price indexes for telecom market: an application to european market of leased...

Price indexes for telecom market: An application to European market of leased lines


Authors Dimitris VAROUTAS 1 Konstantina DELIGIORGI

Christos MICHALAKELIS Thomas SPHICOPOULOS

(University of Athens)

Abstract

The development of telecommunication technology is in general highly correlated with

the evolution of prices for telecommunication services. This chapter deals with the study

of prices for telecommunication services and the construction of a price index by

hedonic approach. A description of the theoretical models and methodologies by

functional forms of both hedonic approach and matched model is given and application

of these models in European telecommunication market is performed. Evidence from

Central and Eastern Europe outlines telecom market behavior and contributes to better

understanding of Europe.

1 Corresponding author: Dimitris VAROUTAS, Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Ilisia, GR15784 Tel: +302107275318, Fax: +302107275601, E-mail: [email protected]

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1 Introduction

1.1 An overview of the telecommunication sector in Central - Eastern

and Western Europe.

The telecommunication industry is one of the most rapidly growing sectors around the

world. The trigger that initiated the changing processes was mainly the transition from

monopolies to more competitive markets. However, this transition didn’t occur

simultaneously in all countries. In fact, some countries followed at a very late time, even

after year 2000, whereas some haven’t fully integrated their market structures yet so as

to converge to competitive markets. The above considerations evidently apply for the

Europe as well, regarding telecommunications. The most important affecting factors are

increasing investments in telecommunication networks, liberalization of markets and of

course the rapid technological change. Generally speaking, the globalization of the

economy in Europe provoked the necessity of a regulatory framework by the

commission of European countries.

The consequences of market liberization in telecommunications are related to social

changes recorded, as new products and services were issued and spread out to the

consumers who, in turn, faced a variety of new choices to maximize their perceived

utility. This triggered a shifting to the existent demand-supply equilibrium, which forced

telecommunication companies to heavily reconsider their strategic plans in order to

satisfy consumers’ new needs and demands. At that point the need for the existence of

national telecommunication regulators was apparent, for making the necessary

legislation establishments.

Following progress steps of Western European, Eastern European countries are moving,

noticeably fast, towards catching up with the Western Europe countries in investments

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dedicated to telecommunications. Although the level of existing investments in Eastern

European countries is quite lower than that of Western countries, the rate by which

Eastern countries are investing in telecommunications is remarkable. For example,

Estonia’s and Czech Republic’s investments in this area are higher than the Western

Europe’s average.

1.2 From monopoly to competition

Initially, a typical governmental approach was that telecommunications were so

important that it should be necessary to have governmental and monopolistic handle

and protection. The reason for this was for strategic purposes, as well as for military

security reasons. However, during the last two decades of the 20th century

reconsideration of the optimal strategy led to liberalization decisions, which happened

with an observable time lag for many countries in Europe. For example England adopted

liberization in telecommunications in 1991 whereas Greece in 2001. Moreover, Bulgaria,

Lithuania and Romania are expected to make their move to a competitive

telecommunications market in 2006.

Legislation alone doesn’t seem to be enough for the liberalization process as the

transition from a central planned economy into a competitive market is a quite

complicated procedure. This is possible to be counterbalanced if the customer is

adequately informed and able to react towards his surplus maximization, in order to

achieve better level of his utility. However, generally this is not the case. Within Europe

there is an obvious difference between North and South. In some European countries

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telecommunications achieved a great development, but this could have been driven by

European Community policies as this seemed a prosperous choice for attracting

investments and boosting competition.

Table 1 Year of telecom market liberization for countries of Europe

Country Telecom market liberalization

United Kingdom 1991 Sweden 1996 Nertherlands 1997 Italy 1997 Belgium 1998 Austria 1998 France 1998 Germany 1998 Spain 1998 Luxembourg 2000 Ireland 2000 Portugal 2000 Greece 2001 Czech Republic 2001 Estonia 2001 Polland 2001 Slovenia 2001 Hungary 2002 Latvia 2003 FYROM 2003 Bulgary 2006 Lithuania 2006 Romania 2006

Source: Yankee Group Europe, (1997)-Regulatory Developments, (2000)

Sweden could be considered as the example of a major telecom representative where

no monopolies exist. On the other hand, privatization raised other social problems, like

staff reduction

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1.2.1 Rapid technological change.

Rapid technological improvements turned out to be the main drivers for improving the

quality of the offered services. Innovations in electronics established telecommunications

industry as an international market. The main benefit is the international industrial

cooperation which made Research and Development sectors provide industry with better

products, as everyone contributed to the whole, instead of working within its strict

boundaries. An indicative example of this cooperation is the digitalization of the

telecommunication networks.

“if knowledge is increasing in a society and it is more than a sign for modern economy-it

will be the problem for organizational changes and integrative steps, it is necessary to

name this society “Knowledge society:”2

This is true and is already a fact, leading to a number of changes in countries’ and

companies’ profile. Companies needed resources, which is translated into people,

engineers and capitals. For example, a country like Austria would be very small to

achieve this. Switzerland was the last one that tried this development by its own.

Another reason for this international cooperation was derived from the manufacturing of

smaller and smaller parts. These parts are constructed in countries which have cheap

‘labor costs’. As a result companies were merged, because they could not afford

competition among each other. In Eastern Europe the quality of networks is generally

low, with digitalization in low levels especially in Ukraine, Moldova, Belarus and Bulgaria.

2 Günther J.‘Regulation of telecommunications in Europe ‘

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1.2.2 Operators and Carriers

“Operators” are used in the context of network operators that install, manage and

operate telecommunication transmission network to offer public services on telephony or

on network (leased lines). They are classified into local operators who offer services to

users who live in specific areas and into national operators who offer services no matter

where the user lives. Following the corresponding Commission’s legislation they have to

provide a minimum set of leased lines according to specific technical standards.

Across Member states a different number of operators can be found that have

license/authorization to offer network services with different entrance times in the

market. As a result, users and consumers continue to enjoy an increased number of

potential choices, accompanied by reduction in prices. This forces operators to

continuously develop new pricing plans and scenarios considering different prices for the

same service and different target groups.

In Eastern Europe a few operators are making much profit. Low Internet penetration

suppresses demand. The average Internet penetration rate in countries which have

recently joined the European Union (EU), was around one third lower than in the old

Member States of Europe, in the middle of 2004.

In addition to the above, carriers are promoted across Europe. Carriers are companies

that buy lines and are independent of technical changes so they can be more efficient.

Their activities include mainly buying big capacities at lower prices and renting them to

customers. This is expected to lead to a continuing reduction of prices in the competitive

market, since customers can choose among several carriers. Moreover, number

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portability is an important benefit for the customers as they are able to switch to

another operator retaining the same phone number

1.2.3 Delivery and repair times of telecommunication services.

The period between the time that a user makes a request for a leased line provision

until this request is fulfilled, is defined as delivery time. There is no straightforward way

for measuring it for each country, due to the number of existing operators and the

various estimation methods applied. However, it seems that serious delays still remain in

some countries. Germany, Austria, Ireland delivery periods vary from four to seven

months. On the contrary, Greece and Luxembourg have relatively quick response times.

This problem impacts the telecommunication sector and makes customers demand

better facilities and services.

As to repair time, it is defined as the period between the time that a failure message is

reported to the provider of the leased line, until it has been re-established and it is

notified back in operation to user. As in delivery time, repair times vary, sometimes

remarkably, across member states. They also depend on the type of the particular

leased line. In Sweden, for example, repair time for a 2 Mbits/s line is about one day,

whereas in Germany it is approximately 15 hours, while for 64Kbit/s is less than ten

hours. However, comparing each year’s services performance to the corresponding past,

major improvements in the quality of service provided and the response times are

observed. This of course is primarily driven by the continuous competition among

companies, a major consumer benefit of a competitive market.

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Once again, these aspects point out an important impact on the market, because the

quality of service and the response time for repairing it, in case any damage happens,

shows a continuing competition among companies, so as customers be satisfied and

prices declined.

2 Development of a price index methodology

2.1 Theoretical background

Econometric methods have been widely used to calculate price indexes since a long time

ago. Typical examples are price indexes for cars (Griliches, 1961), refrigerators (Triplett

and Mc Donald, 1977) and of course for computers (Cole 1986). Furthermore, regarding

information technology such indexes are met in (Cartwright and Smith, 1988) and

(Moreau, 1991). Statisticians use econometric methods in the U.S.A but the root of

hedonic approach, which is a part of economic research, goes back to (Waugh, 1928),

(Court, 1939) and (Stone, 1954, 1956).

Someone has to choose between two types of econometric methods: a) hedonic

methods and b) matched model methods, each of which has both advantages and

disadvantages to present.

One approach is to apply the ‘hedonic methods’, such as two - period method, single-

period method, two - period method with an indicator for new models, or single-

regression method. Such kinds of indexes are commonly used for products, which face

rapid technological changes.

Hedonic methods refer to regression models in which product or service prices are

related to product characteristics. In general, hedonic methods are based mainly on the

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idea that a service and its consequent observed price is a bundle of characteristics and

that consumers buy these characteristics, instead of the product itself. These methods

can be used to construct a quality-adjusted price index of a service. (Berndt, 1991) and

(Triplett, 2000) described an overview on hedonic price equations, whereas (Rosen,

1974) states that from a large amount of product varieties, consumer chooses without

influencing prices. Therefore, consumers maximize utility and producers maximize

profits. In hedonic studies it is possible to adjust the price of a service for its quality not

quantity. All of them are based on some estimated coefficients that are inflicted on the

characteristics of the products in both periods; m and m+1. The participating

coefficients can be estimated separately for each year, or can have observations of two

or all years together and estimate a common set of coefficients. The advantage of this

method is that calculations are easy and fast. Indeed hedonic methods are very fast to

apply but the disadvantage is that index price can change even if no new products are

existed, or all prices remain the same.

Another approach is to apply a matched model method such as chained Laspeyres

(LCPI), (LPI), chained Paashe (PCPI), (PPI), chained Fisher, chained Tornqvist or

chained geometric – mean (Okamoto and Sato 2001). A classic Laspeyres index cannot

deal with such complexity due to rapid technological changes or the introduction of new

products (services). In LPI method, an index shows how much the product would cost in

period m+1, relatively to what it cost in period m. Other price indexes function in the

same way, though with slight differences.

The hedonic price indexes are commonly used as approximations to the true cost-of-

living indexes (COLI) which indicate how much money a consumer would spend in

period m+1, as compared to the corresponding amount he needed in period m, in order

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to maintain the same level of his utility in period t as in period t0 (Jonker, 2001). The

solution to this is the determination of the consumer’s profile so as to react towards a

varied and fast-changing supply of products. But how can this profile be determined

when everyone has different needs and requirements? No matter what profile is

decided, it will be a hypothesis and an assumption that will correspond to a specific

model. Moreover, consumer’s desire is not stable and this is not unreasonable because

there is a great offer as the ‘goods’ of technology become more and more attractive.

However and according to this approach, the price index is constructed only using the

prices of products, which are available in two adjacent periods. Finally, with the matched

models approach products’ prices of identical quality are compared between two

periods.

2.2 Matched model method (Laspeyres method)

According to Laspeyres and in order to create a price index, the number of units sold in

a period m (for example a month) are observed and the average unit price in the period

m and m+1. These data are used as input to the following formula:

∑

∑

=

=+

+ = n

iimim

n

iimim

mm

qp

qpI

1

11

/1 (Eq. 1)

As aleady mentioned, price indexes’ construction is based on the matched model

method of Laspeyres with chaining average unit prices which refer to a previous period,

among units sold in the same period.

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The problem with such a price index is that a basket of products does not remain the

same over time. Furthermore, it is the case that some products disappear from the

market (especially in the telecom market) and some others are modified, so someone

has to introduce new products in order to preserve the same indifference basket of

customers’ preferences. If quality changes are ignored the resulting price index will be

biased.

2.3 Hedonic method

The term ‘hedonic methods’ refers a ‘hedonic function’ f(X) use in economic

measurement, where

(Eq. 2) i iP = f(X )

with Pi being the price of a variety i (or a model) of a product and Xi a vector of

characteristics associated with the variety. The hedonic function is applied on different

characteristics among varieties of the product, in order to calculate the price index.

As soon as the determination of the characteristics that should be considered is

achieved, then for a number of N telecommunication products in period m and in period

m+1 the following set of equations is calculated:

i,m 0 1 1,i 2 2,i i,mln (p ) = b +b *X + b *X + u i = 1,...,N (Eq. 3)

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i,m+1 0 1 1,i 2 2,i i,m+1ln (p ) = b +b *X + b *X + u (Eq. 4)

where bi are the participating coefficients that must be estimated.

2.3.1 True and candidate model by using the hedonic function

The main assumption of this approach is that there is a set of consumers having

preferences over some characteristics of a service. The construction of a price index is

complicated by product-pricing limits such as different charges for various

characteristics. Therefore the definition of a basket of services should come first and the

model that describes the attributes (characteristics) of a product and their prices is given

by the function:

(Eq. 5) iP =f(Xb)+u

where X =(x1,x2,……xn) is an nxp matrix of random regressors, xi and b are px1 vectors

and f(Xb) is an nx1 vector with i-th component f(xi΄b) (i=1,2,3,….n). Moreover, the

error term u, for a given X=x, is assumed to follow the N (0,σ2 I nxn) distribution, σ

being an unknown scalar. Another assumption is that f is an unknown function and b

estimators have unitary norm. The model described in (Eq. 5) constitutes the unknown

“true model” which generates the observed data. The candidate models are considered

in a way similar to the construction of the true model, by defining the characteristics

that are of importance to the construction of the price index. Following the above, the

main objective remains the estimation of the minimum distance between the true and

candidate models. The result of this procedure is the choice of the model from a pool of

candidate ones, that best fits to the data generated by the true one.. The above can be

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implemented using the single-index models regression analysis, which is presented in

the following sections.

2.3.2 Single- Index Model

Regression analysis is a common approach used for identifying the relationship between

a response variable, y, and a vector of regressors, x. Quite often, a linear regression

model of type E(y)=x’b is used for estimating the impact of the regressors to the

expected response, E(y). If more precise estimation are needed, single-index models

can be used which are of the type E(y)=f(x’b), where the link function f is unknown.

This approach is quite advantageous as the use of single-index models mitigates the risk

of misspecifying the link function and has the ability to overcome the curse of

dimensionality and the capability to extrapolate beyond the support of x.

Single-index models can be estimated by using iterative or direct methods. In the first

case, the goal is to obtain a consistent estimator of f, f , and solve the consequent

nonlinear optimization problems to obtain the consistent estimator for b, b .

On the other hand, direct methods are not iterative and provide a consistent estimator

of b without requiring estimation of f.

Whichever of the two methods is followed the result will be the same. However, in the

iterative method, computation is intensive, whereas the use of direct methods involves

easier computations, as the b relative weights are estimated by the SIR (Sliced Inverse

Regression) estimator, (SIR , Li 1991,JASA) which does not consider the estimation of

function f a prerequisite.

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Once the relative weights bi, , are estimated, an index z is constructed

and by applying LPR (Local Polynomial Regression) (Simonoff 1996) the estimation of

the nonlinear link can be achieved.

SIRb SIRbXz ˆ=

)(ˆ zfprice =

As soon as f, ˆ ˆf and b are estimated by iterative or direct method, covariance is

computed as in (Eq. 6)

nbXfYbXf )}ˆ(ˆ{})ˆ(ˆ{ˆ 2 −′−Υ

=σ (Eq. 6)

By using the most suitable model is determined from a variety of candidate

models by applying the Akaike Information Criterion (AICc criterion), which is a measure

of the discrepancy between the true and the candidate models (Naik and Tsai, 2001).

)ˆ,ˆ,ˆ( 2σbf

2.3.3 The AICc criterion

In order to find out what is the best model from a variety of candidate models that

describe a product with a set of characteristics the following equations are used:

(Eq. 7) 20d(f,b,σ )=E {-2*logf(Y)}

where f(Y) shows the possibility for the candidate model and E0 shows expectation

under the true model.

Because of the heavy computations involved in evaluating the above distance, due to

the unknown function f, without having great inclination the following assumptions

facilitate the computation of the AICc value.

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Assumption 1. There following equation holds YHXbf np≅*)(~. This has the meaning

that there exists a smoother matrix Hnp so that f is the projection of Y through that hat

matrix.

Assumption 2. Similarly, *)(*)}(~{ 00 XbfXbfE ≅

Assumption 3. In addition to the above,

*)}(ˆ{*)ˆ(~*)(~)ˆ(ˆ XbfYHbbVXbfbXf p −≅−≅− holds,

where XXbfbXbfVVVVVH bbp *).(~|/)(~~,~)~~(~*

1 =∂∂=′′= =− and .f is the derivative of

f

The Akaike Information Criterion (AIC) is then given by the formula of (Eq. 8)

nHHHHtrnHHHHtr

AICnppnpp

nppnppc /}2)ˆˆˆˆ({1

/)ˆˆˆˆ(1ˆlog 2

+−+−

−+++= σ (Eq. 8)

where is obtained by replacing b* and VVVVVH pˆ,ˆ)ˆˆ(ˆˆ 1 ′′= − Vinf ~.~

with estimators

, evaluated at and tr is the trace of the corresponding matrix. fandb ˆˆnpnp isHH bXXb ˆ=

The best fitting model is considered the one having the smaller AICc value.

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3 Model evaluation

3.1 Telecommunication services - National leased lines

A leased line is a permanent connection between two sites. By connecting to a Network

Access Point (NAP) via a leased line, someone has a permanent access to the Internet

for his whole network. Prices usually depend on the distances and on the speed rate of

transmission. A leased line offers guaranteed better access connections.

For the needs of the study of the present chapter, national leased line data from 1997

until 2003 have been used, covering three (3) categories of distances: 2 km (local

circuits), 50 km and 200 km.

In order to properly reflect the tariff structures used in some countries, the circuits may

be considered in one of two different ways, depending on tariff structure. It is important

to recognize that both of these are correct, and will depend on the tariff elements used

in the pricing.

Some operators apply termination charges per local end, without necessarily covering

the local tail circuit within that charge. This situation would correspond with the second

method above. Some carriers offer 2 Mb/s circuits as both structured and unstructured.

In this analysis only unstructured circuits are included. Also, some carriers offer different

types of leased lines, often in the form of “basic circuits” and circuits in a managed

network. Only “basic circuits” are included in this analysis, as the managed network

services are not comparable between carriers. Lately, a few carriers have decided not to

publish their prices for some or all types of leased lines, which consequently makes it

increasingly difficult to present a full overview of the prices and to compare the results

of the research.

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Finally, four (4) types of circuits are covered: 64 kb/s, 2 Mb/s, 34 Mb/s and 155 Mb/s

but there are prices only for 64 Kb/s and 2Mb/s. The bitrates of such services in the US

due to different technical specifications recommendations are 56 kb/s instead of 64 kb/s,

and 1.5 Mb/s instead of 2 Mb/s. The prices have not been adjusted to reflect these

differences.

Structures for earlier years may be different, but care has been taken to make sure that

prices are based on comparable circuit definitions. All prices are presented in EURO per

year, excluding VAT.

Table 2 Classification of leased lines for different technical characteristics

1: When tariff specifies local tail

prices separately, in addition to

main circuit.

2: When tariff specifies a single

price for the circuit, end to end.

Local tail length Main circuit

length

Local tail length Main circuit

length

2 km circuit 1 km 0 0 2 km

50 km circuit 2 km 46 km 0 50 km

200 km

circuit

2 km 196 km 0 200 km

(Note: The local tail length is per tail, i.e. there will be 2 such tails with each circuit.)

Source : Report on Telecoms Price Developments from 1998 to 2002

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For all these reasons prices for 64kb/s and 2Mb/s are compared for all countries of

Europe so as to reflect the same tariff structure. This overview about data prices and

circuits appears in ‘Report on Telecoms Price Developments from 1997 to 2000’ and

‘Report on Telecoms Price Developments from 1998 to 2002’ which are produced for

European Commission by Teligen Ltd [Teligen (2000), (2002)].

As it can be observed, national leased lines tariffs still show significant variations across

Member States.

However, from 1997 to 2003 there are significant reductions in many counties such as

Belgium, Spain, Italy and Luxembourg. For 64kbit/s lines in all circuits (2km, 50km and

200km) the most noticeable fact is that charges remained stable for two or three years

depending on the country. It seems as if companies imitate one another in setting

higher or lower prices. Greece appears to be offering the most expensive services

among Member States for local (2km), 50Km and 200Km in 64kbi/s circuits, whereas the

cheapest ones are provided by Germany. In addition, it is noticeable that in 2002, there

are slight increase in tariffs in Belgium and Austria. For 2Mbits/s lines the reduction is

larger than in 64kbit/s lines. Denmark is the cheapest country for local distances, but

Austria and Luxembourg present an important reduction between 1997 and 2002 while

the UK had an increase from 2001 to 2002. For 50km and 200km circuits, Sweden

presents a remarkable difference from most other countries, followed by Denmark.

On the contrary, in 2Mbit/s lines for 2km distances, Netherlands, Greece, Spain and

Portugal present the highest tariffs, but Greece and Spain moved towards a greater

price reduction than the others. Remarkable downward trend from year 1997 to 2002 is

recorded in Luxembourg, which corresponds to 30% per cent.

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In addition to the above, for the category of 2Mbit/s over 50km, the most expensive

services are provided in Spain and Netherlands followed by Italy and Greece though

Italy has the greatest reduction from 1997 to 2002 from all other countries in Europe.

Spain also presents highest service prices for 2Mbits /s circuits over 200km followed by

Greece and Portugal with a significant price lag from Sweden.

3.2 Models with non linear functions

As described above, a price index for telecommunications can be calculated after

suitably defining a basket of products.

Table 3 European countries leased lines data

BELGIUM LUXEMBOURG DENMARK AUSTRIA GERMANY PORTUGAL GREECE NETHERLANDS SPAIN UNITED FRANCE SWEDEN IRELAND ITALY

The following paragraphs present an example of the above, in which the characteristics

of the product are determined, so as to show the quality of this product. As soon as the

characteristics of a product are determined, applying the hedonic method the product’s

price is estimated. Otherwise the price of a product is estimated “manually”, by

comparing the new product with the most similar old one.

It is assumed that telecommunication products have two characteristics: Distance (dist)

and Transmission rate per second (MB). These two characteristics are widely used from

telecom operators for valuating and selling leased lines across Europe. Table 3 presents

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the European countries participating in evaluation of the method and Table 4 presents

the average values for the two categories of leased lines.

Table 4 Leased lines average price evolution (€) in Europe (1997-2003).

”Rate, Distance”

/ Year

"64,2" "64,50" "64,200" "2048,2" "2048,50" "2048,200"

1997 2.871,93 6.907,29 9.261,07 11.264,64 41.912,93 69.998,07

1998 2.734,29 6.402,00 8.413,71 10.427,14 38.824,29 62.488,29 1999 2.256,00 5.240,57 6.832,29 9.279,43 32.476,29 52.024,29 2000 2.283,43 5.058,00 6.489,43 8.548,29 28.122,00 46.458,00

2001 2.086,29 4.558,29 5.915,14 6.843,43 22.652,57 37.056,86 2002 2.088,86 4.556,57 5.864,57 6.698,57 22.310,57 36.326,57 2003 1.993,71 4.128,86 5.352,00 6.109,71 18.359,14 31.285,71

(Source Teligen , 2002)

The first step regards estimation of, the single-index model. The data are sorted in

ascending order, according to their prices, Pi, and they are divided into a pertinent

number of slices, as equal as possible. For this study and according to the number of

observations (forty two) two slices are adequate, each containing twenty one

observations. Without needing to specify the unknown link function f(.) the SIR

estimators are derived as: )971898.0,235402.0(=SIRb

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SIR Directions

0,00

100,00200,00

300,00400,00

500,00

600,00700,00

800,00

1993

,71

2088

,86

2283

,43

2871

,93

4556

,57

5058

,00

5352

,00

5915

,14

6402

,00

6698

,57

6843

,43

8413

,71

9261

,07

1042

7,14

1835

9,14

2265

2,57

3128

5,71

3632

6,57

3882

4,29

4645

8,00

Prices

Est

imat

ed P

rice

s

Figure 1 Plot of Price (Pi) against SIR directions

Plot of Price versus b*Χ

0,00

10000,00

20000,00

30000,00

40000,00

50000,00

60000,00

70000,00

80000,00

17,0

1

17,0

1

17,0

1

17,0

1

63,6

6

63,6

6

209,

45

209,

45

63,6

6

484,

05

484,

05

209,

45

209,

45

484,

05

530,

70

530,

70

676,

48

676,

48

530,

70

676,

48

676,

48

Average

Figure 2 Plot of prices versus b*X

The SIR estimates are obtained (SIR dierections) (Figure 1) and the plot of prices versus

linear combination of b*X is plotted (Figure 2). Then, by applying local polynomial

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regression (LPR) with a Gaussian Kernel smoothing (Figure 3), an initial estimate of the

shape of the link function kf is acquired.

Figure 3 Local Polynomial Regression with Kernel Smoothing

Trying to examine the relationship between the price of a leased line and the distance

and the transmission rate several shapes of potential link functions are tested, as

candidate models, and the most suitable one that gives the smallest AICc value, is found

to be the one described by (Eq. 9):

(Eq. 9) 0 1 2ln (p) = b +b *dist + b *MB

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Even link functions such as hyperbolic sin or hyperbolic cosine give almost the same

results with the non linear functions.

Although linear model on logged price is not comparable with all the other models,

because of the AICc value, linear model on logged scale has an advantage.

Working in the logged scale using a linear model results shows better fitting

performance. In addition, because of the high R2 value (coefficient of determination),

which compares the variability of the residuals in the model to the variability of the

dependent measure, the model is expected to have a good predictive power.

3.3 Constructing the price index

Supposing that there are N telecommunication products in period m and m+1, the

proposed hedonic price index can be calculated by the following equation:

1/ 1 1 2 1 2ˆ ˆ ˆ ˆ ˆ ˆ( ) (m m m m )I f b Dist b MB f b Dist b MB+ += + − + (Eq. 10)

Using data such as those presented in Table 4, for selected European countries, it can

been observed (Figure 4 and Figure 5) that prices for different distances cannot be

directly compared across different capacities. However, there are similarities and

patterns that should be evaluated. Moreover and according to economic theory, it is

obvious that as the consumer’s demand shifts toward better services corresponding

prices are increased. Because of this behavior, there is no explicit pricing policy for both

characteristics (distance and transmission rate).

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Average annual pricing evolution across Western Europe (bandwidth, distance)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1997 1998 1999 2000 2001 2002 2003

Pric

e in

64,2 64,50 64,200

Figure 4 Evolution of prices for 64 Kbps leased lines in Western Europe (average

values)

Average annual pricing evolution across Western Europe (bandwidth, distance)

0

10000

20000

30000

40000

50000

60000

70000

80000

1997 1998 1999 2000 2001 2002 2003

Pric

e in

2048,2 2048,50 2048,200

Figure 5 Evolution of prices for 2048 Kbps leased lines in Western Europe (average

values)

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This behavior fits to the hedonic approach and this can be observed by calculating the

hedonic price index and its evolution for several years. This is presented for the case of

European countries’ leased lines market (Figure 6). A simple regression over this index

can be used in order to calculate price for similar products and/or forecasts for next

years.

Hedonic Price Index evolution

y = 0,0069x + 0,8803R2 = 0,0529

0,75

0,8

0,85

0,9

0,95

1

1,05

98/97 99/98 00/99 01/00 02/01 03/02

Years

Price

Inde

Figure 6 Hedonic price index evolution for the case of leased lines market in Europe

4 Aspects and Conclusions

The purpose of this analysis is to show the parameters that influence the prices of

telecommunication services and to foresee the trend of prices of telecommunication

services (or products) and especially national leased lines over time across all European

countries by applying a hedonic method for some defined characteristics. This method

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works better when there is a variety of important characteristics but less satisfactorily

when these change rapidly over time.

Leased lines have important and specific characteristics indeed and their prices vary

slowly over time. The results give a view of telecommunication prices over time and

show how the prices will be fluctuated the next year.

The application of these econometric methods, following the definition of products’

characteristics, provides a reliable and accurate method able to produce an exact

estimate of prices both for new products and over next years. The validity of the model

and the appropriate selection of the functional form that has been chosen to relate price

to characteristics can be validated over next years and more observations.

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