This article was downloaded by: [Mount Allison University 0Libraries]On: 15 September 2014, At: 16:43Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

Regional StudiesPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/cres20

Telecommunications Infrastructure and RegionalEconomic Development: The Case of PolandAndrzej Cieślik a & Magdalena Kaniewska b

a Department of Economics , Warsaw University , ul. Dluga 44/50, Warsaw, PL-00241,Poland E-mail:b Department of Economics , Warsaw University , ul. Dluga 44/50, Warsaw, PL-00241,PolandPublished online: 18 Aug 2010.

To cite this article: Andrzej Cieślik & Magdalena Kaniewska (2004) Telecommunications Infrastructure and RegionalEconomic Development: The Case of Poland, Regional Studies, 38:6, 713-725, DOI: 10.1080/003434042000240996

To link to this article: http://dx.doi.org/10.1080/003434042000240996

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Regional Studies, Vol. 38.6, pp. 713–725, August 2004

Policy Debates

Telecommunications Infrastructure and RegionalEconomic Development: The Case of Poland

ANDRZEJ CIESLIK* and MAGDALENA KANIEWSKA*†*Department of Economics, Warsaw University, ul. Dluga 44/50, Warsaw PL-00241, Poland.

Email: cieslik@wne.uw.edu.pl†Centre for Social and Economic Research (CASE), ul. Sienkiewicza 12, Warsaw PL-00944, Poland

(Received April 2003: in revised form January 2004)

C A. and K M. (2004) Telecommunications infrastructure and regional economic development: the case ofPoland, Regional Studies 38, 713–725. We develop a theoretical model that establishes a link between telecommunicationsinfrastructure and the regional level of income. This relationship is subsequently tested using Polish regional panel data for the1990s. The empirical results confirm that there exists a positive and statistically significant causal relationship betweentelecommunications infrastructure and income at the regional level. With causality running from telecommunications toincome, telecommunications policy should be viewed as an important part of regional policy aimed at reducing spatial incomedisparities in Poland. We can expect that EU enlargement and the harmonization of Polish telecommunication law with EUregulations should contribute to more even regional development in Poland.

Telecommunication infrastructure Regional income disparities Law harmonization European Union Accession

C A. et K M. (2004) Infrastructure des telecommunications et le developpement economique regional: etudede cas de la Pologne, Regional Studies 38, 713–725. Cet article cherche a developper un modele theoretique qui etablit un lienentre l’infrastructure des telecommunications et le niveau des revenus regionaux. Par la suite, on teste ce rapport employant desdonnees regionales polonaises par echantillon permanent pour les annees 1990. Les resultats empiriques confirment l’existenced’une correlation etroite et statistiquement importante entre l’infrastructure des telecommunications et le revenu regional. Etantdonne que la causalite va des telecommunications au revenu, une politique en faveur des telecommunications devrait etreconsideree comme partie integrante d’une politique regionale destinee a reduire les ecarts des revenus regionaux en Pologne.L’elargissement de l’Ue et l’harmonisation de la loi polonaise sur les telecommunications, conjointement avec la reglementationde l’Ue, devraient contribuer a un reequilibrage du developpement economique regional en Pologne.

Des infastructure telecommunications Ecarts des revenus regionaux Harmonisation de la loi Union europeennneAdhesion

C A. und K M. (2004) Telekommunikationsinfrastruktur und regionalwirtschaftliche Entwicklung: der FallPolen, Regional Studies 38, 713–725. Die Entwicklung eines theoretischen Modells dient dazu, eine Verbindung zwischenTelekommunikationsinfrastruktur und regionaler Einkommenshohe herzustellen. Diese Beziehung wird anschließend mit Hilfepolnischer Regionaldatenlisten fur die neunziger Jahre gepruft. Die empirschen Daten bestatigen, daß eine auf Regionalebenepositive und statistisch signifikante Kausalbeziehung zwischen Telekommunikationsinfrastruktur und Einkommen besteht.Angesichts der von Telekommunkatione ausgehenden und sich auf Einkommen auswirkenden Kausalitat liegt es nahe,die Kommunkationspolitik als wichtigen Bestandteil der Regionalpolitik anzusehen, doch Ziel es sein sollte, raumlicheEinkommensunterschiede in Polen abzuschwachen. Es ist erwarten, daß die Erweiterung der EU und die Harmonisierung despolnischen Telekommunkationsgesetzes mit den Bestimmungen der EU zu einer gleichmaßigeren Regionalentwicklung inPolen beitragen wird.

Telekommunkationsstruktur Regionale Einkommensunterschiede Harmonisierung der GesetzeEuropaische Union Beitritt

0034-3404 print/1360-0591 online/04/060713-13 ©2004 Regional Studies Association DOI: 10.1080/0034340042000240996

http://www.regional-studies-assoc.ac.uk

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

714 Policy Debates

C A. y K M. (2004) Infraestructura de telecomunicaciones y desarrollo economico regional: el caso dePolonia, Regional Studies 38, 713–725. Desarrollamos un modelo teorico que establece un vınculo entre la infraestructura detelecomunicaciones y el nivel regional de renta. Esta relacion se comprueba posteriormente utilizando el panel de datosregionales de Polonia para los anos 90. Los resultados empıricos confirman que existe una relacion causal positiva yestadısticamente significativa entre la infraestructura de telecomunicaciones y el nivel de renta regional. Con una causalidad queva en direccion de telecomunicaciones a renta, las polıticas de telecomunicaciones se deberıan ver como una parte importantede la polıtica regional dirigida a reducir disparidades espaciales en el nivel de renta en Polonia. Podemos esperar que laampliacion de la Union Europea y la armonizacion de la ley de telecomunicacion Polaca con las regulaciones de la UE debecontribuır a un desarrollo regional en Polonia mas equitativo.

Infraestructura de telecomunicaciones Disparidades regionales en los ingresos Ley de armonizacion Union EuropeaAccesion

JEL classifications: R53, L96, O18

INTRODUCTION of EU regulations in the field of telecommunicationsmight spur regional growth and narrow the scope for

The rationale for regional policies has long been a regional development assistance requested from thehotly debated issue both among economic theorists current EU Member States. The provision of networkand policy-makers. In light of neoclassical models based connections and wide access to advanced telecommuni-on constant returns to scale, perfect competition, pro- cation services in disadvantaged regions might thenduct homogeneity and the absence of transaction costs, contribute to a more even distribution of economicregional income disparities should decrease over time activity on the territory of Poland and lead to a decreasedue to flows of goods and factor mobility. However, the in regional income disparities.departure from these unrealistic assumptions, recently The role of telecommunications infrastructure inpostulated by the New Economic Geography literature, economic development has been frequently emphasizedreveals that this does not always need to be the case both by practitioners and by theorists. For example, inand it tries to explain why regional income differences the special World Bank report Knowledge for Developmentmight persist over time. In particular, as discussed by, (1999), telecommunications is seen as a means of fastfor example, K (1996), O and acquisition and dissemination of information. RP (1998), F et al. (1999), N (2001), and W (2001, p. 910) argue that ‘where theF and T (2002) and O et al. state of the telephone system is rudimentary, communi-(2002), the existence of increasing returns combined cations between firms is limited. The transaction costswith transport costs might lead to a process of cumula- of ordering, gathering information and searching fortive causality and, consequently, to the concentration services are high. As the telephone system improves,of economic activity in a limited number of the costs of doing business fall and output will increaseagglomerations. for individual firms in individual sectors of the

From a practical point of view, regional policy, economy’.despite general criticism concerning its effectiveness Telecommunications infrastructure differs signifi-and growing scepticism about the use of particular cantly from other types of infrastructure. Unlike trans-policy tools, has been pursued in almost every Euro- portation infrastructure (e.g. highways, railways,pean Union (EU) Member State.1 At the level of the airports) that reduces transaction costs on trade inEU, the case for regional policy is articulated in the goods, telecommunication infrastructure lowers trans-Treaty of Rome, and over time regional policy has action costs of trading ‘ideas’. While the developmentbecome one of the key policies within the EU. With of telecommunications infrastructure contributes toEU expansion on the horizon, the European Regional regional convergence by increasing the efficiency ofDevelopment Fund, which channels financial assistance market operations, the overall impact of improvedto address structural economic problems and reduce transportation infrastructure is ambiguous and differsinequalities between different regions across the EU, from one region to another. For example, the construc-will come under increased pressure. For example, all tion of motorways in Southern Italy opened up peri-regions of Poland, the poorest, most populous and most pheral regions to goods produced in the North andagricultural country of Central Europe, are eligible for contributed to the decline of the South (F , 1983).Objective 1 under the arrangements of the current Investment in telecommunications infrastructure hasstructural funds. important implications for the speed of convergence

The expected costs of the enlargement, however, between countries and regions. In particular, Mmight be overestimated since they do not take into P. (1998) demonstrates that the rate of convergenceaccount beneficial dynamic effects generated by between the regions of the EU increases significantlyharmonization of Polish regulations with European law. once the regional stocks of telecommunications infra-

structure are taken into account. This is in line withIn particular, the present paper argues that the adoption

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 715

C (1999), who shows that investment in tele- the relationship postulated by the theory is subject toempirical tests and causality analysis in the context ofphones is substantially more productive than investment

on average due to the existence of externalities. More the estimation of fixed and random effects. The paneldata approach allows us to address the omitted variablerecently, M (2003) concluded that there should

be a radical change in regional policy priorities: ‘rather problem. Another attractive feature of the present studyis the use of a comprehensive regional dataset forthan financing highways, it will be necessary to pro-

mote technological convergence among regions, which Poland – the largest of the EU accession countries.The paper is organized as follows. The secondwill involve public programs for telecommunications,

the Internet, and training of human capital’. In the section presents a simple theoretical model that relatesthe supply of intermediate inputs, including telecom-light of the above evidence, it is important to investigate

whether the development of telecommunications infra- munications infrastructure, to the regional level ofincome. The third section describes the data and esti-structure could potentially contribute to regional con-

vergence in the EU candidate countries. mation results. The fourth section is devoted to causal-ity tests. The fifth section concludes with policyUnfortunately, to date very few researchers have

focused on the role of telecommunications infra- recommendations and guidelines for future research.structure in regional economic development in Centraland Eastern Europe, and in particular for Poland such

TELECOMMUNICATIONS-studies are non-existent. A notable exception in this

AUGMENTED MODEL OF REGIONALcontext is a cross-section study by M and

INCOME DISPARITIESS (1998) who use simple ordinary least squares(OLS) methods to investigate the impact of telecom- Economic theory often views telecommunications

infrastructure as one of intermediate inputs that appearmunications investment on economic growth in transi-tion countries. Their work, however, lacks a truly in the production function (A , 1990).

According to an early distinction proposed by Hregional dimension, since a region is defined in a verybroad sense as it is meant to represent a particular (1965), governments can affect the provision of two

types of intermediate inputs: (1) technical infrastructurecountry in Central and Eastern Europe. Moreover,their approach is purely empirical and does not relate that supports various types of directly productive activi-

ties and (2) investment in people, which includes publicto any theory that poses some problems with theinterpretation of their results. education. Technical infrastructure in general decreases

the transaction costs of doing business, and telecom-The main goal of the present paper is to investigatethe relationship between telecommunications infra- munications infrastructure in particular affects the costs

of exchanging information among firms and enforcesstructure and the regional level of income. It is demon-strated that there exists a positive and statistically backward and forward linkages among them, while

education positively affects the productivity of an aver-significant causal relationship between telecommuni-cations infrastructure and regional incomes that could age worker. Therefore, government actions aimed at

promoting better access to telecommunications net-serve as a basis for the development of a policy aimedat narrowing regional disparities in Poland. To study works as well as a higher level of public education can

be expected to reduce regional income disparities bythis relationship, a simple but instructive model basedon a regional production function is used. The use of increasing the productivity of private inputs in the

poorest regions.production functions to study the impact of infra-structure on economic development, although not The present paper adopts the theoretical framework

previously used by D F and V (1995)necessarily in the regional context, has a long traditionin the economic literature. Frequently cited examples to study the determinants of regional income inequality

in Spain and augments it with a measure of regionalbelonging to this genre include A (1989),C et al. (1994), D F and V telecommunications infrastructure. This approach

allows us to view access to telecommunication facilities(1995), F (1999) and D F (2002),to mention but a few.2 as one of the most important intermediate inputs in

the production process and to model it as one ofAlthough the present approach and the goal of thestudy might look similar to some of the papers cited the arguments appearing in the regional production

function. Although the framework used allows us toabove, a number of important differences can be iden-tified. First, a theoretical relationship between tele- study potential impacts of various policy tools, we

concentrate entirely on the role of telecommunicationscommunications infrastructure and the level of incomeis provided drawing on a well-established framework infrastructure. The approach presumes that government

regulations, and, in particular, the harmonization ofin economics: a regional production function. Thisframework serves as the basis for the estimating equa- the Polish telecommunication law with EU regulations,

can promote regional development through improvedtion and allows control for a number of characteristicsthat might affect the level of income in addition to the access to telecommunication services, especially in the

least developed regions.stock of telecommunications infrastructure. Second,

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

716 Policy Debates

Model set-up form regional production function capturing specifi-cally the impact of the telecommunications infra-

The model used assumes a two-stage production pro-structure on the level of aggregate regional output:cess. First, a number of geographically disperse firms

within a region produce intermediate inputs that are XióBciKac

i L (1ña)ci Hbc

i P ci T qi S (1ñcñcñq)i (2a)

then supplied to final good producers. This two-stageproduction process can be modelled using a nested By defining �óac, �ó(1 – a)c, �ò�óc, AóBc, �óbc,regional production function. Although Poland is still one obtains a reduced form regional productiona transition economy that represents a low level of function (equation 3) that describes the impact ofintraregional systems of production links, this assump- intermediate goods and of transport and telecommuni-tion is not far from reality if one interprets intermediate cations infrastructure on the level of regional output ofinputs in a broader sense as specialized services such final goods:as advertising, consulting or financial intermediationwhose roles greatly increased with the development of XióAiKai Lbi H l

i P ci T qi S1ñañbñcñqi (3)

the market economy in the 1990s. Following D F and V (1995), it is assumed that produc- Like in traditional neoclassical models, it is assumedtion of intermediate goods requires only private inputs that under perfect competition, capital is perfectlyof capital and labour whose productivity depends on mobile between different regions and moves ins-the average stock of human capital per worker in the tantaneously across the country in response to even theregion. The output level of intermediate goods, Yi , smallest differences in its marginal product.3 The capitalin region i can be described by the Cobb–Douglas flows across regions until its marginal product is equal-production function: ized at all locations, whereas road and telecommuni-

cations infrastructure, labour, and human capital areYióBiKai L1ña

i Hbi (1)

fixed factors whose supplies are predetermined exogen-where B is the total factor productivity, K is the stock ously in a region and do not depend on differences inof firms’ capital, L is employment, H is the average their marginal products across locations.stock of human capital per worker in the region, and It is quite obvious why infrastructure is treated herea and b are constant parameters. as a fixed factor, but assuming fixed amounts of labour

The final output produced in region i, Xi , is assumed and human capital in the region might look like anto depend positively on the amount of intermediate oversimplification and require some further explana-goods reaching final assembly facilities. However, this tion. However, at this stage, it is argued that thedepends on the level of transport costs that can be assumption of the immobility of labour and humanmodelled using Samuelson’s ‘iceberg’ assumption capital, which translates into the lack of migrationsimplying that a fraction of intermediate inputs is lost across regions, can be defended since it is not very farin transit. Transport costs are assumed to rise with the from reality in many current and prospective EUarea of the region, S, but decrease with the stock of Member States including Poland. In addition to thepublic roads network, P. usually quoted reasons for the low mobility of labour

However, unlike D F and V (1995), within the EU, in Poland relatively low mobility ofthe volume of output is also allowed to depend people can be attributed to the underdeveloped marketpositively on the density of the telecommunication for housing, to the low level of educational skills ofnetwork, T. It is assumed that a higher density of workers (especially those from rural areas) and to thetelecommunications network decreases coordination underdeveloped public transportation infrastructurecosts between intermediate inputs providers and final that halts shuttle migration.goods producers. The demand for capital in the ith region is equal to

At the aggregate level, it might be reasonable to its regional marginal product, which is a function ofassume that the regional final goods production func- the following variables:tion is characterized by the constant returns to scale(CRS) property. Thus, if the intermediate goods input,

MPKióLXi

LKi

óaAiKañ1i Lbi H l

i P ci T qi S1ñañbñcñqithe size of the region, the road and telecommunications

networks are increased simultaneously by the same percent, one can expect an equal increase in the final

óaAiKai Lbi H l

i P ci T qi S1ñañbñcñqi

Ki

óaXi

Kioutput. Consequently, the second stage productionfunction can be written as:

This means, other things being equal, that an improve-XióYciP ci T qi S1ñcñcñq

i (2)ment in the regional telecommunications infrastructureraises the productivity of capital in that region andwhere cò�ò�[1, c[0, �[0 and �[0 to ensure thatstimulates investment that eventually translates into atransport costs increase with the land area.

Plugging (1) into (2) allows us to obtain the reduced higher level of regional output.

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 717

Equilibrium where

'ó(K/X)a

1ñaIn equilibrium, the marginal product of capital in theith region must equal the market real interest rate, r,

is the previously defined economy wide capital-outputwhich is the same in all regions under capital marketcoefficient in the national economy raised to the powerintegration:(�/(1ñ�)).

Dividing both sides of (8) by Li yields the averageMPKió

aXi

Ki

ór (4a) product per worker in the ith region:

Inverting, a conditional demand function for private QióXi

Li

ó'A1

1ñai L

bñ1

1ñai H

l1ñai P

c1ñai T

q1ñai S

1ñañbñcñq1ña

icapital in the ith region is obtained: (9)

Taking logs of (9), one obtains equation (10) thatKió

aXi

r(4b)

constitutes the basis for the empirical analysis discussedin a greater detail below:

An integrated capital market clearing condition requiresthat the sum of the regional demands for capital equals

lnQió ln'ò�1

1ña� lnAiò�bñ1

1ña� lnLithe aggregate capital stock available in the nationaleconomy, K, at a given moment of time:

ò�l

1ña� lnHiò�c

1ña� lnPi (10)Kó;N

iKió;

N

ia

Xi

róar;N

iXió

arX (5a)

This allows one to determine the equilibrium realinterest rate in the national economy as follows: ò�

q1ña� lnTiò�

1ñañbñcñq1ña � lnSi

róaXK

(5b) The reduced form per-worker regional productionfunction (10) allows us to identify the relationship

where X is the volume of aggregate output in the between the level of regional income per worker andeconomy that is the sum of regional output, two types of policy tools that could potentially beXó&N

i Xi , and N is the number of regions within a employed by the government. These tools includecountry. physical infrastructure represented in the model by two

Substituting (5b) into (4b) allows one to express the sets of exogenously determined variables: P (transporta-equilibrium stock of capital in region i: tion infrastructure) and T (telecommunications infra-

structure), and educational level, H. The immediateeffects of these tools are reflected in the increasedKióarñ1XióaXi�

K

aX�ó K�Xi

X�óXi�K

X� (6)productivity of private inputs through decreased trans-action costs of transporting intermediate inputs and

where (K/X) is the capital-output coefficient in the information dissemination, and increased educationalnational economy and (Xi/X) is the share of the ith levels of employees.region in the national output. The proposed approach to modelling the effects of

In equilibrium, the stock of capital in the ith region regional policy, however, takes into account two aspectsis a function of the national capital stock and the of government actions. First, a direct impact of theregional endowments of immobile factors. Since it is government policy on regional output throughdifficult to obtain reliable annual data on regional increased regional productivity; second, an indirectcapital stocks in Poland, it was decided, following D effect of decreased income disparity through relocation F and V (1995), to eliminate them from of capital across regions in response to resulting changesour regional production function. Substituting (6) into in its relative productivity at various locations. Both(3) then yields: aspects of these actions are in line with the aim of

increasing per-worker output in less developed regionsand reducing at the same time interregional incomeXióAi�

K

X�a

Xai Lbi H li P ci T qi S1ñañbñcñq

i (7)disparities.

Having divided both sides of (7) by Xai and raising to EMPIRICAL RESULTSthe power (1/(1ñ�)), one obtains:

Statistical methodology and data

The theoretical relationship between telecommuni-Xió'A

1

1ñai L

b1ñai H

l1ñai P

c1ñai T

q1ñai S

1ñañbñcñq1ña

i (8) cations infrastructure and the level of income in the

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

718 Policy Debates

region, derived above from the model of regional of the shadow economy and unregistered revenues.income inequality, can be tested empirically using Therefore, it was decided to approach the issue ofPolish regional panel data for the 1990s. To obtain the regional incomes from the side of expenditure. Givenestimating equation, equation (10) is transformed by the lack of reliable and comparable data on income,adding time subscripts and the error term. This allows per-worker retail sales seems to be a good proxy forempirical equation (11) to be obtained, i.e. a logarith- the real purchasing power of people across regions.mic relationship between income per worker and factor Retails sales per worker, expressed in zloties, wereendowments in the ith region at time t, including converted into fixed prices of 1997 using the consumertelecommunications infrastructure. This relationship price index (CPI).then becomes: The estimating equation used five explanatory vari-

ables representing regional characteristics: lit , hit , pit , titqitó{tòaitòhl litòhhhitòhppitòht titòhs sitòeit and sit . The main explanatory variable was the state of

(11) telecommunications infrastructure in region i at timet, tit , while the remaining variables constituted the

where � is a factor common to all regions that depends conditioning set. N and N (2003) defineon the national capital/output ratio, and lower case telecommunications infrastructure as ‘the transmissionletters denote the logarithms of A, L, P, H, T and S,

media, which includes wired and wireless networks,respectively, while � is the error term. The expected

satellites and antennas, together with routers and othersigns of parameters on the explanatory variables indevices that control the transmission path of informa-equation (11) are �h[0, �p[0, �t[0, �s\0 and �l\0.tion’. They argue that it also includes ‘the software thatTo estimate these parameters, standard fixed and ran-is used to send, receive and manage signals that aredom effects panel data techniques are used (H,transmitted and the various types of end users’ equip-1986). The primary advantage of the panel datament that enable users to originate and terminateapproach over previous studies based on simple OLScommunications’.methods is that it allows the reduction of the effects of

Various segments of telecommunications infra-the omitted variables bias that may arise in cross-sectionstructure have experienced different rates of techno-regressions. This problem is of particular importancelogical progress over the last few years. This progressin the case of regressions using regional data, especiallyhas been very significant in wired networks wherewhen many regional characteristics cannot be properlythe change from analogue to digital systems and theidentified and omitted from the estimating equation.introduction of packet-switching technology allowedTo obtain the estimates of parameters in equationfor the use of the Internet. Rapid improvements in(11), data are used for 49 Polish regions (former voivod-telecommunications, computer and information tech-ships) and 10 years between 1989 and 1998. This yieldsnologies resulted in the convergence of computer and490 observations. Sample choice was determined bytelecommunications infrastructures to a commondata availability. The sample is limited downwards toinformation infrastructure.1989 because this was the first year when comprehen-

Unfortunately, in the present study due to datasive political and economic reforms began in Poland.limitations, telecommunications infrastructure tit isThe sample is limited upwards to 1998 because thisproxied by the number of telephone subscribers perwas the last year for which regional data were available100 000 inhabitants.6 This measure is a fairly roughunder the previous administrative division.4 All dataproxy that does not capture many aspects of modernused in the empirical study were culled from varioustelecommunications infrastructure related to Informa-issues of the Regional Statistical Yearbook (Roczniktion and Communications Technologies such as, forStatystyczny Wojewodztw) and Statistical Yearbookexample, Internet access. This limits the scope of the(Rocznik Statystyczny) of Poland, published regularly bypresent research only to telecommunications density.the Polish Central Statistical Office (CSO), Warsaw. ATherefore, future studies should also take into accountregion is meant to represent the former administrativethe quality aspect of telecommunications infrastructureunit (voivodship) under the previous administrative divi-and its ability to provide advanced telecommunicationsion in Poland that was in force between 1975 andservices.1998.

Data on total employment in region lit concern allThe dependent variable used measuring the regionalpeople who perform work that yields income and arelevel of income per worker (qit) was proxied by theexpressed in 1000s of people. Educational level, hit , inretail sales per worker expressed in constant 1997 prices.the region was proxied by the high school attainmentThe use of this proxy was motivated by regional dataindex. This index is defined as the ratio of high schoolon income not being comparable across periods due tostudents to all people aged 15–18 years.7 The choicethe changes in statistical methodology that took placeof this index was motivated by the fact that the majorityin the early 1990s.5 In addition, in the present authors’of the young people who graduate from such schoolsview, available regional income data might not fully

reflect the actual level of income due to the existence continue their education at colleges and universities

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 719

Table 1. Variables used in the empirical study: pooled data summary statistics

StandardVariable Measure Mean deviation Minimum Maximum

Income (retail sales per worker) zloties in 1997 prices CPI deflated 12 851.6 4606.4 4442.3 35 933.8Employment ’000s of workers 321.2 246.7 97.2 1707.1Educational level secondary school attainment index (%) 23.2 5.7 12.7 46.7Road infrastructure km 925.1 318.5 217.0 1715.0Telephone infrastructure telephones per 100 000 inhabitants 12 233.8 5750.3 4690.0 41 890.0Area km2 6381.3 2219.9 1523.0 12 327.0

that may not be in the region, and after returning statistically significant at all usually accepted levels ofsignificance.10 The goodness of fit, measured by thehome they constitute a pool of highly qualified labour.8

Variable pit reflects the stock of public road infra- adjusted R2, improves remarkably. Most of theremaining explanatory variables that appear in thestructure in the region. There are many potential

measures of public road infrastructure. Hard surface estimating equation have also the expected signs andare statistically significant except for the total employ-public roads can be split according to their location

(within cities and outside cities) and according to their ment and human capital variables.To examine the robustness of the above results,importance (interstate, regional, county, local roads).

In addition, there are data available on hard surface regional heterogeneity is allowed for and equation (11)is estimated using the fixed effects estimator. Theand improved surface public roads. The present study

concentrated entirely on interstate roads in the region. estimates of the model parameters after relaxing theassumption of equality of constant terms are reportedThese roads belong to the improved hard surface roads

category and include major roads of national impor- in column (3) of Table 1. The computed high value ofthe F-test (with pó0) clearly rejects the equality oftance. They constitute the most important links

between regions and the highest priority is given to constant terms across regions and confirms the appro-priateness of accounting for regional heterogeneity.their maintenance. The total length of these roads is

expressed in kilometres. However, allowing for individual effects to vary acrossregions does not change the previous qualitative resultsThe last explanatory variable, sit , is the area of the

region expressed in squared kilometres (km2). In the concerning the estimated relationship between theregional level of income and the measure of telecom-period covered by our sample (1989–98), the area of

the regions did not change. Therefore, in the fixed munications infrastructure in the region in any impor-tant manner. This relationship still remains positive andeffects estimation, it will be treated as a part of indi-

vidual regional fixed effects. statistically significant, although the magnitude of theestimated parameter is now much smaller. AnotherDescriptive statistics on the variables used in the

estimating equations are shown in Table 1. Note that important difference is that the parameter on the vari-able proxying for the level of human capital in theboth retail sales per worker and telephone density vary

greatly across Polish regions.9 region changes its sign and is no longer statisticallysignificant. There is also a change in the parameter’ssign on the total employment variable that now

Parameter estimates of the modelbecomes negative and in line with theory’s predictions,but it remains statistically insignificant.Before turning to the panel data approach, as a useful

point of reference, the results obtained using the pooled Thus far, it has been assumed that individual effectsfor particular regions were fixed. However, in realitydata and simple OLS – the approach frequently used

in earlier cross-section empirical studies – are also they might be randomly generated. Therefore, column(4) of Table 2 also reports parameter estimates obtainedpresented. This approach assumes unrealistically that

individual effects are constant and equal for all regions. using the random effects estimator. Similarly, as in thecase of the fixed effects, the high value of the Breusch–The estimated values of the model parameters from

equation (11) are given in Table 2. Pagan LM test for the random effects (with pó0)confirms the importance of controlling for individualStarting from estimating the original model proposed

by D F and V (1995) that includes heterogeneity. To check whether the estimated coeffi-cients under these two techniques differ statistically, ano measure of telecommunication infrastructure, the

results are compared with the telecommunications aug- Hausman specification test was performed. The highvalue of the Hausman test (with pó0) would argue inmented model of regional inequality. The estimation

results based on this approach are shown in columns favour of the fixed effects approach. However, there isa problem with this interpretation because these two(1) and (2), respectively, of Table 2. It turns out that

the measure of telecommunication infrastructure enters sets of results are not directly comparable. The Haus-man test only compares the coefficients estimated bythe estimating equation with the right sign and is

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

720 Policy Debates

Table 2. Estimates of equation (11) based on Polish regional data, 1990s. The dependent variable is per-worker retail sales

Pooled data approach Panel data approach

Variable (1) OLS (2) OLS (3) FE (4) RE

Area ñ0.776*** ñ0.637*** — ñ0.732***(0.071) (0.063) (0.115)

Employment 0.020 0.056** ñ0.300** 0.006(0.029) (0.025) (0.139) (0.044)

Telephones — 0.469*** 0.166*** 0.244***(0.036) (0.049) (0.041)

Roads 0.951*** 0.769*** 0.527* 0.893***(0.082) (0.072) (0.277) (0.127)

Schooling 0.331*** ñ0.329*** 0.116 ñ0.009(0.052) (0.068) (0.124) (0.093)

Constant 8.750*** 6.082*** — 7.442***(0.349) (0.358) (0.595)

Adjusted R2 0.410 0.560 0.271 0.533F-test (p) — — 10.41 ñ

(0.000)LM test (p) — — — 361.93

(0.000)Hausman test (p) — — — 21.83

(0.000)Number of observations 490 490 490 490

Notes: 1. Standard errors are given in parentheses.2. Significant at the ***1, **5 and *10% levels, respectively.3. FE, fixed effects; RE, random effects; OLS, ordinary least squares.

both techniques, while the fixed effects estimator treats S (1998). These studies demonstrated that, onthe one hand, telecommunications infrastructure pro-the area variable, which is constant over time, as a part

of the individual effects. Hence, a complete comparison motes economic activity and contributes to a highergrowth, while on the other hand, higher economiccannot be made. However, even in the case of a random

effects estimator, the qualitative positive and statistically growth means that more income is spent on telecom-munications services that stimulates further investmentssignificant relationship between telecommunications

infrastructure and the level of income still holds. Sum- in telecommunications. In the light of this evidence,the present study should be extended to tackle theming up, it is concluded that there exists a positive and

statistically significant relationship between telecom- causality issue.To investigate the causality issue, M andmunications infrastructure and regional income that is

robust across various empirical specifications of the S (1998) are followed. They use the procedureproposed by H (1981), which combinesmodel.G ’s (1969) causality test with A’s (1970)final prediction error (FPE) criterion. To test thehypothesis that x causes y, simple OLS is first used to

GRANGER CAUSALITY TESTSregress y on its m lagged values. Then, using thecriterion of FPE minimization, the optimal number ofThe mere existence of a statistically significant relation-

ship between telephone density and the level of income lags, m*, is determined. The next step treats y as acontrolled variable with the number of lags fixed atin the region is not sufficient to argue that the develop-

ment of telecommunications infrastructure would lead m*, and x is treated as a manipulated variable with nlags. Regressing y on its m* lagged values and n laggedto a higher level of regional income. Equally, this

relationship could work well in the other direction. values of x, and applying the FPE criterion allows thedetermination of the optimal n*. If FPE (m*) from theNamely, the higher level of income might be associated

with the higher demand for telecommunication ser- first step is larger than FPE (m*, n*) from the secondstep, then there is a weak evidence that x causes y. Tovices and lead to a new investment in telecommuni-

cations infrastructure.11 obtain strong evidence that x causes y, one needs totest the hypothesis that parameters on the lagged valuesThe economic literature provides extensive empirical

evidence on the existence of a two-way causal relation- of x are jointly equal to zero. An F-test can be used totest this hypothesis. The rejection of this hypothesisship between investment in telecommunications infra-

structure and economic development. Frequently combined with the condition that FPE (m*, n*)\FPE(m*) would yield strong evidence in favour of xquoted studies include H (1980a, b), C

et al. (1991), E (1995) and M and causing y.

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 721

Table 3. Granger causality analysis

Variable (1) OLS (2) OLS (3) OLS (4) FE

Income (ñ1) 0.721*** 0.688*** 0.621*** 0.295***(0.048) (0.049) (0.049) (0.049)

Income (ñ2) 0.167*** 0.130*** 0.075 ñ0.137***(0.050) (0.050) (0.050) (0.049)

Area — — ñ0.237*** —(0.060)

Employment — — 0.021 ñ0.764***(0.023) (0.173)

Telephones (ñ1) — 0.092*** 0.125*** 0.138**(0.030) (0.041) (0.061)

Roads — — 0.271*** ñ0.893(0.071) (0.983)

Schooling — — ñ0.044 0.262*(0.064) (0.154)

Constant 1.088*** 0.789*** 1.972*** —(0.259) (0.265) (0.393)

Adjusted R2 0.736 0.742 0.756 0.331Final prediction error 0.032 0.031 — —F-test (p) — 9.760 9.410 5.070

(0.002) (0.002) (0.025)Number of observations 392 392 392 392

Notes: 1. Standard errors are given in parentheses.2. Significant at the ***1, **5 and *10% levels, respectively.3. FE, fixed effects; OLS, ordinary least squares.

The optimal numbers of lags for regional income CONCLUSIONSand telecommunications infrastructure variables along

The present paper has investigated the relationshipwith the calculated values of final prediction errors andbetween telecommunications infrastructure and theF-tests are given in Table 3. The results shown inregional level of income using panel data for Polishcolumn (1) are obtained for the specification whereregions from 1989 to 1998. The theoretical frameworkregional income is regressed only on its lagged values.used allowed the identification not only of the directThe results obtained for the specification that includesshort run impacts of improvements in telecommuni-the lagged value of telecommunications infrastructurecations infrastructure, but also of its long-run exter-are reported in column (2). Comparing these two setsnality effects associated with capital flows across regionsof results, it can be seen that the lagged value ofin response to changes in its marginal product at varioustelecommunications infrastructure has the expectedlocations. The development of telecommunicationssign and is statistically significant already at the 1%infrastructure in the regions characterized by the lowestlevel. Hence, there is strong evidence that the highertelephone density additionally would be accompaniedlevel of regional income is caused by increased tele-by increased private investment caused by increasedphone density in the region.12

productivity of capital relative to other regions. OverHowever, it would be good to check whether thetime, the resulting capital inflows to the regions withabove result holds when the specification of the estimat-expanding telecommunications infrastructure woulding equation is altered and the estimation method iscontribute to income convergence across regions.changed. To investigate the robustness of the results,

The empirical results presented herein support thecolumn (3) presents the results obtained for the speci-positive and statistically significant causal relationshipfication that also includes other variables present in thebetween telecommunications infrastructure and theestimating equation (11) which were derived from thelevel of income for Polish regions. With causality run-theoretical framework in the previous section. It turnsning from telecommunications to income, this clearlyout that when these variables are included in the presentsuggests the necessity of taking into account the roleestimating equation, the lagged value of telecommuni-of telecommunications infrastructure in developing acations infrastructure remains statistically significant atpolicy aimed at promoting regional development. Inthe 1% level. Finally, the estimation method is changedother words, regional policy cannot be conducted inde-and the fixed effects estimator is used instead of simplependently of telecommunications policy, but insteadOLS. The results shown in the last column of Table 3these two policies should be closely coordinated.essentially confirm the previous results, although the

The model used, however, overlooks the problem ofsignificance level of the telecommunications variablechanges from 1 to 5%. infrastructure financing. In their comprehensive survey

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

722 Policy Debates

paper, N and N (2003) conclude that there tools to support the execution of the obligation. Thenew law on supporting regional policy introducedis no unique model of financing telecommunications

infrastructure and no single set of regulatory policies in Poland in 2000 has not assigned any priority toinfrastructure development in general, and telecom-appropriate for all countries. The policy on infra-

structure development is country specific and depends munications infrastructure in particular (Dz. U.,2000b). Therefore, the transparent system of regulationson the availability of investment funds, development

stages, institutional arrangements and other factors. In aimed at improving the access to infrastructure shouldbe established and regulatory ambiguity must bethe present authors’ view, the government should play

a crucial role in stimulating telecommunication invest- removed before Poland is admitted into the EuropeanUnion.ments in the least developed regions of Poland through

regulation and subsidy programmes. In particular, we The host of issues regarding financing constraints anddesigning the optimal policy of telecommunicationsthink that the implementation of EU Directives onto

Polish telecommunications law and other appropriate infrastructure development that goes beyond the scopeof this paper clearly deserves closer attention in futurelaws including that on public–private partnership will

result in higher private investments in the telecom- studies. Therefore, identification of the most efficientways of exploiting in practice the causal relationshipmunications sector. For example, assuring common

access to telecommunication services across the coun- between telecommunications infrastructure and thelevel of income investigated in this study shouldtry, possibly by means of adopting universal service

regulations, as advocated by G et al. (2000), or become a target of future research.sharing the cost of network expansion in the leastdeveloped regions should reduce regional income

Acknowledgements – The authors are grateful to Tomdifferences. These actions could be complemented byBjorkroth, Jerzy Mycielski, Michael Ryan, the participantsinstituting special programmes for the development ofat the European Regional ITS Conference, Madrid, Spain,access to advanced telecommunication services in theand the anonymous referees for valuable comments and

least developed regions similar to those implemented suggestions.earlier in EU Member States.

Given that the lack of adequate telecommunicationsystems in some regions might constitute an obstacle NOTESto economic development, in the late 1980s and early

1. For a critical survey of EU regional policies, see1990s the European Commission implemented twoM R. (1998).

programmes to improve access to telecommunication 2. M (1992) has summarized much of the earlyservices in specific disadvantaged regions of the Mem- literature on the public capital hypothesis.ber States (E E C, 3. We are interested in studying the long-run impact of1986; CEC, 1995). The STAR programme (1986– telecommunications infrastructure on regional incomes91) was aimed at providing small- and medium-sized and therefore assume perfect mobility of private capital

across regions. Our estimates of long-run elasticitiesenterprises with better access to advanced telecom-presented in the empirical part of the paper can differmunication services. This programme was followed byfrom short-run elasticities because they incorporate thethe Telematique programme (1991–93), which sup-externality associated with capital flows. If private capitalported the development of data communication ser-is not fully mobile in the short run, the actual impact ofvices in less favoured regions.13 Possibly, similarinfrastructure provision will be smaller than estimated.programmes could also be instituted in the new EU

4. Since 1 January 1999, Poland has had a new system ofMember States with the financing scheme under the local government and the previous 49 voivodships werestructural funds arrangements or credit lines from other replaced with 16 larger regions, each of which is splitEuropean financial institutions. into counties (poviats).

In the late 1990s, there was a strong tendency, 5. The annual data on per capita incomes for Polish regionshowever, to put more institutional pressure on the EU are only available for selected years. The official data

compiled by the CSO are not available until 1995.Member States to ensure the effectiveness of telecom-Therefore, for the sake of data comparability, it wasmunication service-oriented programmes with thedecided to use per-worker retail sales as the measure ofEuropean Parliament passing a directive that institutedregional incomes.a universal service obligation (CEC, 1998).14 The

6. Although there are arguments in favour of using mainnecessity of law harmonization before the accessionlines as the measure of telecommunications infra-resulted in a number of new regulations on telecom-structure, the relation between receivers and main lines

munication and regional policies in Poland. In 2000, is reasonably linear (C et al., 1994). Detailedthe regulation instituting a universal service was intro- regional datasets on digitized lines, number of faults, etc.duced into the new telecommunication law (Dz. U., were not available for Poland.2000a). However, to date the legislator neither decided 7. It could be argued that the number of high schoolwho would be under the obligation to provide a graduates constitutes only a small fraction of the total

workforce. Therefore, in addition to using the currentuniversal service nor gave the institutional financial

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 723

after the inclusion of the lagged value of per-workervalue of this variable, we also experimented with itsretail sales to the estimating equation where telephonelagged values. The results obtained, however, were verydensity is treated as the dependent variable, the computedsimilar, which can be attributed to a very low variabilityvalue on the F-test does not allow the rejection ofof this index over time.the null hypothesis that this lagged variable is not8. The number of college and university students cannotstatistically significant. Therefore, the feedback runningbe used as another proxy for the level of human capitalfrom income to telecommunications infrastructure doesin the region because tertiary education in Poland in thenot seem to be very important. Using the words ofperiod covered by the present sample had been highlyM and S (1998), the present resultsconcentrated in several most advanced regions, whileindicated a weak evidence of reverse causation.some less developed regions reported no such students.

13. The European Commission has also been participatingSince our regressions have been estimated in logs, thisin the development of telecommunication programmeswould impose some additional problems.in the EU candidate Member States since the early9. For further details on the telephone density in Polish1990s. In Poland, the European Commission representa-

regions, see the Appendix.tion set the priorities for telecommunication pro-

10. The usually accepted levels of significance are meant to grammes with the total budget of ¤13 million from therepresent the 1, 5 and 10% levels. PHARE fund. The main targets of these programmes

11. The present paper is concerned with determining the differ from those implemented earlier in the EU as theyimpact of telecommunications on the level of income. have concentrated on the preparation of telecommuni-Therefore, this section limits itself to presenting the cation sector for restructuring and improving access toresults for causality running from telecommunications telecommunication services in rural areas through theinfrastructure to income, and not the other way round, implementation of the adequate institutional framework.although both were tested. 14. Directive 98/10/EC stipulates that ‘Member States shall

12. A similar procedure was also applied to test for causality ensure that the services set out in this Chapter are maderunning from income to telecommunications infra- available to all users in their territory, independent ofstructure. However, this relationship does not work very geographical location, and, in light of specific national

conditions at an affordable price’.well. Although the final prediction error drops slightly

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

724 Policy Debates

APPENDIX: TELEPHONE DENSITY IN POLAND PER 100 INHABITANTS,1989–98

10-year Per cent

No. Region 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 average increase

1 Warszawa 18.41 18.91 19.58 21.06 24.15 27.08 31.35 35.76 39.15 41.89 27.73 127.542 Biała Podlaska 5.71 6.69 8.63 10.94 11.83 12.54 13.65 15.70 19.00 21.81 12.65 281.963 Białystok 9.47 10.57 11.67 13.28 14.21 16.18 18.09 20.23 22.14 24.99 19.08 163.894 Bielsko-Biała 6.47 6.73 7.24 7.79 8.16 8.68 10.93 14.69 16.66 18.73 10.61 189.495 Bydgoszcz 9.06 9.71 11.54 13.29 14.01 15.77 17.22 18.59 19.71 23.47 15.24 159.056 Chełm 6.65 6.97 7.79 9.07 9.50 9.96 12.45 15.47 16.87 18.21 11.29 173.837 Ciechanow 5.10 5.26 5.64 6.11 6.56 8.03 9.87 11.37 14.20 16.71 8.89 227.658 Czestochowa 4.69 5.08 5.56 6.92 9.42 11.01 13.09 14.82 16.02 18.71 10.53 298.939 Elblag 6.08 6.47 7.02 7.70 9.46 10.54 12.56 14.44 16.20 18.66 10.91 206.91

10 Gdansk 9.86 10.25 10.95 12.51 13.27 14.02 15.46 18.92 23.31 26.57 15.51 169.4711 Gorzow 7.01 7.38 7.88 8.24 8.85 10.60 11.84 13.88 16.09 21.29 11.31 203.7112 Jelenia Gora 6.68 7.08 7.25 7.75 8.20 9.10 11.86 14.87 16.31 19.12 10.82 186.2313 Kalisz 6.71 7.41 8.07 8.62 9.39 10.70 11.72 14.48 17.25 22.27 11.66 231.8914 Katowice 6.42 6.67 7.02 7.52 8.95 11.19 12.81 13.70 15.94 21.57 11.18 235.9815 Kielce 7.53 8.18 8.91 9.70 10.38 11.43 12.32 14.33 16.61 19.61 11.90 160.4216 Konin 5.09 5.36 5.68 6.77 7.95 9.43 11.02 12.57 14.22 17.63 9.57 246.3717 Koszalin 9.35 9.65 10.57 11.62 12.87 14.45 15.94 17.74 19.02 21.52 14.27 130.1618 Krakow 11.09 11.76 13.18 14.33 14.84 16.77 20.22 22.88 25.39 27.46 17.79 147.6119 Krosno 5.33 5.65 6.16 7.04 7.31 8.28 9.48 10.21 11.98 13.70 8.51 157.0420 Legnica 7.29 7.60 8.12 9.82 12.16 14.23 15.97 17.29 21.81 25.49 13.98 249.6621 Leszno 7.24 7.65 8.14 8.87 9.66 10.53 12.26 14.56 17.09 21.17 11.72 192.4022 Lublin 8.10 8.52 9.59 10.27 10.97 11.87 13.71 15.68 19.87 24.53 13.31 202.8423 Łomza 5.54 5.89 6.22 6.70 8.33 10.19 11.02 12.95 14.82 18.86 10.05 240.4324 Łodz 14.36 14.92 15.90 17.79 20.51 23.52 26.04 28.44 30.82 33.82 22.61 135.5225 Nowy Sacz 5.79 6.13 6.82 7.63 8.06 9.09 9.92 11.40 13.60 16.74 9.52 189.1226 Olsztyn 8.30 8.80 9.30 9.98 11.87 13.58 15.23 16.37 18.30 20.82 13.26 150.8427 Opole 5.21 5.47 5.92 6.56 7.13 8.42 11.66 15.56 18.32 20.34 10.46 290.4028 Ostrołeka 4.95 5.12 5.98 6.76 7.17 7.50 9.44 10.63 11.74 14.19 8.35 186.6729 Piła 6.70 7.10 7.72 8.65 9.98 11.96 13.17 14.44 17.04 21.83 11.86 225.8230 Piotrkow 6.81 7.18 7.61 7.89 8.55 10.59 11.88 13.81 14.91 17.15 10.64 151.8431 Płock 6.74 7.19 7.87 8.54 9.22 10.56 13.27 14.52 16.41 20.47 11.48 203.7132 Poznan 9.29 9.96 11.13 12.44 14.65 16.60 18.23 19.79 22.39 26.56 16.10 185.9033 Przemysl 5.30 5.67 5.89 6.17 6.48 6.68 8.22 10.13 12.41 13.94 8.09 163.0234 Radom 6.47 6.78 7.29 8.32 9.57 10.59 11.22 12.96 15.84 17.89 10.69 176.5135 Rzeszow 5.27 5.52 5.98 6.74 7.41 8.17 10.32 12.04 14.03 19.38 9.49 267.7436 Siedlce 5.03 5.37 5.63 6.02 6.77 7.22 8.35 8.09 10.50 15.23 7.82 202.7837 Sieradz 5.70 6.25 7.17 7.96 8.39 9.23 10.87 13.31 15.23 18.13 10.22 218.0738 Skierniewice 6.08 6.45 7.17 7.61 7.85 8.32 9.62 11.94 14.87 18.27 9.82 200.4939 Słupsk 8.70 9.03 9.66 11.08 11.96 12.53 13.47 15.03 16.34 19.24 12.70 121.1540 Suwałki 5.84 6.27 6.95 8.04 10.22 11.75 12.72 14.11 17.05 19.40 11.24 232.1941 Szczecin 11.47 12.05 12.65 13.37 14.98 17.02 19.86 21.73 24.22 26.66 17.40 132.4342 Tarnobrzeg 5.60 5.87 6.28 6.50 7.71 9.01 9.67 10.74 13.85 15.77 9.10 181.6143 Tarnow 5.69 6.44 7.36 8.47 9.43 10.14 11.32 12.63 14.24 20.49 10.62 260.1144 Torun 7.48 7.91 8.74 10.44 10.86 11.51 12.33 14.98 17.44 24.69 12.64 230.0845 Wałbrzych 6.82 7.22 7.83 8.78 9.74 11.02 12.21 14.81 16.59 19.67 11.47 188.4246 Włocławek 6.64 7.04 8.24 9.74 11.62 12.48 13.28 13.78 15.09 17.81 11.57 168.2247 Wrocław 11.41 11.74 12.14 12.65 13.70 15.00 17.15 20.18 23.29 26.66 16.39 133.6548 Zamosc 5.95 6.38 6.95 7.57 8.11 9.05 10.42 12.10 13.62 15.60 9.58 162.1849 Zielona Gora 6.37 6.77 7.22 7.87 8.71 10.11 12.11 14.30 15.99 18.79 10.82 194.98Weighted average for

Poland 8.21 8.62 9.31 10.25 11.47 12.98 14.84 16.91 19.31 22.78 13.47 177.47

Source: Central Statistical Office, Warsaw.

REFERENCES

A H. (1970) Statistical predictor identification, Annals of the Institute of Statistical Mathematics 22, 203–217.A C. (1990) Information technology and the demand for telecommunications service in the manufacturing industry,

Information Economics and Policy 4, 45–55.A D. A. (1989) Is public expenditure productive?, Journal of Monetary Economics 23, 177–200.

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014

Policy Debates 725

C D. (1999) Telecommunications and Aggregate Output. CAERII Discussion Paper 56, Harvard Institute for InternationalDevelopment, Cambridge, MA.

C D., F M. and P R. (1994) Infrastructure and growth, in B M., P L. and PE. S. (Eds) International Differences in Growth Rates: Market Globalisation and Economic Areas, pp. 113–147. St Martin’s, New York.

CEC (1995) Development Prospects of the Central Mediterranean Regions. Regional Development Studies. European Union,Brussels.

CEC (1998) Directive 98/10/EC of the European Parliament and of the Council of 25 February 1998 instituting a universalservice, Official Journal of the European Communities L101/29.

C S O (various years) Regional Statistical Yearbook. CSO, Warsaw.C F. J., P E. B., C E. K. and G M. A. (1991) Telecommunications infrastructure and economic

growth: an analysis of causality, Telecommunications Policy 15, 529–534.D L F A. (2002) On the sources of convergence: a close look at Spanish regions, European Economic Review 46, 569–599.D L F A. and V X. (1995) Infrastructure and education as instruments of regional policy: evidence from Spain,

Economic Policy 20, 13–51.D. U. (2000a) Law on telecommunications of 21 July 2000. No. 73, Position 852. Warsaw.D. U. (2000b) Law on supporting regional policy of 12 May 2000. No. 48, Position 550. Warsaw.E P. (1995) Telecommunications and economic growth: empirical evidence from ASEAN countries. School of

Economics and Commerce Discussion Paper 9529. Latrobe University, Melbourne.E E C (1986) Council Regulation No. 3300/86 of 27 October 1986 instituting a Community

programme for the development of certain less favoured regions of the Community by improving access to advancedtelecommunication services (STAR programme), Official Journal of the European Communities L1305/1.

F R. (1983) Cumulative process of deindustrialization in an open region: the case of Southern Italy, 1951–1973, Journal ofDevelopment Economics 12, 277–301.

F J. G. (1999) Roads to prosperity? Assessing the link between public capital and productivity, American EconomicReview 89, 619–638.

F M., K P. and V A. J. (1999) The Spatial Economy: Cities, Regions and International Trade. MIT Press,Cambridge, MA.

F M. and T J. F. (2002) Economics of Agglomeration: Cities, Industrial Location and Regional Growth. CambridgeUniversity Press, Cambridge.

G F., L J. J. and S W. W. (2000) Competition, universal service and telecommunications policy indeveloping countries, Information Economics and Policy 12, 221–248.

G C. W. J. (1969) Investigating casual relations by econometric models and cross-spectral methods, Econometrica 37,24–36.

H M. N. (1965) The structure and determinants of local public investment expenditures, Review of Economics and StatisticsMay, 150–162.

H A. P. (1980a) The Role of the Telephone in Economic Development. Institute for Communications Research, StanfordUniversity.

H A. P. (1980b) The Role of Telecommunications in Economic Development. Institute for Communications Research, StanfordUniversity.

H C. (1981) Autoregressive modelling and money-income causality detection, Journal of Monetary Economics 7, 85–106.H C. (1986) Analysis of Panel Data. Econometric Society Monograph 11. Cambridge University Press, Cambridge.K P. (1996) Urban concentration: the role of increasing returns and transport costs, International Regional Science Review

19, 5–30.M G. and S S. J. (1998) CEE telecommunications investment and economic growth, Information Economics and

Policy 10, 173–195.M P. (1998) Can regional policies affect growth and geography in Europe?, World Economy 21, 757–774.M P. (2003) Public policies and economic geography, in F B. and P L. (Eds) European Integration, Regional

Policy and Growth, pp. 19–32. World Bank, Washington, DC.M R. (1998) Regional Policy in the EU: Economic Foundations and the Reality. CEPR, London.M A. H. (1992) Infrastructure investment and economic growth, Journal of Economic Perspectives 6, 189–198.N M. I. and N B. (2003) Telecommunications infrastructure and economic development. Paper presented at 14th

ITS Europe Regional Conference, Helsinki, Finland.N J. P. (2001) Of hypes and hyperbolas: introducing the New Economic Geography, Journal of Economic Literature 39,

536–561.O G. and P D. (1998) Agglomeration in the global economy: a survey of the New Economic Geography,

World Economy 21, 707–731.O G., T T. and T J. F. (2002) Agglomeration and trade revisited, International Economic Review 43,

409–435.R L. H. and W L. (2001) Telecommunications infrastructure and economic development: a simultaneous

approach, American Economic Review 91, 909–923.W B (1999) Knowledge for Development. Oxford University Press, Washington, DC.

Dow

nloa

ded

by [

Mou

nt A

lliso

n U

nive

rsity

0L

ibra

ries

] at

16:

43 1

5 Se

ptem

ber

2014