In order to distinguish the regions we created

dummies for each region as well as dummies that define the two groups according o their

dimension.

It is important to state that this indicator measures the salary differences of typical occupations in

an electricity distributor, such as the maintenance electricians. Here we compare the salaries in

different concession areas where the employees have the same occupation.

38

The average salary was obtained by the representation of the total number of workers according

to CBO (Brazilian Classification of Occupations), weighed by the median salary (NOTA

TCNICA No.192/2014-SRE/ANEEL).

=

=1

=1

Where: n is the total number of CBOs considered, according to the table below.

TABLE 6 - Composio dos CBOs do Ofcio 376/2009- SRE/ANEEL

Descricao Resumida CBO Proprios Terceirizados Total

Eletricista 951105 2226 24428 26654

Eletricista de Alta-tensao 732120 8129 7193 15322

Agente administrative 411010 6034 3755 9789

Auxiliar tcnico de eletricidade de linhas de transmisso 732105 3957 4505 8462

Anotador de consumo de energia eltrica, gua e gs 519940 972 5551 6523

Auxiliar administrativo de pessoal 411005 3747 1883 5630

Auxiliar de eletrotcnico 313105 3922 827 4749

Atendente central telemarketing 422315 1465 2188 3653

Ajudante de eletricista 715615 671 1586 2257

Tcnico de eletricidade 313130 1718 459 2177

Engenheiro eletricista 214305 1695 93 1788

Operador de teleatendimento hbrido (telemarketing) 422310 25 1451 1476

Contramestre (produo de energia eltrica, gs e captao de gua) 860115 1031 110 1141

Analista de comercializao 253120 932 138 1070

Eletricista instalador de alta e baixa tenso 731125 7 964 971

Administrador 252105 901 37 938

Chofer 782305 44 886 930

Zelador 514120 13 877 890

Eletrotcnico (produo de energia) 313110 836 51 887

Agente de segurana ferroviria 517330 22 827 849

Advogado 241005 296 509 805

Operador de eclusa 861205 804 0 804

Analista de comrcio eletrnico (e-commerce) 212405 371 407 778

39

Operador de quadro de alimentao (subestao de distribuio

de energia eltrica) 861110 629 72 701

Montador 374420 0 697 697

Tcnico de manuteno eltrica 313120 585 21 606

OUTROS - 9797 6573 16370

Source: ANEEL

So the concessionaires operating in regions where labor work is more expensive, will have their

operational cost reduced for the comparison while those operating in regions where labor is less

expensive will have their operational cost increased.

Table 7 Distributing Firms Divided by Regions

REGION FIRMS

RIO DE JANEIRO LIGHT

SOUTHEAST

ESCELSA

ELFMS

ENF

AMPLA

EDEVP

CPFL PAULISTA

CNEE

CAIUA

BANDEIRANTE

CPFL PIRATININGA

ELEKTRO

EEB

CSPEE

CLFM

SAO PAULO ELETROPAULO

NORTHEAST

SULGIPE

ESE

CEAL

CELPE

CEPISA

CEMAR

COELBA

EPB

COSERN

EBO

NORTH CELPA

40

ELETROACRE

CERON

CERR

AMAZONAS

BOA VISTA

CENTRAL-WEST

CHESP

CELTINS

CELG

ENERSUL

CEMAT

DISTRITO FEDERAL CEB

SOUTH

NOVA PALMA

AES SUL

CEEE

IGUACU

EFLUL

COOPERALIANCA

CELESC

RGE

MUXFELDT

HIDROPAN

ELETROCAR

DEMEI

FORCEL

COPEL

COCEL

CFLO

Source: ANEEL

41

4. RESULTS AND DISCUSSION

4.1. FIXED EFFECTS MODEL

As mentioned earlier, the idea is to first carry out a preliminary explanatory analysis permitting

us to simply observe the effects and significance of our variables. We use the fixed effects model

to perform this analysis.

Eleven models are presented in tables 1 and 2 of Appendix C. The first model includes all the 63

distributors considered for analysis in this work. The eleventh model only presents the groups

into which the distributors were divided considering their dimension. Group one specifies the

larger firms. The remaining models represent the different regions into which the concessionaires

were separated according to their salary levels.

Despite having created all these various models for analysis, we shall observe that, in terms of

significance and the effects these variables have over the total operational cost, these models

have produced very similar results.

The variable materials is shown to have a 1% significance level in all the models, producing an

increasing effect on the total operational costs as it is increased by one unit. The variables third-

party services, insurance, taxes and staff also seem to have an increasing effect on the dependent

variable with a 1% significance in all the models as they increase.

The variables mentioned above make up our inputs. Now considering the output variables in this

study we can observe for the distributed energy (Market average), in MWh, that it indicates a

positive relationship with the independent variable although it is not significant. This, however,

is only observed in the first model. The remaining models consider this variable to be at a 1%

significance level elevating the value of the total operational costs as it increases.

Two quality variables were also included in our model, technical losses and DEC (Equivalent

Duration of Interruption per Consumption Unit). The latter indicates the number of hours on

average that a consumer remains without current during a period (usually a monthly period).

These two variables show a 1% significance level with an increasing effect over the total costs as

they increase.

42

The two variables, insurance and DEC, seem to have the largest increasing effect over total cost

while the volume of distributed energy (Market average) and technical losses seem to have

produced very small effects on our dependent variable.

The tables also show the intraclass correlation, rho, which indicates the percentage of the

variance which is due to differences across panels.

4.2.STOCHASTIC FRONTIER MODEL

Here we present the results obtained using the Stochastic Frontier Model. We used the following

models:

= 0 + 1 + 2 + 3 + 4 + 5 + 6

+ 7 + 12 + 23 + 34

+ 45+ 56 + 67 + 78 + 89 +

and

= 0 + 1 + 2 + 3 + 4 + 5

+ 6_1

On table 8 we can see the results obtained.

TABLE 8 - Results for the Stochastic Frontier Estimations Using the Half-

Normal Model

Dependent Variable = Logdo (Operating Costs)

Variables Coefficients Std. Error z Statistic p-value

Logy 0.055 0.007 7.90 0.000

Logm 0.060 0.005 12.49 0.000

Logz 0.014 0.002 6.31 0.000

Logw 0.508 0.006 86.04 0.000

Logseg 0.010 0.002 4.03 0.000

Logst 0.337 0.008 40.27 0.000

Logt 0.012 0.003 6.94 0.000

dr2 0.004 0.005 0.85 0.397

dr3 -0.068 0.015 -4.47 0.000

dr4 0.201 0.005 42.87 0.000

dr5 0.050 0.009 5.40 0.000

43

dr6 0.119 0.010 12.12 0.000

dr7 -0.024 0.012 -2.01 0.044

dr8 -0.0315 0.004 -7.14 0.000

dr9 0.028 0.010 2.90 0.004

Constant 0.876 0.030 28.81 0.000

Explanatory variables for technical inefficiency

logfec -1.206 0.277 -4.36 0.000

logendist 0.729 0.225 3.23 0.001

logdec 1.350 0.270 5.00 0.000

logptec -1.539 0.287 -5.37 0.000

logshare -0.023 0.342 -0.07 0.947

grupo_dg1 1.254 0.295 4.25 0.000

const -0.511 4.72 -0.11 0.914

Source: by the author

The adjustments made to the econometric model were very good, with a correlation coefficient

of 99% between the observed value and the predicted values as presented in Appendix B.

As can be observed on the results table above, the estimated coefficients are all significant. The

elasticity results show the dependent variable is highly responsive to payroll and third party

services. The elasticity values are 0.51 and 0.34 respectively.

The sum of the elasticities is less than 1 thus indicating decreasing returns to scale.

It is demonstrated that the dummies for the regions were statistically significant with the

exception of dummy dr2 which presented a coefficient of 0.004 indicating weak evidence against

the null hypothesis.

These results differ from those obtained by Tanuri-Pianto and Sousa (2009) in that, in their work,

the location of the firm did not affect the costs since none of the coefficients associated with the

regional dummies were significant. It is relevant to mention that they only divided the firms into

four regions: Northeast, South, Southeast and Center-West. Only the Center-West region

appeared to be significantly more efficient than Southeast, the control region.

The explanatory variables for technical inefficiency indicate that the variable logfec (frequency

of interruptions per consumer) has significant effects in the efficiency of these firms with an

44

estimated coefficient of -1.21. This indicates that the number of interruptions per consumer

negatively affects the efficiency of firms. The variable logendist ( energy not distributed in

MWh) positively affects the efficiency of the firms with a coefficient of 0.729.

The variable logdec (duration of interruptions per consumer in hours) appeared to be statistically

significant, positively affecting the efficiency of the firms. The results obtained can be compared

to those obtained by Tanuri Pianto and Souza (2009). The variables logfec and logdec have the

same effects on the efficiency of firms. The variable logfec produces the expected results which

state that the number of interruptions contributes to increase the inefficiency. On the other hand,

the variable logdec contradicts our expectations that the longer the interruptions the greater the

inefficiency. Note, however that, the fact that the variable logdec includes both interruptions not

programmed and programmed interruptions which are those destined to the network

maintenance, can be explaining the sign of this coefficient. This is because it is reasonable to

assume that programmed interruptions that contribute to the efficiency of the system, in general,

have a superior duration to the interruptions which are not programmed; they would be

predominating over the more frequent interruptions explaining thus the positive sign associated

to the variable logdec (TANURI PIANTO; SOUZA, 2009).

Technical losses (logptec) is seen to positively affect the efficiency of firms having a coefficient

of -1.54.

The market share appears to be insignificant to explain the efficiency of firms with a coefficient

of -0.02.

The variable grupo_dg1 (group of larger concessionaires) is also statistically significant with a

coefficient of 1.25. This shows that it negatively affects the efficiency of firms.

45

5. CONCLUSIONS

This work is crucial when it comes to making an assessment of the performance of electricity

distributors in Brazil. The methodology used is greatly supported by economic theory giving us

the tools and flexibility to use our assumptions in order to carry out systematic analyses that can

be used for regulatory purposes.

In this study we used the fixed effects models to analyze panel data. The choice of panel data

allowed us to use more observations than we would in a cross-sectional data set permitting us to

obtain more efficient estimators of the unknown parameters. According to Coelli et all (2005),

using panel data makes it possible to relax some assumptions that are necessary to disentangle

the separate effects of inefficiency and noise, we can obtain more consistent predictions of

technical efficiencies and we can explore changes in technical efficiencies over time.

The results obtained using the fixed effects estimation method allowed us to explore the effects

of our variables over the total operational cost.

We also estimated stochastic cost functions for the distributors of electrical energy in the same

period. These functions were used for the calculation of the efficiency of the firms and for the

analysis of certain characteristics of the operational efficiencies.

The results revealed that operating costs are highly responsive to the variables payroll and third

party services. These demonstrated a positive relationship with the dependent variable. The

explanatory variables for technical inefficiency demonstrated curious results, some of which

were unexpected. The quality variables such as energy not distributed and DEC (the duration of

interruptions per consumer in hours) can negatively affect the efficiency of the firms. Other

studies also obtained the same result for DEC which also contradict the expectations that the

longer the interruptions the greater the inefficiency. Tanuri Pianto and Souza (2009) explain that

DEC includes the interruptions which are not programmed as well as the programmed ones

which are destined to network maintenance. It can be assumed that programmed interruptions

that contribute to the efficiency of the system, usually have a superior duration to the unexpected

interruptions.

46

BIBLIOGRAPHY

AIGNER, D.J .; LOVELL, C. A. K.; SCHMIDT, P. Formulation and estimation of stochastic

frontier production function models. Journal of Econometrics. North-Holland, v. 6, p. 21-37,

1977.

ARELLANO, M.. Panel data econometrics Oxford: Oxford University Press, 2003

ARCOVERDE, F. D.; TANNURI-PIANTO, M. E.; SOUSA, M. C. S. Mensurao das

eficincias das distribuidoras do setor energtico brasileiro usando fronteiras estocsticas. In:

ENCONTRO NACIONAL DE ECONOMIA, 33., 2005, Natal. Anais

BATTESE , G. E.; COELLI, T. J. A model for technical inefficiency effects in a stochastic

frontier production function for panel data. Empirical Economics, v. 20, p. 325-332, 1995.

BAUM F. C.. An Introduction to Modern Econometrics Using Stata. Stata Corp LP College

Station, Texas, 2006

BOGETOFT P., Otto L. Benchmarking with DEA, SFA, and R., 2011

CATAPAN, E.A. A privatizao do setor eltrico brasileiro: os reflexos na rentabilidade e

solvncia das empresas distribuidoras de energia. Tese (Doutorado em Engenharia da

Produo) Programa de Ps Graduao em Engenharia da Produo, Universidade Federal de Santa Catarina, Florianpolis. 2005.

CATAPAN, E.A. Empresas congeners do setor eltrico indicadores economic-financeiros 2001 a 2005. COPEL, Curitiba, 2006

CHARNES, A.; COOPER, W. W.; RHODES, E. Measuring the efficiency of decision making

units. European Journal of Operational Research, v. 2, 1978.

COELLI, T. J.; RAO , D. S. PRASADA; BATTESE , G. E. An Introdution to Efficiency and

Productivity Analysis. 3 ed. London: Kluwer Academic Publishers, 1998.

COELLI, T. J. et al. An introduction to efficiency and productivity analysis. 2nd ed. Springer,

2005.

DTE(2000), - Choice of model and availability of data for efficiency analysis of Dutch network

and supply businesses in electricity sector, Netherlands Eletricity Regulatory Service.

FARRELL, M.J. The measurement of productive efficiency. Journal of The Royal Statistical

Society, Series A, CXX, Part 3, 253-290. 1957

FERREIRA, C.K.L. Privatizao do setor eltrico no Brasil: In: PINHEIRO, A.C. e

FUKASAKU, K. (Orgs.). A privatizao no Brasil: o caso dos servios de utilidade pblica.

Rio de Janeiro: BNDES-OCDE, 2000.

47

FERREIRA, C.K.L. Privatizao do Setor Eltrico no Brasil s.1, s.ed., 1999.

FITTIPALDI, E.H.D. Leiles de comercializaode energia eltrica: Um modelo para o

Mercado regulado no Brasil, Tese de Doutorado em Engenharia da Produo Programa de Ps Graduao em Engenharia da Produo, Universidade Federal de Pernambuco, 2005

GUJARATI, D.M.. Basics Econometrics, McGraw-Hill., 1995

GREENE, W. H. Econometric Analysis. 5 ed. New Jersey: Prentice Hall, 2002 (Chapter 16,

p.429: Estimation Frameworks in Econometrics; Chapter 17, p.501-505: Maximum Likelihood

Estimation)

GREENE, W. H. Econometric Analysis. 5 ed. New Jersey: Prentice Hall, 2002

JONDROW, J.; LOVELL, C. A. K.; MATE rov, I. S.; SCHMIDT, P. On the estimation of

technical inefficiency in the stochastic frontier production function model. Journal of

Econometrics. North-Holland, v. 19, p. 233-238, 1982.

KUMBHAKAR, S. C.; LOVELL, C. A. K. Stochastic frontier analysis. Cambridge, 2000.

LIMA, J.L. Polticas de governo e desenvolvimento do setor de energia eltrica. Centro de

Memria da Eletricidade no Brasil. Rio de Janeiro, 1995.

SCHMIDT, P.; LOVELL, C. A. K. Estimating technical and allocative inefficiency relative to

stochastic production and cost frontiers. Journal of Econometrics. North-Holland, v. 9, p. 343-

366, 1979.

SOUZA, M.V.P.; SOUZA, R.C.; PESSANHA, J.F.M. Custos Operacionais Eficientes das

Distribuidoras de Energia Eltrica: um estudo comparativo dos modelos DEA e SFA. Gesto da

Produo, So Carlos, v.17, n.4, p. 653-667, 2010.

SOLLERO, M. K. V.; LINS, M. P. E. Avaliao de eficincia de distribuidoras de energia

eltrica atravs da anlise envoltria de dados com restries aos pesos. In: SIMPSIO

BRASILEIRO DE PESQUISA OPERACIONAL, 36., 2004, So Joo del Rei. Anais

TANURI-PIANTO, M.E.; SAMPAIO DE SOUZA, M.C.; ARCOVERDE, F.D. Fronteiras

Estocsticas para as Empresas de Distribuio de Energia Eltrica no Brasil: Uma Anlise de

Dados de Painel. Estudos Econmicos, So Paulo, v.39, N.1, p 221-247, Jan-Mar 2009

VINHAES, E.A.S. A reestruturao da indstria de energia eltrica brasileira: uma

avaliao da possibilidade de competio atravs da teoria de mercados contestveis. Dissertao (Mestrado em Economia) Universidade de Santa Catarina, Florianpolis, 1999.

WOOLDRIDGE M. J. Introductory Econometrics: A Modern Approach. South-Western. Ohio.

2nd

ed., 2003

48

OTHER SOURCES

ANEEL Agncia Nacional de Energia Eltrica. Available at: .

ABRADEE Associao Brasileira de DIstribuidores de Energia Eletrica. Available at:

http://www.abradee.com.br/

49

APPENDICES

APPENDIX A

Model with panel data Stochastic Cost Frontier Estimation

. frontier logdo logy logm logz logw logseg logst logt dr2 dr3 dr4 dr5 dr6 dr7 dr8 dr9, uhet(logfec

logendist logdec logptec logsh

> are grupo_dg1) cost

50

APPENDIX B

Postestimation predictions and partial correlation

APPENDIX C

Fixed Effects Model - Results

TABLE 1

DR1 DR2 DR3 DR4

(1) (2) (3) (4) (5)

Estimation

Method

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Materials 1.003 1.059 1.090 1.127 1.080

51

.0467* .037* .039* .041* .039*

Third-party

Services 0.982 0.963 0.953 0.959 0.946

.012* .007* .007* .007* .007*

Insurance 2.909 4.708 3.947 3.670 3.866

.490* .387* .395* .413* .394*

Taxes 0.882 1.028 1.069 0.997 1.064

.042* .035* .037* .044* .036*

Payroll 1.021 0.969 0.976 0.977 0.975

.008* .005* .005* .005* .005*

Market

(average) 0.000 0.006 0.006 0.005 0.005

0.001 .000* .000* .000* .000*

Technical Losses 0.006 0.009 0.005 0.009 0.006

.002* .001* .001* .002* .001*

DEC 173.538 137.088 184.135 158.660 212.574

55.814* 41.192* 42.940* 43.761* 43.595*

Constant -1401.738 -2096.857 -1701.212 -1283.188 -1787.610

2302.754** 524.756* 548.877 560.353** 546.729*

Observation

Number 607.000 607.000 607.000 607.000 607.000

rho 0.738 0.830 0.047 0.649 0.114

Source: by the author. Notes: Standard Error in red. * 1% significance **

5%significance

TABLE 2

Dependent Variable = Total Operational Cost

DR5 DR6 DR7 DR8 DR9 DG1

(6) (7) (8) (9) (10) (11)

Estimation

Method

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Panel

Fixed

Effects

Materials 1.094 1.094 1.095 1.099 1.089 1.095

.039* .039* .039* .039* .039* .039*

Third-party

Services 0.953 0.947 0.955 0.954 0.953 0.953

.007* .007* .007* .007* .007* .007*

Insurance 4.026 3.895 3.984 3.924 3.970 3.976

.393* .395* .398 * .401* .396* .396*

52

Taxes 1.051 1.057 1.055 1.057 1.056 1.074

.036* .036* .037* .037* .037* .037*

Payroll 0.976 0.975 0.975 0.975 0.973 0.973

.039* .005* .005* .005* .005* .005*

Market

(average) 0.006 0.006 0.006 0.006 0.006 0.006

.000* .000* .000* .000* .000* .000*

Technical

Losses 0.006 0.006 0.006 0.006 0.006 0.006

.001* .001* .001* .001* .001* .001*

DEC 207.530 199.614 177.256 180.980 181.632 154.670

43.422* 43.420* 43.207* 43.189* 43.056 * 44.381*

Constant -391.582 -1303.512 -1583.527 -1518.414 -1600.668 -596.925

638.194 554.403** 549.746* 556.153 548.724* 703.060

Observation

Number 607.000 607.000 607.000 607.000 607.000 607.000

rho 0.214 0.102 0.034 0.003 0.114 0.038

Source: by the author. Notes: Standard Error in red. * 1% significance ** 5%significance