Publisher: Claude-Yves Charron Orbicom Jointly created in 1994 by UNESCO and Université du Québec à Montréal (UQAM), Orbicom, the Copyright  Orbicom, 2003 Network of UNESCO Chairs in Communications, embodies 28 Chairs and over 250 associate members in 71 countries with representation from All rights reserved. No part of this publication communications research, ICT for development, may be reproduced or modified without journalism, multi-media, public relations, the prior permission of the publisher. communications law, and more. The international Free PDF copy available on Orbicom’s collaboration of academics, corporate decision website: http://www.orbicom.uqam.ca makers, policy consultants, and media specialists makes Orbicom a unique network and constitutes a truly multidisciplinary approach to the promotion of Orbicom International Secretariat communications’ development. Since 1996, Orbicom Université du Québec à Montréal has general consultative status with the Economic P.O. Box 8888, Downtown Station and Social Council of the United Nations. Orbicom Montreal (Quebec), Canada, H3C 3P8 is engaged in a number of efforts focusing on ICTs, including assessment instruments such as Digital Review of Asia Pacific and Monitoring the Digital Divide ...and beyond. In 2002, Orbicom received the UNESCO/UNITWIN award for the quality of its projects. To find more about Orbicom, please visit its trilingual website http://www.orbicom.uqam.ca or contact [email protected].

Published in association with NRC Press, Canada Institute for Scientific and Technical Information. Version française aussi disponible.

ISBN 2-922651-03-7

Legal deposit - Bibliothèque nationale du Québec, 2003 Legal deposit - National Library of Canada, 2003 Monitoring the DIGITAL DIVIDE ...and beyond

Scientific Director and Editor: George Sciadas

Agence canadienne de Canadian International développement international Development Agency

Dedicated to the memory of

Tony Zeitoun

Tony, on behalf of the Canadian International Development Agency, supported the project enthusiastically from its inception, followed its every turn, took pride in its accomplishments and was pioneering the planning of its future course.

Acknowledgements An Orbicom project, in collaboration with the Canadian International Development Agency, the InfoDev Programme of the World Bank and UNESCO.

Scientific Director and Editor: George Sciadas

Main contributors Paul Dickinson, Joanna Chataway, Paul Quintas, David Wiel, Fred Gault, Brenda Spotton Visano, Susan (Sam) Ladner, Aqeela Tabussum, Xiaomei Zhang, Vana Sciadas.

The strategic direction and encouragement provided by Claude-Yves Charron was indispensable in carrying out the project. The project would not have been possible without the daily attention of former Ambassador Pierre Giguère who spared no effort in coordinating its every aspect. Magda Fusaro, Lucienne Sabourin and Valérie Harvey were instrumental in the delivery of the final product and their efforts are greatly appreciated.

We are grateful to Michael Minges and Esperanza Magpantay from the ITU and Diane Stukel from the UNESCO Institute for Statistics for supplying data promptly and efficiently, as well as providing expert advice.

Many individuals and their organizations provided input and contributed in numerous ways over the course of the project. Special thanks are due to Ramachadran Ramasamy from MIMOS, Malaysia.

We are also grateful to the NRC Press, Canada Institute for Scientific and Technical Information, for their role in the publication of this volume. Table of Contents

ENCOUNTERING DEVELOPMENT: A NEW MODEL AND METHODOLOGY TO MONITOR THE DIGITAL DIVIDE ...... by Claude-Yves Charron III

FOREWORD...... by Abdul Waheed Khan V

PREFACE ...... by José-Maria Figueres & Bruno Lanvin VII

EXECUTIVE SUMMARY ...... IX

Chapter 1 GENESIS OF THE PROJECT...... 1 Objectives and terms of reference 2

Chapter 2 THE FRAMEWORK AND THE MODEL ...... 5 2.1 The concepts 5 2.2 An operational model 8 Empirical considerations 11 Data gaps 12 2.3 Distinguishing features 13 Chapter 3 OVERVIEW OF RECENT TRENDS...... 17

Chapter 4 THE EMPIRICAL APPLICATION ...... 23 4.1 Magnitude of the Digital Divide 23 4.2 Causes of the Digital Divide 30 Infodensity and Info-use 30 Component analysis 32 Networks 32 Skills 36 ICT Uptake 39 Intensity of use 42 4.3 The evolution of the Digital Divide 43 Inside the Digital Divide 47 Country analysis 50 Drivers of the evolution 52 Chapter 5 COUNTRY PROFILES ...... 57

Chapter 6 MACROECONOMIC PERSPECTIVES ...... 83 6.1 Infodensity as an Input into an Aggregate Production Function 85 6.2 Competitiveness 87 6.3 Further Research 88 Table of Contents (cont’(cont’d)d)

Chapter 7 FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE - A PRIMER...... 89 7.1 Introduction 89 Knowledge and information 90 Knowledge comes in many forms 91 Context matters 92 Local/Indigenous knowledge 93 7.2 Knowledge Capabilities 94 Knowledge creation 96 Social dimensions of knowledge capability 98 Cross-boundary knowledge processes 99 Absorptive capacity 101 7.3 Lessons from the Industrial World 103 7.4 The North-South Knowledge Divide 106 Measuring knowledge globally 106 Traditional Knowledge - The San People and the hoodia plant 110 Knowledge divide in intellectual property 114 Knowledge networks and the development of the Indian software industry 116 7.5 Conclusion 117

Chapter 8 METHODOLOGICAL ISSUES ...... 121 8.1 The data 121 8.2 Discussion of indicators 122 Infodensity 123 Info-use 126 8.3 Reference year and country 128 8.4 Technical specifications and indexes 129 8.5 Sensitivity analysis 132 Chapter 9 DATA SOURCES AND DEFINITIONS ...... 133

REFERENCES ...... 139

STATISTICAL ANNEX ...... 145

ENCOUNTERING DEVELOPMENT: A NEW MODEL AND METHODOLOGY TO MONITOR THE DIGITAL DIVIDE by Claude-Yves Charron Secretary General of Orbicom and Vice-rector of Université du Québec à Montréal

Creating digital opportunities is not something that happens after addressing the “core” development challenges; it is a key component of addressing those challenges in the 21st century.

(G-8 Creating Opportunities for All: Meeting the Challenge, 2001).

Monitoring the Digital Divide ...and beyond is a major initiative of Orbicom. It was launched at the request of network members from the South who, while appreciative of the potential of information communication technologies (ICTs) in supporting sustainable development, were also apprehensive about the dangers of leaving the majority of the people in the South behind with no access to ICTs. An initial concept called The Digital Divide Index (DDI) was first designed as an instrument which would track the diffusion and uptake of ICTs over time and across economies and regions.

The DDI was proposed by the Orbicom Research Committee in 2000. The idea was later incorporated into an action plan which was approved by Orbicom’s general membership. The research itself started in 2001 under the leadership of the Scientific Director of the project, Dr. George Sciadas of Statistics Canada, with the development of a conceptual framework and a model which was pretested in nine countries. The initiative was then titled Monitoring the Digital Divide. The work received funding and encouraging support from the Canadian International Development Agency (CIDA), first and main contributor, and from the InfoDev Programme of the World Bank and UNESCO. As the project evolved, it attracted the interest and the co-operation of numerous technical and intellectual partners. The project is one of several Orbicom initiatives addressing issues of access, impact and trust of ICTs. They include the recently published Digital Review of Asia Pacific 2003-2004 and Generating Trust in Online Business published in 2002.

III Monitoring the Digital Divide ...and beyond is unique. It goes beyond connectivity and e-readiness issues. Its cohesive conceptual framework and transparent statistical methodology logically incorporate skills into YVES CHARRON YVES CHARRON YVES CHARRON YVES CHARRON YVES CHARRON connectivity measurements, and offer intuitive benchmarking and rich analytical perspectives for decision makers. In addition, the work introduces UDE- UDE- UDE- UDE- UDE- A A A A A primers on the correlation between ICTs and competitiveness and on the CL CL CL CL CL

role of knowledge in development, two areas in need of deepening and - further study, but which hold much promise.

This initiative is of special relevance to the implementation of the Action Plan of the World Summit on the Information Society, which calls for a realistic international performance evaluation and benchmarking, through comparable statistical indicators and research results. This approach clarifies the magnitude and the transformation of the digital divide, in its domestic and international dimensions over time, and provides an essential methodological framework to monitor progress in the use of ICTs to achieve internationally agreed development goals, including those of the Millennium Declaration. It also allows each stakeholder involved to follow how, with concrete indicators, the transformation from the digital divide to digital opportunities evolves.

With the advent and convergence of ICTs, the world has entered a new era which will witness deep cultural and social changes, and global communication systems which are more interactive and participatory in operation. This new dynamic invites us to cope with still unfamiliar ways of riding the waves in a sea of change. We must learn how to use this new dynamic to ascertain how ICTs are key components in facilitating the achievement of the United Nations’ Millennium Development Goals. Encountering Development: A New Model and Methodology to Monitor the Digital Divide the Digital Monitor to Development: Model and Methodology A New Encountering

IV FOREWORD by Dr. Abdul Waheed Khan Assistant Director-General for Communication and Information / UNESCO

Modern societies are currently undergoing a number of fundamental transformations caused by the growing impact of the new communication and information technologies on all aspects of human life. Some experts go so far as to speak of a revolution comparable to the invention of the alphabet or printing. But this revolution brought about by the new technologies has to confront a major challenge, namely the extreme disparities of access between the industrialized countries and the developing countries and those in transition, as well as within societies themselves. Indeed, the real issue is how to take account of the human dimension of the “digital divide” between and within countries. Despite increased awareness, the rich-poor divide in economic well-being is growing. The challenge now lies in enlisting technology as an ally in the movement for development and social equity. How can we help “maintain, increase and diffuse knowledge,” as UNESCO’s Charter requires, in this radically new context?

It is increasingly clear that our ability to cope with the “Digital Divide” or the “Knowledge Divide” will become the primary measure of success at both the micro and macro levels. In this sense, information and knowledge are becoming central to development and to attaining the Millennium Development Goals.

This work comes at an opportune time. To measure the Digital Divide most attempts had concentrated on such aspects as connectivity measurements and e-readiness for a restricted number of countries. The Orbicom research breaks new ground with a conceptual framework that goes beyond infrastructure to examine the content and dimension of the Digital Divide through the inclusion of existing and reliable education data. In addition, it has the merit of covering a substantial part of the planet with an emphasis on developing countries. In this sense, Monitoring the Digital Divide... and beyond is an essential tool for policymakers, donors and other stakeholders concerned with access to information and the acquisition of knowledge and skills as a means to bridge the Digital Divide.

V The fact that education or “skill” data are incorporated in a monitoring instrument of the Digital Divide confirms in my view that education, both in traditional and in new settings, is the key to creating equitable knowledge AHEED KHAN AHEED KHAN AHEED KHAN AHEED KHAN AHEED KHAN societies. There are two types of linkages between ICTs and education. The first is the use of education and training, formal and informal, to create IT-literate societies. Enabling all citizens to use ICTs with ABDUL W ABDUL W ABDUL W ABDUL W ABDUL W

confidence, in both their personal lives and working environments, is a - declared policy in some countries.

The second type of linkage is the use of education and training systems to

Foreword achieve learning goals that do not necessarily have anything to do with ICTs themselves. After some years of mixed results from technology-driven strategies that focused on equipping educational systems with ICTs, we now need to exchange experiences on approaches where the education or training goals determine the use of ICTs rather than the other way around. I am certain that one conclusion of this exchange will be that age- old methods of education delivery are unable to meet adequately the growing demand for learning and knowledge sharing. Initial signs of this incapacity have already led to several innovations: open learning, distance education, flexible learning, distributed learning and e-learning.

It is also evident that ICTs are excellent tools for facilitating access to scientific journals, libraries, databases and advanced scientific facilities. Another positive aspect is their potential to improve the collection and analysis of complex data. Monitoring the Digital Divide... and beyond is a very good example of innovative uses of data and their transformation into an insightful synthesis.

An increased flow of information is, in itself, not enough to lead to holistic and multidimentional development. In building equitable knowledge societies, there is a need for facilitating social, cultural, economical, political and institutional transformation. UNESCO’s concept of knowledge societies is based on fostering social development and community participation to ensure that all social groups could equally benefit from the new communication and information technologies. Monitoring the Digital Divide ...and beyond could well pave the way for approaches that would address this issue, particularly in the context of developing countries.

VI PREFACE by José-Maria Figueres CEO of the World Economic Forum and Chairman of the United Nations ICT Task Force and Bruno Lanvin Manager of InfoDev at the World Bank and Co-editor of the Global Information Technology Report (GITR)

The information revolution differs from previous industrial revolutions in many respects. First, it is not only based on a wave of concurrent technological innovations (in informatics on one hand and in telecommunications on the other), but also underpinned by a number of externalities (network externalities, knowledge-sharing effects) which have never been experienced before in the world economy. Secondly, it challenges many of the ‘distances’ that have until now separated different players and components of this same world economy. By redefining geographical distances, the information revolution has been the true engine of globalization. By redefining economic distances (between rich and poor) it has the power to become one of the engines of a world free of poverty.

To achieve this goal, however, many obstacles still need to be overcome. Before we can decide on how we should attempt to overcome them, we have to know how high, how far, how deep those obstacles are. Measurements and scorecards will therefore be at the heart of future efforts to address the so-called digital divide and identify the digital opportunities which will increase average income while diminishing income inequalities. This is where Orbicom’s work fits so adequately.

While other efforts and entities are concentrating their approach and focus on the ways in which ‘e-readiness’ relates to overall competitiveness and international development goals (GIT Report, UN ICT Task Force), or on specific implications of the information revolution on employment (ILO), trade (UNCTAD) or education (UNESCO), the Orbicom report is a remarkable attempt to offer a global set of indicators a remarkable

VII attempt to offer a global set of indicators (infostate) showing how the availability of ICTs and access to networks can be a misleading indicator ANVIN ANVIN ANVIN ANVIN ANVIN if it neglects people’s skills, and if ICT networks and skills combined (infodensity) are not matched by a measurement of what individuals, business and countries actually do with such technologies (info-use). It also offers important perspectives into the central role that e-policies and knowledge have started to play in determining how countries will fare in the global competition to benefit from the information revolution and move away from poverty.

As the world prepares for the two parts of the the World Summit on Information Society (Geneva, 2003 and Tunis, 2005), the work produced JOSÉ-MARIA FIGUERES & BRUNO L JOSÉ-MARIA FIGUERES & BRUNO L JOSÉ-MARIA FIGUERES & BRUNO L JOSÉ-MARIA FIGUERES & BRUNO L JOSÉ-MARIA FIGUERES & BRUNO L

by Orbicom to describe, measure and monitor the Digital Divide has a - very distinct and important role to play. It offers a fresh and broad-ranging perspective of the ways in which ‘info-ready’ countries differ from ‘info- challenged’ ones. Governments, international organizations, business, Preface non-governmental organizations, academia and civil society as a whole will be all the more interested in building a development-supportive, open and vibrant information society now that they will have at their disposal a reliable, action-oriented and diversified set of indicators to measure both the intensity of their efforts and the level of their impact. Those of us who are involved in providing such indicators have all the reasons to welcome and salute Orbicom’s effort in this domain.

VIII EXECUTIVE SUMMARY

The widely held belief that the proliferation, diffusion and appropriate utilization of ICTs presents enormous opportunities for economic and social development is thwarted by the realization that uneven access and capacity to use them poses serious threats as it could accentuate already existing and sizeable gaps between haves and have-nots.

The Digital Divide is rooted in the heart of Information Society issues. While it has attracted a lot of attention and much has been learned from studies of internal country divides, there has been so far no systematic way to quantify the Digital Divide and monitor its evolution across countries. This work offers the international community such an instrument, the application of which illuminates the issues involved with particular emphasis on developing countries. Unique features are:

! a cohesive conceptual Framework, which goes beyond connectivity measures and logically incorporates skills, as well as offers rich analytical linkages ! explicit measurements both across countries at a given point in time and within countries over-time, in such a way that comparisons are not reduced to changing rankings from year to year ! policy relevant results on a component-by-component basis ! immediate benchmarking against the average of all countries (Hypothetica) and the planet as a whole (Planetia) ! use of existing and reliable data sets with a sound and transparent statistical methodology

The conceptual Framework introduces the notion of a country’s “ICT- ization” or Infostate, as the aggregation of Infodensity and Info-use. Infodensity refers to the stocks of ICT capital and labour, including networks and ICT skills, indicative of a country’s productive capacity and indispensable to function in an Information Society. Info-use refers to the uptake and consumption flows of ICTs, as well as their intensity of use. It is differences among countries’ Infostates that constitute the Digital Divide. Since Infostates are dynamic and ever-evolving, the Digital Divide is a relative concept. Any progress made by developing countries must be examined against the progress made by developed ones.

IX Y Y Y Y Y The empirical application of the model covers a great number of countries. Measurements of networks are offered for 192 countries, covering 99% of the population of the planet; of skills and overall Infodensity for 153 countries, representing 98% of the population; of Info-use 143 countries and overall Infostate 139 countries, both accounting for 95% of the global population. The results are based on 21 variables, reliable, tested and EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR

available to all, and extends over the 1996-2001 period. - - - - -

The findings illuminate the questions of the magnitude and the evolution of the Digital Divide that we set out to answer:

...and beyond ! The Digital Divide between developed and developing countries is huge. Western European countries (including all Scandinavian, the Netherlands, Switzerland, Belgium, Luxembourg, the U.K. and Germany), the U.S., Canada, Hong Kong, Singapore, S. Korea, Japan, Australia and New Zealand have achieved very high Infostates, whereas Digital Divide Digital African countries are heavily concentrated at the bottom of the list. In particular, Chad, Ethiopia, the Central African Republic, Eritrea and Malawi, accompanied by Myanmar and Bangladesh, carry the tail. With the average country, Hypothetica, valued at 100, top countries have

Monitoring the Monitoring Infostate values exceeding 200, whereas the bottom is as low as 5! Literally, decades of development separate the haves from the have-nots.

! Both Infodensity and Info-use contribute to the Digital Divide, with networks and ICT uptake more than other components. Although century-old wireline telecommunications networks are a cause of the Divide, the gaps are more pronounced in newer technologies - the Internet, computers and cell phones. Skills, as measured by education indicators, also contribute significantly to the Divide, and this is more the case as we move from generic to more specific measurements. If anything, the lack of better measurements in this area underestimates the extent of the Divide.

! Over time, Infostates increase across all countries but to varying degrees. In an overall sense, the Digital Divide is closing. This, however, is happening at a very slow pace and is mostly attributed to relative progress by countries in the middle of the distribution. Countries at the bottom continue to lose ground.

! The same ICTs that cause much of the Digital Divide are also the ones behind its slow closing. Progress is being made in Internet use, cell phones and Internet networks. Even though such progress is substantial in some countries, the road ahead is very long. If left on its own this dismal situation is unlikely to improve in any significant way within our life times. Concerted action will be required by the international X Y Y Y community to alleviate the problem and see that as we move on we do Y Y not leave substantial population masses behind, with all the negative consequences that this entails.

! A close correlation exists between Infostates and per capita GDP. Initial study reveals that for every point increase in Infodensity, per capita GDP increases anywhere between $136 and $164. There are notable EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR EXECUTIVE SUMMAR

- - - exceptions, though. Countries with similar GDPs can have very different - - Infostates and vice versa. This speaks to the importance of national e-policies and e-strategies, implying that their design and implementation matter.

As well, a primer on knowledge offers food-for-thought on the complexities ...and beyond associated with its role in development. Knowledge confers the capacity for action and this distinguishes it from information. The significance of tacit knowledge is complemented with the nuances surrounding

indigenous knowledge. Case studies are used to place these issues in Divide Digital context. This think-piece offers a critical assessment of the creation and transmission of knowledge but, most importantly, it emphasizes the absorptive capacity of a country as a challenge for development with several policy implications. This refers to the capability to track and

assimilate external knowledge and put it to productive uses. the Monitoring

XI

Chapter 1 GENESIS OF THE PROJECT

ignificant energy has been expended in recent years to understand the implications of the rapid rise to prominence of Information and Communication Technologies (ICTs), and concerted efforts have been SS devoted to untangling the linkages between their diffusion, use and economic development. Numerous initiatives have been launched in parallel by national, international and non-governmental organizations, including the upcoming World Summits on the Information Society (WSIS), as if to solidify the instinctive belief that we are witnessing something fundamental with profound consequences, some immediate and others over the longer term. Throughout this creative turmoil, the issue of the Digital Divide emerged to occupy a central position among Information Society issues. Simply understood as the gaps between ICT ‘haves’ and ‘have-nots’, it matters enormously to the extent that ICTs represent an historic opportunity for the evolution of our societies and have the potential to accentuate already existing and sizeable imbalances.

This elevated interest is being accompanied by the realization of the importance of measurements. It is in that vein that many voices have been raised in recent times for the need to develop an instrument that would quantify the Digital Divide and systematically monitor its evolution. As an area of investigation, the Digital Divide is multifaceted. It represents the area of overlap between the economic and the social aspects of the Information Society and serves as a prime example of the need for broad, multi-disciplinary approaches. Surely it involves the ever-important issues of deployment of infrastructure and access to it, but it also involves actual uses of ICTs, coupled with their efficacy, intelligence and applications. It extends to include the necessary skills, the evolution of which brings to the forefront issues of training and learning. Moreover, social exclusion and further marginalization of parts of the population, with all their implications, can be dealt with wherever masses of people live. This adds a spatial dimension to the Digital Divide, equally applicable within and across countries. Several other dimensions exist, related to gender, age, family-type and others.

1 The realization of many of the promises of ICTs, including phenomena like e-commerce and e-government, also relates closely to the Digital T T T T T Divide. At a minimum, work on this area requires a combination of diverse subject matter expertise and statistical knowledge. Analytical work concerning internal country digital divides has been carried out (e.g. U.S. 1995, 1998, 1999, 2000, 2002, OECD 2001, Sciadas 2002) and much has been learned. Moreover, methodological approaches and statistical techniques have been developed. The impetus behind this project has been the need for the development of an instrument that GENESIS OF THE PROJEC GENESIS OF THE PROJEC GENESIS OF THE PROJEC GENESIS OF THE PROJEC GENESIS OF THE PROJEC would quantify the Digital Divide across countries, as well as monitor its evolution. This is indispensable in the formulation of national and

1 - international e-strategies, as the emphasis is increasingly placed on ICT- for-development policies. It will help ascertain the relative status of countries and, especially, monitor the relative progress both across

Chapter Chapter countries and components of interest within countries. Such an instrument will provide the international community with a useful mechanism towards: ! the identification of the state of affairs and relative needs among countries; ! the allocation of investments to their most appropriate uses, and; ! the monitoring of performance.

This is where this project aims to contribute.

Objectives and terms of reference As this is a new area of investigation, it is generally characterized by selective application of concepts and lack of widely understood terminology, definitions and overall nomenclature. This can add to the list of complexities surrounding work in this or related areas. Our focus is on the Digital Divide, which is ICT-centric. While ‘divide’ is generally understood, the word ‘digital’ is a misnomer. Digitization is undoubtedly at the center of the recent technological advances, but our domain of interest encompasses more conventional, non-digital ICTs.

Furthermore, the stakeholders established clearly the overall objectives of the project: develop a model, grounded on a sound conceptual framework, the empirical application of which will make possible the systematic measurement of the state and evolution of the Digital Divide internationally. Unlike measurement practices in other areas, where phenomena of interest are measured at a given point in time and the ensuing analyses rely on annual changes in country rankings, the

2 objective was twofold: create a methodology that will make it possible to quantify the Digital Divide and monitor its evolution both ! across countries at a given point in time, and; ! within countries over time. Moreover, it was stipulated that the development will be guided by the following terms of reference: ! Place emphasis on developing countries; GENESIS OF THE PROJECT GENESIS OF THE PROJECT GENESIS OF THE PROJECT ! GENESIS OF THE PROJECT GENESIS OF THE PROJECT

Rely on a modeling approach that yields policy-relevant results; ! Focus on ICTs, but be broader in scope than pure 1 - connectivity measures.

Several implications stem from the above, which were addressed during Chapter the development of the conceptual framework (Orbicom 2002). A synopsis of this is offered next.

3

Chapter 2 THE FRAMEWORK AND THE MODEL

he Digital Divide represents the newest addition to already existing, enormous chasms in the stage of development and the standard of living among countries. Like other well-known imbalances, its measurement TTand analysis require a rigorous framework rather than ad hoc approaches. As the issue immediately invokes comparisons, we must clearly define what is it that divides, as well as establish plausible links between ICTs and economic development. Is it the availability of telecommunications networks that divides and impedes progress? Or is it the existing stocks of computers, cell phones and Internet connections? Or, perhaps, it is the use of such ICTs rather than their stocks that matter more? Could it be that, more than the quantity of ICTs and the intensity of their usage, it is the intelligence of their usage and other intangible qualities that matter? Or all of the above put together? Why? The framework that was developed in Phase I of this project provides the necessary conceptual underpinnings for the systematic quantification and monitoring of the Digital Divide. A synopsis is provided here.

2.1 The concepts The conceptualization begins with the basics. The overriding issue of a society concerns the quality of life of its people. While this relates to all kinds of intangibles, including matters of social and cultural relationships, development efforts do not set out to improve people’s inner happiness, but their economic well-being, current and potential. ICTs are no exception. Consistent with this, while the economy is situated all along within the broader socio-economic, geopolitical and cultural environment of a country, ICTs are treated as an economic and social reality. They are here to stay and the benefits associated with them will necessarily be a function of the way we put them to use.

Next, a distinction is made between consumptive and productive functions. Following economic theory, the standard of living of the people depends largely on their consumption of goods and services. Current consumption is determined by current production, adjusted

5 6 6 6 6 6 Chapter 2 - THE FRAMEWORK AND THE MODEL I Thus, each constituentcomponent. forin infostates canbeidentified amongcountries.Dividesofcourse or aggregate thetwo andarrive ‘ICT-ization’, atthedegree ofacountry’s refers ofICTs. totheconsumption flows Technically, itispossibleto ICT labourstocks andindicative of productive capacity, whileinfo-use overallcountry’s andlabourstocks, capital which and are ICTcapital country’s consumables. Inthatsetting, theframework developed thenotions ofa The natureofICTs isdual;they areboth productive assets, aswell as productive capacitydetermines economicwell-being inthefuture. consumption determines theireconomicwell-being today, thecountry’s and, by extension, toeconomicdevelopment. Therefore, whilepeople’s way, somethingthatbringsusto thewholeissueofeconomicgrowth of expanding inasustainable theproduction capabilitiesofacountry consumption tomorrow. Butover timewe mustconfront theproblem allocation -foregone consumption today (investment) for increased for foreigntradeandsociety’spreferencesregardingintertemporal All thesearemeasurable. in andoutofthelabourforce, all affect drain, andbrain theskillsstock. produced andconsumed.Attrition, obsolescence,training,movements stocks andaugmentingthem).Thesameholdstruefor labourskills, (gross investment andlabour –replenishing theused-upICTcapital demand)orwillbeaddedback stock tothecapital consumables (final will beintheform ofICToutputs,which willbe absorbedas At oftheoutputs theendofnumerous production processes,part together withskilledICTlabourintheprocessing ofagriculturaloutput. andsimpleto toolsareused producetelecommunications services, computersinstance, together withrelatively unskilled labourareused withoutaone-to-one correspondence.ForICT goodsandservices, ICT andnon-ICTfactor inputs arecombinedto produceICTandnon- them all. improvements andICTs andproductivity gainsareinstrumental affect over timethey areallexpandable. Factor growth, technological technology with which they arecombinedinproduction but arefixed, time, theproductive becausethefactor capacityisfixed stocks andthe quantity andqualityofitsfactors ofproduction. Atany given pointin NFODENSITY Infostate Infostate Info-use Infodensity Infodensity . The Digital Divide is then defined astherelative difference Divideisthendefined . TheDigital :

The productive isdeterminedby the capacityofacountry =consumption ofICTs/period flows =aggregationofinfodensity andinfo-use = sumofallICTstocks (capital andlabour) and Info-use . Infodensity refers to thesliceofa I ICT stocks, asitisfor allother forms andlabour. ofcapital this formulation, produced outputwillbeanincreasingfunctionofthese individuals, butasthestock oftheICT equipment made between ICT a prerequisite togeneratingconsumption Inthatvein, adistinctionis flows. Thus,building‘consumptivetechnological goodsandservices. capacity’is increasingly complex asconsumption expands from to staples complex involves theuseofboth andskills,both ICTcapital ofwhich arebecoming thatwould satisfyultimateneeds.Infact,of ICTservices ICTconsumption ICT capitalcomprises consumption (exports). maycountry manufacture ICTgoods,which willnot beseenindomestic ICT goodsandskillscancomefrom Alternatively, imports. adeveloping crucial. TheissuesareInfodensity andInfo-use. andconsumed Capitalized It isevident fromtheframework thatdomesticproductionofICTs isnot provides aschematic oftheframework. can bedealtwithmoreappropriately withcaseorimpact Figure1 studies. technological innovations). Such and examinations are outsideourpurview productivity inbusinesses(organizationalinnovations accompanying the derived satisfaction, inthecaseofindividuals,andtoissue of of use(how much), to itmatters know useis.Thisrelates how to “smart” consumption pertimeperiodcanbemeasured.Inadditiontotheintensity Thenthe corresponds to ICTgoodsandintensityofuseto ICTservices). NFO - Infostate USE :

Clearly, uptake ofICTgoodsisindispensablefor theconsumption . ICTlabourcanbeperceived not somuch asacollectionof { aorcapital labour uptake C klsICTinfrastructure ICT skills network andICT consumption consumption consumption consumption consumption productive productive productive productive productive capacity capacity capacity Economy capacity capacity ICT intensity infrastructureandICTmachinery Figure 1 ICT uptake intensity ofuse skills ofthoseinthelabourforce. In . (Roughly, uptake } } Infodensity Info-use 7 7 7 7 7 Chapter 2 - THE FRAMEWORK AND THE MODEL 8 8 8 8 8 Chapter 2 - THE FRAMEWORK AND THE MODEL can beachieved over time. Consequently, absolute thereisnopre-set, upper limitofinfostate that The sameholdstruefor skills,with obviousimplications for productivity. the Internet,ceilingwould have moved upwards immediately after. ICT hadachieved 100% penetration anduseratespriortothearrival of today meansnothing for tomorrow. For available ifevery instance, expandable over time.Even asconsumables,achieving complete uptake consumption, ICTs areclearlynot boundedupwards butinstead are factor stocks andthecontinuousintroduction ofnew ICTs in Considering theintuitive andinextricable linkofICTs withtheoverall and trade. from actualproductionbothbecauseofcapacityunderutilization clearly to theproductive capacityofthecountry, butitisdifferentiated unemployed refers orunderutilizedlabouranditsskills.Thesupply-side does not increaseproductive capacity. Thesameholdstruefor roads, abandonedfactories andrusted telecommunications networks productive stocks ratherthantheiravailability. Having underutilized As well, whatreallymatters for development istheutilization ofthe forAn analogousrelationshipcanbespecified labourskills. isnotprominent.entailed, Theframework shows that: ICTsector,Thus, thesupply-side for althoughimportant allthespillovers Moreover, build-upsare majorinfrastructure accompanied by small accruingto increase withthenumberofusers. itsusers, the benefits the well-known externalities. Simply put,thevalueofanetwork and their own idiosyncracticnature.Oneoftheirmajorfeatures concerns with conventional networks comewith analysesofgoodsandservices, They combine to form machinery, equipment andnetworks. Compared and cables,tokeyboards, printers, andswitches. sophisticatedrouters N only byat hand,itisconstrained limitations. data can beadapted to examinations. Dependingontheapplication detailed and ICT intensity ofuse.Thisstructureoffers considerableflexibility their constituent components: ICTskills, uptake ICTcapital, and and Info-use. Each canbemeasured andexamined separately, ascan domestic production –net =householdspending+net exports investment (business ETWORKS and government) +businessspendingcurrent government spending : The buildingblocks ofthemodelare thenotionsofInfodensity 2.2 Anoperationalmodel

ICT capital comprisesICT capital allkindsofmaterialgoods,from wires U (ETS2002). withbasicliteracy starts oftheoverallisolation butthey arepart continuumofpeople’sskills, which consumption ICTskillscannot move beviewed Furthermore, in inparallel. fornot unreasonabletoassumethatICTskillsnecessary production and advanced,itis Untilitisfurther measuring ICTskillsisatanearlystage. used for individualconsumption too ortheotherway around. Work in productive andconsumptive functions,i.e.skillsacquiredonthejobcanbe employees andconsumers.Such skillsaretransferable back andforth between country, largenumberofindividualsare sinceavery involved asboth consumption-related skillsandrelatedtotheproductive capacityofa overlap oftheir production.Thereissubstantial they between notpart are other formal but orinformal trainingandconsumeICTgoodsservices, ICTs -studentsandseniors.They skillsatschool obtain orthrough some force age,therearealsothosebelow andabove thelimitswhoconsume a waiterorcarmechanic. Whilethelabourstock includesthoseoflabour occupationisacomputerwhose primary programmer, butalsoasecretary, the useofICTs such becomesmore skillsareusedby pervasive, people versus non-ICToccupationsoremployment inICTsectorindustries. As S networks willbeassociated withInfodensity. capacities -althoughpossiblewithcreative accounting.Inthemodel,ICT networks to categoriesofuse,such asbetween consumptive andproductive telephony. toapportion Animplication ofthisisthatitpracticallyartificial andlocal such ofvoice orlongdistance astransmission anddata, services, networks areusedfor residential andbusinessuse,aswell asfor avariety of consumption Telecommunications andproductionofmany services. marginal costsofconnections.Itisthesamenetworksthatareusedfor watching television orplaying withthekids.Whenanew the ICT enters 24-hour-day constraint. UsingtheInternet willleadto reduced timeof least, willbesubstitutionsinconsumptionvery there duetotheinescapable (substituting abroadband Internetconnectionfor Inthe dial-upservice). on acellphoneby prolonging thelife ofclothing) ordisplacement take place.They costs cancomeeitherintheform (spending ofopportunity not justconsumption ofICTs. Asmore ICTs areconsumed,substitutions According totheframework, whatmatters inasociety isoverall consumption, consumption ofInternet services). makes telephonecallspossible,andthecomputer thatallows the (Examplesfuture consumption would flows. includethe telephoneset that ofhouseholds’consumptiveparts capacity-which determinescurrent and indispensable for theconsumption andcanbeconsidered ofICTservices take placefor consumption are durability tohappen.ICTgoodsofvarying transformations ofaproductive nature involving raw materialsandskillsdo KILLS PTAKE :

: The ICTlabourstock isreallyaset ofskills,asopposedtoICT

Although householdsareseenasaconsumptive sector, 9 9 9 9 9 Chapter 2 - THE FRAMEWORK AND THE MODEL 10 10 10 10 10 Chapter 2 - THE FRAMEWORK AND THE MODEL Internet Telecommunications Networks notwithstanding. provide robustnessincomparability, atthebottom substitutions and depth inactualinvestigations. However, themaincomponents (see Figure2),something thatcanbeexploited andprovide bothlatitude In applyingthemodel,we mustbecognizantofitstree-like structure internal to acountry. other for characteristics divides important ofdigital theunderstanding by gender, urbanandrurallocations,income,level ofeducationand groupsofhouseholds/individualscanbedifferentiatedFurthermore, and typeofinstitution(publicadministration, education,health). sector,industry andgovernments by level (national,regional, local) and analysescanbeaccommodated.Businessessplitby sizeor desired disaggregation. detailed For sectoralmeasurements instance, In principle,ICTuptake andintensity ofusagealsopermitany level of be found over time,cannotbeknown apriori. consumerchoices andthereforereflecting positive. Whatbalancewill substitutions donotrepresentforce-feeding. They willberegarded as ofICTsproportions inconsumption isnottheobjective, these consumables willchange. Althoughcontinuouslyhigherrelative consumption basket, ofICTandnon-ICT therelative proportions Cable ICT capital Machinery Infodensity ICT labour Figure 2 Skills INFOSTATE ICT uptake Info-use ICT intensity N to their origin. backin order tobeabletraceanalytically theexplanations ofthefindings carried outfrom thebottom up,asexplained inthetechnical specifications, their aggregationacross different unitsofmeasurement. Theexercise is Box, p.15). Theseareconverted toindexes, amethodthatmakes possible each component (seeText ofthemodelispopulatedby indicators suitable under theframework, themodelingapproachrelies onindicators. Practically, A considerations andtheproject’stermsofreference. Statistical manipulationmust becombinedwith,andguidedby, subjectmatter of existingindicatorsandtheirlopsidedavailabilityamongcountries. concepts. Suchanexerciseinvolvesseveralnuances,includingtheconstraints an operationalmodelwhichapproximatespragmaticallythepurityof for businesseswithaneye onmarket size. is unlikely tohappen.Theavailable stocks ofICTs andtheirutilizationmatter level ofInfostate thanamuch largerone,say, India.Inabsoluteterms,this relative like terms. Thus,asmallcountry Luxembourg canhave ahigher the model,asopposedto itsbusinessusefulness,Infostates are expressed in A interactive, upondigitization,iseven morefitting. included intheapplicationhere.Thatitispossible for such mediatoturn will belargelyneutralizedwithrespectto thosecomponents, they willbe countries. Therefore, althoughthecomparison amongdeveloped countries component ofICTs isindispensablewhentheemphasis isondeveloping While thismay make senseinthatcontext, inclusionofthe information them uninteresting incomparative analyses.Aprimeexample istelevision. have achieved such awidespread penetration inthesecountriesthatitmakes either ignoredordownplayed. Thisissobecausetheoldertechnologies work involving developed countries,theinformation component ofICTs is thanone-wayinteractivity rather provision ofinformation. Frequently, in technologies. Thenewer onesare mainlyassociated withtwo-way technological convergence between new andoldernetworks and fromindicators theskills’continuum.Moreover, ICTs aretheproduct of ICT skillscanonlybeimperfectly approximated atpresent,withgeneral suchdata, asavailability ofcomputers athomesvs.businesses.Aswell, them getblurred sectoral andjudgmentisneededintheabsenceofdetailed production andconsumption activities.At times,theboundariesbetween N ATURE RELATIVISTIC

INDICATORS

OF ICT For theframework measurementpurposes, asaguidefor serves Empirical considerations ’

MODEL APPROACH S :

ICTs are technologies’ ‘general-purpose andpermeate : Whilealternative empirical applicationsareadmissible : Consistent withtheneedfor policyrelevance of 11 11 11 11 11 Chapter 2 - THE FRAMEWORK AND THE MODEL 12 12 12 12 12 Chapter 2 - THE FRAMEWORK AND THE MODEL In the course oftheresearch,In thecourse thefollowing were limitations identified: R global population. in Info-use and139 inoverall Infostate, covering more than95%ofthe and therefore Infodensity, covering 98%ofthepopulation, 143 countries covering 99%ofthepopulationplanet, 153 countriesinskills total 192 countriesareincludedinthemeasurements ofnetworks, While adheringtotheuseofexisting fromcredible data sources, in in Chapter8. indetail notesare togetherwithexplanatory contained specifications, seen asaregionoftheplanet. Themethodologyanditstechnical if viewed and,inthissetting, couldbe each country asonecountry calculations. Inthiscase,thevaluesarethoseofplanet asawhole, alternative benchmark, Planetiaiscreated andincludedinthe This offers immediate andintuitive initialbenchmarking. Asan thatrepresents theaverage valuesa country ofallcountriesexamined. asareference, country Hypotheticathan choose wascreated, aspecific with prioryears –explained fullyinthemethodology section.Rather to continueto exist. Appropriate linkagefactors were usedto compare year duetotheavailability ofadditionalindicators-which areexpected the recent evolution ofICTs. 2001 waschosen asthereference (base) over the1996-2001 tocapture sufficient period,withtimeseriesdata Infostate components over time.Theempirical applicationextends makes possiblethemonitoringofevolution ofeach country’s The reference facilitates comparisons country andthereference year the modelcallsfor andareference areference country year tobeused. evolution Divideduetotheconstant Digital ofinfostates everywhere, such amission,itwillhavemademodestcontribution. gaps,proves ofdata requirements helpfultowards andidentification If thepresentframework,inconjunctionwith itsinformation information and knowledge-based societies withadevelopment angle. develop information ongoingstatistical of concerningmatters Clearly, there isample to room international for effort aconcerted EFERENCE ! !

COUNTRY Data gaps insufficient qualityofsomeindicators. insufficient lack ofanadequate numberofindicators,

AND

PERIOD : Consideringtherelativenatureof research agendaandoffers thefollowing: somewhat related areas.Theapproach herecontributes totheoverall (hence theneedfor thisproject), several attempts have beenmadein have Divideacross beenmadeinmeasuringtheDigital countriesperse ! ! ! ! ! within thecontext ofthe study. rubust, reproducible inatransparent, anddefensible way A methodology which makes theempirical application to all. The bestexisting data,reliableandavailable period tothenext. constrained tocomparing changed fromone rankings and comparisons ofevolutions donot have to be evolutions andcomparisons. Therefore, benchmarking of Time-series thatmake data possiblethequantification and developing countries. unbounded upwards, both inthecontext ofdeveloped decomposition allofwhich to are constituent parts, Divideandits A realistic depictionoftheDigital otherwise. well asmakes possibleanalyticallinkages, economicor A cohesive framework which provides aperspective, as Work inthisareaisfullofchallenges. Whilenomajorattempts 2.3 Distinguishingfeatures 13 13 13 13 13 Chapter 2 - THE FRAMEWORK AND THE MODEL 14 14 14 14 14 Chapter 2 - THE FRAMEWORK AND THE MODEL will befairer. different technological platforms amongcountries,sothatcomparisons the caseofICTs ‘technology-neutral’. Thisismeantto neutralize away Inaddition, they from,adesired state. shouldbeunbiasedandin be unambiguouslylinked towhether or not we aremoving towards, or should be‘well-behaved’, thatis,thedirection oftheirmovement should availability.considerations concerningdata Ideally, chosen indicators broad knowledge andexperience, inconjunctionwithpractical However, isthussomewhatChoosing indicators ofanart. itdoesrequire onesto more substitute. suitable find we donot gainby productive merely Itismore addingindicators. to investment intheproduction ofmoreandbetter indicators. Inprinciple, for longer term, ofinterest gapscanbeidentified andstatistical areas through acombinationofreasonablenessandavailability. Over the selected inthiscase.Indicators arepractically Simplicity isavirtue for will befutileandcanonlyleadto perfection inactivityandparalysis. everyindicatorhasitsstrengths Thequest andlimitations. In short, their construction. usedin meaningfully onlyonthebasisofabsolute figures denominators have changed. Thus,growth canbecomputed rates onemustinstance, know thatlikely both andthe thenumerators to theircompilation change, aspossible.When penetration rates for matter around information them,includingasmuch ‘metadata’ related toindicate.Thisrequires knowledge ofthesubject indicators purport In conjunctionwithalltheabove, onemusthave knowledge ofwhat banking. but inadequateforabankthatcontemplatestheofferingofInternet equipped withmodemsmay beusefulinformulating policieson access, Knowing thepenetrationparamount significance. ofcomputers intendedonly thecontext is also of useofindicators butthespecific generally moreusefulfor highlightingdifferences ofsomescale.Not from casestudies. Indicatorsconfused withdetailed are findings issues.Themessagesconveyedof specific by theirusemust not be Indicators, usefulasthey are, arenot substitutes for analyses detailed within which they areused. Invariably, dependsonthecontext thevalueofindividualindicators or qualitative, andcanbeexpressed invariousunitsofmeasurement. any kind,shapeorform. They canbesimple orcomplex, quantitative movement andtheorder ofmagnitudechange. Indicators comein components, andtoon theirimportant illustrate thedirectionoftheir Indicators areextremely usefulto focus thediscussions ofcomplex issues INDICA INDICA INDICA INDICA INDICA T T T T T ORS ORS ORS ORS ORS International incoming telephone traffic minutes percapita International incomingtelephone traffic minutes percapita International outgoingtelephone traffic Broadband users users/Internet Internet usersper100 inhabitants PCs per100 inhabitants Residential phonelinesper100 households equippedhouseholdsper100 households TV Gross enrollment ratios Adult rates literacy International bandwidth(Kbsperinhabitant) hosts Secure servers/Internet Internet hosts per1,000 inhabitants subscription per100 households Cable TV Cell phonesper100 inhabitants lines/mainlines Digital Waiting lines/mainlines Main telephone linesper100 inhabitants Tertiary education education Secondary education Primary INFOST INFOST INFOST INFOST INFOST Infodensity Infodensity Infodensity Infodensity Infodensity Info-use Info-use Info-use Info-use Info-use Netw Netw Netw Netw Netw Intensity Intensity Intensity Intensity Intensity Uptake Uptake Uptake Uptake Uptake Skills Skills Skills Skills Skills orks orks orks orks orks A A A A A TE TE TE TE TE 15 15 15 15 15 Chapter 2 - THE FRAMEWORK AND THE MODEL

Chapter 3 OVERVIEW OF RECENT TRENDS

efore the empirical application of the model, it is fitting to have a brief overview of developments that would be useful in establishing the BBperspective against which to interpret the findings. The era of Information Societies is characterized by substantial movement, and a great deal of parallel developments are ongoing for some time now. A substantial part of these is related to ICTs, an area which has generated much excitement and considerable hope. Certainly, substantial progress has been made over the last few years. This progress, however, has been uneven – both across countries and technologies.

One of the most spectacular advances of our times has come from the cell phone. While, overall, wireline telecommunications networks continue to expand, over the last 15 years or so mobile networks and handheld sets have expanded to such an extent as to rival these networks, long-time part of the planet’s landscape (Chart 1).

Chart 1. Cell phones versus mainlines

millions

1,000 Mainlines

800

600

400 Cell phones

200

0 1996 1997 1998 1999 2000 2001

17 Just within the period of examination, from 1996 to 2001, the number of cell phone subscriptions on a global scale increased from 20 per 100 mainlines to 92 (Table 1). (By 2002, cell phone subscriptions had al- ready overtaken mainlines - ITU 2002).

Table 1. Ratios of cell phones over mainlines 1996 1997 1998 1999 2000 2001

Mainlines 738,024,874 792,364,485 845,747,660 904,405,000 980,857,493 1,050,289,871 Cell phones 144,984,882 214,744,940 318,382,755 492,581,789 739,036,475 962,512,345 OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS

Ratio 0.20 0.27 0.38 0.54 0.75 0.92 3 -

This is more impressive considering that wireline telecommunications networks have been around since the 19th century, and that they are Chapter Chapter still growing at a healthy rate. Over the 1996-2001 period, they expanded at an average annual compounded rate of growth of 7.3% to exceed one billion mainlines in 2001. At the same time, waiting lists declined substantially and, on a planetary scale, the networks achieved average digitization of 90%, something that makes possible the offering of value- added services. Even such growth, though, pales in comparison to the 46% annual average of cell phone subscriptions1.

Perhaps more indicative of the impressive diffusion of cell phones is the fact that in many countries the number of cell phone subscriptions already exceeds fixed telephone lines. While this was the case for a couple of countries earlier on (i.e. Cambodia and Finland in 1997 - the former with practically non-existing wireline network, the latter with a highly advanced one), this was the case in 101 countries (of the 192 examined) in 2001, 41 of which over the last year alone. This phenomenon that exemplifies leapfrogging is particularly visible in Africa, where in several instances cell phones outweigh mainlines by a factor of seven or more. Table 2 shows the top countries with a ratio of cell phones to mainlines of two or more. There are several others that have certainly already crossed that threshold by now. In many cases, the jumps have been spectacular.

In parallel, our times have been witnessing the phenomenal introduction and diffusion of the Internet, a technology even younger than the cell phone with less than a decade of life - at least in its commercial incarnation. While the penetration of PCs continues to increase noticeably, as does that of TV sets, cable and other ICTs, the path followed by the Internet is only comparable to that of the cell phone.

1 Subscriptions is a lower number than users, as every line may be used by multiple users.

18 Their trajectories are comparable Table 2. High-ratio countries, 2001 and they are set apart, both cell phones/mainlines exceeding by far the growth of other ICTs. In 2001, the number Congo D.R. 7.5 of Internet users was already half Gabon 6.9 that of cell phone subscribers. Congo 6.8 These movements are shown for Cambodia 6.7 selected indicators, in index Uganda 4.9 form, in Chart 2. Mauritania 4.5 Morocco 4.0 Even more recently, we see Paraguay 4.0 OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS Philippines 3.5 OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS

interest of some scale in the Cameroon 3.1 deployment of technologies for Rwanda 3.0 3 - broadband access, expected to Tanzania 2.9 help materialize many of the Lesotho 2.6 Internet promises. Indeed, Madagascar 2.5 broadband matters and has Côte d’Ivoire 2.5 Chapter started to take hold. So far, Togo 2.5 though, this is happening quite Venezuela 2.4 asymmetrically. The data reveal Botswana 2.2 that in 2001 only 45 countries South Africa 2.2 had broadband users2 and Guinea 2.2 several among them had already Benin 2.1 achieved sizeable penetration Seychelles 2.1 among their Internet users. Chad 2.0

Chart 2. Growth of selected ICTs

index 7

6 Internet users 5

4 Cell phones

3 PCs

2 Int’l traffic

1 Mainlines

0 1996 1997 1998 1999 2000 2001

2 Broadband is defined here to include DSL and cable modems. 19 In 2001, the number of broadband users just exceeded 31 million, 35% of which were in the United States. However, in terms of penetration among Internet users, South Korea topped the list with 32%, followed by Hong Kong (24%) and Canada (21%).

A related issue concerns a country’s international bandwidth. This is shared by everyone and determines the capacity to handle data flows in and out of the country and, consequently, their speed of transmission. Again, the situation here is characterized by enormous gaps between haves and have-nots. Considering the existing architecture on the planet, the distribution of bandwidth is extremely uneven. Over recent years, in particular, several countries are experiencing a bandwidth glut, having OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS

built over-capacity, while others are in dire straits. Obviously, prices are

3 - central to the issue of deployment, but some progress is being made. Among other things, international bandwidth and its architecture impact on the pattern of traffic among countries. In terms of Kbs per capita, the list is dominated by European countries, namely the Netherlands, Chapter Chapter Denmark, Belgium, Sweden, Switzerland, Norway, the United Kingdom, France and Luxembourg. Several African countries barely register on that scale - after many decimals. At the same time, as ICTs increasingly penetrate governments, businesses and people’s lives everywhere, their usage is naturally increasing. Just one indication is provided by the amount of time spent on the phone. Never in history have humans been so talkative! Average combined incoming and outgoing traffic nearly doubled from 1996 to 2001. On average, each person on the planet talked for 20 minutes, up from 12 in 1996. Once again, the differences among countries are huge. Table 3 contains the most and the least talkative countries.

Table 3. Time spent on the phone, 2001 (average minutes per capita) Top Bottom

Luxembourg 908 Gibraltar 796 Burkina Faso 1.8 Bermuda 698 Indonesia 1.7 Ireland 442 Nepal 1.7 Hongkong 431 India 1.7 Singapore 409 Kenya 1.6 United Arab Emirates 384 Mozambique 1.6 Switzerland 349 Nigeria 1.4 Macau 326 Central African Rep. 1.4 Bahrain 307 Madagascar 1.2 Cyprus 301 Bangladesh 1.1 Canada 289 Myanmar 0.7 St. Vincent and the Grenadines 258 Tanzania 0.7 Qatar 256 Chad 0.6 Barbados 253 Uganda 0.5 New Zealand 223 Ethiopia 0.5

20 Outgoing and incoming international traffic are basically the mirror image of one another. Some countries are known to originate many more calls than they help terminate and vice versa, while others have a very balanced traffic pattern. Table 4 shows countries in each group.

Therefore, we have ample evidence for extremely uneven ICT deployment and use among countries, even before we measure the Digital Divide. OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS OVERVIEW OF RECENT TRENDS

Table 4. Ratios of outgoing over incoming telephone traffic, 2001

high around 1 low 3 -

United Arab Emirates 4.1 Brunei Darussalam 1.1 Nigeria 0.3 Bermuda 3.0 Sweden 1.1 Romania 0.3

United States 2.5 Samoa 1.1 Mali 0.3 Chapter Saudi Arabia 2.2 Spain 1.1 Djibouti 0.2 Botswana 2.0 Belgium 1.0 Jamaica 0.2 Norway 2.0 United Kingdom 1.0 Cape Verde 0.2 Hongkong 1.9 Netherlands 1.0 Albania 0.2 Iran 1.8 Central African Rep. 1.0 Cameroon 0.2 Singapore 1.6 Kuwait 1.0 Peru 0.2 Japan 1.6 Chad 1.0 Mongolia 0.2 Luxembourg 1.6 Thailand 1.0 Gambia 0.2 Israel 1.5 Austria 1.0 India 0.2 Guinea 1.5 Argentina 1.0 El Salvador 0.2 Qatar 1.5 Denmark 0.9 Sri Lanka 0.2 Barbados 1.5 Bahrain 0.9 Bangladesh 0.2 Greece 0.9 Sudan 0.2 Ireland 0.9 Myanmar 0.2 Canada 0.9 Honduras 0.2 Costa Rica 0.9 Pakistan 0.2 Eritrea 0.2 Cuba 0.1 Ecuador 0.1 Viet Nam 0.1 Philippines 0.1

21

Chapter 4 EMPIRICAL APPLICATION

hat follows represents the first large-scale application of the model across a great number of countries. These countries represent up to 99% of the population of the planet. The sole criterion for not including the WW remaining countries, either in their entirety or in specific components of the model, has been data availability. Research efforts are already underway to improve the coverage even more in subsequent applications.

4.1 Magnitude of the Digital Divide The results of the empirical application are illuminating, conducive to rich analysis, and address directly the questions we set out to answer. One of the first key questions for which answers are sought is: How big is the Digital Divide? The following results quantify its magnitude through the measurement of countries’ Infostates. As well, analytical insights are derived, starting from Infostate levels and zeroing- in on specific components of interest.

Infostates The model was run and the Infostate figures obtained for 2001 are shown in Chart 3. The results make it immediately clear that the gaps are huge. Top countries achieve index values of 230, while countries at the bottom assume values as low as 5. Literally, many decades of development separate the haves and the have-nots.

Countries with the highest Infostates are from Western Europe (including all Scandinavian countries, the Netherlands, Switzerland, Belgium, Luxembourg, the U.K. and Germany), the U.S. and Canada from North America, Hong Kong, Singapore, S. Korea and Japan from Asia, as well as Australia and New Zealand. Countries at the bottom of the Infostate list are concentrated heavily in Africa, with Chad, Ethiopia, the Central African Republic, Eritrea and Malawi at the very bottom, accompanied by Myanmar and Bangladesh.

23 24 24 24 24 24 Chapter 4 - EMPIRICAL APPLICATION Chart 3. Chart United Arab Emirates United Arab United Kingdom Czech Republic United States New Zealand Luxembourg Infostates, 2001 Netherlands Switzerland Hongkong Singapore S. Korea Germany Denmark Australia Slovenia Portugal Hungary Belgium Sweden Norway Canada Estonia Finland Iceland Cyprus Austria Greece Ireland France Japan Spain Israel Malta Italy 01010202005 0 5 0 250 200 150 100 50 0 250 200 150 100 50 0 Trinidad andTobago Brunei Darussalam Slovak Republic HYPOTHETICA South Yugoslavia Costa Rica PLANETIA Venezuela Argentina Barbados Colombia Mauritius Lithuania Romania Lebanon Malaysia Uruguay Bulgaria Jamaica Panama Bahrain Croatia Mexico Poland Kuwait Russia Turkey Macau Belize Latvia Africa Brazil Qatar Chile Chart 3. Chart Saudi Infostates, 2001 El Salvador Philippines Kyrgyzstan Guatemala Zimbabwe Nicaragua Botswana Honduras Indonesia Sri Lanka Paraguay Mongolia Viet Nam Morocco Thailand Moldova Ecuador Armenia Namibia Senegal Georgia Ukraine Guyana Albania Tunisia Jordan Samoa Bolivia Gabon Arabia Oman China Egypt Cuba Peru Iran Fiji 01010202005 0 5 0 250 200 150 100 50 0 250 200 150 100 50 0 (cont’d) Central African Rep. Central African Papua NewGuinea Burkina Faso Mozambique Côte d’Ivoire Madagascar Bangladesh Cameroon Mauritania Lao P.D.R. Cambodia Myanmar Gambia Tanzania Pakistan Ethiopia Djibouti Uganda Zambia Guinea Nigeria Algeria Angola Malawi Yemen Eritrea Ghana Sudan Kenya Benin Nepal Chad Syria Togo India Mali 25 25 25 25 25 Chapter 4 - EMPIRICAL APPLICATION 26 26 26 26 26 Chapter 4 - THE EMPIRICAL APPLICATION euesres-71--goserlmn nscnaye.()6. 68.5 62.8 grossenrollmentinsecondaryed.(%) 23.9 - 3.2 - 3,476,224 - 721 cellphones/100inhabitants 1,652,974 - 1,838,475 738,320 - - - avg. int’ltraffic(min.) broadband users 5,013,085 1,208,715 Internet users 84,669 4.5 140,017 5,111,652 PCs 755,130 residential mainlines 5,470,249 television households 3,030,239 217,186 bandwidth (Kbs) 3,843,869 servers secure 8,012,021 4.6 Internet hosts 31,590,591 cable 7,318,065 cell phones 24,491,984 digital lines waiting lists mainlines avg. familysize households population with theirown analyticalusefulness. with thatoftheplanet asawhole(Planetia). Several differences emerge, It is,however, instructive to compare thesituationofsuch acountry tobenchmark averages.it isnot customary theresults againstplanetary reference. Whenpoliciesare contemplated, implemented ormonitored, comparisons onourplanet today, are themainunitsof ascountry-states regardless ofitspopulation.To alargeextent, thisdescribeswell everyday carrieseffectivelyIn thiscontext, thesameweight country every existing andnot theaverage data, inexistence. ofeach country andevery that sense,itisthebestaverage thatcanbederived from country the on 139 countries,bothaccountingfor 95%oftheglobalpopulation.In use itisbasedon143 countries,whilefor theoverall Infostate itisbased average of153 countries,representing98%ofthepopulation,for Info- for relatedtoskillsandoverall indicators Infodensity itisbasedonthe of 192 countries,accountingfor 99%oftheplanet’s populationin2001; is: for related indicators tonetworks, Hypothetica represents theaverage on adifferent numberofcountriesfor each component measured. That used intheempirical applicationofthemodel.Consequently, itisbased withvaluesequaltotheaverageHypothetica isacountry ofallcountries 1,8,8 1,610,15 918,983,387 ,4,647,645,778 6,345,,654 ,1,0 5,205,437 3,510,202 9620 60 nul19 2001 1996 annual 96-01 2001 1996 495,758 ,9,9 - 3,291,702 - 8,391,398 A Pr A Pr A Pr A Pr A Pr 4,7 - - 242,370 2,248 ofile ofHypothetica ofile ofHypothetica ofile ofHypothetica ofile ofHypothetica ofile ofHypothetica 6. 46.0 564.0 1. 16.0 110.3 7. 54.2 772.0 46.0 563.9 3. -8.4 -35.5 5211.9 75.2 8.2 3.8 48.3 20.5 218.7 52.1 11.0 7.3 68.7 42.3 RWHINFOSTATE INDICATORS GROWTH . 1.8 1.4 9.5 7.1 avg. atn it/anie 3613.5 90.0 23.6 75.6 digital lines/100mainlines waiting lists/mainlines C/0 naiat . 11.9 6.0 1.3 11.5 1.4 - avg. int’ltraffic(min/capita) broadband users/100Internetusers Internet users/100inhabitants PCs/100 inhabitants 99.5 residential mainlines/100households 95.2 television households/100households gross enrollmentintertiaryed.(%) gross enrollmentinprimaryed.(%) bandwidth mainlines/100 ieayrt % 9481.8 79.4 14.4 3.0 16.6 10.2 - 2.7 literacy rate(%) secure servers/1000Internethosts Internet hosts/1000 inhabitants cable/100 households Kscpt)-0.4 - (Kbs/capita) inhabitants 5625.5 15.6 49.4 66.0 43.5 24.3 60.0 19.8 9823.3 19.8 rabn sr - 517,957,445 138,451 246,293,088 - 352,987,126 avg. int’ltraffic(min.) broadband users 141,757,527 Internet users 3.9 232,073,274 962,512,347 PCs 16,256,512 981,437,198 residential mainlines 26,883,289 television households 144,984,906 1,050,287,870 bandwidth 581,805,970 41,699,801 secure servers 4.0 738,022,878 Internet hosts cable 1,538,308,119 6,065,393,554 cell phones 1,405,068,386 digital lines 5,662,460,963 waiting lists mainlines avg. familysize households population Ks - (Kbs) the perspective. together accountfor one-third oftheplanet’s populationhelps establish being thepopulations.Realizing, for thatChinaand India instance, In effect, Planetia istheweighted average ofallcountries, theweights Hypothetica’s. Examples would becableandInternethosts. than others, Planetia’s indicators exhibit values higherthan combinedpopulationshavewith substantial penetration rates higher telephone mainlines,waiting listsandPCs.Conversely, whencountries hasvalueshigherthantheplanet,where theaverage aswell country as as much asalargecountry. Examples would betheaverage family size, sizeable penetration ratesand,intheaverage sense,they areweighted thoseofHypothetica. trail indicators Many smallcountriesmay have When largecountriesfall below substantially average, Planetia’s both theirlevels of“connectivity”,aswell astheirpopulationbases. so becauseofthesizeabledifference amongcountrieswithrespectto ofHypothetica’s, whereasothertimesthey exceedshort them.Thisis timesthevaluesassumedbyOften individualindicators ofPlanetiafall the samevalues. Hypothetica andPlanetiaare assumedtobethesameandareassigned are ratherthanabsolute measuredinpercentages figures, ratios empirical literacy andgross application.Inskills,where rates enrollment Consequently, itsvaluesarethesumsofall countriescovered inthe Planetia representstheplanet atlarge,ifitwasviewed asonecountry. 8442437119,956,344,502 68,464,264,327 2,2,0 775,610,119 1,139,220,901 523,020,105 945,502,372 38800490,463,619 73,868,010 9620 60 nul19 2001 1996 annual 96-01 2001 1996 1,611,148,326 36,113,180 A Pr A Pr A Pr A Pr A Pr ofile ofPlanetia ofile ofPlanetia ofile ofPlanetia ofile ofPlanetia ofile ofPlanetia 6. 46.0 564.0 1. 16.0 110.3 7. 54.2 46.0 772.0 563.9 3. -8.4 -35.5 838.2 3.8 48.3 20.5 5211.9 75.2 218.7 11.0 52.1 7.3 68.7 42.3 . 1.8 1.4 9.5 7.1 RWHINFOSTATE INDICATORS GROWTH ------avg. gross enrollmentin rabn sr/0 nentues-7.4 - 81.8 avg. int’ltraffic(min/capita) 79.4 broadband users/100Internetusers Internet users/100 PCs/100 68.5 99.5 residential mainlines/100households television households/100households 1.0 62.8 95.2 2.6 gross enrollmentinsecondaryed. (%) - gross enrollmentinprimary ed. (%) 5.7 literacy rate(%) 17.3 bandwidth secure servers/1000Internethosts 13.0 Internet hosts/1000 cable/100 households cell phones/100 digital lines/100mainlines waiting lists/mainlines mainlines/100 inhabitants naiat . 8.5 4.3 inhabitants Kscpt)-0.3 - (Kbs/capita) ihbtns2615.9 2.6 inhabitants naiat . 23.4 2.9 inhabitants naiat . 8.1 1.3 inhabitants tertiary ed. tertiary ed. (%) 9824.3 19.8 2119.8 12.1 50.4 74.1 37.2 67.3 6522.9 16.5 93.4 78.8 27 27 27 27 27 Chapter 4 - THE EMPIRICAL APPLICATION 28 28 28 28 28 Chapter 4 - THE EMPIRICAL APPLICATION 60% tomorethan90%of theaverage.Thisgroupaccountsforabout Group 25countries withInfostate Ccontains valuesranging from Infostates combined, accountfor lessthan18% ofthe g Macau andMalaysia from Asia.Groups AandBwithabove-average from Latin Croatia andLithuania),together withChile,Uruguay andArgentina the Czech Republic, Hungary, theSlovak Republic, Poland, Latvia, Greece), countriesfromtheformer East South European Italy, countries(Portugal, Cyprusand Spain,Malta, less than5%oftheworld’s population,assomeare small.Herewe find is onlymarginallybelow). These24 countriescollectively accountfor Infostates tobeabove theaverage (withtheadditionofLithuania,which advanced countrieswithsufficiently The secondgroup (B)contains of countries. countries andothers, andnot withinthisgroup treatment. Themodelsetoutto quantifythedifferences between these caneasilychange depending ontheexactparticular methodological should not beemphasized. Values andrankingsamongthetopten in Kong, Singapore, S.Korea andJapanfrom Asia.Theirexact positions America, withtheadditionofAustralia, New Zealand,aswell asHong, all from Western Europe (includingallScandinavian) andNorth (or closeto it).They accountfor 13% oftheglobalpopulationandare advanced Infostates, 10 ofwhichha 22countrieswithhighly- contains Thetop,groupA, analytical purposes. Based ontheseresults,the139 countrieswere groupsfor splitintofive similar comparisons). shown. (Alternative approaches couldinvolve or bilateral,regional 1,be identicalto Chart except thatthe 100 would to0-andisnot shift more positive ornegative thenumber, themore so.Theendresultwould performance andanegative numberbelow average the performance; would becomputed. A positive number would indicateabove-average the differences between countries’Infostate values fromHypothetica as thebenchmark against which iscompared. each Practically, country of reference. Oneway to itsmagnitudeistouseHypothetica establish Being thedifference between Infostates, Divideneedsapoint theDigital bottom end(which areobviously boundedat0). distribution (which low isunbounded)relative thevery to valuesatthe by highvalues thevery oftheInfostate index atthetop endofthe average. Thelocationofthemeanvalueindex isstrongly influenced accounting for 18% ofthepopulationhave Infostate values above the population areperforming below average, whileonly45countries in Infostate measurements,94 countriesaccountingfor 82%ofthe the average country, Hypothetica. Specifically, from the139 included It isalsoimmediatelyevidentthatthemajorityofcountriesarebelow America, UAE andBahrain from states andonly the Arab ve valuesmorethantwicetheaverage ern block (Slovenia, Estonia, lobal population. Table 5. of thepopulationthey represent,are shown inTable 5. over 20% oftheaverage. Thesegroups, together withtheproportions Bangladesh andMyanmar. TheirInfostates from range only5%tojust Non-African countriesinthisgroup are Pakistan, Lao,Yemen, Nepal, population, andisfor populatedby Africancountries. themostpart Finally, 30countrieswithabout14% groupEcontains oftheglobal (Botswana, Zimbabwe, Senegal,Togo, C Saharan Africa,withabove-average performance for thecontinent African countries(Tunisia, Egypt,Algeria) andseveral othersfrom sub- Armenia, Kyrgyzstan, MongoliaandVietnam). We North alsofind Paraguay, etc.), Honduras aswell Nicaragua, asAsiancountries(Iran, American countriesarehere (Ecuador, ElSalvador, Guyana, Bolivia, populations, such asthePhilippinesandIndonesia.Many Latin because thisgroup Chinaandothercountrieswithsizeable contains below incountriessubstantially live masses theaverage. Thisisso and 60%oftheaverage. the populationofplanet. TheirInfostates are between one-quarter Group D,isagroupof38countriesthataccountsformorethanhalf Mauritius) andonlyThailandfromAsia. and SaudiArabia),acoupleofAfricancountries(SouthAfrica Colombia andPeru), countries(Qatar, Arab Kuwait, Jordan, Oman Rica,Panama, Costa Venezuela,with LatinAmericancountries(Brazil, 13% ofthepopulationandrepresentsageographically diverse group Ttl 199. 100.0 95.4 139 Total Country groups Country ru Number ofcountries Group 01. 14.6 53.6 13.3 4.9 13.9 51.1 13.6 12.6 4.7 13.0 30 38 25 24 22 E D C B A This group is This group is indicative of that ô te d’Ivoire andGambia). ol Infostate world % ofpopulation huge population 29 29 29 29 29 Chapter 4 - THE EMPIRICAL APPLICATION 30 30 30 30 30 Chapter 4 - THE EMPIRICAL APPLICATION Infodensities. Malaysia withabove-average Info-use values butbelow average Argentina andChile.Thereverse isthecasefor and Macau,Bahrain Lithuania andCroatia, andamong LatinAmericancountries,Uruguay, Republic, Slovenia, Estonia, Hungary, Poland, Slovak Republic, Latvia, Infodensity thanInfo-use are, amongEastern Europeans, theCzech African countriesbeingpresent.Countrieswith highervalues in comparatively better. Smalldifferences exist atthebottom, withmost Canada, HongKong, Singapore, S.Korea andtheU.S. are doing and Scandinavian countriesdominate Infodensity, whereas inInfo-use aggregate are different. not Amongthetopgroup,Netherlands very Overall, thegapsbetween thetop andbottom countriesineither main aggregates arequitesmall. also inthebottom 30for Infodensity. So,thedifferences acrossthetwo (Group E)are inthebottom30of Info-use, andonlyone(Kenya) isnot lowvery endofthetophalf.Allbottom 30countriesinInfostate and Info-use moved highendofthebottom from thevery halftothe Similarly, thosemoving upfrom thebottom to thetop halfinInfodensity individual components ofboth Networks andUptake. Moreover, 20ofthetop22countries inInfostate are inthetop22of which isnotinthetop 22ofInfodensity, butdropsslightlyto25th. 22 ofboth Infodensity andInfo-use. Theonlyexception isS.Korea, ingroupAaccording toclassified theirInfostate valuesareinthetop well 2and3).Morespecifically, 21 asAnnex Charts ofthe22countries the countrieswithingroups remainlargelythesame(seeTable 6,as with low orintermediate Infostates. Althoughtheexact rankingchanges, in one,itishighboth applytocountries oftheothers. Similarfindings between Infodensity, Info-use andoverall Infostate. ishigh Ifacountry Generally, there isahighdegreeofconsistencyincountries’rankings average inInfodensity, thisisthecasefor 52countriesinInfo-use. more concentrationinthemiddle,andwhile43countriesareabove the higher andthebottom are lower. At thesametime,thereisslightly greater thanthatofInfodensity. Boththetopvaluesofindex are that therangebetween haves andhave-nots inInfo-use issomewhat in eitherofthemare not different. substantially We however, doobserve, and Info-use contribute theDivide.Theorders to ofmagnitudeinvolved multitude ofsources. To beginwith,both mainaggregates ofInfodensity The huge Digital Divide identified above Divideidentified comesfromThe hugeDigital a Infodensity andInfo-use 4.2 CausesoftheDigitalDivide Table 6. mn64.3 64.7 66.9 67.8 69.1 70.8 Oman Thailand 72.3 72.6 Jordan 72.9 Colombia Yugoslavia 73.4 Jamaica 75.5 Venezuela 74.5 79.9 Panama 83.0 Russia Romania South Africa Belize Turkey Mexico ugra86.8 87.4 Costa Bulgaria Lebanon uat88.1 90.8 Kuwait Trinidad and Qatar rzl91.6 91.2 Barbados Brazil artu 92.5 98.7 101.5 Mauritius Lithuania Malaysia rai 102.3 104.8 107.2 Croatia Latvia Poland retn 107.9 Argentina hl 110.8 109.9 Uruguay Chile aa 111.2 Macau lvkRpbi 111.7 Slovak Republic Brunei Darussalam ari 116.3 121.2 Bahrain Greece ugr 122.6 122.6 Emirates United Arab Hungary yrs128.0 129.0 Cyprus Czech Republic soi 140.5 Estonia at 143.4 Malta pi 144.3 Spain tl 148.5 Italy lvna149.1 Slovenia otgl151.7 Portugal sal159.0 Israel rne168.9 France rln 175.6 Ireland aa 178.7 Japan .Kra183.8 S. Korea uti 184.7 Austria utai 189.5 185.2 New Zealand Australia ntdKndm190.2 United Kingdom emn 191.8 Germany igpr 194.3 Singapore cln 195.6 Iceland uebug197.1 Luxembourg iln 199.5 Finland ogog202.6 Hongkong egu 202.6 Belgium owy214.4 Norway wteln 216.8 Switzerland ntdSae 217.9 United States ehrad 224.2 Netherlands aaa224.8 Canada emr 230.0 Denmark wdn230.5 Sweden YOHTC 0. -100 0. - 100.0 - 100.0 - 100.0 HYPOTHETICA PLANETIA ia86.0 Rica Infostates andrankings,2001 Tobago ne akIdxRn ne Rank Index Rank Index Rank Index 113.4 0. -9. -127- 102.7 - 98.5 - 100.6 nott noest Info-use Infodensity Infostate 90.6 69 68 67 66 65 64 63 62 61 60 59 58 57 56 55 54 53 52 50 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 32 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 10 11 8 8 7 6 5 4 3 2 1 100.4 104.5 124.1 132.7 104.6 133.0 103.9 146.0 136.3 137.1 142.3 140.4 144.3 155.5 153.5 163.2 172.6 162.9 145.6 174.3 174.8 186.5 196.2 181.8 155.8 180.5 183.1 215.9 161.9 201.7 223.8 210.6 199.5 232.6 194.6 222.9 228.2 115.9 116.1 114.3 114.8 116.1 111.6 59.6 68.0 64.4 68.1 70.3 71.2 69.5 81.4 77.6 79.7 81.8 70.6 76.8 90.2 71.1 94.4 83.4 75.5 86.8 70.9 82.1 96.9 79.7 86.0 87.2 94.2 73 69 70 66 64 60 65 54 57 55 53 63 58 47 61 45 51 59 49 62 52 44 55 39 50 43 36 34 38 37 41 48 34 33 46 32 40 31 42 24 30 29 27 28 26 22 23 18 17 19 25 16 15 13 21 14 12 20 10 11 9 5 7 3 6 8 1 4 2 103.9 102.9 101.4 107.4 104.3 101.8 105.3 141.8 107.5 103.7 143.6 143.8 157.6 144.9 150.0 146.3 157.2 154.1 148.0 164.6 174.8 178.7 196.1 232.0 195.8 196.1 192.5 184.4 202.3 242.3 212.1 184.4 253.4 203.6 205.4 223.1 237.9 216.1 259.8 237.4 232.8 116.2 119.7 117.3 110.7 113.1 113.9 211.9 69.4 61.6 69.5 67.5 67.9 70.3 75.3 64.7 68.6 67.6 67.9 80.7 83.1 76.3 79.9 91.6 94.5 86.6 87.3 94.7 99.0 62 72 61 67 64 60 59 70 63 65 64 56 55 58 44 57 52 46 51 36 48 54 41 53 34 43 50 49 47 42 35 33 40 45 32 39 31 38 24 37 30 27 29 25 26 28 23 22 21 15 17 15 18 19 14 10 19 13 12 11 7 3 2 8 4 9 1 5 6 yi 26.6 21.9 Pakistan Syria ey 21.0 26.8 Kenya Gambia au e una20.2 Papua NewGuinea Côte d’Ivoire artna20.2 Mauritania ni 27.9 India jbui20.1 Djibouti oo28.4 Togo aeon18.7 Cameroon hd5.2 6.1 6.5 Chad Ethiopia 7.8 Myanmar Rep. Central African 10.5 Mozambique lei 29.0 Algeria aba18.6 Zambia rte 8.5 9.5 9.9 10.6 Eritrea Malawi Mali Angola abda11.2 11.5 12.1 13.5 Burkina Faso 12.9 Cambodia 14.3 Uganda Guinea Madagascar Sudan Tanzania itNm29.2 29.4 Viet Nam Senegal agaeh9.5 Bangladesh 17.0 Yemen Lao P iei 14.4 Nigeria ibbe30.2 Zimbabwe ea 14.5 15.4 16.2 Nepal Benin Ghana Sri Lanka lai 32.2 32.4 Albania Cuba ao 33.5 33.7 37.5 37.6 38.4 40.4 Gabon 38.6 Honduras Morocco 41.3 Indonesia 41.9 44.5 Nicaragua Mongolia 45.0 47.0 Egypt 48.0 Kyrgyzstan 49.0 48.9 Guatemala Armenia 49.4 Paraguay Bolivia 50.3 Tunisia 51.4 50.4 Moldova Iran 53.7 Guyana 53.9 Botswana 54.1 Philippines 54.7 China 58.4 58.2 El Salvador Namibia 61.8 Ecuador Samoa Georgia Ukraine Fiji Saudi Arabia Peru DR 17.3 .D.R. ne akIdxRn ne Rank Index Rank Index Rank Index nott noest Info-use Infodensity Infostate 26.9 10.7 31.7 51.9 61.5 109 108 107 106 105 139 138 137 136 131 104 135 133 132 130 129 128 127 126 125 124 123 103 102 133 122 101 121 120 100 110 112 112 114 115 116 117 118 119 111 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 19.1 26.0 21.0 26.1 29.2 19.5 28.5 21.4 25.2 16.9 10.2 25.6 21.4 14.9 10.5 12.8 19.7 17.9 14.9 17.4 18.2 26.9 23.7 23.6 21.3 14.9 35.5 17.2 17.3 20.8 39.6 19.4 42.1 28.2 44.8 32.8 36.4 40.9 38.3 43.7 37.6 46.0 48.3 43.9 56.3 52.5 37.7 53.4 38.4 40.4 60.7 57.1 48.5 48.8 58.1 52.6 68.1 58.7 68.1 61.1 57.7 57.3 11.6 11.4 7.3 7.4 8.1 6.6 9.4 8.5 106 105 101 102 108 125 138 137 136 133 107 139 126 130 132 129 121 126 122 134 120 104 109 135 131 126 124 123 103 100 119 114 117 116 110 113 115 118 111 111 99 93 90 87 98 91 95 89 97 86 85 88 79 82 96 80 94 92 72 78 84 83 75 81 66 74 66 71 76 77 37.1 23.0 17.0 27.7 24.8 20.9 27.4 19.0 31.9 23.9 32.9 16.3 10.9 10.8 19.4 31.7 36.3 14.6 14.1 10.6 14.0 25.6 16.8 12.1 25.3 13.5 24.6 37.2 25.0 38.8 34.5 38.4 34.1 43.5 37.1 36.4 45.2 35.9 42.0 61.0 44.7 62.4 60.4 41.8 44.5 54.5 55.2 49.6 55.2 42.9 50.9 49.6 55.9 65.5 66.6 34.7 11.2 11.5 3.7 5.1 5.2 6.0 6.1 9.5 8.9 6.4 7.4 9.9 9.6 8.6 103 108 104 101 100 139 138 137 136 125 126 102 135 127 130 131 134 133 128 129 124 120 105 132 122 106 123 121 109 107 115 112 114 110 117 113 118 119 116 111 92 95 91 89 98 90 98 85 92 94 82 96 87 73 83 71 74 88 84 78 76 80 76 86 79 80 75 69 68 97 31 31 31 31 31 Chapter 4 - THE EMPIRICAL APPLICATION 32 32 32 32 32 Chapter 4 - THE EMPIRICAL APPLICATION N are revealed. of thistypeanalysis,relative strengths andweaknesses differs widelybothacross indicators –contributes, indicators – andindeedeach measuredby area specific uptake andintensityofuse.However, theextent towhich each ofthese responsible.Thisincludesnetworks,Infostate ispartially skills,ICT be noted fromtheoutset thateach constituentcomponent andevery of are associatedwithindividualcomponents ofmeasurement.Itshould Dividewhich, causesoftheDigital inevitably,we canidentifyspecific denoting a greater variabilitythantheoverall indexes. the top andbottom within each groupare much largerinNetworks, unveiling anadditionaldimensionto thedivide.Thedifferences between are alsopresent groups withineach examined previously, ofthefive with thelargerdividewhennetworks are concerned.Theincreased gaps that ofthehigheraggregatesInfodensity andInfo-use andisconsistent are above theaverage. much Thisrepresentsaproportion lower than From the192 inthecomputations, only49countries countriescontained 1). Annex Chart index, together withselectedindicators, are shown inTable 7(seealso the countrieslaggingbehindarelargeones.Thevalues for theNetworks Hypothetica hasavaluehigherthanPlanetia, indicatingthatsomeof involvedthe distances inthedeployment ofnetworks inthesecountries. times lower thanthetop.Thesegapsare tellingandclearlyindicate very values for networks lessthan1%oftheaverage andmorethan400 bottom Thevery represents thetail. Bangladesh andHaiti,carrying top, andAfricancountrieswiththeadditionofMyanmar,the very with theNetherlands, Scandinavian countriesandSwitzerlandoccupying by afew points-asitisboundedatzero. Theusualculpritsdominate, top increases by more than150 pointswhereas thebottom isonlyfalling While both thetop endismoving higherandthebottom endlower, the much greaterthanithasbeeninany oftheaggregates examined sofar. between top andbottom values,asmeasuredby theNetworks index, is between haves isthatthegap thingtoobserve andhave-nots. Thefirst the case,though(for seeIndia). instance, (48.5) andMexico’s much moreso(90.2vs. 76.3). Thisisnot always China’s Info-use index (54.5) issixpointshigherthanitsInfodensity with lower levels ofInfodensity dousewhatthey have. For instance, above itinInfo-use -albeitmarginally. Thisindicatesthatlargercountries It isnoteworthy thatPlanetia isbelow Hypothetica inInfodensity, but ETWORKS : Networks areamajorcontributortotheoverall Divide Digital Looking beneaththeaggregates ofInfodensity andInfo-use Component analysis and acrosscountries.Intheprocess networks. Intotal, 69of192 countriesare above average. we Tajikistan alsoencounterBhutan, andCubawithunderdeveloped indicators. At thebottom, inadditionto thecountriesmentionedearlier, are closetothetop andfare comparatively much better thaninother this list, whilecountriessuch asLuxembourg, andAustria Israel,Italy ranked by mobilenetworks, to emerge.Taiwan differences start tops mobile networks. Althoughthegapsremainatsamescalewhen done better. However, many developing countrieshave leapfrogged to infrastructure andsubscription base, developed countrieshave again have entered latelyinthefray theirwireless andexpanded significantly phone technology andtheInternet. Whilemany developing countries At thesametime,anewer ofnetwork part gapsiscreated by thecell proceeding quickly. usage. Overall,waitinglineshavedecreasedanddigitizationis Internet connections,faxlines–oraccommodationofexpanded the explosion ofdemandfor new lines,includingmultiplelines–for progress indeveloped hasbeensteady countriestoo.Thisissodueto (ITU 2001). Someprogress indeveloping countriesnotwithstanding, telecommunicationsnetworksfixed arewell-documented over theyears the ageofsuch networks. Discrepanciesamongcountriesrelated to Cambodia from Asia.75countriesare above average, though,indicating meet Congo,Liberia,Chad, NigerandRwandafrom Africa,and and thechannel islandsofGuernsey andJersey. At bottom thevery we countries overall. TheseincludeGibraltar, Bermuda,theCayman islands countries have quite densenetworks, advanced denserthanmore range from to practically zero. morethan400 Inwireline, several smaller Ranking countrieswiththewireline telecommunicationsindex yieldsa in Table 7). networks andtheInternet arebasedonmoreindicators thancontained shown inAnnex Table 1. (Bearinmindthattheindexes for wireline newness, variability Divide.They andcontribution totheDigital are Internet arecomputed andanalyzed,reinforcing thecorrelation between hold truewhentheindicators for wirelineandmobilenetworks andthe nextcountries withpenetration tonothing). Byandlarge,thesefindings higherthanHypothetica, indicativesignificantly ofthelargenumber (Here, unlike thecaseinoverall networks, Planetia hasavalue varies widelyamongcountries,variability iseven largerinInternet hosts. Internet. ofmainlinesandcellphonesubscriptions While penetration relatively morepronounced inthenewer the technologies, particularly cell phonesubscriptions andtheInternet reveals thatthegapsare Analysis atthelevelofindividualindicatorsfortelephonemainlines, 33 33 33 33 33 Chapter 4 - THE EMPIRICAL APPLICATION 34 34 34 34 34 Chapter 4 - EMPIRICAL APPLICATION rga 0. 831. 21.1 15.5 8.7 28.3 50.0 13.5 11.9 104.4 40.1 102.6 35.0 16.8 75.1 35.7 38.0 New Caledonia 1 13.3 1 33.7 120.3 Uruguay 52.9 49.8 Slovak Republic 84.9 45.5 46.1 11.7 43.8 73.4 Emirates 14.8 United Arab 37.5 134.2 122.7 35.4 Aruba 43.4 29.9 135.9 88.3 62.1 Cayman Islands 14.8 137.9 Brunei Darussalam 138.1 46.7 143.7 47.1 Greece 48.6 24.2 18.1 73.7 Andorra 22.3 Hungary 147.4 150.3 149.3 Estonia 40.2 50.2 70.4 61.1 Spain 79.9 Greenland 23.9 22.1 87.5 84.8 153.6 S. Korea 53.0 20.6 Italy 46.1 13.3 77.4 159.5 156.4 90.7 Virgin 160.1 Islands(U.S.) 55.9 166.7 Slovenia 86.9 35.6 Faroe Islands 42.5 46.6 60.5 47.9 Guernsey 58.8 33.4 178.6 Jersey 89.2 179.1 174.0 57.3 40.1 179.6 Malta 72.4 58.6 Czech Republic 77.4 118.0 185.1 Bermuda 194.1 81.7 47.1 57.6 200.3 Portugal 190.5 48.5 57.4 Israel 29.4 46.8 Gibraltar 207.6 85.9 86.5 France 217.4 54.1 211.8 68.2 Japan 222.9 58.0 66.4 Singapore 34.1 224.5 106.4 96.3 New Zealand 63.4 Ireland 241.0 241.4 74.7 Austria 46.2 36.2 251.7 372.9 Australia 170.7 31.4 49.8 82.5 Hongkong 60.0 67.6 Iceland 45.1 252.5 80.4 92.0 72.8 Germany 272.5 253.2 104.8 79.0 262.6 Taiwan, China 275.3 66.7 54.8 United Kingdom 78.0 72.8 73.9 74.0 Liechtenstein 163.4 282.2 Belgium 310.8 301.0 73.2 Canada 72.2 335.2 76.7 United States Luxembourg 340.8 343.5 Finland 62.1 344.5 Sweden Switzerland 378.9 Denmark Norway Netherlands Table 7. Networks, 2001 ewrsFixed Networks ne 10/0 /1000 /100 /100 index 3. 594. 25.5 40.1 25.9 132.6 22.6 37.5 63.5 153.5 222. 9913.5 15.4 39.9 61.6 28.9 34.0 12.2 17.6 313. 21.5 31.0 23.1 35.8 59.4 21.1 55.6 67.9 37.8 106.9 59.9 47.7 76.4 37.1 96.6 77.0 57.3 58.7 100.5 81.5 73.2 oieInternet Mobile aii 7264552.5 0.5 0.8 1.2 5.5 11.3 1.3 1.8 18.8 30.0 6.4 12.3 14.5 2.4 7.6 12.4 8.5 9.9 1.8 208.1 37.2 36.8 9.0 5.3 17.2 37.8 0.9 0.2 39.4 5.4 0.1 Saudi Arabia 42.9 0.2 43.2 24.3 Namibia 10.9 26.3 Botswana 43.9 0.6 1.4 19.7 44.0 Thailand 29.3 1.3 45.4 10.9 Colombia 2.1 40.0 Oman 24.4 15.9 27.5 1.5 45.6 Samoa 0.5 10.9 46.1 Russia 20.5 7.6 14.3 46.9 2.1 Tonga 26.3 18.7 19.8 Venezuela 48.5 49.1 23.0 Bahamas 46.5 2.7 22.9 17.2 0.4 T.F.Y.R. 48.1 Macedonia 49.6 Qatar 2.1 49.8 18.4 1.5 Jamaica 52.0 16.4 1.6 43.4 Belize Costa Rica 54.9 22.9 13.0 38.6 Yugoslavia 39.4 5.4 2.6 55.1 29.5 Barbados 3.1 18.7 58.4 French Guiana 20.8 3.3 60.2 Romania 28.5 24.2 22.7 0.9 0.9 Puerto Rico 61.3 62.0 31.4 Dominican Rep. 19.1 62.8 11.1 25.6 Panama 20.7 19.8 71.5 Macau 9.1 1.0 35.9 63.2 Lebanon 63.6 2.6 9.6 Kuwait 69.7 50.9 43.0 70.3 Turkey 21.7 63.6 South Africa 3.2 46.0 71.7 71.2 16.7 Mauritius 4.7 13.7 8.0 45.7 Malaysia 26.7 21.8 Bulgaria 7.3 53.9 75.0 9.6 Martinique 72.6 3.0 37.7 34.2 77.2 Guam 77.2 26.1 Trinidad and Tobago 28.5 10.6 38.3 27.7 23.3 Guadeloupe 45.6 80.8 Mexico 12.8 22.3 12.7 87.3 27.9 31.3 Bahrain 92.8 63.1 Brazil 84.8 19.3 Seychelles 30.7 25.9 93.7 94.0 French Polynesia 23.4 Croatia 22.4 98.6 29.5 97.0 Chile 15.9 Lithuania 98.7 99.2 Cyprus PLANETIA 17.3 Latvia Argentina 100.0 Poland HYPOTHETICA ewrsFixed Networks ne 10/0 /1000 /100 /100 index 412. 960.6 4.8 39.6 26.8 14.6 54.1 11.0 56.9 5.3 19.7 24.0 72.1 463. 0.4 31.6 34.6 23.4 15.9 17.3 oieInternet Mobile Table 7. ni 1238060.1 0.3 0.0 0.6 0.2 0.1 6.9 0.9 3.8 0.0 0.3 8.0 9.9 0.1 3.1 0.2 11.2 0.0 3.6 0.6 0.0 11.0 11.2 2.5 4.1 1.7 0.3 0.0 4.7 4.0 4.2 Turkmenistan 8.7 India 13.1 2.6 11.9 0.0 Maldives 2.9 4.3 10.9 13.5 13.5 Solomon Islands 9.2 0.1 Marshall Islands 13.9 3.2 14.5 2.2 10.4 Senegal 0.9 0.0 Honduras 15.1 8.1 Kiribati 0.2 15.2 16.9 15.2 Gambia 0.4 0.5 9.9 Tunisia 0.6 5.2 16.2 Guyana 3.1 0.0 0.1 Zimbabwe 7.8 5.0 3.0 Egypt 0.7 0.1 17.1 3.5 Côte d’Ivoire 57.6 20.5 2.9 17.2 17.4 Sri Lanka 14.0 0.1 0.3 Iran 16.4 17.4 41.0 3.0 Mongolia 0.2 0.2 18.2 Kyrgyzstan 18.3 4.1 1.7 1.4 Albania 18.7 19.0 Indonesia 9.4 9.4 1.1 Nicaragua 20.9 0.1 31.7 28.8 Armenia 6.3 Reunion 12.0 0.4 23.3 5.4 23.4 Gabon 11.0 Morocco 24.2 0.7 0.3 23.8 Cape Verde 3.1 13.7 5.1 Saint Lucia 0.1 0.5 Belarus 0.4 3.6 6.7 Azerbaijan 25.3 24.7 14.6 0.4 Bolivia 19.8 5.9 theGrenadines 0.6 12.1 10.4 15.0 St. Vincent and 25.9 Swaziland 17.6 6.1 0.1 7.8 China 26.1 4.2 27.3 0.8 9.7 El Salvador 27.8 17.4 Moldova 28.4 6.4 1.2 28.0 0.3 Kazakhstan 6.5 9.9 0.4 Ecuador 29.6 32.8 Suriname 4.4 11.2 31.4 6.2 Philippines 32.2 16.7 Peru 32.3 Georgia 21.2 36.1 42.9 Guatemala 12.9 Paraguay 36.2 Grenada 35.1 36.5 Islands Marianas Northern Fiji Ukraine Jordan Networks, 2001 ewrsFixed Networks ne 10/0 /1000 /100 /100 index 2177090.1 0.9 7.7 12.1 0.2 0.1 4.5 3.6 1.8 4.4 15.8 16.0 0.1 7.2 0.0 14.3 6.5 22.2 0.1 22.7 13.4 24.5 10.2 25.9 (cont’d) . 040.5 20.4 5.1 oieInternet Mobile yna . . . 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.6 0.8 0.0 0.3 0.0 0.1 0.0 0.0 0.4 0.8 0.4 0.0 0.8 0.4 1.4 0.1 1.3 0.3 0.0 1.2 0.3 0.0 1.1 1.3 Myanmar 1.6 0.2 0.0 Eritrea 0.0 0.2 0.0 0.0 1.6 1.0 1.7 0.6 Bangladesh Chad 0.0 0.0 1.7 1.6 0.5 2.0 Ethiopia 0.6 0.3 0.9 2.3 Sudan 0.0 0.0 Burundi 0.3 0.0 0.4 0.5 0.0 Congo D.R. 0.2 0.5 2.6 2.6 Liberia 0.0 0.5 0.1 0.0 0.5 Niger 1.2 3.1 1.2 Haiti 2.4 2.9 3.4 Rep. Central African 0.8 3.6 3.5 0.0 4.1 0.1 0.2 0.0 Angola 10.3 0.1 Mozambique 0.0 2.2 Malawi 4.4 1.3 1.0 4.6 4.6 0.9 Bhutan 0.0 0.5 Nigeria 0.7 4.7 Mali 0.4 0.0 4.8 1.2 Tajikistan 0.3 0.5 0.3 0.1 Uganda 0.0 5.0 Syria 0.5 5.5 6.7 0.0 Yemen 5.5 0.1 0.1 5.5 0.2 0.6 Nepal 0.5 0.1 0.0 1.0 Madagascar 5.7 2.0 Sierra Leone 0.8 6.0 0.5 0.2 0.0 1.5 0.0 Ghana 6.3 0.1 1.3 Guinea 0.0 0.7 0.0 0.3 Uzbekistan 1.2 6.4 6.6 1.5 Cambodia 0.3 0.1 5.1 0.4 0.1 Lao P.D.R. 2.6 6.7 0.0 6.9 6.5 4.8 Burkina Faso 3.8 6.1 0.0 6.9 Papua NewGuinea 6.9 1.1 1.9 1.0 Djibouti 0.1 2.6 0.7 0.1 Cameroon 7.3 7.4 0.8 0.9 Rwanda 0.9 7.9 0.0 Tanzania 1.0 8.0 0.6 1.9 Cuba 10.9 8.7 Viet Nam 9.0 9.1 4.3 Algeria 2.3 1.0 9.1 Lesotho Congo 1.0 9.1 Zambia 9.2 Benin Togo 9.8 Libya Pakistan Kenya Mauritania ewrsFixed Networks ne 10/0 /1000 /100 /100 index oieInternet Mobile . 0.0 0.4 0.0 1.7 35 35 35 35 35 Chapter 4 - EMPIRICAL APPLICATION 36 36 36 36 36 Chapter 4 - THE EMPIRICAL APPLICATION Annex Table 2). educationareshown in Tableand tertiary 8.(For individualindicessee forindicators andgross literacy rates enrollmentinprimary, secondary positions thanthey did innetworks. Theskillsindex, together withthe Estonia, Lithuania,Belarus,Ukraine andBulgariatooccupy higher concerns theriseofEastern Europeancountries,Russia, Latvia,Slovenia, differences compared intherankings tonetworks. One observation to alow of18.5, with 85of153 countriesabove theaverage. There are Differentiation iscomparatively low, from ranging ahighvalueof155 exclusivelycome ontop, whileatthebottomwe find Africancountries. In theoverall index, Sweden, Australia, theU.K., FinlandandGermany much differentiation isoffered by more generic indicators. trueamongdeveloped countries,wherenot Thisisparticularly tertiary. gaps increase aswe move to moreadvanced levels ofeducation,especially ofICTs.with thepervasiveness Thisisreinforced by thefact thatclearly underestimate thegaps,toextent thatICTskillsmove inparallel usedareanalysed indetail, clearlytheindicators such thatthey somewhat thesevere gapscausedbynetworks. not Whileskillsare this reason,inthecomputations oftheindices, ICTskillsmoderate information for sufficient stage, agoodassessmentdoesnot exist. For considering thatwork early onmeasurementsinthisareaisatavery S do not have such networks atall. contributes to alesserextent totheDivide.However, many countries are not anywhere widespread yet. Cableisanadditionalnetwork that represented. However, thesetechnologies thatrelate to e-commerce, do not reveal agreat deal,butmany aredisproportionately islandstates index for explained reasons inthemethodology section.Secureservers much intheactual Divide,itisnotdeteriorates reflected theDigital skewed –even withinthecontext ofdeveloped countries.Whilethis Related to thatistheissueofinternationalbandwidth,which isextremely access untilfairly recently. Itisexpanding, though. Divide. Many countries,especiallyinAfrica,didnot have any measurable a revolutionary new technology butalsoasamajorsource oftheDigital have highvalues.Therefore, very theInternethasemergednotonlyas 44 ofthe192 countriesare above average –andmany ofthosethatare Divide,comesfromcontributes moreto thefact theDigital thatonly SyriaandVietnam. Anadditionalindicator ofhowalso find the Internet are practicallynon-existent. Inadditionto Africancountries,herewe barely register anything above zero. Inthesecases,Internet networks have valuesthatcouldexceed 1,000, whereasagoodnumberofcountries becomes really extreme. Top countries(U.S., Iceland,Finlandetc.) By far, thelargestdividecomesfrom Internethosts, wheretherange KILLS : ICT skillsarealsoacauseoftheDigitalDivide.However, Table 8. aahtn1039. 888. 30.9 16.5 88.5 108.5 52.1 41.1 162.3 98.8 36.1 84.3 85.6 87.3 40.8 99.4 99.0 121.6 98.1 40.0 120.3 120.1 103.8 101.4 43.3 94.2 Slovak Republic 98.6 94.1 109.4 97.0 99.0 38.2 105.2 Kazakhstan 103.2 126.2 123.2 97.6 122.4 50.5 42.1 49.9 Brazil 102.0 98.5 78.0 101.6 126.3 Czech Republic 52.7 98.4 99.3 126.9 99.6 95.9 Kyrgyzstan 99.6 110.1 128.3 Macau 93.3 46.3 128.3 99.3 99.7 107.3 100.9 Uruguay 48.0 56.0 129.8 Bulgaria 99.0 97.3 99.0 113.9 98.5 47.7 Hungary 96.7 131.1 130.1 131.2 95.1 84.4 Ukraine 52.5 103.8 102.5 57.5 131.2 Barbados 120.1 99.0 108.6 Switzerland 48.7 95.2 100.8 91.7 96.9 131.3 Greece 99.7 50.2 99.0 132.3 Italy 108.7 60.5 101.3 132.4 55.5 103.0 Israel 132.5 113.6 99.6 57.7 102.3 Germany 92.2 99.8 63.1 101.4 132.9 Argentina 121.2 99.0 134.5 99.0 64.1 Belarus 100.2 135.0 90.9 99.6 92.5 Japan 47.5 99.6 135.1 83.3 103.7 Lithuania 53.6 99.7 100.3 135.5 109.1 60.0 Estonia 99.0 135.9 116.7 59.4 99.8 107.8 Iceland 136.3 119.4 102.6 Portugal 136.3 99.6 115.6 104.9 Slovenia 99.0 136.8 98.6 Poland 99.0 137.0 105.1 Austria 137.2 99.0 55.0 97.7 Latvia 137.6 58.9 140.9 Russia 124.5 Ireland 57.0 128.2 97.9 France 107.5 73.9 142.1 141.1 Canada 147.1 101.9 99.0 Spain 126.0 63.3 142.8 99.0 99.0 United States 105.0 70.0 144.6 101.6 144.3 S. Korea 99.0 145.4 160.8 Netherlands 99.0 148.8 149.3 New Zealand 102.1 150.0 Denmark 109.9 99.0 Norway 99.0 99.0 154.9 Belgium 155.4 151.9 Finland United Kingdom Australia Sweden Skills, 2001 klsLiteracy Skills ne (%) index 901109. 72.6 95.2 101.0 70.0 99.0 114.6 101.4 99.0 Primary Secondary TertiaryPrimary Secondary 0. 7330.3 87.3 29.8 103.0 94.6 104.3 77.6 94.1 101.1 9912469.2 112.4 99.9 59.5 156.4 98.9 Enrollment (%) cao 0. 181505. 17.6 0.9 57.4 74.0 13.5 15.1 115.0 128.1 91.8 70.0 78.3 16.4 93.4 101.3 101.5 110.4 83.3 20.2 107.0 10.9 93.2 95.7 85.3 8.0 73.3 99.6 Ecuador 103.5 102.0 102.0 84.0 75.5 Belize 87.3 101.8 59.6 28.5 78.2 Dominican Republic 104.3 102.9 28.2 Costa Rica 24.6 98.5 59.3 143.8 Albania 98.7 105.4 70.3 Fiji 99.0 89.0 105.7 23.3 101.9 11.6 Jamaica 105.9 20.7 98.7 Trinidad and 92.8 T 69.8 104.6 73.4 Armenia 105.9 27.4 85.6 87.9 75.3 81.7 Samoa 112.4 14.0 105.9 106.0 27.9 119.7 107.2 Gabon 71.9 113.2 91.9 Venezuela 78.5 98.6 71.2 9.3 108.1 33.3 South Africa 91.4 94.3 108.3 Malaysia 24.8 108.5 104.3 21.5 83.8 61.1 93.5 Qatar 94.4 99.3 42.3 83.9 Colombia 108.8 88.9 99.0 109.1 98.8 Guyana 34.9 100.9 110.1 28.6 75.7 Mexico 90.6 106.3 99.0 35.7 98.5 69.2 Hongkong 24.2 111.4 87.7 111.7 92.3 95.5 98.9 Tajikistan 20.0 79.6 112.7 112.0 111.6 Moldova 84.5 86.5 100.8 Luxembourg 25.2 93.4 92.1 29.0 115.9 113.5 Mongolia 90.3 28.8 101.9 113.6 86.0 Bahamas 101.3 113.7 82.1 96.6 96.8 27.3 Malta 80.8 113.9 114.6 103.2 Lebanon 97.2 91.3 34.5 82.3 Panama 127.6 114.9 87.9 Jordan 98.4 115.1 90.2 72.9 31.2 98.8 Bolivia 43.8 115.4 115.7 35.3 Cuba 37.5 77.3 98.2 95.5 Cyprus 74.1 48.8 81.9 115.8 Bahrain 75.4 99.0 112.6 Croatia 89.5 94.3 116.1 94.8 Peru 95.1 102.7 92.5 Romania 116.5 115.6 1 95.7 95.9 Brunei Darussalam 116.9 117.4 80.8 117.8 Georgia 118.6 Philippines Singapore Thailand Chile Libya bg 104.6 obago klsLiteracy Skills ne (%) index 619. 0. 1. 14.4 112.6 104.2 91.6 16.1 841048. 6.5 80.8 100.4 98.4 Primary Secondary TertiaryPrimary Secondary 2. 9523.1 16.0 59.5 60.2 124.0 106.8 1. 7315.2 87.3 111.4 Enrollment (%) 37 37 37 37 37 Chapter 4 - EMPIRICAL APPLICATION 38 38 38 38 38 Chapter 4 - EMPIRICAL APPLICATION oo7. 841423. 3.7 0.3 39.1 10.5 35.7 3.0 124.2 48.7 58.4 136.9 30.6 8.4 2.6 70.2 5.0 61.0 101.6 6.1 94.0 71.0 58.0 37.0 32.8 41.9 72.7 83.3 43.3 14.7 102.2 115.0 73.5 96.9 Togo 11.8 8.5 11.5 69.2 109.1 Malawi 32.0 83.9 81.8 74.2 Lao P.D.R. 54.0 79.0 75.3 3.9 38.6 68.2 79.5 India 106.0 15.0 79.8 Kenya 103.5 75.6 89.0 44.5 72.3 Guatemala 70.8 66.8 80.0 5.2 9.9 Lesotho 5.9 85.0 73.0 80.8 95.0 Congo 112.0 82.1 82.2 21.1 Syria 59.9 78.1 61.7 89.3 67.8 Honduras 39.0 83.1 55.6 89.4 Nicaragua 124.6 86.4 7.5 112.2 Myanmar 80.3 85.7 Oman 77.1 84.8 82.7 90.8 Zimbabwe 62.8 91.1 91.0 82.4 99.6 Algeria 15.0 91.9 Saudi Arabia 106.4 56.1 14.6 Swaziland 57.7 4.6 85.8 92.7 Namibia 92.8 10.1 57.0 Iran 100.6 93.1 Kuwait 85.5 59.8 110.0 93.0 El Salvador 9.7 93.9 108.3 Egypt 87.3 24.2 111.2 China 78.1 96.1 11.4 67.1 Emirates 93.5 97.5 United Arab 62.1 98.2 77.1 105.6 Turkey 98.3 91.9 Indonesia 69.3 92.7 98.3 108.6 Botswana 99.2 94.0 Paraguay 84.8 99.4 Tunisia 99.8 Sri Lanka Viet Nam 81.8 81.8 Yugoslavia 100.0 100.0 Mauritius HYPOTHETICA PLANETIA Table 8. Skills, 2001 klsLiteracy Skills ne (%) index 176. 1. 763.3 37.6 113.1 65.6 71.7 22.4 67.8 67.5 17.5 77.1 90.7 54.2 109.3 79.2 91.9 (cont’d) 679. 5112.1 75.1 99.1 76.7 21.7 78.3 117.3 72.1 Primary Secondary TertiaryPrimary Secondary 0. 215.3 72.1 105.9 956. 24.3 24.3 68.5 68.5 99.5 99.5 Enrollment (%) ie 851. 55621.5 1.9 6.2 1.3 15.0 35.5 1.6 0.9 13.8 61.2 16.5 0.7 18.0 18.5 11.5 3.7 26.4 67.0 0.9 15.5 31.8 64.4 73.2 41.0 17.8 1.9 39.9 73.6 40.3 14.7 44.2 Niger 40.9 74.8 41.6 42.0 Burkina Faso 9.6 40.3 0.6 42.0 Mali 38.3 3.6 3.7 65.5 Guinea 42.9 75.0 11.9 Ethiopia 43.3 0.7 Chad 21.8 48.2 21.0 3.5 91.5 Angola 1.9 43.6 5.8 1.7 Senegal 95.5 83.0 45.2 24.1 Djibouti 36.2 45.7 6.9 38.6 63.0 40.7 Republic 28.3 74.4 47.8 Central African 46.8 82.3 76.0 28.8 59.5 Mozambique 6.6 44.0 48.0 Congo D.R. 37.8 48.2 56.7 55.0 Mauritania 48.9 45.7 2.3 52.1 10.8 Benin 2.2 4.6 58.8 Tanzania 100.2 55.6 Pakistan 47.6 21.1 1.7 4.0 14.3 Gambia 50.6 40.6 Eritrea 79.2 58.2 83.8 58.9 10.3 12.1 2.5 103.1 Côte d’Ivoire 30.3 118.2 47.7 64.6 Sudan 67.3 39.3 42.9 118.6 2.8 2.9 62.4 Bangladesh 81.9 58.3 23.5 60.6 62.9 Papua NewGuinea 68.0 94.4 3.3 4.9 65.4 Haiti 18.7 78.2 38.2 63.0 63.2 Madagascar 3.0 49.8 79.0 36.2 Yemen 110.1 19.6 118.0 63.3 Nepal 64.4 18.6 68.7 54.8 Rwanda 80.2 107.8 65.1 66.3 Nigeria 135.8 72.4 72.7 Morocco 68.0 68.3 Zambia 68.0 Cambodia 69.9 Liberia Cameroon Ghana Uganda klsLiteracy Skills ne (%) index 572. 431. 0.9 10.2 44.3 24.8 25.7 1.4 18.4 46.8 62.7 7.0 46.7 23.2 81.3 49.7 54.0 081042. 1.2 29.3 110.4 50.8 Primary Secondary TertiaryPrimary Secondary Enrollment (%) ICT U underestimate theextent Divide. oftheDigital the gapswiden.Consequently, theindex numberspresented to say thataswe move towards ofICTskills, indicators morespecific Considering themovements unveiled by theprecedinganalysis,itisfair in networks. (Malawi). Thus,thegapsarenotfaroffbigonesweencountered the index assumesatopvalue of318.8 (S.Korea) andalow oneof1.8 spread education,where becomeseven morepronounced intertiary countries are ontop,whileTanzania andNiger are atthebottom. The (topincreases value substantially 235andbottom 8).Developed levels education,for ofeducation.Insecondary example, thegap to openupaswe moveLarger gapsstart to higherandmorespecialized developed countriesare inthemiddle. However, Congo,Burundi,Burkina and Faso thetail andNigercarry Uganda, Belize,Peru, Togo Swaziland, andtheDominicanRepublic. countries are ontop.Braziltopsthelistfollowed by Gabon,Malawi, Interestingly, whenranked education,lessdeveloped by primary to 163 while thebottom increasesto35.7, denoting anarrower spread. education.Inasense,thegapissmallerastop goesup in primary improve. Thesame,moreorless,holdstruefor gross enrollment ratios near-complete literacy, whereaslessdeveloped countriescontinueto combined withthefact thatdeveloped countrieshave already achieved good extent, accountedfor by thesmallerdifferences rates, inliteracy The moremodestcontributionofskillstotheDigitalDivideis,a second onlyto Internet networks overall. Only45countries areabove in PCsare higher thanthoseincellphonesandInternetusage, lower intelevision households.Amongmeasured thegaps indicators, inresidentialphonesandmuchusers, whilethey more are contained PCs andtheInternet. GapsarebiggerinPCs,followed by Internet The mainsources ofgapsinuptake areagainthenewer technologies of list. Intotal, 53ofthe149 countriesareabove theaverage. before, together withMyanmar andCambodia,areatthebottom ofthe than inother components. ThesameAfricancountrieswe have seen Hypothetica. Thelatterdenotes thatlarge countriesare lower inuptake than itwasinnetworks, whilePlanetia ismorethantenpointsbelow change higher intherankingsisthatS.Korea ispositionedsignificantly (U.S.) is288.7andthebottom (Chad)just0.8.Noticeable amongthe Divide.Thetopvalue general isalsoamajorsourceoftheDigital Thus, althoughgapshere arenotashugethoseinnetworks, uptake in largerthaninoverallof countriesmeasuredare significantly Info-use. PTAKE : GapsinICTuptake between thetop andbottom inthelist 39 39 39 39 39 Chapter 4 - THE EMPIRICAL APPLICATION 40 40 40 40 40 Chapter 4 - EMPIRICAL APPLICATION Table 9. hl 2. 536. 0620.1 10.6 27.3 22.5 61.3 12.6 17.9 95.3 68.5 20.3 84.0 18.3 129.6 96.2 28.1 15.4 84.7 16.8 78.0 30.0 124.6 87.9 26.9 Slovak Republic 11.7 115.2 145.6 108.1 156.0 17.5 Chile 130.7 21.8 19.5 25.3 99.3 Czech Republic 96.8 98.9 72.7 27.7 161.1 Malaysia 24.7 101.0 23.0 161.5 Macau 31.0 99.8 161.7 24.6 26.4 146.7 91.4 97.5 Emirates 120.2 United Arab 23.3 164.7 115.0 30.1 32.9 167.4 97.2 184.0 Bahrain 23.3 92.9 38.7 96.0 Spain 87.3 27.6 96.9 185.0 Portugal 39.1 186.6 22.5 33.5 Estonia 95.0 191.1 100.2 94.7 104.0 Italy 54.6 72.5 43.0 Cyprus 196.2 98.2 205.6 93.9 Malta 121.0 37.4 42.3 96.9 38.4 Israel 206.8 209.4 Belgium 81.9 38.2 79.3 212.8 35.8 France 38.7 Slovenia 218.0 30.7 104.7 117.7 91.0 38.7 Ireland 41.2 97.5 97.8 Austria 53.8 94.2 99.0 233.1 103.6 50.8 Gibraltar 226.8 49.1 37.1 229.9 United Kingdom 119.4 234.5 235.2 99.0 118.0 Taiwan, China 42.8 51.6 36.0 Finland 99.7 240.2 84.5 46.4 Germany 89.5 106.4 46.7 51.7 Japan 246.7 97.8 251.0 49.5 42.9 Hongkong 95.7 47.3 96.3 131.2 Switzerland 59.9 251.2 127.6 54.2 Singapore 252.2 51.6 107.7 253.8 99.5 New Zealand 41.8 95.0 119.6 262.9 Netherlands 56.1 99.2 254.0 Australia 142.0 264.7 96.9 92.1 264.9 Luxembourg 94.0 97.0 Norway 266.8 Bermuda 94.4 269.7 271.9 Canada Denmark 274.4 98.6 S. Korea Iceland 288.7 Sweden United States ICT Uptake, 2001 paehouseholds Uptake index (%) VRsdnilInternet Residential TV 021221. 31.5 13.5 102.2 70.2 46.4 50.8 113.0 90.2 anie C users PCs mainlines Enrollment 0. 6633.0 34.9 36.6 36.4 102.1 141.5 46.1 39.3 100.6 50.1 62.5 120.1 1. 8152.1 48.1 116.2 991. 12.5 14.9 14.7 59.9 14.7 68.7 /100 (%) rn5. 746. . 1.6 7.0 4.1 7.7 63.2 5.6 3.8 3.8 4.8 67.4 2.3 3.3 42.5 4.5 5.0 27.4 56.8 62.4 2.7 4.6 4.5 76.6 3.6 52.3 64.9 4.2 4.5 3.2 79.4 68.8 58.2 2.9 50.0 61.9 Iran 65.0 61.9 66.4 3.3 Saudi Arabia 48.8 65.6 3.6 4.7 61.9 Panama 5.0 86.6 7.3 Yugoslavia 58.3 62.4 94.2 4.7 98.0 Peru 5.3 6.9 62.8 54.3 Jamaica 13.4 7.5 63.2 63.2 6.3 86.6 6.0 Suriname 39.8 46.2 95.6 46.3 Romania 3.2 64.0 61.2 4.1 Colombia 63.7 6.8 64.2 79.8 85.8 Oman 35.2 84.7 South Africa 98.8 90.9 64.8 7.1 68.6 Jordan 7.8 77.3 Russia 92.0 77.8 5.6 97.7 71.9 Venezuela 7.5 80.5 Mexico 85.4 9.3 Belize 7.2 92.4 65.9 Brazil 89.8 8.4 91.3 Bulgaria 92.0 9.8 10.1 15.3 Turkey 92.6 8.8 8.8 PLANETIA 8.0 58.9 82.5 8.5 11.1 Lithuania 94.4 12.0 theGrenadines 51.7 96.9 66.4 73.8 74.4 13.3 St. Vincent and 67.7 Lebanon 6.6 75.9 102.2 69.2 97.0 Barbados 98.3 Trinidad Tobago & 98.0 11.9 100.0 16.4 104.0 108.2 HYPOTHETICA 71.4 111.9 11.0 Latvia 11.0 13.2 139.9 13.2 113.3 Argentina 14.7 14.8 77.0 Poland 10.8 85.8 8.1 Kuwait 80.6 9.5 113.4Croatia 80.2 93.0 120.0 101.3 Darussalam Brunei 78.5 75.4 120.5 89.6 Costa Rica 97.5 Qatar 121.8 96.6 123.2 Uruguay 123.8 Seychelles 124.6 Mauritius Greece Hungary ne (%) index paehouseholds Uptake index 1. 376. 709.3 17.0 65.2 83.7 118.2 819. 23631.3 6.3 62.3 96.5 58.1 4.8 11.3 9.2 76.2 6.9 83.9 74.0 93.9 85.3 97.6 VRsdnilInternet Residential TV 462. . 6.5 7.0 25.1 64.6 8.4 8.8 51.7 75.9 10.2 7.3 104.7 98.3 anie C users PCs mainlines Enrollment /100 (%) Table 9. ni 584. 62060.7 1.3 0.6 2.3 3.2 1.2 1.3 16.2 1.0 0.6 2.6 0.9 17.4 42.9 14.9 1.9 0.7 5.0 1.7 9.3 11.1 15.8 21.4 1.4 1.1 31.6 1.5 16.5 1.7 19.7 79.6 20.4 1.1 1.2 10.5 1.0 0.4 66.0 17.7 20.7 India 20.6 1.3 1.1 1.4 Gambia 15.3 54.5 1.9 21.6 1.6 Kiribati 28.4 18.3 2.0 3.0 15.6 Viet Nam 22.8 48.0 14.6 43.3 1.4 22.9 Togo 1.4 40.4 12.3 1.7 Algeria 23.0 1.3 69.1 60.8 2.5 Indonesia 75.6 1.4 24.6 3.0 2.2 Mongolia 0.6 76.1 24.6 28.1 24.9 25.6 Honduras 11.0 0.9 17.1 3.9 2.6 2.1 25.8 Guatemala 36.5 18.5 Paraguay 1.4 59.8 1.5 76.6 Senegal 21.1 2.2 19.8 1.8 25.9 96.6 Syria 2.5 27.0 1.6 27.3 39.7 Cuba 1.2 14.9 15.3 0.9 47.2 30.1 Kyrgyzstan 0.9 5.5 41.9 Nicaragua 89.9 30.3 1.8 31.1 2.6 61.5 56.9 Morocco 2.9 20.2 63.4 Samoa 32.8 3.6 32.7 54.1 1.8 1.9 65.7 Botswana 2.6 55.7 33.8 37.9 Bolivia 2.2 97.0 34.5 4.6 41.5 Philippines 10.9 2.3 39.1 85.7 Egypt 40.1 40.1 Moldova 38.0 87.3 2.6 5.8 39.0 41.2 Armenia Namibia 61.2 44.7 62.9 28.8 2.8 84.9 Ukraine 45.8 Georgia 46.2 26.6 37.2 46.0 El Salvador China 51.4 97.9 Maldives 53.3 Ecuador 55.3 Fiji Guyana Tunisia Thailand Cape Verde ICT Uptake, 2001 paehouseholds Uptake index 398. 81222.3 2.2 38.1 84.5 2.7 43.9 6.9 60.1 40.0 56.5 VRsdnilInternet Residential TV 843. . 4.1 2.6 37.5 88.4 (cont’d) anie C users PCs mainlines /100 hd080803020.1 0.0 0.2 0.0 0.1 0.2 0.3 0.1 1.5 0.1 0.3 0.3 0.8 0.1 2.1 2.0 1.1 0.3 0.1 0.8 0.2 3.1 0.2 1.3 0.9 0.4 0.8 1.4 1.4 0.1 4.2 Chad 1.7 8.7 4.5 0.1 2.8 0.1 2.7 Ethiopia 0.2 1.9 Myanmar 2.9 2.3 0.1 0.4 1.7 0.2 0.3 Rep. Central African 0.2 0.3 6.1 Malawi 5.1 2.8 0.2 8.4 1.8 Cambodia 0.4 0.2 2.3 11.3 0.4 3.1 Uganda 0.1 3.4 4.0 Mali 3.6 24.1 1.4 0.3 23.7 3.4 7.0 Burkina Faso 19.1 0.2 0.7 Mozambique 4.1 4.0 6.6 3.4 10.3 4.1 Madagascar 0.3 0.7 0.1 3.8 1.9 1.6 Guinea 4.3 4.5 Bangladesh 21.3 0.4 2.1 4.9 0.2 0.2 Angola 0.3 0.6 46.0 5.7 Eritrea 3.2 10.6 Tanzania 6.0 0.4 21.9 1.0 2.1 Benin Nepal 39.8 6.1 6.4 93.6 0.3 7.8 2.8 0.5 10.3 Ghana 0.9 0.3 Nigeria 7.5 7.9 1.3 0.4 1.1 8.1 Lao P.D.R. 83.5 47.3 5.7 Zambia 0.8 1.2 10.7 9.8 Cameroon 9.5 6.2 0.8 Yemen 1.5 18.3 0.9 12.1 74.8 Kenya 39.7 0.9 Mauritania 1.7 40.5 8.1 12.7 Sudan 9.0 13.3 13.1 Solomon Islands 13.3 11.0 6.5 Papua NewGuinea 14.1 Pakistan 20.6 Djibouti 22.2 Albania 14.6 Gabon 14.9 Côte d’Ivoire Sri Lanka Zimbabwe paehouseholds Uptake index 064373390.5 3.9 7.3 0.4 4.3 0.7 10.6 12.6 46.5 14.2 VRsdnilInternet Residential TV 9938030.2 0.3 3.8 29.9 anie C users PCs mainlines . . 0.1 0.1 0.2 1.1 0.2 0.2 0.4 0.3 0.4 1.7 0.9 1.0 /100 41 41 41 41 41 Chapter 4 - EMPIRICAL APPLICATION 42 42 42 42 42 Chapter 4 - THE EMPIRICAL APPLICATION I the individualindices). per100users areshown inhabitants inTable 9.(Annex Table 3contains households, residential phonelinesper100 households,PCsandInternet The Uptake index, togetherwiththe Chad, Malawi andEthiopia. sizeable group ofcountriesregisteredextremely low values, especially 90 countriesare above average andseveral morenot far behind,stilla countries, itisstill quite anissueinlessdeveloped ones.Even though cause ofdifferentiation developed amongvery andsomewhat-developed Somewhat similaris thecaseoftelevision households.Whilenota than thosefor PCs. countries continueto smallvalues–afew besubjecttovery even smaller half thecountries(74 of149) above are theaverage. However, thebottom Residential phonelinesgive riseto comparatively smallergaps.Here the bottom arebarely registering. countries below 10, ofwhich 32are below 5.Anumberofcountriesat extremes arebigger, withonly44countriesabove average and42 representing asizeablegap.Infact, theconcentrationissuch thatthe not far behind.Thetopvalue is521.7 andthebottom just0.2,therefore DividebyThe contributionto thegapsinInternet theDigital usersis African, withtheinclusionofAlbania,ArmeniaandYemen. average, while39countrieshave lessthan10. values Theseare mostly be aproblem. telecommunications networks andusage.Even televisions continueto contributioncontinuesto andmobile bemadeby fixed significant is mostly duetothenewer technologies of theInternet andPCs,whilea everything the same.Much Divideobserved matters ofthehugeDigital Therefore, mattersintheInformation whileeverything Society, not as theothers. statistics, isalsoaDividecausebut not asmuch by internationaltraffic This technology isexpected to evolve more.Telephone usage,asindicated the leaderamongInternetusers,followed by HongKong andCanada. asmuch -seemethodology). S.Koreathe index thisisnotreflected is practically two-thirds ofthecountriesdonot have any such (In users. represents thenewest technology andtheasymmetriesare huge– gaps inoverall Info-use. Broadband unevenly useisvery spread.It moderates somewhat theUptake outgoing telecommunications traffic ontheuseofbroadbandandinternationalincoming indicators NTENSITY

OF

USE : Whilenot measuredindependently, theinclusionof values oftheindicators for television across components). among countriesandover time(andinamore detailed treatment, movements areaffected by thedifferent degreesofchange in group B.Several othermovements are too, detectable sincerelative Malaysia, Croatia, Poland andArgentina –countriesthatwere classified Hypothetica in1996 toabove itin2001. Thishasbeenthecasefor signs ofrelative evolution, assomecountrieshave moved frombelow doubled theirInfostates inthissix-year period.We canalsoget early modest. We cansee,for thatHypothetica instance, andPlanetia nearly Some countrieshave progress, madeconsiderable whileothers more at any degrees. time,butprogress wasmadeeverywhere –to varying the casefor year. each intervening andevery hasregressed No country period, Infostates andthishasbeen increasedineach country andevery asthebeginningofthislineinquiry. Overand serves the1996-2001 Infostates areshown inTable 10. usefultoknow Thisisvery andquantify, studied. Ranked by their2001 values,thetimeseriesofcountries’ they increasedsignificantly. Inaddition,they increasedfor year every examination, theInfostates ofallcountriesincreased and,onaverage, immediatelythatoverFirst, we can observe theperiodunder of whatisanalyticallypossible. on aspectsofindividualtechnologies. Whatfollows offers onlyasample desired detail, such asgrouping countriesby region,incomeorfocusing performance. Moreover, they atvariouslevels canbeperformed of context thatwould by shedlightoncausaleffects linkingpoliciesto complex,be very especiallywhenoutsideintelligenceisbroughtinto investigation oftheevolution Divide.Such oftheDigital analysescan as well asindividualindicators. Thismakes possibleanin-depth but ratherby explicitly monitoring Infostates, theirmaincomponents, comparisons reduced tochanges inrankingsfromoneyear thenext, to questions headonandprovide answers. Theseare not basedon the unique features ofthismodelisthatitcanreally take thistypeof Which countriesare makingmoreprogress, howfast andwhy? Oneof questions, such Divideclosingorwideningover as:IstheDigital time? DivideevolvingDigital policy viewpoint, however, afar more interesting questionis: themaincontributingfactorsas well totheDivide.From asidentified a howso far hugeitis,whoissubjecttoandwhatextent, quantified That a Digital DivideexistsThat aDigital isknown. Theempirical application 4.3 TheevolutionoftheDigitalDivide ? Thiskey policyissuerevolves aroundaset of How isthe 43 43 43 43 43 Chapter 4 - THE EMPIRICAL APPLICATION 44 44 44 44 44 Chapter 4 - EMPIRICAL APPLICATION Table 10. mn2. 674. 815. 64.3 52.8 64.7 66.9 48.1 55.6 56.0 42.6 67.8 48.9 49.0 36.7 62.3 41.9 69.1 70.8 40.8 20.0 58.6 39.1 64.0 66.3 31.3 72.3 72.6 51.8 32.1 49.5 54.5 72.9 20.7 65.8 74.6 44.1 44.2 49.8 63.8 61.9 73.4 57.2 37.2 38.0 40.5 50.6 75.5 51.9 68.7 48.7 29.3 Oman 34.2 48.8 69.7 79.9 42.8 59.3 39.7 Thailand 43.3 83.0 62.3 70.9 36.7 51.7 Jordan 32.1 35.1 73.6 53.4 66.0 Colombia 38.1 86.8 Yugoslavia 63.6 48.0 52.4 28.2 Jamaica 87.4 77.0 51.6 31.3 45.3 88.1 Venezuela 46.6 82.5 65.0 42.2 36.4 82.6 Panama 75.5 53.7 35.0 78.7 Russia 53.9 66.0 46.3 Romania 75.4 48.6 55.5 South Africa 38.1 69.1 Belize 38.6 60.2 Turkey Mexico Costa Rica Bulgaria Lebanon Kuwait aa 167. 579. 0. 90.8 102.4 91.6 91.2 92.4 77.3 79.6 65.7 92.5 63.9 69.9 72.6 98.7 82.2 52.1 63.9 51.6 87.4 66.9 43.6 53.3 74.8 58.2 36.6 47.8 101.5 67.7 100.0 102.3 44.0 90.5 104.8 92.3 92.5 54.0 34.0 97.8 82.6 83.1 107.2 Trinidad and T 76.5 40.0 83.8 94.3 72.6 Qatar 73.2 67.7 107.9 73.0 82.3 Barbados 60.3 64.5 59.8 99.4 110.8 109.9 55.9 Brazil 59.5 71.0 50.5 55.4 100.7 105.5 49.2 111.2 84.2 Mauritius 46.1 79.5 57.4 96.8 99.6 Lithuania 65.9 66.6 46.0 83.7 93.0 HYPOTHETICA 51.6 55.3 70.2 PLANETIA 116.3 86.2 42.2 Malaysia 96.8 48.8 53.1 73.5 121.2 Croatia 91.2 64.5 110.9 Latvia 78.5 100.0 122.6 Poland 84.2 67.6 107.8 Argentina 128.0 63.9 99.4 70.8 63.8 Uruguay 131.1 47.2 88.0 62.3 120.2 Chile 143.4 140.5 109.0 76.4 Macau 124.2 134.2 93.1 144.3 115.7 107.0 Slovak Republic 61.5 148.5 132.9 68.9 93.5 93.3 102.6 Brunei Darussalam 143.6 116.3 85.7 121.7 69.5 80.1 Bahrain 102.0 103.0 149.1 70.2 Greece 90.4 52.6 64.4 76.3 88.4 132.8 151.7 Emirates United Arab 159.0 77.0 123.6 134.1 63.0 73.4 150.2 Hungary 107.1 121.6 168.9 140.9 95.8 Cyprus 109.9 159.2 129.7 175.6 93.5 Czech Republic 80.7 147.6 113.0 166.0 178.7 Estonia 127.5 78.8 96.4 145.2 166.3 112.1 131.0 Malta 183.8 150.9 96.3 111.5 172.0 Spain 184.7 135.3 93.6 150.1 183.3 120.4 Italy 117.2 163.5 103.0 Slovenia 100.6 144.8 189.5 Portugal 83.7 126.5 179.5 111.3 Israel 164.0 152.0 France 140.8 132.3 Ireland 194.3 195.6 132.1 112.9 184.4 188.7 116.2 Japan 166.9 173.4 126.2 S. Korea 199.5 148.5 155.8 197.1 107.0 189.7 Austria 135.1 141.3 180.2 180.4 1 New Zealand 202.6 115.0 124.3 202.6 164.0 168.7 186.5 Australia 148.3 196.1 156.4 167.0 127.8 174.8 United Kingdom 138.0 148.3 117.8 152.6 Germany 137.2 216.8 132.9 Singapore 118.1 203.5 114.9 Iceland 217.9 188.3 145.2 224.8 224.2 206.7 167.0 Luxembourg 215.4 213.4 230.0 194.1 148.8 199.4 Finland 194.9 230.5 179.7 216.0 131.1 185.3 Hongkong 167.4 222.7 165.8 204.0 166.7 144.6 212.7 Belgium 187.0 148.2 146.5 129.2 192.8 162.5 Norway 174.3 140.7 Switzerland 150.3 United States Netherlands Canada Denmark Sweden Evolution ofinfostates obago 9619 9819 002001 2000 1999 1998 1997 1996 624. 987. 4790.6 84.7 71.7 59.8 49.5 36.2 737. 399. 0. 113.4 104.3 91.7 83.9 73.0 67.3 190.2 180.0 168.3 148.0 129.0 15.0 6. 7. 0. 1. 214.4 210.8 200.4 179.8 166.0 416. 757. 74.5 71.8 67.5 61.9 54.1 447. 289. 100.6 92.6 82.8 72.7 64.4 4. 6. 8. 191.8 182.0 160.6 141.0 986. 6486.0 76.4 67.6 59.8 538. 0. 111.7 100.0 87.4 75.3 129.0 114.7 101.0 87.5 0. 1. 122.6 116.8 108.7 185.2 172.5 161.3 hd12162126515.2 5.1 6.1 6.5 2.6 5.1 6.3 2.1 4.4 9.5 4.8 1.6 9.9 2.2 8.2 7.8 1.2 3.3 8.3 1.9 6.2 9.5 3. 7.3 2.5 6.0 10.6 1.2 4.6 12.3 3.3 6.9 9.3 10.5 2.2 4.1 10.2 3.8 1.7 10.2 5.8 8.8 6.9 3.5 11.5 3.1 8.2 5.2 6.9 Chad 12.1 4.2 3.1 10.7 6.8 Ethiopia 10.8 4.5 5.8 9.2 2.0 Myanmar 13.5 9.6 5.3 Rep. Central African 6. 7. 12.9 4.2 7.9 10.9 14.3 Eritrea 5.4 2.4 12.7 14.4 11.8 4.8 6.0 11.8 5.0 Malawi 14.5 15.4 11.2 10.8 4.6 8.4 Bangladesh 8.9 13.0 2.5 3.9 9.2 4.2 8.2 10.0 Mali 3.6 10.6 6.1 16.2 6.5 Mozambique 9.2 8.9 5.9 4.9 17.0 13.0 Angola 1.9 5.3 7.4 7.3 4.9 Burkina Faso 14.8 13.0 4.0 18.6 3.6 Cambodia 6.5 5.8 13.2 10.6 3.1 Uganda 17.7 10.8 18.7 9.3 4.5 Guinea 15.4 9.2 16.5 Madagascar 20.2 7.2 11.6 12.1 Sudan 5.7 15.9 21.0 9.6 6.0 Tanzania 7.2 8.0 26.6 17.6 Nigeria 12.9 9.2 4.1 21.9 6.2 21.7 13.1 Nepal 6.1 12.6 18.7 Benin 26.8 17.4 10.0 5.4 4.0 10.8 Ghana 15.8 18.9 8.5 8.8 27.9 Yemen 14.3 3.0 17.1 28.4 Lao P.D.R. 7.6 6.9 23.4 11.6 13.2 23.9 Zambia 29.0 19.1 3.0 8.2 9.7 17.3 Cameroon 22.6 15.8 29.2 Djibouti 13.7 6.5 7.3 29.4 22.2 21.7 13.2 Mauritania 10.8 25.3 11.8 14.9 17.9 10.5 Papua NewGuinea 30.2 4.2 Kenya 20.7 13.0 8.9 12.1 27.4 32.2 Pakistan 32.4 15.6 9.9 7.8 22.9 22.6 Syria 33.5 26.5 11.8 17.6 5.5 Gambia 19.3 28.2 21.6 8.9 Côte d’Ivoire 13.9 16.1 33.7 19.1 17.8 India 37.5 17.6 6.5 13.2 28.6 16.8 15.0 Togo 33.4 13.7 37.6 10.3 25.5 12.7 Algeria 12.7 25.9 38.4 32.1 Viet Nam 21.5 40.4 6.2 38.6 22.7 34.7 27.5 Senegal 16.8 35.5 36.2 16.8 Zimbabwe 29.5 41.3 23.7 12.7 29.7 26.0 Sri Lanka 12.8 25.9 33.5 21.8 23.2 41.9 19.1 Albania 44.5 22.9 25.2 17.7 Cuba 19.9 35.8 15.5 40.8 45.0 47.0 19.7 18.3 Gabon 15.8 11.6 29.6 36.1 40.7 42.6 9.7 Honduras 24.3 49.0 26.9 36.2 37.5 Morocco 48.9 6.2 18.6 42.1 23.5 Indonesia 31.3 30.7 45.5 49.4 13.5 35.0 Nicaragua 17.3 24.1 25.2 34.1 Mongolia 45.3 26.4 17.7 50.3 20.9 29.8 Egypt 51.4 32.3 22.5 46.4 15.5 50.4 19.1 Kyrgyzstan 44.7 25.4 18.1 41.0 Guatemala 44.1 11.2 36.3 24.2 53.7 Armenia 29.5 37.9 26.0 21.5 Paraguay 53.9 48.3 14.1 34.1 54.1 Bolivia 19.6 46.6 36.0 10.2 24.7 44.4 54.7 Tunisia 15.7 41.4 34.6 58.2 21.0 Moldova 28.0 46.0 31.8 24.9 Iran 49.4 24.1 39.1 61.8 28.7 Guyana 17.6 41.2 20.4 31.5 40.2 Botswana 53.2 24.4 36.5 12.5 26.8 Philippines 33.1 49.0 31.7 20.3 China 27.6 42.2 26.2 El Salvador 35.4 Namibia 31.5 Ecuador Samoa Georgia Ukraine Fiji Saudi Arabia Peru 9619 9819 002001 2000 1999 1998 1997 1996 062. 274. 7151.9 47.1 42.4 32.7 25.3 20.6 61.5 52.3 46.6 31.0 23.3 23.3 041. 751. 20.2 19.8 17.5 12.7 10.4 48.0 35.8 30.9 21.3 17.4 481. 1826.9 21.8 18.1 14.8 31.7 29.4 24.9 21.2 . . 1617.3 11.6 9.4 7.6 . . 8.5 8.0 4.7 7 10.7 11.2 8.6 9.6 7.0 8.3 2 1 561. 20.1 16.7 15.6 58.4 54.2 47.0 3 This shouldbeinterpretedinthe relativesenseandshouldnotbeconfusedwithlowerinfostatevalues. total of111 countriesimproved theirsituationandonly28‘deteriorated’ lowand countrieswithvery Infostates candeteriorate. We thata find even high Infostates countrieswithvery canimprovethis specification, improve In theirsituationwhileotherswillbesubjecttodeterioration. low highandvery very Infostates, some countrieswill,by necessity, Benchmarking vis- Infostates ofhighlyadvancedcountries. countries below average morethanthe increasedproportionately average increasedmore thanthetop,which meansthattheInfostates of gap between thebottom countriesandtheaverage. Alternatively, the the gapbetween thetopcountriesandaverage closedmorethanthe Divide.Even closingDigital a generally visualinspectionreveals that at thetop andthebottom ends.This increased steepnessisindicative of 4.The2001shown inChart lineliesvisibly‘inside’the1996 line,both overallfirst glimpse the evolution into Divide.Thisis oftheDigital is alsomoving upwards over time)andplotting thevalueswe canget a and 2001, whilekeeping Hypothetica constant (at100, sincetheaverage asthebasisforthen willserve relative comparisons. Doingsofor 1996 andHypotheticavalues ofeach for country each year available. This One way to beginistocompute thedifferences between theInfostate generalize findings. of today’stopcountries.Butmoresystematicanalysisisneededto for thecountriesatbottomofInfostatescaletoachievelevels movements thatsupportstheargumentdecadesareliterallyneeded Germany’s in1996 (107). Itisexamination ofthepacesuch 50. Analogously, Argentina’s2001 value(107.9) wascomparable to Botswana years. by Ofcourse, 2001 infive Botswanahadmoved upto the Infostate value ofBotswana in1996. Therefore Malicaughtupto their 1996 levels. Thenearly10 pointsachieved by Maliin2001, was arrived thereveryslowly(orMauritaniamuchfaster),asevidencedby and Djibouti,for hadsimilarInfostates instance, in2001, butDjibouti movements -allofwhichareupwardsbutatdifferent“speeds”.Mauritania answer thequestionsposed,includingprecisionintrackingrelative But theDigitalDivideisarelativeconceptandmuchmoreneededto below average. Moldova -mostofthesecountrieswere, andstillare, Botswana, Argentina,Samoa,Mauritius,Kyrgyzstan, Jordan and progress was comparatively madeby S.Korea, followed closelyby from belowthose thatstarted average. Ontheother hand,themost African Republic, andCosta Rica-theonlycountriesamong Qatar thatthishasbeen thecaseforthan others), we theCentral alsofind U.S., Canada,Scandinavian countries,New ZealandandKuwait more While, onaverage ithasbeen countriesatthetop thatlostground (the à -vis Hypothetica ratherthanbetween countrieswith 3 . 45 45 45 45 45 Chapter 4 - THE EMPIRICAL APPLICATION 46 46 46 46 46 Chapter 4 - THE EMPIRICAL APPLICATION Chart 4. Chart Chart 5. Chart denotes aclosingdividevis- 2001 apositive number 4.Inthisspecification, thatwere plottedinChart bysubstantiated computing thechanges ofthedifferences in1996 and The slowclosingoftheDigitalDivideinanoverallsensecanbefurther though, andthere areexceptions. top oneslost ground.Thedegree towhich thishappenedvarieswidely, most countriesgainedground against Hypothetica, whilemany ofthe is displayed form 5,which ingraphical clearlyreveals inChart that widening divide–themoreso,biggerchange inthedivide.This -60 -50 -40 -30 -20 -10 10 20 30 40 0 10-0 5 01010200 150 100 50 0 -50 -100 -150 The evolution Divide(1) oftheDigital The evolution Divide(2) oftheDigital countries à -vis theaverage andanegative numbera 1996 2001 countries Table 11. B-C B-D D-E C-E B-E A-B A-C A-D A-E 80.5 B-C 100.0 B-D 100.0 D-E 44.6 C-E 100.0 B-E 100.0 A-B A-C 100.0 100.0 A-D 100.0 A-E 92.3 83.1 Hypothetica 73.2 E D 64.5 C B 55.4 A Hypothetica E D C B A Group The following areofinterest to advanceourknowledge: consistent withthosefound divides. studies indetailed ofinternalcountry of analysisproduces several allofwhich are interesting findings, the 1996-2001 AofTable period.(Thisisshown inpart 11). Thistype each year, aswell astheirrespective change ofgrowth andrates over To begin,theaverage Infostate valuesfor each grouparecomputed for groupings introducedDivide atthelevel earlier. country ofthefive analysis therefore proceeds toinvestigate theevolution oftheDigital requires comparisons; which thus,itmatters iscompared towhat.The thesubject shown divides.Bydefinition, inanalysesofinternalcountry in anoverall ashasbeen thedevil sensecanbeobtained, isinthedetails Evolution Divide,bygroup oftheDigital (A) Infostates (D) Changesindifference (C) DigitalDivides (B) normalizedInfostates While usefulinsightsontheevolution Divide oftheDigital Inside theDigitalDivide 9619 9819 0020 change 2001 2000 1999 1998 1997 1996 0. 0. 278. 3776.8 109.6 83.7 109.4 122.5 108.6 89.2 158.8 129.1 186.4 109.6 167.3 135.2 92.7 193.0 105.6 174.5 140.2 100.0 197.9 180.4 102.6 144.9 202.3 107.7 186.1 153.0 205.6 192.6 210.2 199.9 205.7 209.6 212.4 214.9 218.1 199.9 189.8 174.2 155.4 138.6 120.8 1. 1. 1. 2. 2. 123.1 122.0 120.3 119.7 114.9 110.4 534. 764. 5545.7 82.0 45.5 27.6 83.6 63.9 46.0 25.8 85.2 63.9 23.4 47.6 62.6 87.7 44.8 21.9 86.1 62.0 19.4 45.3 60.7 84.9 17.6 57.2 41.1 77.4 38.4 76.6 35.1 74.3 32.0 72.1 28.8 70.0 25.5 65.1 123.1 112.6 100.0 87.6 74.1 61.2 411. 342. 554. 70191.4 114.8 27.0 41.4 41.1 77.4 35.5 70.6 29.2 61.8 23.4 52.8 18.6 45.2 14.1 36.1 . . 011. 2713.5 12.7 11.7 10.1 9.3 7.9 . . . . 171. . 211.0 9.2 13.5 11.7 9.7 7.4 6.0 4.4 0527-. 060.2 -0.6 -1.5 -6.9 -6.6 2.7 -8.4 -5.6 -6.1 -0.5 -3.5 -7.2 -5.0 -5.9 -7.3 -4.6 -5.8 -7.7 -8.1 -6.5 46-. 44-. -6.6 -4.8 -4.4 -3.3 -4.6 . . 25-. -1.6 1.8 0.0 -1.6 0.3 2.4 1.3 -2.5 1.5 0.7 0.6 1.6 -1.0 2.5 1.3 1.2 4.0 1.8 3.5 3.0 1996-2001 20101.3 62.0 9165.5 79.1 growth 47 47 47 47 47 Chapter 4 - THE EMPIRICAL APPLICATION 48 48 48 48 48 Chapter 4 - THE EMPIRICAL APPLICATION 4 higher thanthatofthehaves, butmust beatleast asmany timeshigherastheratio oftheirinitialdifference. 2002) thatthisisnot thecase.For thedivideto closethera the have-nots divides,however, asaclosingdivide.In internal country ithasbeendocumented(Sciadas Sometimes thegrowth intheabsolute gapisinterpreted asagrowing divideandthehigherrates ofgrowth of a smaller absolute increase because they start from low very a smallerabsolute increasebecausethey levels start highlevel,very whereas ofgrowth thehighrates ofthe“have-nots” yield “haves” froma resultinahigherabsoluteincreasebecausethey start and relative magnitudesinvolved. Thelower rates ofgrowth ofthe This isdirectlyrelated to between theinescapableinterplay absolute measured. Theseare CofTable shown inpart 11. groupings Dividebetween canbemeaningfully Digital ofthefive pairs BofTableout, andthey inpart arecontained 11. Itisonlythenthatthe Therefore, computations basedonanormalizedaverage were carried and Eritrea). Togo, Kyrgyzstan, Gabon,Vietnam, Mauritania, Ethiopia,Botswana (The highest ofInfostate rates growth were achieved bySyria,Sudan, ! ! ! only (!)65%. increasedby doubled, whilethoseofthefirst of thesecondgroup (withabove average countries) than tripled,asalmost didthoseofgroupD,thevalues 11. IndeedtheInfostate values ofthebottom group more andcanbeseen clearlyinthelast2002) columnofTable (Dickinson andSciadas1999, OECD2001, Sciadas Divideresearch inDigital also beenaconsistent finding are higherthanthosewithInfostates. Thishas The ratesofgrowth ofcountrieswithlower Infostates between haves andhave-nots occursdespitethefact that: malized. Thisincrease inthe(unadjusted)differences Divideiscomputedthe Digital theaverage mustbenor- 1996). Theaverage isalsoincreasing, though,andbefore divides(U.S. study ofinternalcountry from first thevery have beenfound Similarfindings group A. consistently easily by thevaluesofgroup subtracting Efromthoseof absolute terms, increaseover time.This canbeverified The differences between thetop andthebottom, in the differences inInfostates are. used. Butthisisnot Divide– ameasureoftheDigital holdstrueforand thisofcourse groupings thecountry hadahigherlevel ofInfostateevery country year, every Over time,Infostates increaseacross theboard. Each and te ofgrowth ofthehave-nots not onlymustbe 4 . explained DofTable above -part 11. basedonthechangealternative ofthedifferences, as specification can alsobeseenwiththe are separatedbyhugegaps.Thesefindings middle groups andtheonlygainsareagainst countrieswithwhomthey top. Theproblem withthebottom group remains.Itisoutpacedby overall becausethemiddlegroupsaremakinggoodprogress againstthe does not Divideisclosing close.ThemessagethenisthattheDigital all other groups. Even thedividewithgroup D,which isjustabove, from thisanalysisthatthebottom group (E)actuallylosesgroundagainst the topthanleast-connected group. At thesametime,we can see faster, indicatingthatcountriesingroupsB,CandDcatch upmore to between thetop groupandeach oftheother groups –infact, thepaceis bottom groups isslowly closing.Thisisalsothecasefor thedivides groupings. We cannow seethatthedividebetween thetop andthe 6for Divide.TheyareplottedDigital inChart theselectedpairsof The directionofthesemeasuresovertimepointstotheevolution Chart 6. Chart 100 150 200 250 50 0 Evolution ofdividesbetween groups country 1996 9719 9920 2001 2000 1999 1998 1997 B -C B -D D -E C -E B -E A -B A -C A -D A -E 49 49 49 49 49 Chapter 4 - THE EMPIRICAL APPLICATION 50 50 50 50 50 Chapter 4 - THE EMPIRICAL APPLICATION 1998, since it remained flat over1998, thelast coupleofyears sinceitremainedflat oftheperiod). managed to ground gainsubstantial (dueto itsperformance inthe1997- asZambia,but fromof Botswana aboutthesamestate thatstarted marginal over withtheperformance theperiod.Thiscontrasts sharply from amuch in1996,started higherstate itsprogress hasbeenrather endedhigherthanZambia.AlthoughZambia same time,Mauritania it duringthe1997-1999 period,onlytofall At the behindagainin2000. Sudan improved, in1996, slightlybehindMauritania to itstarted surpass The underlyingpatterns ofevolution are telling.Whilethesituationof in Infostate ispresentedinAnnex Tables 4,5and6). displayed 7. inChart (Theresultsfor theentire list ofcountriesincluded period ofexamination. Theevolution ofacross-section ofcountriesis values by keeping theaverage country, Hypothetica, over constant the performances. Oneway todosoisthrough normalizingtheInfostate even withregards insightscanbeobtained more to comparative Chart 7.Chart 100 120 20 40 60 80 0 9619 9819 002001 2000 1999 1998 1997 1996 Patterns ofclosingdivides (1) Carrying theanalysisto thelevelCarrying ofindividualcountries, Country analysis HYPOTHETICA Mauritania Argentina Botswana Republic Malaysia Zambia Sudan Jordan Brazil Slovak is contained inTableis contained 12 anddisplayed 8. inChart against Hypothetica. Anexample ofanothercross-section ofcountries Infostates by theratiosofnormalizedvaluescountry taking Analogous snippets ofdifferent patterns ofbehaviour canbeobtained edged ahead. behind Malaysia, though,theirprogress wasmuch stronger andthey line (at100). Whileboth theSlovak Republic andArgentinastarted average in1996 butendedabove itby 2001, crossingHypothetica’s Argentina andMalaysia examples below are ofcountriesthatstarted good progress,approachingHypothetica.TheSlovakRepublic, achieved Brazilhadin1996 thestate -while thelatter alsoexperienced Also visibleisthesteadyprogress madeby Jordan; by 2001 ithad Table 12. Chart 8. Chart .Kra07060606050.5 0.9 0.5 0.9 9.4 19.3 3.0 0.6 1.9 1.0 10.0 18.3 3.3 2.4 2.1 9.5 0.6 1.0 1.0 32.3 1.1 4.4 2.8 2.3 0.6 10.6 1.0 1.1 34.9 1.2 3.3 4.4 2.8 11.2 39.3 0.7 1.0 1.2 1.1 4.0 5.1 3.3 13.2 45.7 Hypothetica S. Korea 6.6 9.0 Poland 3.5 Chile Angola Chad 9.0 South Gabon China Kyrgyzstan 50 10 15 20 25 30 35 40 45 0 5 fia12121212131.3 1.3 1.2 1.2 1.2 1.2 Africa Patterns ofclosingdivides (2) Benchmarking ratios 961997 1996 9619 9819 002001 2000 1999 1998 1997 1996 9819 002001 2000 1999 1998 . . 1.0 0.9 1.0 1.0 1.0 1.0 HYPOTHETICA Kyrgyzstan S. Korea Africa S. Angola Poland Gabon China Chad Chile 51 51 51 51 51 Chapter 4 - THE EMPIRICAL APPLICATION 52 52 52 52 52 Chapter 4 - THE EMPIRICAL APPLICATION networks andtheInternet–outgrowing thetopgroup by afactor of the bottom group, experienced the highest rates ofgrowth both inmobile countries withhigherInfostates. Itisnoteworthy thatgroup D,andnot Internet. Thiswas much more sointhehave-not countries thaninthe Most ofthegainscamefrommobile networks, followed closelyby the from low very levels. Zimbabwe, Togo, Syria,Ethiopia,Nepal,Botswana andSamoa-starting growthExtraordinary was experienced by Kyrgyzstan, Mauritania, during theperiod,much greaterthanthe142% for thetop group. we compute thatnetwork growth for thebottom group exceeded 500% 184.5 and113.2, respectively. Working withthe 139 Infostate countries, ICT networks anduptake account for mostofthegrowth, registering divide between earlier. thetop andbottom groupsidentified components. Thesemovements accountfor thesomewhat diminished 13). Thesameholdstrue,butiseven morepronounced,withinspecific was consistently higheramongthehave-not (orhave-less) groups(Table about 74% andInfo-use by 87%. growth Consistent withearlierfindings, closing. Onaverage, between 1996 and2001, Infodensity increasedby existence Divide,they oftheDigital alsoaccountalmostequallyfor its As much asInfodensity andInfo-use accounted almost for equally the groupings andHypothetica. interest. Aswell, itdoessoseparatelyfor each country ofthefive above, theanalysisdigsdeeper into components of andindicators individual indicators. Asample ofthelatterfollows next. only attheInfostate level, butatthelevel ofaggregate components or performances canbecompared. Aswell, they not canbeperformed against any benchmark orgroup ofcountriesdesired,and country Similar analysescanbecarriedoutnot onlyagainstHypothetica but ended even higher. Korea, theonlycountryinthiscross-sectiontostartaboveaverage, did not.BothChileandPolandoutperformedHypothetica,whileS. parallel paths.Chinaalsoimproveditsrelativeposition,whileS.Africa was alsomadeby Kyrgyzstan andGabon,two countriesthatfollowed much bettersituation.Inthebeginningofperiod,noticeableprogress times below theaverage. Angola’sprogress wassmaller, from a starting of thescale,maderelativeprogress,butstillremainsatalmosttwenty Here wecanseethatChad,oneofthecountriesconsistentlyatbottom To uncover themainforces ofthemovements identified Drivers oftheevolution AE - Table 13. A -E Hypothetica E D C B A Hypothetica E D C B A Networks Group Hypothetica E D C B A however, stillbarelyregistered valuesin2001. Oman, Tonga, Turkmenistan, andRwanda.Many Bhutan countries, huge growth aswell. InInternet, growth wasledby Kyrgyzstan, Samoa, levels. Many otherAfricanandEastern Europeen countriesexperienced from extremely andHaiti,againcountriesthatstarted lowMauritania experienced by Zimbabwe, Botswana, Syria,Mozambique,Togo, vis- explain to agoodextentThese findings why thebottom group lostground higher thanitsmobilenetwork growth (Table 14). In addition,group Dwastheonlygroup whoseInternet growth was bottom group onlyslightly, thedifference intheInternet wassubstantial. seven andeight,respectively. However, whilein mobileitoutgrew the change indifferences, 1996-2001 divides normalized values growth, 1996-2001(%) à Detailed component analysis, by componentanalysis, group Detailed -vis group D.Inmobilenetworks, phenomenalgrowth was 0. 6. 3. 3. 1. 8. 2. 4. 2. 1. 2. 199.9 120.8 212.4 122.6 245.5 129.1 189.5 119.8 138.0 134.9 263.6 108.7 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 100.0 100.0 100.0 100.0 100.0 123.1 100.0 110.4 199.9 100.0 218.1 126.3 100.0 109.7 212.4 100.0 229.2 144.1 100.0 121.8 245.5 100.0 275.3 121.4 100.0 111.8 189.5 208.8 124.6 123.9 138.0 143.9 120.6 105.4 263.6 309.3 0. 5. 208. 9. 7. 7. 4. 2. 0. 1. 186.4 210.2 200.2 222.6 240.3 272.9 173.9 198.7 82.9 92.0 258.7 306.9 9620 9620 9620 9620 9620 962001 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 1996 415. 0. 0. 657. 947. 618. 6177.4 36.1 80.3 123.1 36.1 61.2 78.2 126.3 58.7 29.4 144.1 75.4 57.1 36.5 121.4 108.4 64.1 100.5 124.6 53.3 116.1 14.1 120.6 37.0 022. 389. 854. 552. 394. 5541.1 25.5 77.4 40.4 65.1 23.9 80.3 29.7 67.6 15.5 78.2 42.7 62.8 28.5 75.4 93.5 63.6 93.8 108.4 107.2 20.6 10.2 53.3 40.2 511009. 0. 741004. 0. 351005. 100.0 55.4 100.0 53.5 100.0 46.9 100.0 57.4 100.0 93.7 100.0 35.1 . . 875. . 561252351. . 13.5 4.4 12.2 41.1 3.5 14.1 40.4 5.2 12.8 1.2 29.7 7.3 15.6 5.8 42.7 55.1 16.4 48.7 93.5 87.9 4.9 0.8 20.6 3.6 . . 205. 011. . . . 227913.5 7.9 12.2 6.6 5.2 2.5 15.6 10.1 55.1 52.0 4.9 2.3 82902. 262. 23.8 22.4 32.6 24.8 9.0 48.2 0. 31194305253211.0 191.4 114.8 65.5 101.3 245.3 216.8 122.1 115.1 73.2 350.5 309.3 165.8 152.3 90.2 169.4 160.8 106.6 89.3 58.2 13.1 6.3 7.9 7.3 2.3 502.9 475.8 277.1 225.7 142.6 8. . 421328. 80.5 86.9 113.2 74.2 6.7 184.5 klsIfdniyUtk noueInfostate Info-use Uptake Infodensity Skills 53 53 53 53 53 Chapter 4 - THE EMPIRICAL APPLICATION 54 54 54 54 54 Chapter 4 - THE EMPIRICAL APPLICATION

Table 14. hns152195.5 195.2 phones (tertiary) phones C 1. 3. 0. 4. 081401. 294687100100.0 100.0 8.7 4.6 32.9 13.7 114.0 50.8 246.4 105.2 739.5 414.8 Internet use PCs 100.0 16.2 100.0 13.3 100.0 0.2 97.1 0.0 3.6 100.0 0.1 77.3 68.3 62.6 7.5 2.5 27.2 0.2 98.7 0.9 1.6 95.3 16.6 25.2 77.3 110.2 2.5 13.5 107.8 7.7 116.8 78.2 96.7 115.7 208.8 52.0 15.6 23.6 120.3 120.1 156.3 514.3 113.0 296.3 106.8 55.9 enrollment 274.0 224.1 literacy Internet mobile wireline C 831411441119999.0 9.9 100.0 100.0 9.9 70.4 64.5 101.7 39.8 29.3 98.2 141.1 517.6 152.1 113.5 651.1 111.1 145.7 16.5 120.3 124.4 61.7 159.4 580.5 119.1 58.0 residential 1,273.8 152.0 2,702.4 TV households 123.9 1,568.3 8.9 134.1 177.5 123.7 86.3 168.5 3,282.2 3176.3 enrollment 2,996.7 2,225.8 659.7 (combined) 419.9 enrollment literacy 553.8 4.4 9.7 78.3 Internet 1.0 901.0 50.2 mobile wireline 43.5 518.1 783.6 38.4 14.0 4.9 0.2 Internet use 0.2 PCs residential phones 381.5 10.8 430.1 households TV 22.3 4.5 enrollment (tertiary) enrollment (combined) literacy Internet mobile wireline C 0. 7. 291382. 73691. . . 03100.0 50.3 4.4 2.3 16.5 6.9 100.0 57.3 91.0 25.5 33.5 23.9 123.8 52.9 85.3 73.3 371.7 208.5 127.6 117.2 138.4 Internet use 132.6 PCs 145.1 residential 138.3 households TV enrollment (obnd 152.5 (combined) (etay 204.0 (tertiary) advanced networks. inFinland andLichtenstein,also observed countrieswithhighly AfricanRepublic.Congo D.R.andtheCentral Minordeclineswere Guyana andFrench Polynesia, whiletherewere declinesinCongo, did not increase in French Guyana,Samoa,Kenya, Morocco, Armenia, Tanzania, Bycontrast,thesenetworks SaudiArabiaandBrazil. Bhutan, networks wasledby Sudan,followed by Albania,Sri Lanka,CapeVerde, D, which grew almost timestheaverage. three Growth inthewireline The highest growth networks infixed wasexperienced againbygroup Analysis by indicators andgroup Analysisbyindicators normalized values growth, 1996-2001(%) 2. 2. 7. 9. 1. 3. 076. . . 0. 100.0 100.0 6.9 4.1 60.0 40.7 430.0 28.7 137.6 114.9 1,366.9 106.2 192.4 2,966.2 175.4 461.9 222.0 221.6 5. 7. 4. 0. 0. 2. 578. 161. 0. 100.0 100.0 16.2 11.6 81.3 65.7 100.0 100.0 123.8 3.3 101.1 2.0 205.7 143.4 32.6 17.5 278.5 251.3 101.1 67.3 202.1 146.1 354.4 289.8 9620 9620 9620 9620 9620 962001 1996 2001 1996 2001 1996 2001 1996 2001 1996 2001 1996 553611. 6. . 16041. . . 20100.0 12.0 2.3 0.0 16.2 0.4 51.6 3.4 164.1 12.8 356.1 55.5 4. ,8. ,9. ,2. ,0. 733.0 6,806.4 4,024.5 1,397.7 1,186.8 542.1 Hypothetica E D C B A 5. 1. 3. 431748. 943. 599. 100.0 90.5 45.9 38.9 89.4 81.8 107.4 94.3 133.4 117.1 159.3 2. 1. 6. 211055. 61941. 12100.0 81.2 13.1 9.4 66.1 53.4 100.5 82.1 167.1 116.4 226.1 7. 2. 4. 0. 1. 049. 3050.7 43.0 98.8 90.4 118.6 104.1 147.4 129.4 176.0 5. 6. 0. 2. 585. . . 81100.0 88.1 6.1 3.7 52.9 35.8 121.2 101.2 169.4 154.5 665701. 0. . 640113100100.0 100.0 1.3 0.1 46.4 1.4 102.7 15.7 597.0 96.6 2. 4. 059. 633. 0. 100.0 100.0 36.8 26.3 93.8 80.5 140.3 128.8 974. 6313.5 23.2 10.5 66.3 38.8 17.9 47.5 23.7 9.3 19.7 22.5 13.9 . . . 3.0 9.1 3.5 2.2 . 3. . 96100100.0 100.0 19.6 0.3 135.3 3.3 . 0. . 67100100.0 100.0 26.7 1.0 204.5 6.6 0. 100.0 100.0 Mauritania andBrazil. Mauritania led growth were Sudan,Ghana,Albania,SriLanka, China,Lao, growth Again saturation. was led by thebottom group. Countries that situation inthetop groupremaininglargelyunchanged, asitiscloseto More modestgrowth occurredinresidential phone lines,withthe Gambia, Armenia,Moldova andJamaica. with highergrowth were CapeVerde, Latvia, Bangladesh,Honduras, weak, albeitstrongerproportionately thanthetop group.Countries in thethree intermediate groups. Growth inthebottom group was Growth secondinICTuptake inPCswasadistant placemainly andtook Togo, Morocco, BeninandChina. Papua New Guinea,Algeria,Bangladesh,Kyrgysztan, Moldova, Lao, Syria, Sudan,Gabon,Vietnam, Myanmar, Chad, Eritrea, Mauritania, technology anditsuniversal appeal.Countriesthatledthisgrowth were movements areindicative ofthecontinuinginroads made by thenewest and exceeded thatofmobilephoneswhich strong. These wasvery But even inthetop two groups, growth inInternet usagewas thehighest ushering inthenew technology, fromnext which tonothing. againstarted uptake butalsoinnetworks. Thisismostlydueto thetwo bottom groups increased by733%,more than any other indicatorused,not onlyin Internet use,which exploded It intheperiodunderconsideration. that ofthesecondgroup(B).Overwhelmingly, thisgrowth camefrom 90% for thetopgroup. Thefourth group (D)hadagrowth rate double Growth inICTuptake was350%for thebottom groupcompared to Samoa, Libya, Malaysia, Vietnam,Latvia,Niger, EthiopiaandMongolia. and wasstronger inDjibouti,Kyrgyzstan, Rwanda, Yemen, Ghana, stronger thanthetop -growth wasledby thesecondgroup ofcountries education, whilegrowth was stillstrong amongthebottom groups -and Mozambique, Bolivia,Mali,Thailand,AlbaniaandYemen. Intertiary enrolment Bangladesh,Jordan, -Liberia,Uganda,Ethiopia,Brazil, topping thelist. Amoremixed group ofcountriesledgrowth inoverall Niger, AfricanRepublic, Gambia,Central Liberia,EthiopiaandMali came primarilyfrom AfricancountrieswithChad,Burkina Faso, Benin, This wastruefor bothliteracy andenrollment. thegains Inliteracy have-not countries,withgroup Eoutgrowing groupAby afactor ofsix. higher skills,growth andgenerallymoresoamongthe wassignificant room to grow aswe amongagoodnumberofcountries.Again, move to variability than otherInformation Society indicators andtheyhave little education,inparticular, aresubjectto less enrollment inprimary slowly, andfor reasons relatedto theirmeasurement.Literacy rates and The growthinskillswaslowbothforinherentreasons,asimprove 55 55 55 55 55 Chapter 4 - THE EMPIRICAL APPLICATION 56 56 56 56 56 Chapter 4 - THE EMPIRICAL APPLICATION Chart 9. Chart – althoughthemovement ispainfullyslow. Divide are alsotheonesthatmove moreinthedirectionofalleviatingit networks. Thus,thesamefactors thataccountthemost for theDigital for bytheuseofInternet, followed by mobilephonesandInternet over theperiod.Clearly, much oftheupward movement isaccounted groups, aswell asitoffers comparisons withtheaverage movements each factor contributed theclosinggapbetween to thetop andthebottom 1996 9).Thisshows ofHypothetica ratios (Chart theextent towhich 2001. Then,their2001/1996 ratiowasplotted, togetherwiththe2001/ group were computed for each indicatorandfor theyears 1996 and closing oftheDigitalDivideoverall, theratiosoftop andthebottom To summarize,therelative contributionofindividualfactors intheslow Mozambique, Pakistan, Syria,BangladeshandNepal. stronger gainswere registered inEritrea, Gabon,Lao,Ethiopia,Gambia, Among countrieswithconsiderableroom togrow, proportionately Even households. moremodestgrowth tookplaceinthecaseofTV 10 12 2001/1996 0 2 4 6 8 ratios Contributors Divide totheclosingDigital PCs

Literacy HYPOTHETICA E / A

Enrollment

Tertiary ed.

Wireline

TV households

Res. phones

Internet

Mobile

Internet use Chapter 5 COUNTRY PROFILES

imilarities and differences in the patterns of infostate growth among countries have been identified. These are useful in assessing a country’s comparative performance, identifying its relative strengths and SS weaknesses and, at a more detailed level, linking the changes to specific initiatives and policies. The latter is directly applicable to individual countries. Such exercises can then lead to the redrafting, refinement or calibration of such initiatives and policies in a way that would reflect the accumulated performance experiences. What works and what does not work, what works more and in what context, can be uncovered and understood. This will be accomplished best if it is not based solely on the experiences of the country concerned, but rather drawing on the reservoir of experiences everywhere - some of which will be inevitably more relevant to the reality of a country than others.

Such investigations can be made for country groupings, whether they are arrived at through Infostate rankings, incomes, regional or other considerations, or for country pairs. Moreover, they can entail a comprehensive approach, encompassing simultaneously all areas of interest or they can be more targeted to a well-defined priority area, say, telecommunications policy. Early examples of this approach were provided in Chapters 3 and 4. In any case, the specific situation of an individual country is always the starting point, and this involves both the identification of where things stand and the dynamics of arriving there. What follows shows in graphical form the situation of each country with respect to a set of several key indicators used to arrive at Infostates, as well as the country’s evolution over the 1996-2001 period. In addition, it contains the 2001 situation of the average country, Hypothetica,for easy benchmarking. (All values are in index form).

57 58 58 58 58 58 Chapter 5 - COUNTRY PROFILES Internet users Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic residential phones residential phones Internet users residential phones PCs PCs PCs HYPOTHETICA 1996 values ARGENTINA ALGERIA TV households TV households TV households 100 100 120 100 150 wireline wireline wireline 20 40 60 80 20 40 60 80 50 0 0 0 mobile enrollment mobile mobile enrollment 2001 values enrollment Internet literacy Internet literacy Internet literacy 2001 average values (Hypothetica) int’l telephonetraffic Internet users int’l telephonetraffic Internet users residential phones int’l telephonetraffic residential phones Internet users residential phones PCs PCs PCs ARMENIA ALBANIA TV households ANGOLA TV households TV households 100 120 100 120 140 wireline 100 20 40 60 80 wireline wireline 20 40 60 80 20 40 60 80 0 0 0 mobile enrollment mobile enrollment mobile enrollment literacy Internet Internet literacy Internet literacy Internet users Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs 1996 values AUSTRALIA BARBADOS BAHRAIN TV households TV households TV households 1000 400 600 800 200 100 150 200 250 wireline 100 150 200 250 wireline wireline 50 50 0 0 0 mobile mobile enrollment enrollment mobile enrollment 01vle 2001average values (Hypothetica) 2001 values Internet literacy literacy Internet literacy Internet Internet users int’l telephonetraffic int’l telephonetraffic Internet users Internet users residential phones int’l telephonetraffic residential phones residential phones PCs PCs PCs BANGLADESH BELGIUM AUSTRIA TV households TV households TV households wireline 100 150 200 250 300 350 wireline 100 100 150 200 250 300 350 wireline 50 20 40 60 80 50 0 0 0 mobile mobile enrollment mobile enrollment enrollment Internet Internet literacy literacy literacy Internet 59 59 59 59 59 Chapter 5 - COUNTRY PROFILES 60 60 60 60 60 Chapter 5 - COUNTRY PROFILES int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic Internet users Internet users residential phones residential phones residential phones PCs PCs PCs 1996 values BOLIVIA EIEBENIN BELIZE BRAZIL TV households TV households TV households wireline wireline 100 120 140 100 120 140 100 120 wireline 20 40 60 80 20 40 60 80 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment 2001 values Internet literacy Internet literacy Internet literacy 2001 average values (Hypothetica) int’l telephonetraffic Internet users Internet users int’l telephonetraffic residential phones Internet users int’l telephonetraffic residential phones residential phones PCs PCs PCs BRUNEI DARUSSALAM BOTSWANA TV households TV households TV households 100 100 150 200 250 wireline 100 wireline 20 40 60 80 wireline 50 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment literacy Internet Internet Internet literacy literacy Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic residential phones Internet users residential phones residential phones PCs PCs PCs 1996 values CAMBODIA UGRABURKINA FASO BULGARIA CANADA TV households TV households TV households 100 200 300 400 500 600 700 100 wireline 100 150 200 wireline wireline 20 40 60 80 50 0 0 0 mobile mobile enrollment mobile enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values Internet Internet Internet literacy literacy literacy int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic Internet users Internet users Internet users residential phones residential phones residential phones CENTRAL AFRICAN REPUBLIC PCs PCs PCs CAMEROON TV households TV households TV households wireline 100 100 100 20 40 60 80 20 40 60 80 20 40 60 80 wireline 0 0 0 wireline mobile mobile mobile enrollment enrollment enrollment Internet Internet Internet literacy literacy literacy 61 61 61 61 61 Chapter 5 - COUNTRY PROFILES 62 62 62 62 62 Chapter 5 - COUNTRY PROFILES int’l telephonetraffic int’l telephonetraffic Internet users Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values COSTA RICA CHINA TV households TV households CHAD TV households 100 100 120 140 100 120 140 160 wireline wireline wireline 20 40 60 80 20 40 60 80 20 40 60 80 0 0 0 mobile mobile enrollment enrollment 2001 values mobile enrollment Internet Internet literacy literacy literacy Internet 2001 average values (Hypothetica) int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones Internet users residential phones residential phones PCs PCs PCs COTE D’IVOIRE COLOMBIA CHILE TV households TV households TV households wireline 100 150 200 100 wireline wireline 100 120 140 160 50 20 40 60 80 20 40 60 80 0 0 0 mobile enrollment mobile mobile enrollment enrollment Internet literacy literacy Internet Internet literacy int’l telephonetraffic int’l telephonetraffic Internet users Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values DENMARK CROATIA CYPRUS TV households TV households TV households 100 200 300 400 500 600 700 800 wireline wireline 100 120 140 160 100 150 200 250 300 wireline 0 20 40 60 80 50 0 0 mobile mobile mobile enrollment enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values Internet Internet literacy literacy Internet literacy Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic Internet users Internet users residential phones residential phones residential phones PCs PCs PCs CZECH REPUBLIC DJIBOUTI TV households TV households TV households CUBA wireline 100 150 200 250 300 100 100 120 50 wireline 20 40 60 80 wireline 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment Internet literacy Internet Internet literacy literacy 63 63 63 63 63 Chapter 5 - COUNTRY PROFILES 64 64 64 64 64 Chapter 5 - COUNTRY PROFILES Internet users Internet users Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values EL SALVADOR ECUADOR ESTONIA TV households TV households TV households 100 150 200 250 300 100 120 140 100 120 140 wireline wireline wireline 50 20 40 60 80 20 40 60 80 0 0 0 mobile mobile enrollment mobile enrollment enrollment 2001 values Internet literacy literacy Internet Internet literacy 2001 average values (Hypothetica) Internet users int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs ETHIOPIA ERITREA EGYPT TV households TV households TV households 100 100 100 120 140 wireline wireline 20 40 60 80 20 40 60 80 20 40 60 80 wireline 0 0 0 mobile mobile mobile enrollment enrollment enrollment Internet Internet literacy Internet literacy literacy int’l telephonetraffic int’l telephonetraffic Internet users Internet users int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs 1996 values GAMBIA FRANCE TV households TV households TV households FIJI 100 150 200 250 300 100 120 100 wireline wireline wireline 20 40 60 80 50 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values Internet Internet literacy literacy Internet literacy Internet users int’l telephonetraffic Internet users int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs GEORGIA FINLAND GABON TV households TV households TV households 1,000 100 120 140 wireline 200 400 600 800 100 120 140 20 40 60 80 wireline wireline 20 40 60 80 0 0 0 mobile enrollment mobile enrollment enrollment mobile Internet literacy literacy Internet Internet literacy 65 65 65 65 65 Chapter 5 - COUNTRY PROFILES 66 66 66 66 66 Chapter 5 - COUNTRY PROFILES Internet users int’l telephonetraffic Internet users int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values GERMANY GREECE GUINEA TV households TV households TV households 100 100 150 200 250 300 350 wireline 100 150 200 250 300 350 wireline 20 40 60 80 50 wireline 50 0 0 0 mobile mobile enrollment mobile enrollment enrollment 2001 values Internet literacy Internet literacy literacy Internet 2001 average values (Hypothetica) Internet users int’l telephonetraffic int’l telephonetraffic Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs GUATEMALA GUYANA TV households GHANA TV households TV households wireline 100 120 140 100 100 wireline wireline 20 40 60 80 20 40 60 80 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment Internet Internet literacy Internet literacy literacy Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs 1996 values HONDURAS HUNGARY TV households TV households TV households INDIA 100 100 wireline 100 150 200 250 wireline 20 40 60 80 wireline 20 40 60 80 50 0 0 0 mobile mobile mobile enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values enrollment Internet Internet literacy literacy Internet literacy Internet users Internet users int’l telephonetraffic int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs HONGKONG INDONESIA ICELAND TV households TV households TV households 1,000 200 400 600 800 100 120 100 150 200 250 300 350 400 wireline wireline wireline 20 40 60 80 50 0 0 0 mobile mobile enrollment mobile enrollment enrollment Internet Internet literacy literacy Internet literacy 67 67 67 67 67 Chapter 5 - COUNTRY PROFILES 68 68 68 68 68 Chapter 5 - COUNTRY PROFILES Internet users int’l telephonetraffic int’l telephonetraffic Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values JAMAICA TV households ISRAEL TV households TV households wireline IRAN 100 120 100 150 200 250 300 350 400 100 120 140 20 40 60 80 wireline wireline 50 20 40 60 80 0 0 0 mobile enrollment mobile mobile enrollment enrollment 2001 values Internet literacy literacy Internet literacy Internet 2001 average values (Hypothetica) Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs IRELAND JAPAN TV households TV households TV households ITALY 100 150 200 250 300 350 400 100 150 200 250 300 350 100 150 200 250 300 350 400 wireline wireline wireline 50 50 50 0 0 0 mobile mobile enrollment mobile enrollment enrollment Internet literacy Internet literacy literacy Internet Internet users Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values KYRGYZSTAN S. KOREA JORDAN TV households TV households TV households 100 120 140 100 120 140 100 200 300 400 500 wireline wireline 20 40 60 80 20 40 60 80 wireline 0 0 0 mobile mobile enrollment enrollment enrollment mobile 01vle 2001average values (Hypothetica) 2001 values Internet Internet literacy Internet literacy literacy Internet users int’l telephonetraffic Internet users int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs LAO P.D.R. KUWAIT KENYA TV households TV households TV households 100 120 100 100 150 200 wireline wireline 20 40 60 80 wireline 20 40 60 80 50 0 0 0 mobile mobile enrollment enrollment mobile enrollment Internet Internet literacy literacy literacy Internet 69 69 69 69 69 Chapter 5 - COUNTRY PROFILES 70 70 70 70 70 Chapter 5 - COUNTRY PROFILES Internet users Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values LITHUANIA LATVIA MACAU TV households TV households TV households 100 120 140 160 100 150 200 100 120 140 160 wireline wireline wireline 20 40 60 80 50 20 40 60 80 0 0 0 mobile enrollment mobile mobile enrollment 01vle 2001average values (Hypothetica) 2001 values enrollment literacy Internet literacy Internet literacy Internet Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic Internet users Internet users residential phones residential phones residential phones PCs PCs PCs LUXEMBOURG MADAGASCAR LEBANON TV households TV households TV households 100 100 120 140 160 100 200 300 400 500 wireline wireline 20 40 60 80 20 40 60 80 wireline 0 0 0 mobile mobile enrollment mobile enrollment enrollment literacy Internet Internet Internet literacy literacy Internet users int’l telephonetraffic int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones Internet users residential phones PCs PCs PCs 1996 values MAURITANIA MALAWI TV households TV households TV households MALI 100 100 wireline 100 20 40 60 80 wireline 20 40 60 80 wireline 20 40 60 80 0 0 0 mobile mobile mobile enrollment enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values Internet Internet literacy Internet literacy literacy Internet users Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs MAURITIUS MALAYSIA MALTA TV households TV households TV households 100 150 200 250 300 100 150 200 wireline wireline 100 150 200 250 50 50 wireline 50 0 0 0 mobile mobile enrollment enrollment enrollment mobile Internet literacy Internet literacy Internet literacy 71 71 71 71 71 Chapter 5 - COUNTRY PROFILES 72 72 72 72 72 Chapter 5 - COUNTRY PROFILES Internet users Internet users Internet users int’l telephonetraffic int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs MOZAMBIQUE 1996 values MONGOLIA MEXICO TV households TV households TV households 100 100 120 140 wireline 100 120 140 wireline 20 40 60 80 wireline 20 40 60 80 20 40 60 80 0 0 0 enrollment mobile mobile enrollment mobile enrollment 01vle 2001average values (Hypothetica) 2001 values Internet literacy literacy Internet Internet literacy int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs MOROCCO MYANMAR MOLDOVA TV households TV households TV households 120 100 140 100 120 wireline 100 120 wireline wireline 20 40 60 80 20 40 60 80 20 40 60 80 0 0 0 mobile enrollment mobile enrollment mobile enrollment literacy literacy Internet Internet literacy Internet Internet users int’l telephonetraffic Internet users residential phones Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones PCs PCs PCs NETHERLANDS 1996 values NICARAGUA NAMIBIA TV households TV households TV households 1,000 100 100 120 wireline 200 400 600 800 wireline 20 40 60 80 20 40 60 80 wireline 0 0 0 mobile mobile enrollment enrollment mobile enrollment 01vle 2001average values (Hypothetica) 2001 values Internet literacy literacy Internet Internet literacy int’l telephonetraffic Internet users Internet users int’l telephonetraffic Internet users residential phones int’l telephonetraffic residential phones residential phones PCs PCs PCs NEW ZEALAND NIGERIA TV households NEPAL TV households TV households 100 100 wireline 100 200 300 400 500 600 700 800 20 40 60 80 wireline 20 40 60 80 wireline 0 0 0 mobile mobile enrollment mobile enrollment enrollment Internet literacy literacy Internet Internet literacy 73 73 73 73 73 Chapter 5 - COUNTRY PROFILES 74 74 74 74 74 Chapter 5 - COUNTRY PROFILES Internet users int’l telephonetraffic int’l telephonetraffic Internet users int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs PAPUA NEWGUINEA 1996 values PAKISTAN NORWAY TV households TV households TV households 100 120 100 wireline wireline wireline 20 40 60 80 100 200 300 400 500 600 700 20 40 60 80 0 0 0 mobile mobile enrollment mobile enrollment 01vle 2001average values (Hypothetica) 2001 values enrollment Internet literacy Internet literacy Internet literacy Internet users int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs PARAGUAY PANAMA TV households OMAN TV households TV households 100 120 wireline 100 120 100 120 140 160 wireline 20 40 60 80 wireline 20 40 60 80 20 40 60 80 0 0 0 mobile enrollment mobile enrollment enrollment mobile literacy Internet literacy Internet literacy Internet Internet users int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values POLAND QATAR TV households TV households PERU TV households 100 120 140 wireline 100 150 200 250 100 120 140 160 20 40 60 80 wireline wireline 50 20 40 60 80 0 0 0 enrollment mobile mobile mobile enrollment 01vle 2001average values (Hypothetica) 2001 values enrollment literacy Internet Internet literacy Internet literacy residential phones Internet users Internet users int’l telephonetraffic int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones PCs PCs PCs PHILIPPINES PORTUGAL ROMANIA TV households TV households TV households 100 120 140 100 120 100 150 200 250 300 350 wireline wireline wireline 20 40 60 80 20 40 60 80 50 0 0 0 mobile mobile mobile enrollment enrollment enrollment Internet literacy Internet literacy Internet literacy 75 75 75 75 75 Chapter 5 - COUNTRY PROFILES 76 76 76 76 76 Chapter 5 - COUNTRY PROFILES Internet users Internet users int’l telephonetraffic int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs SAUDI ARABIA 1996 values SINGAPORE wireline TV households RUSSIA TV households TV households 100 120 140 160 20 40 60 80 wireline 100 120 140 160 0 100 200 300 400 500 wireline 20 40 60 80 0 0 mobile mobile enrollment mobile enrollment enrollment 01vle 2001average values (Hypothetica) 2001 values Internet literacy Internet literacy Internet literacy Internet users int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs SLOVAK REPUBLIC SENEGAL SAMOA TV households TV households TV households 100 150 200 250 wireline 100 150 200 100 50 wireline wireline 50 20 40 60 80 0 0 0 mobile enrollment mobile mobile enrollment enrollment literacy Internet Internet Internet literacy literacy int’l telephonetraffic Internet users Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs 1996 values SLOVENIA TV households SUDAN TV households TV households SPAIN 100 150 200 250 300 350 100 120 140 100 150 200 250 300 350 wireline wireline 50 wireline 20 40 60 80 50 0 0 0 mobile enrollment enrollment mobile mobile enrollment 01vle 2001average values (Hypothetica) 2001 values literacy Internet literacy Internet Internet literacy int’l telephonetraffic Internet users Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs SOUTH AFRICA SRI LINKA SWEDEN TV households TV households TV households 100 200 300 400 500 600 100 120 140 100 120 wireline wireline wireline 20 40 60 80 20 40 60 80 0 0 0 mobile mobile enrollment enrollment mobile enrollment Internet Internet literacy literacy literacy Internet 77 77 77 77 77 Chapter 5 - COUNTRY PROFILES 78 78 78 78 78 Chapter 5 - COUNTRY PROFILES Internet users int’l telephonetraffic residential phones Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones PCs PCs PCs SWITZERLAND 1996 values TANZANIA TV households TV households TV households wireline TOGO 100 wireline 20 40 60 80 100 200 300 400 500 100 0 wireline 20 40 60 80 0 0 mobile mobile enrollment enrollment mobile 01vle 2001average values (Hypothetica) 2001 values enrollment Internet literacy literacy Internet literacy Internet Internet users int’l telephonetraffic Internet users Internet users int’l telephonetraffic int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs TRINIDAD ANDTOBAGO THAILAND TV households SYRIA TV households TV households 100 120 wireline 100 120 140 160 20 40 60 80 100 120 140 160 wireline wireline 20 40 60 80 0 20 40 60 80 0 0 enrollment mobile mobile enrollment enrollment mobile Internet literacy literacy Internet Internet literacy Internet users int’l telephonetraffic Internet users int’l telephonetraffic int’l telephonetraffic Internet users residential phones residential phones residential phones PCs PCs PCs UNITED ARABEMIRATES 1996 values UGANDA TUNISIA TV households TV households TV households wireline 100 150 200 250 300 100 100 120 140 wireline wireline 50 20 40 60 80 60 20 40 80 0 0 0 enrollment enrollment mobile mobile mobile 01vle 2001average values (Hypothetica) 2001 values enrollment literacy Internet literacy Internet Internet literacy int’l telephonetraffic Internet users Internet users int’l telephonetraffic Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs UNITED KINGDOM UKRAINE TURKEY TV households TV households TV households 100 150 200 100 150 200 250 300 350 100 120 140 160 wireline wireline wireline 50 50 20 40 60 80 0 0 0 mobile mobile enrollment mobile enrollment enrollment Internet literacy Internet literacy Internet literacy 79 79 79 79 79 Chapter 5 - COUNTRY PROFILES 80 80 80 80 80 Chapter 5 - COUNTRY PROFILES int’l telephonetraffic int’l telephonetraffic Internet users Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs UNITED STATES 1996 values VENEZUELA YEMEN TV households TV households TV households 1,000 100 120 140 160 100 120 140 200 400 600 800 wireline wireline wireline 20 40 60 80 20 40 60 80 0 0 0 mobile enrollment enrollment mobile mobile enrollment 01vle 2001average values (Hypothetica) 2001 values Internet Internet literacy literacy literacy Internet Internet users int’l telephonetraffic int’l telephonetraffic Internet users Internet users int’l telephonetraffic residential phones residential phones residential phones PCs PCs PCs YUGOSLAVIA VIETNAM URUGUAY TV households TV households TV households 100 120 140 100 120 140 160 100 120 140 wireline wireline wireline 20 40 60 80 20 40 60 80 20 40 60 80 0 0 0 mobile mobile enrollment enrollment mobile enrollment Internet literacy literacy Internet Internet literacy Internet users int’l telephonetraffic residential phones PCs 1996 values ZAMBIA TV households 100 wireline 20 40 60 80 0 mobile 01vle 2001average values (Hypothetica) 2001 values enrollment Internet literacy Internet users int’l telephonetraffic residential phones PCs ZIMBABWE TV households 100 120 wireline 20 40 60 80 0 mobile enrollment Internet literacy 81 81 81 81 81 Chapter 5 - COUNTRY PROFILES

Chapter 6 MACROECONOMIC PERSPECTIVES5

aving quantified the Digital Divide and its evolution, we proceed to relate the measured Infostates to one key macroeconomic variable: Gross Domestic Product. As a measure of aggregate output and overall HHeconomic performance GDP is heavily correlated with connectivity, a result demonstrated often in studies of the internal divide. Although there are other influential variables interacting in a complex way, we limit the focus of this analysis to the examination of the impact of Infostates and Infodensities on GDP.

The GDP per capita series were reconciled with the Infostate indices for the years 1996-2001. Only those countries for which observations were available for both series and for all years were retained. The combined data set contains a total ranging from 118 to 131 countries, depending on the year. Simple scatter plots of GDP per capita and these indexes suggest a strong positive relationship. Representative of these results are Charts 10 and 11 below plotting Infostate and Infodensity against GPD per capita for 2001. (GDP per capita is measured in purchasing power parity terms). Chart 10. Infostate and GDP per capita, 2001 Chart 11. Infodensity and GDP per capita, 2001

Infostate Infodensity

250 250

200 200

150 150

100 100

50 50 $ (US, PPP) $ (US, PPP) 0 0 0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000

5 Authored by Brenda Spotton Visano, Susan (Sam) Ladner, Aqeela Tabussum and Xiaomei Zhang (York University).

83 84 84 84 84 84 Chapter 6 - MACROECONOMIC PERSPECTIVES Table 15 Botswana Botswana US). Table 15 listsexamples from selectedcountries. (at$24,371per capita US)wasmorethandoubleEstonia’s (at$9,834 Infostates (141 and149, respectively) inthatsameyear, yet GDP Italy’s of Kenya ($1,014 US),in2001. Estonia hadcomparable andItaly Senegal ($5,329USversus $1,600 US)andmore timesthat thanfive points), yet GDP wasmorethanthreetimethatof Algeria’spercapita Infostates (29points) andwere onlyslightlyhigherthanKenya (at21 per-capita differences. For example, AlgeriaandSenegalhadidentical Countries withcomparable Infostates appearsubjecttosizeableGDP- isclearlyvisible,closerexaminationcapita reveals noticeabledeviations. While thepositive relationship between theseindices andGDPper are thesamecountriesasthoseingroups AandBabove. above. Itfollows thatthecountrieswithabove-average GDPpercapita Infostate Empirical groups C,D,andEinChapter4(“The Application”) 10in Charts and11 above) arethesamecountriesappearingin (approximately USin2001 $9,000 lines andindicatedby thevertical thosecountrieswithbelow-averageFurthermore, GDPpercapita isgreaterthehighervalueofindex.on GDPpercapita Specifically, thecross-sectional datasuggest thattheimpact oftheindices Infodensity andInfostate GDPisnon-linear. indicesandpercapita suggestThe scatterplots thatthepositive relationshipbetween the Algeria Algeria Russia Russia Gabon Gabon tl .24,371 .. Italy . Deviations GDPfor inpercapita countrieswithsimilarInfostates, 2001 ihGPP Low High GDP-PC (Infostate) (Infostate) of low of low of low of (Infostate) (Infostate) ...... 5,329 5,329 6,304 6,304 7,876 7,876 8,830 8,830 (149) (29) (29) (34) (34) (50) (50) (73) (73) hlpie . 4,050 ... Philippines ibbe..2,681 ... Zimbabwe odrs..2,520 ... Honduras aac . 3,993 ... Jamaica 5,989 ... Panama eea . 1,600 ... Senegal soi . 9,834 ... Estonia Bolivia ey . 1,014 ... Kenya .... GDP-PC 2,511 (141) (21) (29) (30) (34) (47) (50) (71) (73) ifrnea differenceas% difference as% D-CInfostate GDP-PC 2.%38.1% 425.5% 3.%0.0% 233.1% 3.%13.3% 135.1% 5.%0.0% 150.2% 1.%6.4% 213.7% 2.%2.8% 121.1% 4.%5.7% 147.8% 45 0.0% 94.5% 74 0% 47.4% 6 the results to thesedata differences. of therelationship between Infodensity andGDPpercapita mightideallyexplore thesensitivityof capita calculation,GDPisdivided by thepopulation. Asubsequent andmore detailed examination is measured per100 insomecasesandperhouseholdother persons cases. IntheGDPper is not identicalto thepercapita transformation ofGDP. IntheInfodensity index thecapital stock The “percapita” transformation ofnetworks andskillsinthecalculationofInfodensity index Table 16 capita outputasaproxyfordevelopment. capital andlabourskillsGDPpercapitameasures aggregateper GDP,per capita whereInfodensity captures stock thepercapita ofICT strength of thisrelationship,we examine theimpact ofInfodensity on development attempt ofacountry. Inapreliminary to estimate the by thenatureanddegreeof andbeinginfluenced influencing ofeconomicdevelopmentstate iscomplex, withconnectedness examples from selected countries. Sweden’s Infostate was231 149. compared withItaly’s Table 16 lists GDP inItalyandSweden were $24,371US and$24,930 US,respectively, an Infostate ofonly10 compared withMongoliaat39.Where percapita GDPtothatofMongolia($1,672per capita USversus $1,845 US)but differentvery Infostates. For example, in2001, Bangladeshhadasimilar Contrariwise, somecountrieswithsimilarpercapitaGDPsexhibited Mongolia eao ... 87 ...... Lebanon odv ...... Moldova Bulgaria wdn...231 ..... Sweden Estonia onr GPP) onr GPP) %o o %oflow %oflow (GDP-PC) Country (GDP-PC) Country ihIfsaeLwIfsaedfeec sdifferenceas difference as Infostate Low Infostate High . Deviations inInfostates for GDP, countrieswithsimilarpercapita 2001 ... 39 ...... 141 ...... 87 ...... 49 The relationship between a country’s ‘ICT-ization’The relationshipbetween acountry’s andits Aggregate Production Function 6.1 InfodensityasanInputinto (24,930) (1,845) (9,834) (2,289) (5,748) (6,195) Trinidad & Tobago Bangladesh una...12 ..... Guinea Algeria Gabon tl . 149 ... Italy .. 10 ...... 91 ...... 29 ..... 34 ..... (10,092) (24,371) (1,672) (2,052) (5,329) (6,304) 6 nott GDP-PC Infostate 3.%10.3% 333.3% 0.%11.5% 308.3% 0.%7.9% -1.7% 200.0% 155.9% 49 -2.6% 54.9% 50 2.3% 55.0% 85 85 85 85 85 Chapter 6 - MACROECONOMIC PERSPECTIVES 86 86 86 86 86 Chapter 6 - MACROECONOMIC PERSPECTIVES Table 17 apea Sample 2001 2000 1999 1998 1997 1996 year ** The marginal effect Themarginal ofachange ofInfodensity isgiven onGDPpercapita by:a ** Thereported values oftheparameter estimates correspond to anInfodensity ofabaseequal index to 1;regression errors are * capita onInfodensitycapita for theyears 1996-2001. Table 17 theresultsoflinearregressions below ofGDPper reports In the absence of other input data, the constant term( In theabsenceofother input theconstant data, production function,where postulated isaversion elasticityofsubstitution oftheconstant and GDPpercapita(GDPPC),theaggregate production function Given theapparentnon-linearrelationship between Infodensity (ID) as aninput withthatlaidoutintheFramework. toGDPisconsistent interconnected andglobalized,thisapproach ofmodellingInfodensity of productive connectednessinaworld thatbecomesincreasingly input intonecessary theaggregate production function.Asameasure from thelikelihood thatthecomponents ofInfodensity have becomea Theoretically, thepossibilitythatInfodensity contributes toGDPderives form (with not onlyscaleeffects buttheeffect ofany omitted inputs. Inlog-log . Regression results* of thevariables. normally distributed. 235)(27.56) (203.50) 172)(27.35) (197.21) 158)(26.50) (185.86) 165)(24.43) (166.58) 107)(24.94) (160.71) 136)(24.94) (153.62) .1 .0089188787. 124 77.5 8,738 118 0.879 1.10 9.210 .3 .8 .5 2 ,0 40135 74.0 9,205 127 0.856 1.082 9.332 .9 .0 .4 2 ,4 56134 65.6 8,747 128 0.848 1.004 9.396 .6 .1 .2 3 ,8 73136 57.3 8,488 131 0.822 0.917 9.465 .9 .0 .2 3 ,4 99153 49.9 8,448 131 0.828 0.902 9.596 .9 .4 .2 3 ,8 27164 42.7 8,281 131 0.828 0.844 9.691 tsa)(-tt qae nsml $SPP x0)($) (x100) PPP) ($US insample squared (t-stat) (t-stat) (1a) 1 GDPPC (1) 0 ln ln denoting thenaturallogarithm),equation(1)becomes: lsiiyRcutisma eneffect** mean mean countries R Elasticity GDPPC = a 1

= e a0

a * ID e 0 denotes thebaseofnaturallogarithm: +a a1 1 ln ID fcpt density capita # of 1 D e Info GDP per *(GDPPC/ID) andisevaluated atthemean a 0

) willcapture Marginal reported forreported Infodensity. Themarginaleffects ofaunitincreasein 2001, inclusive, are similarto those theresults(not reported) related change tothepercentage inIn examined theextent towhich change apercentage GDPis inpercapita Employing similartothatusedabove, alogarithmicspecification we that capacity)istherelevant variable for thisanalysis. productiveindicator ofboth capacityanditseffective acountry’s useof competitive we mightexpect anationtobe.Thus,Infostate (asan the greater theexpected uptake andintensity ofuse,andthemore For agiven stock andlabour(asmeasuredby Infodensity), ofICTcapital appropriate tothislineofreasoningisInfostate, ratherthanInfodensity. the aggregateproduction function,asexplored above, thevariable GDP. Whilecloselyrelated to theissueofInfodensity asaninput into itscompetitiveness, asmeasuredbyinfluence thegrowth initspercapita nation’s technological capacityandoverall state ofconnectednessmay theextent towhich thegrowthIn thissection,we considerbriefly ina National Product,real GDPperworker, andthelike. by average annualrates ofgrowth GDP, inpercapita Gross percapita economic performance andproductivity have beenvariouslyestimated National Accounts are available for awiderangeofcountries,overall indicator.of anationisoneimportant from Becausereliabledata the will prove inadequateingaugingit—theoverall economicperformance determine anation’soverall competitiveness—and thusany oneindicator several quantitative andqualitative factors combineandinteract to source ofactualandpotential economicgrowth. primary country’s While closely related toproductivity. Thus,competitiveness asa isidentified and thatoftheInfodensity mean. – asevidenced by theover-time movement ofthemarginaleffects column effectsGDP raising ofInfodensity arehigheratlower levels ofInfodensity Infodensity.increasing acountry’s Theresultsalsoindicatethat the ofraisingGDPpercapita—to interms benefit—measured significant estimators ofthetruevalues,theseresults suggestthatthere isa $124 USto$164 US(calculated around the sample mean).Ifappropriate the Infodensity Index isto anywhere increaseGDPpercapita from For thesamplehere,marginaleffectofanincreasebyonepointin “Competitiveness”—a broad andmulti-faceted concept—is 6.2 Competitiveness fostate. For theyears 1996 through 87 87 87 87 87 Chapter 6 - MACROECONOMIC PERSPECTIVES 88 88 88 88 88 Chapter 6 - MACROECONOMIC PERSPECTIVES 7 and thatofSenegaldropped from272 per cent in1996 to233percentin2001. difference haddropped to148 percent.Thepercentage difference between Algeria’spercapitaGDP For example, in1996, Italy’spercapita GDPwas 2 poorest samplecountries. richest sample countrieswere 22timesricher, onaverage, thanthe times thatofthosecountriesinthelowest whereasin1996 quintile, the those countriesinthetopquintile ofthesample wasmore than24 been widening,not closing.In2001, theaverage of GDPpercapita with the1996 distribution suggests,however, thattheincomegaphas time period.Acomparison ofthe2001 distribution of globalincome of thegapbetween therichest andpoorest countriesover thissame and 2001. Infostates, GDPdifference theper capita diminishedbetween 1996 in thedeviations referred toabove reveal thatfor countrieswithsimilar in Chapter 4suggeststhattheDivideisclosingand,indeed,trend growth. Theanalysisoftheevolution Dividepresented oftheDigital connectedness isstrongly andpositively GDP correlated withpercapita distribution. From theanalysis here, an increasing degreeof andconsider theimplication ofbothreport for globalincome arises whenwe pairtheseresultswiththosepresented earlierinthis A secondphenomenon,andonethatispotentially related tothefirst, Infostate appearstodecline. rises,themarginaleffect ofachangeof GDPpercapita inthemean greater thehigherInfostate. Over time,however, asthemeanvalue sectional analysis,thepositive impact GDPappearstobe onpercapita cross-sectional analysisdoesnot appeartoholdover time.Inthecross- GDPevidentinthe onpercapita the degree oftheInfostate influence One, apparent intheregression above, results reported isthefact that andexplainuntangle two phenomena. important directly related tointernational andinvestment, trade andattempt to expand therangeofindicators ofcompetitiveness to includethosemore Divide mightexplore employed, thesensitivityofresultstodata results found research elsewhere. into Further thisaspectoftheDigital contributing to theoverall economicwell-being ofanation,echoing generally increase. appear todiminishsomewhat,onaverage,overtimeasInfostates Infostate andmagnitudeagain arecomparable insignificance 7 Itfollows, then,thatwe mightexpect toseeasimilarclosing These results suggest acriticalroleThese resultssuggest for connectednessin 6.3 Further Research 09 percentgreater thanEstonia’s; by 2001, this Chapter 7 FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE – A PRIMER8

7.1 Introduction The web provides access to vast amounts of data, information, and codified knowledge. However, many important forms of knowledge cannot be codified and are not available in the digital form required by computer-based information and communication technology (ICT) systems. Consideration of the knowledge divide therefore raises issues beyond the concerns with access to, or participation in, the web and the digital economy, although these remain important and relevant.

In this context, as in many others, knowledge is associated with power and capacity for action. Access to information on the web or anywhere else is useless without the knowledge to understand the content, make sense of it and use it. Arguably, the inability to transform the contents of the web into economic and social value is part of a ‘knowledge divide’ far more significant than the access issue which can be remedied with investment and technology.

While it is undoubtedly the case that a lack of knowledge is disadvantageous, we should bear in mind that knowledge comes in many forms, which are valued differently in different cultures and at different times. A global consideration of knowledge demands sensitivity to this variety.

This paper explores a number of development issues raised by the subject of knowledge. Knowledge is a concept that is inherently complex, and here we can only begin to flag issues that require further attention. We focus in particular on the facets of knowledge that distinguish it from data and information, the plural nature of knowledge, issues around the protection of knowledge resources, or intellectual property, and the key issue of knowledge capability or capacity for action. A number of examples of knowledge issues in specific development contexts illustrate these issues.

8 Authored by Joanna Chataway, Paul Quintas, David Wield (Open University, Milton Keynes, U.K.) and Fred Gault (Statistics Canada). 89 90 90 90 90 90 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER make senseofit,turningitintoactionable new knowledge. it–thatis,usetheirexistingcan understand knowledge andskillsto is itselfinformation, butthatinformation isonlyofuseiftherecipient ‘knowledge’. Knowledge inlanguageorsymbols thathasbeencodified and which anaddedelementofexperience requires to become distinguish itfrominformation, which incontext, canberegarded asdata on action.Knowledge heremay beseenasa‘capacityfor action’to keyA further distinctionbetween knowledge andinformation focuses to thetopic ofsocialknowledge insection7.2. therefore hasasocialdimension,inadditiontobeingpersonal.We return experiences andhelpeach otherlearn(Prusak2001). Knowledge practice (Lave andWenger 1991) –wherepeopleshare similarwork happensinsocialcontextsthrough often practice –communitiesof assimilate thenew. Moreover, thispersonalknowledge acquisition demands alevel ofpriorknowledge and thatenablesustounderstand Italso new thingsdependsonpersonalexperience andreflection. acquisition. Acquiring knowledge, Learning orlearning,requires effort. As Prusak(2001) toknowledge isfundamental strongly argues,practice tutelage, butyou willhave topracticeandlearnfor yourself. cannot buytheknowledge ofhow toplay theviolin–you can buy purchase anengineeringdrawing, arecipe oramusicalscore,butyou knowledge (Polanyi andindeedpersonal are tacit 1958). You can may beregarded asacommoditywhich many canbetraded, forms of Whereas andinformation data that arenot amenableto codification. ride abicycle orplay aviolintoknowing how tobeanempathetic listener, digitised. Therearemany forms ofknowledge, fromknowing how to only thoseforms cansimilarlybe ofknowledge thatcanbecodified andinformation.with data andinformation Data may bedigitised,but knowledge somewhat different divide.Moreover, fromthedigital theconceptof suggest prioritiesandapproaches for work. further case studyinformation neededto illuminatethem.Itthengoesonto and indicators a criticalassessmentoftheissuesandstatistical The paperisaprimeronthesubjectofknowledgedivide.Itprovides posesarangeofchallenges over andabove thoseassociated Consideration ofa Knowledge andinformation knowledge divide raises issuesthatare raises (Baumard 1999, p.59). can tell’ butthat‘we know far more thanwe areprepared to believe’ implication ofPolanyi’s analysisisnot onlythat‘we know morethanwe order toappraiseapatient’ssymptoms andreach adiagnosis.The may beunaware of,andunableto express, therulestheyemploy in reacting 1983). tosituationsormakingdecisions (Anderson Doctors and we are consciousandwhich we canexpress (Polanyi’s foreknowledge), A usefuldistinction canbemadebetween presence ofanexperienced ‘master’. trialanderrorapprenticeship, wherewe inthe learnby observation, our own experiential knowledge inasocialcontext, for example inan Tacit knowledge isnot directlytransferable, although we gain canoften preoccupation ofacademicresearchers). researcher aboutothers’ knowledge seekingto ‘gatherdata’ (aperennial management against others’ attempts to‘capture’ ourknowledge (acurrent awareness (Baumard 1999). Thesetwo aspectscombinetoconspire of ourown knowledge isheldandindeedemployed withoutourconscious activity.demonstrated intheprocessofsomepurposeful Moreover, much practical challenges. We canonlyknow ofitsexistence inothers ifitis of ourown experiences. Tacit knowledge posesmany conceptual and innature,beingbasedonourown tacit fundamentally internalisations communicate to others.For Polanyi (1958, 1966) knowledge is same languageorother to codes,much ofourknowledge isdifficult Whereas information canbeshared the between thosewhounderstand absorb itandactuponit,arekey dimensionsoftheknowledge divide. here thatdifferentials intheabilityto make senseofnew knowledge, andusetheinformationunderstand available onthenet. We shallargue separated from theknower. IntheWest, we tend to thinkabout traditions differ intheirviews oftheextent to which knowledge canbe differently indifferent cultures.Grossly simplifying, Western andEastern a conventionally refers toinequalityofaccesstheInternet,thereisalso In thisvein, Castells (2001) notes thatwhereasthephrase knowledge gap procedural knowledge It is important to recognise thatknowledge torecognise It isimportant is viewed Knowledge comesinmanyforms preoccupation) andthey presentseriouschallenges for the interms oftheknowledge andskillsrequired to uponwhich we draw subconsciouslywhen declarative knowledge digital divide knowledge ofwhich 91 91 91 91 91 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 92 92 92 92 92 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER knowledge creation. of distinguishingdifferent knowledge typesinthecontext of thepoor.especially hurt Insection7.2 we explore other ways in developing countriesthanindeveloped countries,andthatthey distribution ofboth know-how andknowledge aboutattributesisworse the diligenceofanemployee. arguedthattheunequal Thereport knowledge ofthequality ofaproduct, thecredibilityofaborrower, or farming oraccounting;and ‘knowledge aboutattributes’, such as on two typesofknowledge: ‘how-to’ (orknow-how) knowledge, such as The World Knowledge Bank’s report for Development (1998) focused (Western) knowledge asa apprenticeship within a community of expert practitioners apprenticeship withinacommunityofexpert is through guidedjointsocialinteraction during,for example, Indeed, many authorsargue thattheonlyway to transfer knowledge tacit contexts itmust bede-contextualised, ormadeindependent ofcontext. in another context. Itfollows thatinorder totransfer knowledge between What hasvalueandmeaninginonecontext may have little ornomeaning 1995, p.7). away nothing becausethey can’ttake ithomewiththem’(Leonard plant, showingthem‘almosteverythingandwewillbegiving Steel. TheCEO ishappy to tourcompetitors through theChaparral or share between contexts, asisillustrated by theexample ofChaparral and Winter1982). Tacit transfer to organizationalprocessesaredifficult context inwhichand located itwasdeveloped inthespecific (Nelson example, research anddevelopment nature (R&D)activityisofatacit (von Hippel1994). Much oftheknowledge generatedthrough, for and Wenger 1991) totransfer andmay orshare be‘sticky’ anddifficult degrees‘situated’ contexts (Lave andisto varying is createdinspecific tend to emphasise groups thatknow it.Putanotherway, anon-Western approach would to emphasise ofwhatisknown theinseparability fromtheindividualor Converselymanaged andtraded. theEastern traditionsaremorelikely knowledge asa‘thing’orcommoditythatcaneasilybemovedaround, 1981, p.86). technologyhuman contact, transfer may sometimes beimpossible’ (Teece is beingtransferred between organizations:‘intheabsenceofintimate Wenger 1991). Similarly, whereknowledge associated withtechnology Current perspectives onknowledge suggest thatknowledge Context matters knowing thing as a . process rather thantheCartesian

(Lave and assumption thatknowledge isabsoluteandindependentof context. importance of integrating localknowledge ofintegrating importance withother knowledge. (for example Schumacher 1973, Willoughby 1990). We the arguerather resources large-scalecapital-intensive instead equipment ofimporting to promote alternative, smallerscale,technologies which uselocal an approach oflocalknowledge have basedontheimportance tended environmental issues.Indebates aboutindustrialisation, proponentsof oflocalknowledgeevidence oftheimportance inunderstanding have Otherauthors provided oflocalexpertise. on theimportance (RRA)thatfocus Appraisal(PRA)andRapidRuralRural Appraisal inpromoting ideasaboutParticipative have beenenormouslyinfluential achievement of the1990s. Writers such (1997) Chambers asRobert in development practiceandthebroad acceptance of this ideaisakey For many development thinkers today, localknowledge isanessential knowledge.this context-specific Hayek argued,themarket poolsandcommunicates theinteractions of (such asagovernment) can have accesstoallthislocalknowledge, but, circumstances. No orindeedany oneperson kindofcentralisedagency andbrokers, customers suppliers, whoeachknow abouttheir local local knowledge thatis widelydistributed amongsttheplethora of 1945). Hearguedthatthemarket isasystemfor communicatingthe role oflocalknowledge inthefunctioningofmarket (Hayek 1937, time andplace(Hayek 1945, p.3).Hayek placedgreat emphasis onthe circumstances of oftheknowledge oftheparticular the importance economists: ‘...itisfashionableof hiscontemporary today tominimize p.21). Nonaka indicate ‘theshared context for knowledge creation’ (Nonaka ‘ba’ (aJapaneseword which translates approximately to ‘place’)to Nonaka JapaneseauthorIkujiro (1998) usestheterm The influential competencies which were lesstransferable (Hamel to unravel’ strengths werewhereas Japanesefirms’ often‘difficult companies technology tended to bringeasilyimitated to acollaboration, projects involving Western found thatWestern andJapanesefirms may besharedbetween contexts. Arevealing study ofcollaborative differences cultural intheextentThere towhich aresignificant knowledge In 1945, Nobel Laureate Friedrich Hayek criticallyobserved knowledge Local/indigenous et al. contrast their contrast ba approach withtheCartesian et al. 1989). et al et . 2001, 93 93 93 93 93 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 94 94 94 94 94 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER complementary complementary make senseoftheinformation andto learn.Resources, itseems,require available to you. minimalcapabilitywe Thevery needisthecapacityto and you thatlanguagethentheknowledge donot understand isnot knowledge,resource fullofcodified butifitisallwritteninJapanese capabilities to usethisknowledge isa arenotalways present. Alibrary rediscovery ofthe1970s concept (Becker ofhumancapital 1975). The management ofinformation. Asecondpopularthemefocuses ona in theWest, littlebeyond we are-labelledinterest often find inthe Within thecurrent new-found enthusiasmfor ‘knowledge management’ international resources. required between generationanduseoflocalresourcesaccessto industrialisation strategies have thequestion addressed ofthebalance And, morerecently, debates aboutlocalclusteringand accessinternationalmarketsrole inhelpingsmallfirms andtechnology. different have examples, played largeinternationalfirms animportant knowledge, Infact, couldcompete inallofthesevery withlargerfirms. sharingessentialcosts and some to arguethatclustersofsmall firms, manufacturing models(Best1990). SiliconValley was alsousedby applicable asinternationallycompetitive alternatives totraditional of uselocalknowledge andresources mightbemore generally and BadenWurttemburg inGermany ledsometoarguethatthismodel success ofindustrial such districts Italy asEmiliaRomagna innorthern components ofindustrial clusters. Inthe1980s andearly1990s, the Local knowledge andknowledge managementhave beenimportant in theglobalmarket place. that local,contextual knowledge andresourcesassist competitiveness than capitalintensive production.Anassociatedlineofthinkingholds inaneradominatedby knowledge rather international counterparts abetter chance stand ofcompetingunits andsmallfirms withlarger, means oftechnological innovation. isthatsmallerproduction Thetheory older preoccupations withtheabilityofsmallerunitstocompete by of knowledge for isimportant industrialisation have developed outof In theeraofknowledge-intensive production, debates aboutwhatkind Vast knowledge resources exist across theworld, butthe 7.2 KnowledgeCapabilities capabilities inorder to 1996). render themuseful (Grant discussed below. is termed assimilate knowledgefromoutsidetheorganization. Thiscapability that oneofthefundamentalcapabilitiesistobe ableto track and knowledge thatexists, oriscreated, externally. The implication ofthisis within even thelargest organizationisaminutepercentage oftherelevant single organization.Yet ofknowledge theproportion resourceslocated andwhathappens withinthe All thisisapreoccupation withthefirm assimilate it. capability to make senseoftheinformation, itand to understand resources. Thesecapabilitiesarepredicated ontheunderpinning capabilities to managethem:tocommunicate,shareandusesuch Like otherresources, knowledge assetsrequire thecomplementary without any interest ortake-up. Much expensively-gathered information sitsonshelves orondatabases bysupported thecapabilityto shareandusethatknowledge (Hall2003). There are isalsosomeevidence not thatcapture always andcodification experiential nature ofhumanknowledge, andalsoitscontext specificity. ways, problems and stemming withfundamental fromthetacit knowledge management(KM).Such ‘capture’ isproblematicinmany into awidelyavailable resource, hastherefore becomeamajorthemein within organizations,withtheaimofturningindividuals’knowledge ofexisting knowledge ‘capture’ andcodification The identification, over-arching concerniswiththemanagementofknowledge with knowledge thatisnot easilyavailable asamanagementresource: ability to create newknowledge. However, there isarealfrustration alreadyhaveknowledge thatfirms greatlyoutweighs concernfor the orinnovate.function, create profits Thispreoccupation withthe or themany other forms ofknowledge thathelptheorganization assets Firms areencouragedto value,manageandexploit theirknowledge , whether andother patents theseare forms ofintellectual property, Chairman ofHewlett Packard. ‘I wishwe knew whatwe know atHP’–Lew Platt, Texas Instruments. ‘If TIonlyknew Junkins,CEOof whatTIknows’ –Jerry absorptive capacity (O’DellandGrayson, 1998, p.154) (CohenandLevinthal1990) andis resources . 95 95 95 95 95 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 96 96 96 96 96 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 9 ‘development’ or‘generation’. The word ‘creation’ (ofknowledge) isusedhere andregarded asbeingsynonymouswith knowledge toexplore knowledge creation. Thenext sectionusestheconceptofmode1and 2 survival. itistheabilitytocreate new knowledgefields andto innovate thatensures accounts of‘KM’donot even Yet considerthisimportant. inmany emphasis onthekey capabilityofknowledge creation.Indeedsome As noted previously, much Western knowledge managementhaslittle In and other forms ofknowledge createdinthecontext ofpractice. function without,for example, knowledge theubiquitous presenceofcraft contribute to economicandsociallife, andindeednosociety could repeatability. However, itisapparent thatmany other forms ofknowledge knowledge, becauseofitsclaimedobjectivity, transparency and knowledge iswidelythoughttohave greater validitythanother forms of through formal method. Such activity, through thescientific particularly In theWest thereisatendency toprivilegeknowledge thatisproduced or disciplines. existing knowledge,fromdifferentsources,ordomains can alsobecreatedasaresultoffocussedcombination resulting from learning-by-doing inawork environment. New knowledge research anddevelopment (R&D)activities,orinformal, for example human societies. ofknowledge Thegeneration canbeformal, through importance. Thefollowingimportance. 1compares table modes1and2. beenunder-valued,mode 2hashitherto andalsothatitisincreasing in the capabilityto Gibbons produce itiswidelydiffused. unrecorded.application, transientandoften Itistransdisciplinary, and it resultsfrom Itisknowledge practice. created inthecontext of In contrast, mode2knowledge iscreatedinthecontext ofapplication– university syllabusesandprofessional qualifications. grows cumulatively, isstored andforms inlibraries thecontentof knowledge whichgenerating codified istransferable. Mode1knowledge through peerreview, anemphasis onindividualcreativityand homogeneity ofknowledge ahierarchical producers, controlsystem knowledge isproduced ininstitutions,isdisciplinary, withhigh shift inmodesofknowledge creationfrom mode1to mode2.Mode1 The NewProduction ofKnowledge The creation ofknowledge Knowledge creation , Gibbons 9 is a defining characteristic of isadefining et al. (1994) write abouta et al. arguethat required inmany industriallydeveloping countries. Restructuring ofscienceandtechnology infrastructureistherefore ofknowledge.arrangements facilitate both generationandflow mixture ofpublicandprivate institutions.Different institutional applicationsandcontexts itcomesfrom inmindandoften specific a institutions. Inmode2,however, much knowledge isgenerated with Theideaisthatknowledge canbetransferredservices). between these (universities andresearch orpublic institutes)andusedby others (firms knowledge isdevelopedthat scientific inoneset ofinstitutions heavily rooted incolonialmode1institutions, builtontheassumption the scienceandresearch baseinmany developing countriestendsto be For developing countries,such problems.First, changes poseparticular coordinate thesedifferent processes,andbetween different sites. rather thandesignatedsinglelocations.Thisrequires thecapabilitiesto with knowledge, information andproductiongenerated inmultiplesites asamixed-modeIndustrial activity innovation defined isincreasingly and are driven by increasinglycompetitive innovative environments. some evidence to thatmode2practicesruninparallel mode1institutions generation anduseistheimplication for industrialinnovation. Thereis aspect ofthisnew aboutknowledge understanding An important The newproductionofknowledge: Mode1and2knowledge interdependence between modes1and2. However such aconclusionmay underestimate theextent of of practicehasequal,andgrowing, validityandeconomicsignificance. mode 1institutions.Itsuggests thatknowledge createdinthecontext in creation offers considerablehopetocountriesthatare deficient On thefaceofit,recognitionimportancemode2knowledge Source: developed from Gibbons Universal knowledge Exclusive knowledge Homogeneous knowledge formMaintains Hierarchical organization -academic Agenda Discipline based knowledge Scientific OE1MODE2 MODE 1 et al et 1994 Context-specific knowledgeContext-specific Socially distributedKnowledge Heterogeneous Knowledge Transient form Non-hierarchical organization -practice Agenda Trans-disciplinary knowledgePractitioner 97 97 97 97 97 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 98 98 98 98 98 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER of knowledge withoutthe‘knowledge management’label. ofthemanagement practice inorganizationsillustrates thelonghistory routines andstories. The mutually recognisednormsandcompetence, withsharedlanguage, practice (CoP)asasociallearningsystem, unitedby jointenterprise, (Lave andWenger 1991). Wenger acommunityof defines (2000) andmay besaidto formlearning opportunities communitiesofpractice People whosharework experiences andwork problems have similar its own andinterpretation. problems ofdefinition called ‘network itfrom which todistinguish ‘socialcapital’ capital’ carries where peoplework inteams Thiswillbe orcommunitiesofpractice. consider thatsocialnatureoftheseknowledge processes–especially key feature ofbeingamembercommunity. We therefore needto the peopleare thatknow aboutyour (know area ofexpertise who)isa a greater capacitytoactthanthatof allitsconstituents. Knowing who in group interactions androutines insuch away thatthecollective has workplace (know how). Itcanalsoresideingroups ofpeople,created education (know what,know why) andthrough learning-by-doing inthe reside through inpeopleandbeaccumulatedas‘humancapital’ formal dimension –itmay becreated andheldcollectively. Knowledge can consultants and clients in developing country contexts. andclientsindevelopingconsultants country the interactionaroundissuesofknowledge managementbetween play often acrucialrole.consultants However, littleisunderstood about technology andknowledgeor wherebringinginspecific isrequired, institutionsisbeingcontemplatedcases where remodellingofspecific management toolsfor in Particularly copingwithmode2interactions. ofknowledge2 generation andhave beenattheforefront ofnew inmode actors areimportant firms Consultancy firms. by consultancy Third, much bilateral development andmultilateral work iscarriedout be appropriate. capabilities inthefutureandyetitisunclearwhichstructuresmight of thepast isunlikely bethebest to option inbuildingindustrial capabilities, butsimply specialisedR&Dstructures rebuildingthevery to play inbuildingnationaleducationalandcultural amajorrole and universities areinanextremely Universities fragilestate. willcontinue breaking down. For example ofAfrica,R&Dinstitutions insomeparts Second, insomedevelopingcountries,mode1institutionsarealso As hasalready beenpointedout,knowledge hasasocial Social dimensionsofknowledgecapability discovery ofpre-existing communitiesof way they interacted withstudents, governments andbusiness. students, untilthey begantocreatenew knowledge andchanged the were originally repositories ofknowledge passedontogenerationsof Universities, which are key players inthemode1knowledge system, organizations andmarkets becomeinternational. accessed andsharedacross culturalandnationalboundariesas specialisations andbetween disciplines.Increasingly, knowledge is between organizations,withinbetween functional management system’ introducedfromoutsidetheCoPs. communities refused to useanewcomputer-based ‘knowledge (Baumard 1999,in practicearealltacit.’ p.135). TheQantas ofthoughtinscribed of practice,conjecturalknowledge andrepertories the CoPsfavour lessexplicit circulation ofknowledge: ‘...communities favours documents,manualsandcomputerised information, whereas different interpretations ofreality. top-down Qantas’ managementstyle have theirown language,which asBaumardemphasises, indicates group,andthemarketingthe financial group. Each ofthesecommunities thepilotspractice intheAustralian airlineQantas: andtheirretinue, undermine them.Baumard (1999) threecommunitiesof identifies attempts toformally managecommunitiesofpracticefromoutsidemay odds withtheinformality ofcommunitiespractice,andindeed acknowledged by management.Formal managementstyles may beat The challenge posedbysocialknowledge isthatitmay notbe function naturally. practice therefore representoaseswithinwhich knowledge processes (Brownshared understandings andDuguid1991). Communitiesof communicate non-verbally becauseofshared experience, sharedlearning, like jazzmusicians,exchanging truncated andableto phrases were working observed together onaproblem machine, communicating tricks andideas’(Wenger 1998, p.47). InXerox, photocopier engineers other, ‘exchanging information, makingsenseofsituations,sharingnew hold experiences actasresourcesfor incommon.CoPmembers each organizational boundaries,includingpeopleinseveral organizationswho (Baumard 1999) intheorganization.Sotoo itmay transcend unrecognised (Scarbrough 1996) orignoredtaken for granted A communityofpracticeismostoftenaninformalgrouping.Itmaybe Cross-boundary knowledge applytoboundaries Cross-boundary transactions Cross-boundary knowledgeprocesses 99 99 99 99 99 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 100 100 100 100 100 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER because ( substitute inputs from thatfirms external supplierslearning. Heobserved Stigler (1951) linked networks withprogress through specialisationand Guy 1995). currently technology ‘peripheral’ areas(Quintasand risk, share costsandscarce resources, especially withregard tonew or and collaborationsprovide canreducethe themeansby which firms ofknowledgethe integration from many disciplines.Alliances,networks development increasinglyrequires ofnew goods,systemsandservices ofknowledge transactionsacross boundaries.Sotoo the difficulties creates new managementchallenges resulting fromtherisksand resources available to Knowledge theinnovating interdependence firm. argues, theexternal network asasource of hasincreased inimportance Marshall, inbuildingthecasefor industrial districts. AsFreeman (1991) The key role ofexternal before was linkages noted acentury by Alfred quoted inWeber (1989, p.132). Z. Gussin,former VPfor ScienceandTechnology, Johnson&Johnson, even companies thelargest can’tafford todoitallthemselves’ (Robert ‘Technology hasbecomesosophisticated, broad, andexpensive that to beincreasinglydependentonexternal sources ofknowledge: (Richardson 1972,affiliation’ p.895).Today allorganizationsare likely are not islandsbutarelinked togetherinpatterns and ofco-operation hasever beenindependentinknowledgeArguably nofirm terms:‘Firms economic context areexplored inMassey, andWield(1992). Quintas challenges ofuniversity-industryinteractionsinachangingpoliticaland direct commercialisationlinkstoindustryandcommerce.The but increasingly research fundingislinked to, conditionalupon, andoften Universities andresearchinstitutionsaregeneratorsofmode1knowledge, of knowledge’ areexplored (2002). inQuintas have littlecommonground for These‘divisions sharedunderstandings. boundaries,sincedifferent communitiesanddisciplinesmay disciplinary knowledge between organizations,and indeedacrossfunctionaland p.71). Specialisationresultsingreater barriers to thesharingof (Massey, between integration horizontal andWield1992, firms’ Quintas ordination thenew between division oflabourrequiresmore firms... in turndependsonanew divisionof labourrequiringincreased co- achieve. Astwo ofusnoted inanearlierpublication, ‘thisspecialization to provide can theseatlower integratedfirm cost thanthevertically pace Adam Smith)thesesuppliers learn through specialisation 10 earlier (Penrose 1959). similar conclusionsinherseminal Coase ina1970 address to theUSNational Bureau ofEconomicResearch. EdithPenrose had cometo tacit dimension(Polanyitacit 1966). knowledgealien orinappropriate toarecipient.Even hasa codified perspectives, prioritiesandvalues. Thesemayto embedparticular be the context inwhich theknowledge are likely wascreatedandcodified onthecapabilitiesofrecipient. Howeverand absorbdependinpart electronically orthrough print media,andread.Theabilitiesto transmit Knowledge canbetransmitted thatcanbe writtendown, orcodified, our efforts. resource, wecannotevendistinguishthoseareasinwhichtoplace into external learningmoreaboutanyputting effort knowledge particular enoughtoconcludewhetherinternal itisworth capabilitytounderstand appropriate for any other context. Thesecondis that unlesswe have contexts, andthesemaymeaning andvalue notbe withinthosespecific isthatknowledge contexts,others. has iscreated inparticular Thefirst external knowledge oneofthese. We isofcourse shallemphasise two external challenging. Navigating sources particularly thevast array of There are however many factors thatmake absorbingknowledge from paradoxically tolooknot ahead, buttolookaround’ summarisethechallenge as:‘thewayand Duguid(2000) forward is Essentially, must becomeadept atsearching firms andlearning.Brown knowledge includetechniques ofsourcing,sense-makingandacquisition. developments (CohenandLevinthal 1989). Theskillsrequired to absorb research anddevelopment isinorder to track external andunderstand reasons invest why resourcesone oftheprimary in firms significant requires strategies for learning andassimilation(Pavitt 1991). Indeed, (Cohen andLevinthal 1990). Knowledge isnot afreegood;itsacquisition assimilate externally sourced knowledge isknown asabsorptive capacity learning capacity(Amsden2001). andrequiresof alltypesisincreasing importance investment in andassimilate knowledgecapability tounderstand across boundaries zero (Coase1988, Loasby 1999) neoclassical microeconomic assumption was that transactioncostswere is bynomeansunderstood.Barely30yearsagothedominant accepted, althoughthenatureandextentofcostssuchtransactions The importanceofcross-boundaryknowledgetransactionsisnowwidely The capabilityto track, make and senseof, understand Absorptive capacity The Theory ofthe GrowthThe Theory ofthe Firm 10 . Whatisnow clearisthatthe publishedeleven years 101 101 101 101 101 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 102 102 102 102 102 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER and to useittocreatevalue. The key issueisthecapacityto adapt andabsorbthisexisting knowledge people inthedeveloping world withacapacitytoimprove theirexistence. countries, inuniversities, businessesandgovernments, itisnot providing While there isavast accumulationofknowledge intheindustrial in tracking, learningandassimilation processes. from outsidebecomesever Thisrequiresinvestment more important. percentage oftheworld’s new knowledge, andsoabsorbingknowledge knowledge production thateach now year find they produce asmaller organizations. Even countriesthatformerly dominatedmode1 Absorptive capacity may alsobeappliedtonations aswell as 1998, p.441). canandcannotthere do’(Pavitt areclearcognitive limitsonwhatfirms their search activitiesclosetowhatthey alreadyknow: ‘Inthissense, thathave constrain Even capabilities acrossanumberoffields largefirms cumulatively whatcanbelearned. andispath-dependent, constraining and Pavitt 1997, Pavitt 1998). develops Theknowledge baseofafirm knowledge isconstrained by whatthey alreadyknow (Pavitt 1991, Patel capacitytotrack andabsorbexternal As withindividuals,afirm’s quest toabsorbknowledge. employ to Such techniques intheexternal are difficult generallymore example, acquire knowledge byworking withexperienced practitioners. of appliedknowledge. Young research scientistsandphysicians, for Apprenticeships have longbeenusedfor andabsorption thetransmission toshareorabsorb. andareinherently difficult codified, As we saw above, many forms ofknowledge cannot bewrittendown or shared experiences” (Nonaka 1994, p.19). emotions andnuancedcontexts thatareassociated with make from littlesenseifitisabstracted embedded processes. Themeretransfer ofinformation willoften for peopleto each share difficult others’ thinking Without someform ofshared experience, itisextremely key“The knowledge toacquiringtacit isexperience. accumulated experience, itisnow clearthat themeasurement of behaviour.and buildindicators of firm somepilot After and surveys, or research canmake institutes thesemeasurementswithconfidence oftheproject offices obvious butitdoesmeanthatstatistical atthestart about how domanagetheirknowledge. firms Thiswasnot immediately of knowledge managementpracticesandtoarrive atsomeconclusions isthatitactuallypossibleto measure theuseofaset finding The first offer insightsintotheknowledge divide. findings FranceDenmark, andGermany (Foray Someofthe andGault2003). knowledge inbusinessfour managementpractices countries,Canada, and Development (OECD)hasco-ordinated astudyofthe useof in developing countries.TheOrganizationfor EconomicCo-operation oftheknowledgenot divide. This isnot asgoodatitispart justaproblem organizations thatcanconvert anduseknowledge andthosethatare dependsuponnovelty.value, andsurvival Thedifference between use it,have adistinctadvantage inaworld where knowledge generates Organizations thatcangenerateandabsorbknowledge, andstore and including governments. transforming, buttoany organization, doesnot justapplyto firms throughbasic andtactical the complex andstrategic totheradicaland world ofturmoil.Thishierarchy ofmanagementpractices,from the and make andprosper istosurvive ina change aradical ifthefirm level, themanagementmustbepreparedtoabandonexisting vision and astrategic visiontoenableitdealwithchange. At aneven higher reacting tomarket signalsanddevelop amemory, aforesight capacity, environment. At thismorestrategic mustgo beyond level thefirm just navigates towardsfirm itsgoalsinanever-changing economicandsocial inputs, thetransformation process, andoutputs;itisalsoabouthow the However, about isnotjust themanagingofknowledge ofthefirm about clients. and delivery processes, ormanagingbetter theinformation held produce anddeliver products,improving theproduction market morecreative ormoreexports, share useoftheinputs usedto could includesocialresponsibility ininvestment andemployment, greater toallows meetitsmanagement objectives. Theseobjectives thefirm andthento applyitinorder toaddvalue,or from outsidethefirm, capacity to createvalue.Theabilitytoabsorbknowledge, from inside Knowledge confers acapacityfor usethat actionandfirms fromtheIndustrialWorld7.3 Lessons 103 103 103 103 103 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 104 104 104 104 104 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER Communications andaKnowledge ManagementDevelopment Initiative for of example, initsDepartment hasaChiefKnowledge Office developed countries.TheGovernment oftheRepublic ofSouthAfrica, may beacasefor promoting theiruse,whetherindeveloping or have alowerand smallerfirms propensitytousesuch practices,there tothemanagingofknowledgeAs thereappeartobebenefits infirms, iscontrolled for.size offirm even isalsomadethatthiseffect persists when andtheobservation firm ofasuccessful,creative, innovativemanagement ispart andproductive of innovation andpatenting. Thisdoessuggest thatknowledge propensity toinnovate andtopatent,aswell aswithahigherintensity knowledge ispositively managementpractices correlated withahigher intheworkA finding ofKremp isthattheuseof andMairesse(2002) what context. bedoneonwhatdoeswork, morehasto and Gault(2003) andin resultsinForay Whilethere arepreliminary micro andsmallfirm. take accountofwhatworks, andwhatdoesnot, inthe use ofknowledge managementpracticesinthedeveloping world should have Thismay suggestthatany smallerfirms. attempt the toencourage tends todependonthesizeofeconomy andsmallereconomies There isanimplication herefor development andfor policy. Firmsize 20 to50workers. that thethreshold for change varies from according to thepractice mid-sized (50-249) andlarge(250+workers) and firms (20-49workers)firms differ innumberandtypefromthoseusedby (1-19management practicesusedby microfirms workers) andsmall and Rycroft 1999). show Earl andGault(2003) thattheknowledge as opposedto technologies, anditmay have to dowithcomplexity (Kash how for sizematters theadoption ofknowledge managementpractices, technology useare examples. However, theremay beadifference in most economicactivitiesofwhich R&D,patenting, innovation, or Thisisnotanew 2003). insight,assizematters forMairesse 2002, Kremp isthatsizematters (EarlandGault2003, and A secondfinding such activities. derivedautomation andtheindicators of from thesurveys manufacturing technologies (Ducharme and Gault1992) orofoffice preoccupation inthe1980s withthemeasurement oftheuseadvanced knowledge managementpracticesin2001 islittledifferent from the contribute to theknowledge divide. and organizationalstructures may inhibittheuseofknowledge and it isamajoronefor thedeveloping world whereculture,computer literacy with littlecommonground. Whilethisisanissuefor developed countries, boundaries andnoted thechallenges for communitiesanddiscipline looked atthesharingof knowledge across functionalanddisciplinary application, inorder to have has acapacityfor (2002) action.Quintas to literate absorb the knowledge, andthecontext forsufficiently its knowledge, ofthefreely peoplehaveadvantage available codified tobe money knowledge usingthetacit To initshighlyskilled staff. take knowledge cangive away, itscodified The firm butitwillstillmake andapplicationofknowledge.transmission, absorption Itispeople. broadband delivery, thereisstill challenge afundamental for thecreation, andisbecoming moreWhile theweb sowithwireless ispervasive, high value-added problems ratherthanonmany low value-addedones. gainsfrom theformerIn principle,thefirm clientsseeking counsel on cannow accesstheseproductsatnocostthe firm andusetheknowledge. who, inthepast, would have boughtlow value knowledge productsfrom regulations onsafety intheworkplace andrelatedcaselaw. Theclient ofinformation on,say,instruments, ordatabases andcommentary all to see.Thiscouldincludepatentsandother intellectual property for knowledge example, for topublishitscodified thatallows thefirm andtooperation managethatknowledge strategically. Itisthenetwork, the information andknowledge accumulatedaboutallaspectsofits complex to see,for timeinhistory, medium tolarge-sizedfirm the first ofknowledge.codification Itis theelectronic network thatallows the The web, andelectronic networks morebroadly, have promoted the publishing knowledge. the useofweb andtheInternet for storing, and finding, connection thanwithaccesstotheweb. Theemergingissueis transfer,data andpoliciesdealmore withtheavailability ofbroadband countries theissueismore thespeedofconnectionandvolume of access totheweb remainsanissuefor policy, whereas inthedeveloped industrialized countries,ofICTs andtheiruse.Indeveloping countries, inthe certainly oftheanswerattention. Part liesinthepervasiveness, a question ofwhy knowledge isnow asubject that meritssomuch anumberofissues,thereisstill While theOECDproject hasidentified and innovation, aswell asimproving thetransfer knowledge. oftacit with aweb site ( http://www.KM-debate.co.za ) which promotes profitability 105 105 105 105 105 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 106 106 106 106 106 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 11 and tertiary education, with tertiary enrolments given education,withtertiary aweightand tertiary offive. The Harbison-Meyers Indexistheaverage oftheper Resources devoted to education(asaproxy for capacity) knowledge. build acomprehensive pictureofthenatureglobaldistribution agencies. Thoughlimited, inthequest thisinformation to offers astart evidence drawn from ahugeamountofdatacollectedby UNandother The following commonlyusedindicators are examples ofsomestatistical in sections4.2to 4.4. contexts We (Chatawayin specific andWield2000). adopt thisapproach context. Ideally, knowledge we understand needto issues‘ontheground’ What countsasvaluableknowledge dependsonthesocialandpolitical controversial. andoften knowledge isinitself political,sociallydefined by itselusivepart nature.Another complicating factor isthatdefining suggest thatmappingandmeasuringknowledge ismadecomplicated in knowledgeThe previous oftacit sectionsemphasising theimportance by isnostraightforward lookingatglobalnumbers task! (Machlup 1962). Beginningto atthenature grasp ofknowledge inequities pioneering work ontheknowledge economy oftheUS illustrates knowledge isachallenging even task, for asinglenation,asMachlup’s the globalknowledge divide.However, mappingandmeasuring different key knowledge issuesfor development. economy. To thisend,threevignettes arepresented,each highlighting and explore real issuesinthecontext withintheglobal oflocalpractice Howeverdivide, usingavailable data. we alsoseektogobeyond this Meyers index ofskillseducationalattainment Canada ledtheworld inrankingby theHarbison Statistics compiled by UNIDOsuggest thatin1998 between 1995 and1998. Zimbabwe have rankings both descendedin table first 50isSouth Africa(42 first Africa(SSA) inSub-Saharan toappearinthe country Brazil is57 developing at10 country of 87 countries,SouthKorea isranked astheleading is Zimbabwe (68 In thissectionwe begintomapthenature oftheknowledge 7.4 TheNorth-SouthKnowledgeDivide It is important to begintobuildapicture ofthenature It isimportant Measuring knowledgeglobally th , China59 th ). Moreover, SouthAfrica and th th place.Taiwan isplaced23 andIndia69 nd ). Thenext SSA country centage oftherelevant agegroupsinsecondary th . Theonly 11 . Out rd . Patents (asproxy for knowledge creation) absorptive capacity) R&D investment (asproxy for both knowledge and creation out internationalpatents” (UNIDO2002). than others] anddifferences inthepropensity totake of R&D[patenting ismoreprevalent insomesectors R&D elsewhere, differences inthequality ororientation patentingtechnologycompanies’ basedon affiliates variations may bedueto several factors, such asforeign somewhat notes that“The similar pattern.Thereport spending butrankslow inpatents andChinashows a Taiwan. hasconsiderableR&D By contrast Brazil in R&Dspendingandhighpatents, andsodoes HongKong ofpatentlimitations data; SAR low ranks anomalies,whichare highlightsome someimportant correlation andpatents. between Butthere R&Deffort that, asmightbeexpected, ingeneralthereisastrong caution. For notes example, (2000) aUNIDOreport must alsobeviewed Butpatent data with findings. newgenerate knowledge-based creationsandscientific Patent isanother abilityto data measureofacountry’s of theirR&Donbusiness(closetoapplication)R&D. lowBrazil, ChinaandIndia)spendquite proportions volume ofR&Dexpenditureswith significant (like spent more than1%.Andmany developing countries Korea). Onlyoneother developing country, Singapore, compared withjust (South onedeveloping country 1987-97 (Human Development 2001), Report more than2%ofGNPperannumonR&Dintheperiod developed countriesspending withthirteen absorption measureofknowledgeone (albeitpartial) creationand Investment inResearch andDevelopment (R&D)is educational terms. technicalareas ofstrength or inscientific, donot capture anythe statistics certainly particular populations withrelatively higheducationallevels. And average down, butthey alsohave substantial or minimalamountsofeducation,bringingtheoverall and Brazilmay have hugenumbersofpeoplewithno numbers aremisleadingasaproxy for capacity;India littleeducationthe large numbersofpeoplewithvery obvious For limitations. example, incountrieswith Although interesting,thesenumbershavesomevery 107 107 107 107 107 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 108 108 108 108 108 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 12 Footnote weightings. average the value ofeach nationalcharacteristic iscompared totheinternational In order tocombinethesedisparate components intoacommonindex, The RAND(2001) ScienceandTechnology Composite index: Comprehensive indicators ! a wider set of indicators, isthata a widersetofindicators, this more sophisticatedapproach, and onewhich beginstoincorporate Four categories emergefrom theabove. One oftheinteresting resultsof ! ! ! ! ! ! Office (EPO). Office Trademark (USPTO)andtheEuropean Patent Office through theU.S.The numberofpatentsfiled Patent and withexternalcontact knowledge sources; the conclusionoftheirstudiestocharacterise thecountry’s United adjusted States for thosewhochose nottoreturn homeat A measureofthenumbernation’sstudents studyinginthe per millionpeopletocharacterise for theinfrastructure S&T; The numberofuniversities andresearch inthenation institutes level ofinput intoS&T; The percentage ofGNPspentonR&Dtomeasure thesociety’s citizens ofthenationtocharacterise S&Toutputs; and patentsproduced by The numberofS&Tjournalarticles capture thehumanresources available for S&Tactivities; The numberofscientists permillionpeopleto andengineers asaproxyserve for generalinfrastructure; to grossThe percapita nationalproducts(GNP)ofthe country 12 with thefollowing index. to getabroader ideaofscienceandtechnology capacity (RAND 2001, RANDresearchers UNIDO2002). tried cluster indicators to give amorecomprehensive picture limited, anumberofagencieshave triedtobundleor Because any sets oneofthedata taken ontheirown is . significantly moredifferentiated picture significantly of how thereismorethanastraightforward knowledge dividebetween The RANDindex itgives anidea isausefulsnapshot andinparticular capture dynamism. newcomersoutstrip thatofscientific next tothem.Nor doestheindex investment that andsignificant tradition with centuriesofscientific ignores thefact thatthelist ofSACs andSPCsincludeseveral countries tolimitations thisapproach. representation and Itisastillstatistical those which focus oncountingpatents ortelephonelinesthere are still Although theRANDindex particularly isanadvanceonsingleindicators, laggingcountries(SLCs).Scientifically countries(SDCs). developing Scientifically countries(SPCs). proficient Scientifically Advanced Countries(SACs).Scientifically to emerge: and, fordevelopingcountries,perhapsaslightlymorepositiveone,begins SSA, apart from SouthAfrica. SSA, apart laggingcountries.Thisincludesmostof scientifically The remaining of 80countriesfall intothecategory the international mean. investments andsomecomponents oftheindex exceed international average they have madesome positive Indonesia. Althoughthesecountriesarebelow the Mexico, Rica,Egypt Pakistan and Colombia,Costa The next set of24 countriesincludesArgentina,Chile, and India. includes Singapore,Cuba,China,Brazil,SouthAfrica category. Thiscategory capable” asthoseinthefirst international average, butthey arenotas“uniformly overall S&Tcapacityindex valueatorover the includes24This category countrieswhich possessan Taiwan, SouthKorea Israel, andRussia. some developing countriessuch andtransition as includesindustriallyadvanced countriesand category 85%-90% ofalltheworld’s R&D(RAND2001). This in internationally journalsandthey recognised fund published responsible articles for 86%ofallscientific S&T capacitythanthemean.Thesecountriesare haveThe 22countriesincludedinthiscategory greater 109 109 109 109 109 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 110 110 110 110 110 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER blank sheet. Withmany industrialprocesses andproducts thereare ways the feat ofturningnew knowledge to notworking gold,areoften from a following example shows, exploiters ofknowledge, orthosewhoperform older knowledge goesunrewarded often andunacknowledged. Asthe knowledge rests knowledge onoldindigenousortraditional (TK).That software industry. and thethird looksatthedevelopment ofthenetwork-based Indian rights(IPRs), discusses recent debates surroundingintellectual property thecaseofSanpeopleandhoodiaplant.Thesecond in particular vignette concernstraditionalindigenousknowledgeelite. Thefirst and institutions mustberooted intheneedsofallplayers ratherthanan developing world andfocus attentionontheway inwhich international and knowledge for developing countriesandcommunitiesinthe ofdifferentdiscussions which typesofskill highlighttheimportance knowledge divide.Herewe present threevignettes ofcurrent issuesand become apparentinrecentdiscussionofcasesandissuesrelated tothe harder ofmode2knowledge tomeasure. does Buttheimportance disciplinary, andtransientinform, practice-based for example, ismuch mostlymode2(practitioner)knowledge,tacit, which tends tobetrans- knowledge creation ( and indexes to mode1typeof discussedarethatthey roughlyconfirm to note indicators aboutthestatistics, One morethingthatisimportant of ‘development’. problematic projects programmes, inthename andstrategies undertaken this conceptualisationleadstosomebothpoliticallyandpractically andinstitutionsthereunderstandings areanumberofreasons why oftheworldBut not operate withthesamenorms,values, allparts valuable and,asitsoriginscanbetraced,originators canberewarded. knowledge isvaluable becauseitisapparentlyuseful,commercially knowledge divideisaninstitutionalframework. Withinthisframework, However,attention todetail. underlyingtheconcept ofknowledge and might bearathertechnical procedure, which couldbeachieved by knowledge from oneplacetoanother. Andthisinturnimplies thatit that. Itimplies thataresolution mightbeachieved by simply transferring But theconcept ofknowledge divideiseven morecomplicated than rich andpoorcountries.Infact the index shows there areseveral divides. The SanPeople andthehoodiaplant Traditional Knowledge- In someareas clearthatnew itisvery commercially valuable pace Gibbons et al. 1994). The context specific, was claimedby anumberofthoseinvolved tobeimpossible. Thehead initial plansfor drug development sharing.It didnotincludebenefit product derived from thehoodiawillbeshared withtheSanalthough made. Thedifference inthiscaseisthatthecommercial successofany norm was thatnoacknowledgement ofTKhasbeen oftheimportance on indigenousknowledge, aswiththehoodiaandSan.Inpast, initial indicationsofwhich plantsmay have based are usefulproperties from plantsoriginatingintheglobalSouth,frompoorcountries.Often, Many ofthedrugsusedinindustrially developed countriesare derived on theproduct. plansto develop Pfizer aslimmingaid based large pharmaceuticalfirm. a andsub-licensedittoPfizer, Phytopharm CSIR’sfindings confirmed Phytopharm, which medicines. basedontraditional specialisesintrails The CSIRlicensedP57 totheBritishdrugresearch company “P57” (Evans2003). isolated therelevant bioactive compound andin1997 patented itas also usedtheplantasasupplyofwater. Duringthe1980s theCSIR second was by information Sanpeople who suppliedtothemilitary paper from aDutch ethno-biologist whoquoted Sanhunters, andthe Its curiositywasinspired by a1937 two sources. Thefirst, research to test Sanclaimsfor oftheplantin1963. thehungerbusting properties andIndustrialAfrican Councilfor Research Scientific (CSIR)started ofthedrug‘discovery’dieting beganwhentheSouth drug.Thehistory isusingittodevelop a Pfizer people isthatthepharmaceuticalfirm The reason totheSan thathoodiamay bethesourceofnew benefits my children” saidaSanwoman (Evans2003). ina recentinterview orthirsty,hungry andnow itisgoingtomake life better for meandfor hungry. Inthe olddays themenoften went huntingandthey never felt and inNamibia they even give itto theirdogstoeatwhenthey are useful whenonhuntingtrips.“All theSanpeoplehereuseXhoba called) have usedtheXhobaorhoodiaplanttocombathunger, especially For many years theSanpeople(orbushmenassomestill prefer tobe divided inthefuture. equitably in ‘sociallyresponsible’ aremore ways maymeanthatprofits example shows, andother tobehave institutions pressure oncorporations TK isinvolved thishasnot beenthecasealthoughasfollowing through avariety oflicensingorroyalty agreements. Butincaseswhere and allthosewhohave contributedatdifferent alsoget stages recompense in which‘valueadded’termsofknowledgecreationcanbeassessed, 111 111 111 111 111 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 112 112 112 112 112 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER wound healing - a property wellwound known inIndia-wasrevoked healing-aproperty following 1995 totheUniversity ofMississippifor theuseofturmericpowder in due to theabsenceof aninventive step. AndaUSpatentawarded in revoked apatentcovering ofneemtree seeds thefungicidalproperties thefollowingdetails theEuropeanPatent cases“…inMay Office 2000 to patents onTKhave beensuccessfullychallenged. Dutfield Graham plant varieties thatareidenticalto traditionalvarieties. Somechallenges plant breedersrights(aform ofIPRgiven toseeds)beingawarded to for ‘inventions’ knowledge thatarecloselybasedontraditional orof ETC claimtohave uncovered many casesofeitherpatentsbeingacquired inventions 2002). arebased(Dutfield the patentmay deliberately uponwhich fail tocite their thepriorart maybe toolow; institutionsapplyingfor orthecompanies orscientific required standards ofinventiveness being appliedto patentapplications examiners have searches; not hadenoughtimetoconduct‘priorart’ the knowledge. Patent awards have beeninadequatefor thefollowing reasons: awarded inthepast precisely becausethey have ignored traditional (RAFI). ETCandothers arguethatmany patents have beenwrongly formerly known Advancement asRural Foundation International Action GrouponErosion, Technology Group), andConcentration(ETC for compensation for knowledge traditional istheNorth Americangroup ‘biopiracy’ asithasbecomeknown. Oneoftheadvocacy groups arguing many contendthatthistypeofactivityamountsto intellectualpiracyor knowledge Incaseswherenocompensation (TK)holders. isprovided, The P57 dealisanexample where‘bioprospecting’didreward traditional (Evans 2003). year tobeshared between the Sancommunityallover SouthernAfrica marketing P57 in2007, theSanhopeto receive several millionranda meetsany itsgoalof commercialised drugwillbedivided.But ifPfizer knowledge”. No preciseformula asyet exists abouthow from profits the Bushmenas“custodians oftheancientbodytraditional reached acknowledging amemorandumofunderstanding therightsof The endresult wasthatinFebruary theCSIRandSanCouncil 2002 was gainingstrength inindustriallydeveloped countries. responsibilitymovementof indigenouspeopleandwithacorporate that coincided withapoliticalwillinSouthAfricato acknowledge therights took onthecasebehalfofanewly formed Sancouncil.Thiseffort had previous involvement withtheSanhelpingthemto winlandrights, Thesituationbeganto2003). change whenahumanrightslawyer, who of PhytopharmclaimedthattheSanpeople“havedisappeared”(Evans (Dutfield 2002). (Dutfield these activities.ThePhilippinesalreadyhasthis type oflegislation from and indigenouspeoples,to benefits share any financial negotiate to bioprospectors accesstogeneticresources withgovernments sharing’legislation that require the introduction of‘accessandbenefit allstakeholders.regulated inways Measuresmightinclude thatbenefit policy measures areunderdiscussionto ensurethatbioprospectingis andWIPO(Correa2001).debated intheUN, WTO Anumberof knowledge,Issues oftraditional IPRandcompensation have beenwidely traditional knowledge” ( theirdisappearingbiologicalresources andassociated and conserve from improvements inhealthcareandfrom enhancedcapabilityto use least localcommunitiesindeveloping tobenefit countries.Thesestand have Inouropinion,allparties for lost,not localandglobalbenefit. drug discovery, biotechnology usesofbiodiversity andother sustainable to develop transparentandethical innatural-products collaborations project inChiapas,“may have achilling effect ontheabilityofscientists of scientistsandresearchers inNature, saidthatthetermination ofthe which wasinvolved intheproject, writingonbehalfofadiverse group from RAFI.JoshuaRosenthal from USNational InstitutesofHealth, sharing,was terminated asaresultofpressurecommitted tobenefit International Cooperative Biodiversity Group (ICBG),anorganisation programme oflegalbioprospecting inMexico by undertaken the sharingisachieved.communities even ifbenefit Inlate 2001, a of dangerous commercialisation which willundermineindigenous andthosewhoseeanythese efforts form ofbioprospecting asindicative between thelargesectionsofdevelopment communitywhich support protecting indigenousrights(Correa2001). Tensions have arisen although there isdisagreementastotheadequacyofIPRameans and to claimcompensation for TKhave received widespreadsupport, ofgovernmentsEfforts andactiviststooverturn unjust patentdecisions revoked duetolack ofnovelty” 2002). (Dutfield written proof (includinganancientSanskrittext) was thatthepatent the United States. onlywhentheIndiangovernment Itwas provided foreign undocumentedknowledge ifitisnotalsoknown as‘priorart’ in common knowledge inIndiasinceUSpatent rules“donotrecognise succeeded ifithadrelied on theargumentthat‘invention’ was on to note thatthechallenge totheturmericpatent would not have a legalchallenge Hegoes by theIndiangovernment” 2002). (Dutfield Nature , 416, 15: 07 March 2002) 113 113 113 113 113 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 114 114 114 114 114 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 13 being acknowledged. when localknowledge isignored. Sustainable and equitable development rests onlocalknowledge have written extensively abouthow development initiatives andactivities are routinely undermined Development writers such asRobert Chambers (1997) andmany others such asWarschauer (2002) growth andthushelpalleviate poverty, andothers whoargue“equally tobetween stimulate economic thosewhoarguethatIPRsare necessary of IPRs.Theoverarching is argument(summarisedinCIPR2002) to provide members requires allWTO minimum standards ofprotection Rights(TRIPS).ThisagreementRelated AspectsofIntellectualProperty The key institutionaldebatepresentlyisover onTrade- theAgreement p.89). practices (CIPR2002, acomplexBasmati varieties -inshort mixofknowledge forms and environment, andgenetics ofthe practices agricultural soil,climate dependingonauniquecombination of geographically specific claims Basmatiisagenericterm, IndiaandPakistan claimitis upmassivetaking amountsofprofessional andadvocate time.TheUS lines. Butthedispute over focus thegeographical ofBasmaticontinues, withdrewexamination, theirapplicationsoftheBasmatitype thefirm rice breeding linessimilarto Basmati.AfterIndiarequested are- apatentseekingmonopolyover wasgranted breeding various firm disputes protection therightto ofthenameBasmati.In1997 aUSrice India andPakistan, whereBasmatiriceoriginates,andtheUSthat case ofbasmatirice.Along-runningdispute hasbeengoingonbetween One example requiredto ofeffort protect ofthequantity IPRsisthe hard orimpossible toprotect theirknowledge claims. other professionals working inandaround theIPRprotection arena,and rightswithavasttheir intellectualnumberofdifferent property typesof companies andcountrieswithextreme sensitivitytotheneed protect rights (IPR).Thesimplistic divide,oncemore,isbetween nature oftheknowledge divideareassociated withintellectualproperty knowledge counts abroader setof‘development discussions’aboutwhose divide reflect accumulated andshared. Viewed thisway, debatesabouttheknowledge ofhow knowledgeenable amore justandaccurate is reflection ignorant poor. atleast They aboutcreating institutions, which are inpart transferring knowledge from aneruditegroup ofrich countriestothe knowledge divideallow for. Theissuesclearlyarenotsimply about more complicated knowledge ‘map’thanmorecrudedepictionsofa sharingapproach institutionalisesThe benefit formal recognition ofa companies andcountrieswithweaker capabilitieswhich make it Other revealing andtopical examples ofthecomplex Knowledge divideinintellectualproperty 13 . those few those (CIPR 2002). developing countriesbecausecostlyerrorsofpolicy will behardertoear proceed accordingly. for Indeed,itisperhaps moreimportant greater extent inthepast, soshoulddeveloping countriesbefree to variations inhowsignificant they applyIPRs, anddidsotoaneven and socialcircumstances. Justasdeveloped countriescurrently exhibit economic regimes totheirparticular theirintellectualproperty tailoring In summary, theinterests ofdeveloping by countriesarebest served oftheconcession. advantage their legislationandchange bilateralandmultilateralagreements totake countries isnot straightforward, sincemany countriesneedtoamend the protection ofpharmaceuticalpatentsto2116 for theleast developed p.142).2002, Even thedecisioninDohato extend theperiodto legislate India, 25inJamaica,97 inKenya, 20 inTanzania, 128 inVietnam (CIPR differ in widely–around 600 IPoffices example, thenumbersstaffing high for those countrieswithweak overall innovation systems.For The cost ofmeeting therequirementsofTRIPSwillbedisproportionately technological capabilities. poverty, like IndiaandChina,willdiffer from weak countrieswithvery technological livinginextreme capabilitiesandwithhugenumbers widely. Policies onIPRsfor countrieswith relatively advanced andtechnologicalhomogeneous. Theirscientific capabilitiesdiffer and technological capabilities.Developing countriesare not for thoseinterested inbridgingknowledge dividesistheissueofscience and accessto andownership ofplantgeneticresources. Akey aspect local knowledge ofthose leastabletoprotect itformally (like theSan), including accessto medicinesfor poorpeople,protection and oftacit implications forThere are ofimportant arange theknowledge divide, p.1). badly”(CIPR2002, farmers particularly essential medicinesandagriculturalinputs, affecting poorpeopleand rather thandomesticmanufacture. Moreover, theyincreasethecostsof by themarket patentprotection obtaining andto service throughimports, Theyimitation. allow to foreign drive outdomesticcompetition firms even ifdeveloped. They limittheoptionoftechnological learningthrough poorpeoplebecausethey willnotbeableto affordbenefit theproducts, capacity may beabsent.They are ineffective atstimulating research to developing humanandtechnical countries,becausethenecessary vehemently theopposite.IPrightsdolittletostimulateinventionin 115 115 115 115 115 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 116 116 116 116 116 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER the trust in the quality of software services thatismentioned by the the trustinquality services ofsoftware US, Europe,theMiddleEast, Chinaandmany other places,buildsup professionals, andtheirinformal knowledge ofworking andlivinginthe diasporic natureandextensive linksofexperienced Indiansoftware term knowledge ofworking withoverseas clients).Undoubtedly, the software professionals, not to mentionbuildingthemediumandlonger team management(recruitment, andretaining screening,training notice,requiring capabilitiesofprojectand teams ofdevelopers atshort tomobiliselarge US companies highlightedtheabilityofIndianfirms rest ofIndia”. exemplar governance andcorporate ofgoodentrepreneurship to the isthusits and under-appreciated industry contributionofthesoftware andorganization….Apotentiallymodels ofentrepreneurship important growing in turn, hasledtoinnovative ofhumancapital, importance workers, andanalysts.The notonlyprogrammers, butalso managers therelative has“increased valueofprofessionalsoftware industry Arora andAthreye p.253)arguethatthesuccess oftheIndian (2002, simple andmore complex notionsofITandknowledge management. thedifference between high skill–butitismore thanthis,reflecting Arguably thisresultsfrom awinningcombination–lowwage costsand at 56%ayear two-thirds years dueto for to2002, exports. thefive European locations.However, hasgrown theIndiansoftwareindustry setupasalow-wage alternativeeasy to toUSand critiqueanindustry incomparisonstatus withthesoftware giants(Heeks1996). Itisperhaps andthusrelativelyand body-shoppingoriginsmentality ‘low-tech’ beencriticisedfor hasoften The Indiansoftware industry itscallcentre time’ (Merchant p.1). 2003, of Infosys’ 14,000 engineersareonoverseas assignmentsatany one out assignments,whilesome4,000 engineers areabroad onshort-term between two weeks andtwo years. ‘About halfofWipro’s 13,000 software company, Tata (TCS),areabroad for ConsultingServices capabilities to createandpolicethem. regimessetupby thosewiththevastto globalregulatory majorityof complex knowledge capabilitiescanbebuiltunderpressure toconform However, begsthequestionofhow policies tocircumstances tailoring Nearly a quarter oftheengineersemployedNearly aquarter by India’slargest of theIndiansoftwareindustry Knowledge networksandthedevelopment centre ofBangaloreinIndia,GuandongChina,theGuatengdistrict most industrialised zone(SaoPaulo) orindeedManaus,thehi-tech Bay oftheUSA. Areaandother What hopethen,even parts for Brazil’s orbetweenEast and‘therest’ Boston/Cambridge oftheUK, andthe for ustonotice thedifference between, for example, theUK’sSouth edge’ seemssofar removed from therestthatchasms are wideenough most developed regionsandthoselessdeveloped isobvious. The‘leading them. Thatthechasms arevast between economicallyandindustrially the facts concerningdividesandinequalitiesdoingsomething about engineering. thatallowsexpertise managementskillstoaddvaluesoftware international centresandIndia,indeveloping project turnkey use ofdiasporicrelationsbetween IndiansintheSiliconValley, other Firms have builtcapabilitiesthrough amixthatincludesmaking good for example. andR&Dservices, technical support transcription, callservices, data be appliedtoarangeofbusinessprocesses, such aspayroll management, Athreye suggests thattheoutsourced businessmodelcouldinprinciple manufacturing, Wipro intelecoms andR&Dservices. animation, Satyam onsoftware for automated systems intransport andinsurancedomains,Pentafour assetsfinancial oncreating in digital TCSandInfosysincluding hybrid concentrated on US-Indianfirms. entered, development clients.Otherfirms centres dedicated to specific exacting p.1). quality” (2003, like TCSandInfosys Leadingfirms set up and to deliver todifferent outsourced specifications technical services successfullypioneered abusinessmodelbasedontheability Indian firms unglamorous where areaoflargevolume outsourced software services, intheirchosen marketfirms niche. Thismarket niche wasthe to transform capabilities“andbecomeleading aset ofskillsintospecific Athreye emphasizes thefact thatearlyentrantswere abletolearnhow knowledge beingstored inanetwork, orofnetwork capital. employees, aswell astheirexperience. Thisisanexcellent example of from competition byhighlightingthequalityoftheirprocessesand productiontosoftware India.Indiancompanies distinguishthemselves large companies. 185 Fortune companies now ofthetop500 outsource There isaclassictensionindevelopment between mapping 7.5 Conclusion 117 117 117 117 117 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER 118 118 118 118 118 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER economic power, albeitsofar inamodestway. institutions canbepressured to protect therightsofthosewithouthuge economies. Theexamples pointouthow nationalandinternational gain somerecognition for theroleoftraditionalknowledge inmodern people have politicalclimates used advantageous andlegalstructuresto comesfrom SouthernAfrica.TheSan and institutionalmodification differentA very example of change thathighlightspossibilitiesofpolicy IPRs may impact. have significant avery pointed out,institutional reforms such asthosegoverning and trade But,aswe 2003). haveIndia’s pharmaceuticalsector (Chaturvedi, also there isevidencethatthesenew sets ofpossibilitiesarebeingechoed in and innovative And organisationhascreated anew set ofopportunities. capacities. IntheIndiansoftware example, afocus onhumanresources ‘learning environments’ which enhance‘sense-making’andsourcing not Firmsandnetworks static. canbecomemore adept atcreating in ways initialpredictions. thathave Absorptive defied capacitiesare possibilities exist. hasgrown TheIndiansoftware industry incapacity aset of so much ofknowledge ratherthancodified, acquisitionistacit progression. Butpreciselybecausecontext andbecause issoimportant cumulative linearityandorderof pathways doimply acertain powerful diasporiccommunitiesandtheirnetworks. Insomeways some policymeasures andsomecomplementing factors, andalso some thingsmake adifference, asfor example strategies, firm-based and usedependsonwhatyou alreadyhave). Butwe alsoknow that depends onabsorptive capacities(inother words, whatyou canacquire For example, we know thattosomeextent knowledge accumulation that window ofcomplex liesapanorama constraints andopportunities. knowledge, knowledge divides,andrelateddevelopment issues.Through This chapter has hopefullyprovided awindow ontheanalysisof new knowledge basedeconomiesoperate. miss someofthemostextraordinary andinteresting aspectsofhow circumstances. Andyet, notto engageinwhatispossibleto difficult bridge them.Itismuch lesseasyto lookfor whatispossibleinhowever and oftheinternational institutionsthatseemtohave solittleabilityto It iseasyto drown inhelplessand cynicalcritiqueofthe‘facts’ ofdivide, to DarEsSalaam,Dacca,orTegucigalpa. the USA? Let aloneMalaysia, Slovakia orColombia,andsoon‘down’ in SouthAfrica,Prague,orMontereyonMexico’snorthernborderwith institutionally-aware perspective. for amorecomplex, morehumanresource-dependent, more those approaches todevelopment, dominantfor sometime,thatargue The new emphasis onknowledge, to not reinforce surprisingly, serves possible andthuswhatcapabilitiescapacitiesfor actionarerequired. itoffersis not. amore nuancedanalysisofwhatmightbe In short, other hand,itallows seriousways ofassessingwhatispossibleand complicates theprocess ofbridginggaps.Onthe copied, thatcertainly cannot workare not enough,iffirms alone, iftechnology cannot justbe and technology managementandpolicythinking.Iftop-down approaches transcends thenormal‘todolists’ thatcharacterise much ofdevelopment main thingsitcontributesisaglimpse ofaway that ofoperating wayforward intherethinking ofwhatneedsto bedone.Oneofthe practice ofknowledge anditsmanagementhave something to offer asa One thingthatemergesfromthischapteristheideastudyand 119 119 119 119 119 Chapter 7 - FROM DIGITAL DIVIDE TO KNOWLEDGE DIVIDE: A PRIMER

Chapter 8 METHODOLOGICAL ISSUES

8.1 The data The work starts with the raw data. All data used in the empirical application of the model come from well-known sources. They have history and continuity, they are well-tested over time and their pros and cons are well-known. The strictest standard, that of the lowest common denominator, was applied; every series must be available for every country and for each year. Therefore, estimation of missing cells was kept to a minimum.

Nevertheless, in a statistical operation of a large size, where up to 192 countries, 21 indicators and 6 years of observation are utilized, it is not surprising that missing cells existed. Whether the average of two years was used to fill in missing values in intermediate years or the applicable rates of growth, the estimation relied overwhelmingly on a series’ own trend and each country’s own information - rather than donor imputation. (The fact that we deal with series that generally grow over time is tremendously conducive to this type of estimation). In total, the proportion of cells estimated represents 4% of the total, and the quality of the fits is estimated to be in the 95%-99% confidence interval. Only about 1% of the values were imputed from donor information, selected on the basis of geographical proximity rather than through the application of generic rules. While this adds to the complexity of the calculations, it also adds to the specificity of the estimates – although obviously the quality of these fits cannot be assessed.

All in all, considering the approach to use well-known and well-tested indicators, in conjunction with the quality of the raw data, the relatively small proportion of estimated values, and the high fit of the estimates that can be assessed, the data used are deemed to be of the best quality possible.

121 122 122 122 122 122 Chapter 8 - METHODOLOGICAL ISSUES rules, across individualseriesand years. of variation)andwas appliedin asystematicway, onthebasisofset mean, thestandard deviation (thecoefficient (variance)andtheirratio Internet hostsintheUnited States. Thisprocedureisbasedonthe anomalies,suchdata astheproblem withtheallocationoftoo many At thesametime,itisbelieved thatthisprocedure takes care ofsome morethan anythingsome ‘outlier’values,which thedata else. distort procedure. typeandtookcareof Thiswasoftheminimalintervention this typeofindividualseriesanalysiswasusedto applyasmoothing admit to any maximumvalues, sinceInfostates areunbounded upwards, complete moments.Althoughthemodeldoesnot withitsstatistical thorough analysiswas statistical for performed each andevery indicator, Moreover, someindicator seriesaresubjectto anextreme A range. closer to theconceptsthatmodelcallsfor. indicators were combinedto form composite indicatorsthatwould come derived from theraw were data constructed, whereappropriate, and eventual aggregation. Whileinmost casesindividualindicators the raw to indicatorsfor data ofcomparability across countries purposes years for which each variableis available, thenext stepwasto convert Having acomplete set for ofdata allcountries,variablesandthe matter knowledge. application representsacombinationofstatisticalworkandsubject and dataavailability. Thus,theendresult oftheempirical must beexercised, basedonacombinationofsubject-matterknowledge same indicatorcouldbepotentially usedineithercomponent. Judgment indicator betweensuitable modelcomponents becomesblurredandthe applicationisfreeof.Inpractice, sometimes, thechoiceparticular ofa problem ofhighautocorrelation amongindicators, somethingthatour indicators for individual components ofthe model alsoavoids the usedandnotindicators their quantity. Selectinggoodbutnot multiple earlier, thekey tosuch anapproach istheappropriatenessof The empirical applicationisbasedonanindicators model.Asexplained beanapproximationwill inevitably oftheframework. concepts toanempirically appliedmodelingexercise. Any modelfit adaptations mustbemadeinmoving fromtheconceptual purityofthe overallaggregation to acountry’s Infostate. Asisalways thecase, notions ofInfodensity andInfo-use, theirsub-components andtheir The conceptual framework callsfor themeasurement ofthe 8.2 Discussionofindicators N infrastructure readinessandpotential. be restricted tonetworks. Thisisusefulasitindicative ofacountry’s though, thatallow ofnetworks. themeasurement will Thus,ICTcapital level. there areenoughgoodindicators, InthecaseofICTcapital, information exists tomeasureeitherofthesecomponents atadetailed measurement andICTskills.No ofICTcapital adequatestatistical adjustment andthelatterasa‘quality’one. isconcerned.Theformer may beperceivedservices asa‘quantity’ extent of thenetwork, insofar asitscapacitytodeliver value-added the network’s digitizationisusedasan additionaladjustment for the coverage orinthecapacityofswitches), and;second,thedegreeof clearly exceeds supply, eitherintermsofactualgeographical/population (considering thatthelevel ofdemandincountrieswithwaitinglists better theactualextenttelephone ofthenetwork linesinorder toreflect thenumberofwaiting linesisusedtointroduced: adjustthemain first, network thatwe wishto measure,thefollowing two adjustmentsare demonstrated Recognising bythedata. thatitisreally theextent ofthe demand,as amountofunsatisfied where thereisasubstantial realityequallywell indevelopingit cannot countries besaidto reflect assumption works reasonably well inthecontext ofdeveloped countries, will continueto expand, asthiswould beuneconomical.Whilethis In other words, ifthedemandisnot there,itisunlikely thatthenetwork implicit assumption thatover timethedemandandsupplymatch. reason for thewidespread useofthisindicator for many years isan indicator ofmaintelephonelines(persomepopulationmeasure).The telecommunications network, which istraditionally measuredby the needtomeasuretheextentFor we ofthewireline first ourpurposes, ETWORKS ! ! ! ! ! ! ! ! International bandwidth Secure servers Internet hosts Cable connections Cell phonesubscriptions mainlines Digital Waiting lines Main telephone lines : Thefollowing indicators are usedtomeasure networks: Infodensity The measurement ofInfodensity callsfor the 123 123 123 123 123 Chapter 8 - METHODOLOGICAL ISSUES 124 124 124 124 124 Chapter 8 - METHODOLOGICAL ISSUES them asanadjustmentintheInternet hostsindicator. Thisway, they things. Therefore, agoodtreatmentfor istoincorporate secure servers butwould ratherconfuse importance indicate anything ofparticular if expressed interms oftheirsmall populationbases.Thiswould not reasons they are located there, would represent anenormous percentage themereexistencestates, for ofoneortwo whatever oftheseservers, would not reality. besensicalasitcouldseriouslydistort Inmany island to Internet hostsandplacingthisindicator onequalfooting withhosts highly developed smallproportionally countries,theirnumberisvery in many ways, they are not yet awidespread phenomenon.Even in Internetdenote infrastructure. thesophistication However, ofacountry’s indicator, which admittedly issubjectto several caveats. Secure servers different.very However, they both improve ontheInternet hosts From asubjectmatter pointofview, ofeach oftheseis thesignificance 2001 we alsohave andinternationalbandwidth. for data servers secure hosts, anindicatorfor which several caveats For have beenidentified. So far, theextent oftheInternet iscommonlymeasuredby Internet case, amonotonic transformation applied. was fact thattherearealternatives Inthis for whatisstillthemainservice. offered- given viacable,coupledwiththe themultiplicityofservices degree asifthey didnot have, say, awirelinetelecommunications network countries thatdonot have themshouldnot bepenalizedtothesame inthemeasurements,albeit countries thathave themshouldbereflected infrastructure. Thus,theavailability andextent ofcablenetworks in it isundoubtedly anextra valuable piece of theoverall converging Inthatsense, (consideredbroadband) andtelephoneservices. services increasinglyasachannelsignals, butserves for theprovision ofInternet today isnotusedonlyfor andreception thetransmission oftelevision those countriesdevoid ofcablenetworks. Ontheother hand,cable the useofthisindicator would undulybiasthecomparisons against accomplished elsewhere viasatellites anddishesorantennas.Thus, networks hasbeensignalsoftelevision channels, something thatis neutrality. offered by cable Thisissobecausetheprincipalservice indiscriminately would oftechnological violatethedesirableproperty Many countrieshave nocablenetworks atall,andusing theindicator Cable connectivityisanother network indicator, butitposesapeculiarity. and government however, purposes, cannot beisolatedinthedata. should exclude linesusedpurelyby households.Linesusedfor business extent ofthemobiletelecommunicationsnetwork. Ideally, theindicator over areusedasanindicatorofthe time,thatcellphonesubscribers It isforthesamereason,ofparallelmovementdemandandsupply S all traffic. not onlytotheInternetbutallnetworks,asitisusedcarry bandwidth note hereconcernstheapplicabilityofacountry’s A final scale intact. put thedifferences inconceivable scale,whilestillkeeping the and make amonotonic transformation senseof.Again, isperformed to comprehend, to digest extremely difficult will produce dubiousfigures over percapita one, bandwidth country thatofabandwidth-starving that would border themeaningless. Asimple test, ofatop- theratio full extent today of such would massive resultinfigures overcapacity themtotheir ofthe countries’infrastructure, reflecting and form part While thisdoesnot change thefact thatthesebigpipesare indeedthere the presence ofbiginternationalandtransnationalorganizationsthere. Brussels andGeneva have the‘bigpipes’,something notunrelated to of adifferent naturethanInternet hosts. For citiessuch as instance, interconnections andthelike iswell-known andcangive risetobiases infrastructure internationally, withitsphysical backbones, nodes, of prices.However, thestructure andarchitecture oftheInternet Divide,includingtheissue aspectsoftheDigital ofimportant the heart enormouslyintheoverallbandwidth matters scheme ofthingsandisat heralded asmuch superiortoInternet hosts(IDRC True, 2002). indicatorand International bandwidthisemergingasasignificant unduly biasingtheresults inameaninglessway. iskeptbut atthesametimetheirimportance inperspective, without are usedtodifferentiatebetweencountryinfrastructuressomeextent, are used: which progressively becomemoreadvanced. The following indicators Therefore, skillsareapproximated withgenericeducationindicators, across alargenumberofcountriesneededfor internationalcomparisons. withregards todata particularly earlystages, this pointitisstillatvery KILLS :

! ! There isincreasinginterest inwork to measureICTskillsbutat Gross enrollment ratio rate Literacy ! ! ! Tertiary education education Secondary education Primary 125 125 125 125 125 Chapter 8 - METHODOLOGICAL ISSUES 126 126 126 126 126 Chapter 8 - METHODOLOGICAL ISSUES proportion ofhouseholds with atelephone.Unfortunately,proportion thelatter households, andisusedasaproxy for thebetter indicator, namelythe Residential phonelinesare agoodindicatorofuptake among some time. countries, wherepenetrationrateshaveachieved saturationfor Obviously, itdoesnot offer any differentiation amongdeveloped inmany indicate. important countriesasthedata this medium,still vitally asanindicatorofthecapacityto receiveserves information through oftelevision-equipped householdsoverThe proportion 100 households Uptake uptake toarrive atameasureofInfo-use. the available intensity ofuseindicators areaggregatedwiththoseof Therefore, whileuptake assuch, ismeasuredonitsown andidentified capture theintensity ofuseinaway thatcouldbedeemedsatisfactory. It was judged,however, thatnot enoughinformation exists thatwould and ICTintensity of use. improvements besoughtby must theinternationalcommunity. beanarea whereserious this scale,shouldmost definitely this treatmentisthebestwe candoatthispointfor anapplicationof For thisreasonthey areweighed more inthecalculations.Although advanced skillsand,inthatsense,offer abetter proxy for ICTskills. which educationindicators, denote theacquisitionofmore tertiary differentiation isoffered aswe move and toenrollment insecondary More a continuum,withICTskillsembeddedthere 2002). (ETS isconsistentwiththeoriesandframeworksindicators thatview skillsas education, especiallyamongdeveloped countries,theuseofsuch situated earlyontheskillscontinuum-andgross enrollment inprimary While littledifferentiationisofferedbyliteracyrates-astheindicator ismeasured by thefollowing indicators: ! ! ! ! Internet usersper100 PCs per100 Residential phonelinesper100 households Television equippedhouseholdsper100 households The modelcallsfor separate measuresofICTuptake Info-use traffic is the rest of the world’s incoming traffic andviceversa. Moreover, istherest oftheworld’s incomingtraffic traffic outgoing scale,onecountry’s one must becognizantthat,onaplanetary therefore statistics arenot available asindicators.Indealingwithtraffic statistics are not measuredinaconsistent way across countriesand ifadded,would aneven offer National better traffic, view, butsuch areinhabitant combinedandusedasanindicatorofintensityuse. measuredinminutes per Incoming andoutgoingtelephonetraffic monotonic transformation was applied. represents DSL and cableconnections.To mitigatetheseproblems, a something thatcould impose additional biases.Theindicator usedhere controversy surrounds thearea asto whatexactly constitutes broadband, narrowband Internet from broadband users users). Moreover, some argument isthatmoredifferentiates Internet usersfrom than non-users with other indicators would biasthecomparisons. (Implicitly, the no broadband atall,andtreatingbroadband users footing onanequal we encounter asituationsimilartothatofcable:many countrieshave here asbroadband withintensity isassociated ofuse. Once again,though, onbroadbandData usersareavailable onlyfor 2001 andarerelevant of Info-use: The followingintensityofuseindicatorscompletethemeasurement per 100 inhabitants. goodindicatorfor andisexpressedInternet isavery ourpurposes users a goodideaofoverall ICTuptake. for such anindicator inuptake, thisindicatorisusedhereandprovides households, incombinationwiththefocus onnetworks andtheneed differentiate between availability inbusiness,governments and thanuptake. rather ICT capital Given thatitisnotpossibleto equallywellPCs isoneofthoseindicators inmeasuresof thatcouldfit 100 anditiscappedthere. involvinginstances mostly developed countries,theindicator exceeds known for possibleadjustmentsto bemade.Therefore, inseveral aremore andthesenumbers not thanoneline.Thesediffer by country oftheresidentiallinesisthattherelimitation aremany householdswith exists onlyincountriesthathave regular One householdsurveys. ! ! ! International incoming telephone traffic percapita International incomingtelephonetraffic percapita International outgoingtelephonetraffic Broadband users 127 127 127 127 127 Chapter 8 - METHODOLOGICAL ISSUES 128 128 128 128 128 Chapter 8 - METHODOLOGICAL ISSUES the analysis. kept throughout oftheresultsand thecalculations,presentation weighted type-theweights being populations).However, Planetia was averages (effectively,among countriesmorethanplanetary ofthe Hypothetica waspreferred, asourworld still evolves aroundcomparisons While Planetia couldbeagoodalternative reference country, constructed. Planetia andHypothetica areassumedto bethesame). used are expressed andnocumulative totals inpercentages canbe 95% ininfo-use andtheoverall infostate. (Inskills,theavailable indicators population oftheplanet innetworks, 98%inskillsandinfodensity, and each component. In2001, thesecountriesaccountfor: 99%ofthe country. Planetia isbasedonthesumtotal ofallcountriesavailable for theaggregatecontains valuesofthewholeplanet, ifitwasviewed asa be thehypothetical oftheaverage country valuesofallcountries,it usefulness; Planetia represents theplanet. Inother words, ratherthan wascreated withitsown country analytical Another imaginary tool for benchmarking. asausefulanalytical an initialdelineationamongcountries,serving the average ofeach indicatoramongallcountriesexamined andprovides in each component ofthemodel.Ineffect, Hypothetica hasasvalues representstheaveragecreated. Thiscountry amongallcountries used asthebase,Hypothetica was country Rather thanpicking aspecific existindicators andtherefore itcanproduce thebest measurements. was chosen asthebase,becausethisisyear for which additional can continuetoexpand fromyear toyear everywhere. Theyear 2001 admitting allalongto thereality thatinfostates andtheircomponents evolution Divideacrosscountriesandover oftheDigital time,while country. Theseprovide thebenchmarks toquantify the andmonitor too was subjecttoamonotonictransformation. many anomalies.For things,andresults thisreason, incertain thisseries takes placeisafunctionof Theway traffic inhabitants. international infrastructure, itdoesnot representusagebyusage ofacountry’s its the callswere originated andterminated. Even thoughthisrequires routed through countriesintermediatingbetween thosewhere traffic such dataarealsosubjecttoseveralanomalies,asthecapturingof The modelcallsfor areference (base) periodandareference 8.3 Reference yearandcountry For telecommunicationsnetwork; thefixed indices) were arrived atasfollows: to form composite indicators. Theseindicators (andtheassociated While many indicators areusedindividually, someothersare combined pose anupward tomeasurementsover boundary time. and not inallseries. Itshouldbenoted thatthisadjustmentdoesnot standard deviation. Thisproduced onlyafew butusefulmaximumvalues with following rulewasapplied: such thatadmissible minimumvalues areatzero. Specifically, the onlypermissiblemaximumvalues.Theindicatorsestablishes usedare characteristic behaviour ofeach andevery seriesandpracticallyit natureand outlier valueswasapplied.This basedonthespecific the appropriate Thenthesmoothing denominators. adjustmentfor For theInternet; I The gross enrollment indicator; I I gross enrollment Internet fixed CV = for CV>3, for CV<1.5, f being the series’ coefficient ofvariation, beingtheseries’coefficient or 1.53,scalar=2 stands for thevalueofindex, I t Î t I t j i, j i, c i,j for allothercountrieswe have:

=(Vt =(Vt (c) = n i, j i, c ∏ /Vt x _ i /Vt = n t 1 x _ o refers to thereference year and I x o _ i o

,

i, c j i, c ( n c , ) x100 t ) ) x100 i refers to individualindicators, t to any to value of100 for thebaseyear throughout theexercise –for each and It isclearfrom this methodology willhave thatthereference a country one index pointcorresponds point). toonepercentage represent percentage changes, butonlyapproximations (onlyroughly the transformations donotreally thatwere necessary, theendfigures networks andtheintensity ofusecomponent ofInfo-use). Considering then appliedto allprevious years. (Linkfactors are onlyrelevantfor yielded thelinkfactors both for Infodensity andInfo-use, which were time withonlythevariablespresent for the1996-2000 period.This year 2001 were carriedouttwice:oncewithallvariablesandasecond 2001 arenot available for prioryears, thecomputations for thereference In order to take careofthesituationwhereby indicatorsavailable for development across ofinterest. indicators value judgmentthatfavours symmetricalratherthanlopsided The choice ofageometric ratherthananarithmetic meanrepresentsa aswehaveICT goodorservice, noknowledge basistodootherwise. what follows isanunweighted average, indifferent toeach individual Clearly, have onceindicators beenconstructed aspreviously explained, highest level infostate, ofaggregation,acountry’s simply as: Finally, whenwe have both Infodensity andInfo-use, we arrive atthe combined international traffic. where z=6,thatisallthefour uptake indicesplusbroadband usersand and skillsarecombinedintotheInfodensityindexas: We continuelikewise for level thesubsequent ofaggregation. Networks While noindex iscomputed for intensity ofuse,Info-use isarrived atas: Infodensity withk=2. Infostate Info-use

= = = z √ 2 ∏ i (infodensity xinfo-use) = z k 1 ∏ I i = k i 1 , j ( I n c , i ) t , j ( n c , t ) 131 131 131 131 131 Chapter 8 - METHODOLOGICAL ISSUES 132 132 132 132 132 Chapter 8 - METHODOLOGICAL ISSUES of reference placestheemphasis ondeveloping countries. technique ormethod Thisisconsistent ofaggregation. withtheterms out loudandclearisinnoway by affected theexact choice of and thattheirdifference fromcountriesatthebottom ishuge,comes exercise. That thesamegroups of countriescomesupontop,though, information thanisavailable andwould representatotally different by theresults. Thiswouldsupported requiremuch more detailed comparisons basedontherankingofthesecountriescannot be application.Therefore,one another dependingonthespecific can bereached. At leasthalfthecountriesthere cancome upontopof Among thecountriesthattop theInfostate conclusions list, nodefinitive – withtheexception tierofcountries. ofthetop Theystand. were found to beextremely robust to different methodologies change, especiallyamongthetopcountries,conclusions figures andthereforepermutations, anequalset ofestimates. Whiletheprecise tests were carriedout, effectively includingavast numberof conclusions oftheoverall –numerous results thanthefigures –rather Toaggregation, theendfigures. affects therobustness ofthe ascertain is well-known, each different choice, methodof such astheparticular ispossible.As largenumberofpermutations arithmetic means,avery etc.), to grouping variables for aggregation, to usinggeometric or performing monotonic transformations (scalars,logarithms,square roots toindicators, applyingsmoothing techniques, tothealternative ways of into play andvariousalternatives openup.From creatingcomposite country achievedcountry thatin 2001, itcanbesaidto betwo years behind. had20%Internetpenetration in1999 ifacountry instance, whileanother eachdifferent other’s countrieswilleffectively timeline.For reflect components ofinterest ortheoverall Infostate index, thevalues of over indicators, aggregate time.Inasense,forcountry specific made: across countriesatany given point intime,andwithineach So, consistentwiththetermsofreference, two-fold comparisons canbe be moving over time.Itisonlythe baseyear thathasauniform value. reference country. Butthereference scoreisnot Itwill static. country’s for canbecompared theaggregates.Then,each withthe country below 100, whilefor others they willexceed 100. Thesameholdstrue well bethatfor willhave somecountriesindividualindicators values for allother countrieswillassumetheircorrespondingvalues. Itmay every indicator, each component andtheoverall Infostate. Theindices In statistical workIn statistical ofsuch scalenumerousdecisionscome 8.5 Sensitivityanalysis Chapter 9 DATA SOURCES AND DEFINITIONS

All the data used in this report come from well-known and credible sources. As well, they have a history and continuity and are available to all.

Data on telecommunications, broadcasting and information technology come form the ITU. Primary sources to compile these data include annual ITU questionnaires, as well as reports from national statistical offices, telecommunication ministries, regulators, broadcasting agencies and operators. Depending on the specific data series concerned, a variety of other selected sources are also used. For instance, publications of the European Audiovisual Observatory are consulted for broadcasting data, the Internet Software Consortium and the Internet Domain Survey are consulted for statistics on Internet hosts, while data on personal computers are estimated based on stock and shipment data from many national and international sources

The education statistics come from UNESCO and World Bank databases. Interested readers are referred to numerous publications and metadata information available at these sources. Consult http://www.itu.int, http://www.unesco.org and http://www.worldbank.org.

Valiant efforts are devoted into the compilation of these statistics so that they serve the policy and research needs of the international community. However, gaps exist, as identified earlier, and there is need for concerted efforts to address this “information deficit”. Resources for such activities are either scarce or misallocated. The value of quantitative information in a global Information Society setting cannot be underestimated.

ITU data Demography, Economy The indicators in this category are useful for deriving ratios in order to make comparisons across countries. They are generally obtained from international organizations or national statistical offices.

133 134 134 134 134 134 Chapter 9 - DATA SOURCES AND DEFINITIONS obtained by dividing the number of main lines serving households (i.e., byobtained dividingthenumberofmainlines serving Refers is tothepercentofmainlinesinresidences.Thispercentage Residential main lines networkof digital terminationpoints. ofinter-exchangethe percentage orthepercentage lineswhich are digital indicator doesnotmeasure of exchanges thepercentage which aredigital, telephoneexchangesto digital by thetotal numberofmainlines.This by dividing thenumberofmainlinesconnectedpercentage isobtained Refers exchanges. tothepercentofmainlinesconnected todigital This mainlines Digital included inmainlines. subscribers would generallyincludepublic telephoneswhich not are network accesspointsincludingmobilecellularsubscribers.Telephone Inothercasesitrefers toall capacity (ratherthanlinesinservice). used by somecountriesvaries.Incases,itrefers installed tothetotal same asanaccesslineorasubscriber. ofaccess lines Thedefinition commonly usedintelecommunication documents.Itmay notbethe orDirectExchangewith theterm mainstation Line(DEL)which are inthetelephone exchangeport equipment.Thistermissynonymous equipment tothepublic switched network andwhich have adedicated The numberoftelephone linesconnectingthesubscriber’sterminal Main telephonelinesinoperation refer inthiscategory The indicators telephonenetwork. tothefixed Telephone Network inthedomesticeconomy.services national currency. expenditures ongoodsand GDPisthesumoffinal forThe data Gross DomesticProduct in (GDP)arecurrentpricedata Gross domestic product (GDP) based ongrowth rates between censuses. of personswholive together orapersonlivingalone.Estimates are forThe data householdsrefer tothenumberofhousingunitsconsisting Households the defacto populationwithinthepresentboundaries. forThe data populationare mid-year estimates. They typicallyrefer to Population following mobile factors alsoaffect themeasurementoftelephone traffic: thelength ofthechargingFurthermore, unit canbechanged. The orthetimeofday) andacross countries. (depending onthetypeoftraffic is becausethelengthofcharging unitvarieswithincountries time for andisnotcomparable thesamecountry acrosscountries.This not measured comparable inpulsesisoften across telephone traffic Note that tomeasuretelephoneunits usedby traffic. thecountry Minutes refer tothenumberofminutes ofuse.Pulsesrefer tocharging isrecorded. Callsrefer totheactualnumberofcompletedtraffic calls. convention amongcountriesfor measuringtheunit inwhich telephone thereisnostandard Specifically isreported. way telephonetraffic the PublicSwitched Telephone Network. Thereiswidevariationinthe carriedover refer tothevolumeThe indicators inthiscategory oftraffic Traffic arenot included. paging services orradio services, (e.g., Wireless LocalLoop(WLL)),publicmobiledata but shouldnot includenon-cellularsystems. to wireless Subscribers fixed such asDCS-1800, Personal HandyphoneSystem (PHS)andothers) cellularsystems(includingmicro-cellularanalogue anddigital systems Telephone Network (PSTN) usingcellulartechnology. Thiscaninclude which providesmobile telephone service accessto thePublicSwitched Refers telephones subscribingto anautomatic public to usersofportable Cellular mobiletelephonesubscribers refer inthiscategory tomobile(wireless)networks.The indicators Mobile Services unmet demand. registered applicationsandthusmaynotbeindicativeofthetotal technical facilities (equipment, lines,etc.).Thisindicatorrefers to Network (PSTN) which have hadtobeheldover owing to alack of Unmet applicationsfor connectiontothePublicSwitched Telephone Waiting listfor main lines main lines. oraspublictelephone stations) bypurposes thetotal numberof lines which are notusedfor business,government orother professional 135 135 135 135 135 Chapter 9 - DATA SOURCES AND DEFINITIONS 136 136 136 136 136 Chapter 9 - DATA SOURCES AND DEFINITIONS are simply rebroadcasting free-to-air channels becauseofpoorreception. that are cabledtocommunityantenna systems even thoughtheantennas systems (MMDS)).Othercountries includethenumberofhouseholds using wirelesstechnology (e.g.,Microwave Multi-pointDistribution connection. However to subscribers pay television somecountriesreport subscribe to delivered amultichannel by line television service afixed The numberofcabletelevision Refers subscribers. to householdswhich subscribers Cable TV the numberoftelevision households. households may not register, thenumberoflicensesmay underestimate be registered) areusedasaproxy for television households.Since countries, thenumberoflicenses(i.e.,system where television sets must besides householdsmay have receivers (e.g.,businesses).Insome since householdscanhave more thanonereceiver andotherentities receivers. Thisisnot thesameasnumber oftelevisionreceivers Refers tothenumberoftelevision householdsthathave television Television equipped households and networks. refer totelevisionThe indicators inthiscategory broadcasting equipment Broadcasting billedinthecountry. refersmeans thatthedata traffic to volumes basedonpointofbilling.This internationaltraffic to reporting to destinations outsidethatcountry. Many countrieshave now shifted This covers originatinginagiven theeffective country (completed) traffic International outgoingtelephonetraffic destination insidethecountry. witha Effective originatingoutsidethecountry (completed) traffic International incomingtelephonetraffic are included. are includedandwhetherbothautomaticmanuallyplacedcalls completed andother callsare services used;whether callstodirectory (for somecountriesthisisincluded);whether attempted total traffic or servers areconducive toservers theprotection ofonlinetransactions. broken relatively quickly withspecialist knowledge andtools. Secure than 40bits,sincemessagesencodedwithsystems lessthancanbe withakey typicallyoffers encryption A secure server lengthgreater to thesoftware thatmakes information theprocessofserving possible. isalsousedtoreferin response Theterm torequests orqueries. server isahostcomputerA server on anetwork thatsendsstored information Secure servers and thespeedofinformation transmitted. occupies onagiven transmissionmedium. Itaffects boththequantity refers to offrequencies thewidthofrange thatanelectronic signal It measures how onagiven transmissionpath,anditalso fast flows data International bandwidth Only cableandDSLconnectionsareincludedhere,not ISDN. internationally agreed ofwhatconstitutes uponstandard broadband. Refers tofast-speed andalways-on Internet connections.Thereisno Broadband users The numberofInternet users. Estimated Internet users address andthusmay notcorrespondwiththeactualphysical location. Internet codeinthehost network. isbasedonthecountry Thisstatistic The numberofcomputers thataredirectly connectedto theworldwide Internet hosts based onanumberofnationalandinternational sources. single useratatime)inusethecountry. PrimarilyITUestimates The numberofpersonalcomputers (i.e.,designedtobeoperatedbya Personal computers and networks. The indicatorsinthiscategoryrefertocomputerequipment Information Technology 137 137 137 137 137 Chapter 9 - DATA SOURCES AND DEFINITIONS 138 138 138 138 138 Chapter 9 - DATA SOURCES AND DEFINITIONS secondary level. secondary condition ofadmission,thesuccessfulcompletion ofeducationatthe advanced research qualification, level ofeducationshown. Tertiary education,whether ornot toan correspondstothe to thepopulationofagegroup thatofficially Gross enrollment oftotal enrollment, ratioisthe regardless ofage, (%gross)School enrollment, tertiary specialized teachers. offering moresubject-orskill-orientedinstructionusing the foundationsforlifelonglearningandhumandevelopment,by level,of basiceducationthatbeganattheprimary andaimsatlaying level educationcompletes ofeducation shown. Secondary theprovision correspondstothe to thepopulationofagegroup thatofficially Gross enrollment oftotal enrollment, ratioisthe regardless ofage, School (%gross) enrollment, secondary andmusic. social science,art, ofsuchunderstanding subjectsashistory, geography, science, natural reading, writing,andmathematics skillsalongwithanelementary level educationprovides ofeducationshown. children Primary withbasic correspondstothe to thepopulationofagegroup thatofficially Gross enrollment oftotal enrollment, ratioisthe regardless ofage, School (%gross) enrollment, primary on theireveryday life. simple statement readcannot, andwriteashort, with understanding, Adult ofpeopleages15 rateisthepercentage illiteracy andabove who Illiteracy rate,adulttotal (%ofpeopleages15 andabove) UNESCO andWorld BankDefinitions normally requires, asaminimum normally requires, REFERENCES

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145 146 146 146 146 146 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Chart A1.Chart United Arab Emirates United Arab Virgin Islands(U.S.) Brunei Darussalam Cayman Islands United Kingdom Slovak Republic Czech Republic New Caledonia Taiwan, China Faroe Islands United States Liechtenstein New Zealand Luxembourg Netherlands Switzerland Networks, 2001 Greenland Singapore Hongkong Germany Bermuda Denmark Guesney Slovenia S. Korea Gibraltar Uruguay Hungary Portugal Australia Belgium Andorra Sweden Canada Estonia Norway Finland Greece Iceland Austria France Ireland Jersey Japan Aruba Spain Israel Malta Italy 0 0 0 0 200 100 0 200 100 300 0 0 400 300 400 Trinidad and Tobago T.F.Y.R. Macedonia French Polynesia Dominican Rep. HYPOTHETICA French Guiana Guadeloupe South Africa Puerto Rico Seychelles PLANETIA Costa Rica Yugoslavia Martinique Venezuela Botswana Argentina Barbados Colombia Bahamas Lithuania Mauritius Romania Malaysia Lebanon Thailand Jamaica Panama Bulgaria Namibia Bahrain Croatia Mexico Cyprus Samoa Poland Russia Turkey Macau Kuwait Belize Guam Oman Tonga Latvia Brazil Qatar Chile Chart A1.Chart St. Vincent andtheGrenadines Northern MarianasIslands Networks, 2001 Solomon Islands Marshall Islands Tukmenistan Saudi Arabia Côte d’Ivoire Cape Verde Kazakhstan El Salvador Saint Lucia Kyrgyzstan Guatemala Philippines Azerbaijan Zimbabwe Nicaragua Swaziland Honduras Indonesia Suriname Paraguay Sri Lanka Mongolia Maldives Grenada Morocco Moldova Reunion Armenia Ecuador Senegal Georgia Gambia Guyana Ukraine Belarus Albania Tunisia Kiribati Jordan Gabon Bolivia China Egypt India Peru Iran Fiji 0 (cont’d) 0 0 0 200 100 0 200 100 300 0 0 400 300 400 Central African Rep. Central African Papua, NewGuinea Burkina Faso Sierra Leone Mozambique Madagascar Bangladesh Congo D.R. Uzbekistan Lao P.D.R. Cameroon Mauritania Cambodia Myanmar Tajikistan Viet Nam Tanzania Pakistan Rwanda Ethiopia Lesotho Uganda Burundi Djibouti Zambia Guinea Nigeria Bhutan Algeria Angola Malawi Yemen Liberia Ghana Eritrea Congo Sudan Kenya Nepal Benin Niger Chad Cuba Libya Syria Togo Haiti Mali 147 147 147 147 147 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 148 148 148 148 148 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Chart A2. Chart United Arab Emirates United Arab Trinidad and Tobago Brunei Darussalam Slovak Republic Dominican Rep. United Kingdom HYPOTHETICA Czech Republic Infodensities, 2001 United States New Zealand Luxembourg South Africa Netherlands Switzerland PLANETIA Costa Rica Yugoslavia Venezuela Singapore Hongkong Botswana Argentina Barbados Colombia Bahamas Lithuania Mauritius Germany Denmark Romania Malaysia Australia Lebanon S. Korea Thailand Slovenia Hungary Uruguay Jamaica Portugal Panama Bulgaria Belgium Sweden Georgia Canada Ukraine Bahrain Estonia Norway Finland Greece Iceland Croatia Samoa Mexico Cyprus Poland Austria France Jordan Russia Ireland Turkey Kuwait Macau Belize Japan Oman Latvia Brazil Spain Qatar Israel Malta Chile Italy Fiji 01010202005 0 5 0 250 200 150 100 50 0 250 200 150 100 50 0 Central Arican Rep. Central Arican Papua NewGuinea Burkina Faso Mozambique Saudi Arabia Côte d’Ivoire Madagascar Bangladesh Kazakhstan Congo D.R. El Salvador Kyrgyzstan Guatemala Philippines Lao P.D.R. Mauritania Cameroon Zimbabwe Nicaragua Cambodia Swaziland Indonesia Honduras Paraguay Sri Lanka Tajikistan Myanmar Viet Nam Mongolia Tanzania Pakistan Morocco Moldova Ecuador Armenia Senegal Namibia Rwanda Ethiopia Lesotho Uganda Gambia Guyana Belarus Djibouti Albania Zambia Guinea Tunisia Nigeria Angola Algeria Malawi Yemen Liberia Ghana Sudan Gabon Eritrea Bolivia Congo Kenya Nepal China Benin Egypt Niger Chad Cuba Libya Syria Togo India Peru Haiti Mali Iran St.Vincent &theGrenadines Chart A3. Chart United Arab Emirates United Arab Brunei Darussalam Trinidad & Tobago Slovak Republic United Kingdom HYPOTHETICA Czech Republic Taiwan, China United States New Zealand Luxembourg Netherlands South Africa Switzerland Costa Rica PLANETIA Seychelles Yugoslavia Venezuela Singapore Hongkong Info-use, 2001 Argentina Suriname Barbados Colombia Lithuania Germany Mauritius Bermuda Denmark Romania Malaysia Australia S. Korea Lebanon Slovenia Gibraltar Hungary Uruguay Portugal Jamaica Bulgaria Belgium Sweden Canada Bahrain Estonia Norway Finland Greece Iceland Croatia Cyprus Mexico Austria Poland France Ireland Jordan Russia Macau Turkey Kuwait Belize Japan Oman Latvia Brazil Spain Qatar Israël Malta Chile Italy 01010202005 0 5 0 250 200 150 100 50 0 250 200 150 100 50 0 300 Central African Rep. Central African Papua NewGuinea Solomon Islands Burkina Faso Mozambique Saudi Arabia Côte d’Ivoire Madagascar Bangladesh Cape Verde El Salvador Guatemala Kyrgyzstan Lao P.D.R. Cameroon Mauritania Zimbabwe Nicaragua Cambodia Phillipines Botswana Indonesia Honduras Paraguay Sri Lanka Myanmar Viet Nam Mongolia Tanzania Maldives Thailand Pakistan Morocco Moldova Armenia Ecuador Panama Senegal Namibia Ethiopia Georgia Uganda Gambia Guyana Ukraine Djibouti Albania Zambia Guinea Tunisia Nigeria Samoa Angola Algeria Malawi Kiribati Yemen Ghana Gabon Eritrea Bolivia Sudan Kenya China Nepal Benin Egypt Chad Cuba Syria India Togo Peru Mali Iran Fiji 300 149 149 149 149 149 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 150 150 150 150 150 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A1. ava9. 0. 1. 72.9 88.0 116.8 86.9 80.5 108.2 104.8 144.5 100.0 101.4 98.6 113.5 97.0 64.7 100.0 98.7 60.2 99.2 PLANETIA 129.5 100.0 208.9 Latvia Argentina 92.6 81.5 104.4 100.0 Poland 157.2 102.6 275.0 HYPOTHETICA 314.0 115.1 158.8 New Caledonia 120.3 244.8 140.8 91.4 1 Uruguay 208.1 231.7 Slovak Republic 388.7 190.3 200.6 306.5 Emirates 80.4 315.4 United Arab 134.2 101.6 152.6 122.7 Aruba 123.1 135.9 124.8 175.3 369.1 Cayman Islands 259.4 137.9 Brunei Darussalam 101.8 138.1 143.7 213.9 Greece 215.8 197.6 Andorra 307.9 165.5 123.8 Hungary 147.4 150.3 153.1 149.3 Estonia 209.9 183.7 294.4 Spain 255.2 550.7 Greenland 153.6 400.5 388.1 S. Korea 163.9 151.4 242.3 86.2 Italy 159.5 156.4 Virgin 160.1 Islands(U.S.) 323.6 316.3 378.8 91.2 166.7 Slovenia 383.0 397.9 Faroe Islands 148.9 193.7 212.4 253.0 Guernsey 245.6 328.8 178.6 Jersey 407.3 179.1 174.0 229.2 179.6 262.5 Malta 732.9 302.6 274.9 268.1 Czech Republic 185.1 323.3 Bermuda 194.1 809.3 250.2 341.4 200.3 215.8 Portugal 395.2 982.1 221.8 Israel 240.0 218.4 214.2 Gibraltar 207.6 358.9 361.3 France 217.4 247.5 211.8 Japan 222.9 265.3 Singapore 303.9 224.5 New Zealand 233.9 728.5 290.3 660.8 Ireland 241.0 241.4 312.0 Austria 193.2 251.7 151.3 Australia 982.1 982.1 Hongkong 216.1 227.8 274.4 Iceland 565.7 252.5 309.3 188.4 335.9 Germany 384.5 272.5 253.2 262.6 330.3 Taiwan, 499.7 China 717.8 275.3 296.2 United Kingdom 250.7 357.0 304.3 Liechtenstein 309.0 338.3 282.2 Belgium 982.1 310.8 301.0 Canada 334.8 335.2 330.3 United States 320.5 Luxembourg 340.8 343.5 Finland 344.5 274.7 Sweden Switzerland 378.9 Denmark Norway Netherlands Networks, 2001 ewrsW Networks 3. 1. 6. 174.3 167.4 117.0 132.6 155.1 156.8 287.8 153.5 1. 5. 5. 105.3 257.4 155.4 117.6 2214916892.5 166.8 104.9 12.2 0. 2. 147.4 129.4 104.4 245.4 248.2 144.5 254.5 284.0 163.9 523.3 255.0 403.5 321.9 262.4 268.9 688.6 340.5 335.0 256. 160.1 66.3 72.5 rln oieInternet Mobile ireline 8. 202.0 285.1 Table 7. Networks, 2001 eu2. 362. 3.6 2.7 2.9 24.7 62.5 3.9 3.3 25.3 33.6 1.4 15.0 5.6 40.5 85.3 41.8 28.4 26.8 28.0 7.9 41.2 25.3 2.9 20.0 29.6 149.8 18.5 49.3 31.4 Philippines 69.8 17.4 32.2 Peru 32.3 5.2 Georgia 8.1 42.1 36.1 22.9 54.9 Guatemala 9.2 12.2 Paraguay 78.6 36.2 51.5 Grenada 205.1 28.9 36.5 MarianasIslands 31.9 16.5 51.7 Northern 34.9 982.1 7.4 41.4 Fiji 37.2 22.1 65.5 Ukraine 40.6 1.0 6.3 37.8 Jordan 1.2 39.4 18.0 Saudi Arabia 58.6 1.4 42.9 43.2 110.1 Namibia 36.5 82.2 3.8 9.4 43.9 Botswana 44.0 122.5 Thailand 38.7 45.4 Colombia 102.1 174.5 66.4 Oman 125.6 10.0 45.6 Samoa 3.8 46.1 69.2 Russia 62.5 46.9 78.2 14.2 Tonga 82.7 Venezuela 48.5 49.1 Bahamas 3.0 71.8 67.8 18.6 T.F.Y.R. Macedonia 217.7 2.9 Qatar 131.9 14.6 54.1 49.8 Jamaica 68.5 52.0 10.4 121.5 Belize 181.4 11.0 155.2 Costa Rica 95.6 54.9 57.0 161.3 54.1 Yugoslavia 180.2 Barbados 55.1 123.4 17.9 85.5 French Guiana 58.4 21.3 95.0 22.8 60.2 Romania 94.9 116.1 Puerto Rico 6.7 61.3 131.3 5.9 62.0 79.9 Dominican Rep. 62.8 Panama 113.3 86.7 88.9 Macau 298.9 62.6 83.7 Lebanon 6.9 63.2 63.6 65.6 Kuwait 18.1 69.7 232.9 195.1 90.6 Turkey 70.3 265.7 South Africa 69.9 192.4 71.7 71.2 Mauritius 22.3 62.3 Malaysia 207.1 32.5 54.6 122.2 96.4 Bulgaria 225.1 75.0 Martinique 72.6 157.6 65.5 143.0 77.2 Guam 21.2 77.2 113.0 Trinidad and Tobago 141.6 115.6 Guadeloupe 105.5 190.5 80.8 Mexico 1 87.3 Bahrain 105.2 92.8 286.0 Brazil 84.8 Seychelles 93.7 94.0 French Polynesia Croatia Chile Lithuania Cyprus ewrsW Networks 511652. 1.9 25.9 3.5 196.5 47.3 35.1 64.6 36.8 8.7 45.6 14.4 91.1 31.6 46.5 92.0 33.0 49.6 61.2 42.0 56.9 36.2 82.3 107.4 72.1 1018950.2 118.9 01.0 0011236.7 101.2 50.0 rln oieInternet Mobile ireline 6. 4.3 165.7 Table A1. oo91331. 0.3 0.1 10.8 0.5 0.6 3.7 2.3 3.3 0.3 8.0 0.6 36.2 2.3 18.0 9.1 9.4 2.6 0.0 2.6 9.1 0.7 3.1 9.1 6.2 28.8 9.2 0.5 16.5 1.8 0.3 Togo 18.8 9.8 0.9 45.0 0.6 13.1 Libya 11.2 Pakistan 2.4 15.2 11.0 Kenya 0.2 11.2 17.2 7.8 10.8 Mauritania 2.1 0.2 Turkmenistan 15.1 19.3 36.2 India 13.1 9.1 11.9 Maldives 11.9 18.1 0.3 13.5 0.8 13.5 Solomon Islands 24.1 Marshall Islands 13.9 0.4 14.5 6.0 13.5 43.2 Senegal 14.9 6.3 Honduras 15.1 0.3 Kiribati 33.9 15.2 59.5 15.2 1.5 15.5 Gambia 2.3 2.9 41.3 Tunisia 4.3 Guyana 16.2 16.5 16.0 13.0 Zimbabwe 0.4 0.0 12.4 19.5 20.5 2.8 Egypt 17.1 0.6 Côte d’Ivoire 15.4 85.5 240.7 Sri Lanka 9.3 17.2 17.4 31.1 2.3 Iran 68.4 1.0 17.4 Mongolia 187.1 11.8 18.2 1.3 Kyrgyzstan 1.2 18.3 5.8 18.6 7.2 Albania 18.7 19.0 Indonesia 39.4 39.2 Nicaragua 20.9 7.7 72.9 0.5 138.1 Armenia Reunion 28.6 31.5 23.3 23.4 Gabon 22.5 46.1 2.7 Morocco 24.2 23.8 Cape Verde 4.7 1.8 9.9 62.4 21.4 Saint Lucia 0.9 Belarus 15.1 27.9 Azerbaijan 25.3 24.7 34.8 Bolivia 82.6 theGrenadines 30.6 44.5 St. Vincent and 25.9 Swaziland 38.6 China 26.1 27.3 El Salvador 27.8 Moldova Kazakhstan Ecuador Suriname Networks, 2001 ewrsW Networks 213. . 0.4 3.7 33.4 12.1 1.3 18.6 7.6 15.8 0.5 30.1 0.4 62.4 22.2 27.2 0.6 98.2 56.0 24.5 43.9 25.9 (cont’d) 521. 0.2 16.8 45.2 rln oieInternet Mobile ireline Table 7. Networks, 2001 yna . . . 0.0 0.5 0.0 0.1 0.0 0.0 0.0 1.2 0.0 1.8 1.7 0.2 1.4 0.0 0.6 0.0 0.1 1.9 1.2 0.8 0.0 0.8 1.0 3.3 0.3 1.3 1.2 0.1 1.2 1.0 4.6 0.0 1.3 Myanmar 1.6 0.5 0.0 Eritrea 0.1 0.7 2.7 0.0 1.6 2.7 Bangladesh 11.5 Chad 0.0 0.1 1.7 2.2 1.6 2.0 Ethiopia 1.2 2.1 2.3 0.0 Sudan 1.4 0.3 1.8 Burundi 0.1 1.6 0.0 Congo D.R. 1.0 1.9 2.6 10.8 Liberia 0.0 0.1 1.6 0.5 4.8 2.1 Niger 5.0 3.1 Haiti 2.4 2.9 3.4 3.4 Rep. Central African 0.3 8.3 3.5 4.1 0.9 0.1 Angola 17.7 0.4 Mozambique 0.2 4.2 Malawi 4.4 3.1 3.9 4.6 4.6 Bhutan 2.3 0.1 3.1 Nigeria 4.7 Mali 1.4 0.2 4.8 3.2 Tajikistan 1.1 1.0 1.4 0.4 Uganda 0.1 2.3 5.0 Syria 5.5 0.2 Yemen 5.5 17.9 1.0 2.7 0.6 5.5 0.6 1.9 Nepal 0.3 4.0 0.5 Madagascar 5.7 8.4 3.4 Sierra Leone 0.8 6.0 1.8 7.1 0.0 Ghana 5.3 0.1 0.3 6.3 Guinea 1.8 0.2 0.9 Uzbekistan 4.5 6.4 0.1 6.6 6.4 1.4 Cambodia 0.7 1.9 17.0 0.5 Lao P.D.R. 11.0 6.7 6.9 20.1 6.5 Burkina Faso 14.3 4.8 20.1 6.9 Papua NewGuinea 6.9 8.1 2.7 Djibouti 3.1 Cameroon 7.3 7.4 2.7 Rwanda 2.6 7.9 Tanzania 8.0 Cuba 8.7 Viet Nam 9.0 Algeria Lesotho Congo Zambia Benin ewrsW Networks rln oieInternet Mobile ireline . 0.0 1.7 0.0 3.6 0.1 4.0 0.3 6.9 151 151 151 151 151 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 152 152 152 152 152 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A2. oai 1. 2. 1. 93102112.2 120.2 141.8 99.3 106.4 111.6 128.2 180.0 96.0 144.9 120.1 112.9 154.1 108.2 115.8 111.4 119.6 113.2 110.2 94.8 Romania 121.0 95.3 116.6 1 103.2 116.1 Darussalam 120.8 Brunei 116.3 117.8 118.3 67.8 116.5 Georgia 113.2 142.5 127.0 117.0 116.9 117.3 Philippines 117.4 117.8 158.4 98.8 129.2 Singapore 118.6 163.2 Thailand 99.3 Chile 214.0 168.8 138.5 119.0 Libya 148.3 123.1 125.0 106.7 Republic 121.5 121.6 167.7 Slovak 120.3 104.3 143.2 101.9 164.3 Kazakhstan 137.6 177.8 138.5 110.0 127.9 Brazil 143.9 103.7 153.5 115.0 133.7 118.6 Republic 156.9 126.2 102.6 Czech 123.2 133.8 207.3 78.4 119.4 148.4 173.1 Kyrgyzstan 204.9 135.5 126.3 120.4 143.7 Macau 135.1 110.6 126.9 145.5 216.4 140.1 121.5 Uruguay 128.3 99.9 121.8 138.3 107.8 136.2 101.4 Bulgaria 128.3 Hungary 121.9 144.6 139.9 114.5 142.9 197.0 129.8 229.8 Ukraine 118.9 121.0 195.9 148.1 120.4 141.1 Barbados 131.1 130.1 131.2 123.3 116.2 149.6 Switzerland 120.8 215.5 142.4 131.2 109.1 236.4 Greece 101.3 147.8 139.0 199.9 121.0 Italy 143.9 133.9 131.3 145.0 118.5 Israel 206.2 101.8 158.8 121.9 132.3 103.5 Germany 121.0 132.4 248.7 145.1 165.9 102.8 228.1 132.5 Argentina 148.3 134.6 237.0 121.7 121.9 Belarus 150.5 148.0 122.0 259.2 132.9 100.7 Japan 134.5 144.6 161.3 121.0 100.1 263.2 132.7 Lithuania 135.0 150.6 104.2 113.1 195.2 151.5 Estonia 121.7 100.8 135.1 220.1 121.8 153.4 Iceland 159.3 121.9 135.5 117.3 246.4 152.3 135.9 Portugal 157.3 121.0 243.8 120.0 153.7 136.3 Slovenia 149.8 122.0 105.5 136.3 168.8 155.1 Poland 121.7 99.1 155.5 136.8 Austria 318.8 105.6 121.0 298.3 137.0 Latvia 156.3 121.0 137.4 166.2 138.9 137.2 Russia 225.9 121.0 Ireland 119.5 101.6 137.6 101.5 241.8 181.7 140.9 France 168.7 164.5 187.2 108.1 Canada 234.1 119.7 303.4 121.0 Spain 102.5 168.5 142.1 214.8 141.1 United States 183.9 172.9 172.0 121.0 105.5 S. Korea 142.8 102.1 121.0 121.0 259.8 Netherlands 184.2 144.6 144.3 287.7 New Zealand 185.8 145.4 234.7 121.0 Denmark 217.2 149.3 121.0 102.6 Norway 150.0 110.5 Belgium 198.2 151.9 199.5 Finland 121.0 Kingdom 154.9 121.0 United 155.4 Australia Sweden Skills, 2001 2. 2. 1. 0. 2. 124.5 127.5 103.5 122.6 119.1 138.2 121.0 120.1 104.9 123.8 121.0 122.4 klsLtrc noletPiayScnayTertiary Secondary Primary Enrollment Literacy Skills 6111910414714359.3 164.3 104.7 120.4 111.9 16.1 2. 7. 0. 6. 287.6 167.3 244.5 102.0 174.7 228.3 121.0 99.4 190.7 121.0 0. 4. 190.2 144.6 104.3 284.4 164.1 100.5 1. 3. 200.4 130.7 116.2 Table 7. Networks, 2001 rn9. 428. 6914040.7 114.0 86.6 86.9 81.1 160.2 30.6 88.0 85.3 125.1 94.2 91.7 83.7 100.1 91.1 61.6 100.8 107.0 125.4 91.9 59.9 68.6 84.3 82.2 Iran 19.1 92.7 104.9 101.1 83.2 41.7 Kuwait 92.8 136.0 El Salvador 84.3 110.6 87.3 Egypt 108.8 104.5 40.0 China 86.5 111.8 93.9 99.7 Emirates 99.4 106.8 97.9 United Arab 84.4 95.4 46.7 96.1 Turkey 90.6 114.3 97.5 106.2 Indonesia 112.6 98.2 98.3 69.7 Botswana 86.9 109.2 72.3 Paraguay 3.8 113.3 86.0 Tunisia 95.9 99.2 83.9 114.9 Sri Lanka 108.1 103.7 99.4 Viet Nam 115.6 99.8 128.8 Yugoslavia 55.6 62.0 91.3 Mauritius 90.1 112.3 102.2 114.4 100.0 100 114.2 HYPOTHETICA 101.3 67.5 101.5 111.0 PLANETIA 107.6 Ecuador 121.7 94.0 82.9 99.8 101 Belize 44.9 114.0 100.1 117.0 104.3 Republic 32.8 107.0 103.5 102.0 102.0 Dominica 102.0 110.2 Costa Rica 78.6 106.7 87.0 117.1 Albania 103.4 104.3 92.2 144.6 Fiji 86.6 92.6 115.7 101.1 Jamaica 120.4 92.7 120.6 105.4 102.4 102.7 Tobago 129.9 105.7 121.0 Trinidad and 95.8 98.9 105.9 99.3 105.2 Armenia 85.0 102.0 113.5 Samoa 115.2 104.6 105.9 Gabon 110.0 113.0 112.6 104. 107.4 99.8 57.7 Venezuela 105.9 106.0 107.2 113.8 104.1 105.0 114.7 South Africa 97.4 114.7 112.3 105.3 Malaysia 120.5 94.8 103.9 108.1 136.6 104.8 111.8 Qatar 108.3 103.5 84.3 108.5 Colombia 88.3 102.0 89.2 98.1 114.3 Guyana 100.1 122.5 121.3 108.8 129.8 173.8 99.3 Mexico 121.0 109.1 Hongkong 91.0 143.3 110.1 106.9 110.5 103.7 117.5 Tajikistan 121. 120.4 101.0 112.6 107.6 146.5 99.4 Moldova 99.2 128.0 111.4 111.7 112.8 116.7 112.2 Luxembourg 116.3 82.1 121.9 112.7 112.0 101.3 123.4 Mongolia 114.6 105.7 116.5 136.4 117.0 103.5 Bahamas 102.4 113.5 112.6 119.3 123.5 110.4 Malta 118.5 113.6 148.0 97.1 111.0 113.7 105.1 119.9 Lebanon 118.3 117.9 113.9 103.8 111.0 Panama 114.6 91.8 118.9 128.2 Jordan 123.3 114.9 110.7 Bolivia 107.5 121.4 115.1 Cuba 120.3 110.3 115.4 Cyprus 115.7 Bahrain Croatia Peru klsLtrc noletPiayScnayTertiary Secondary Primary Enrollment Literacy Skills 0. 2. 0911018026.6 118.0 101.0 90.9 120.3 104.6 199. 731997. 72.0 79.1 49.7 109.9 109.6 87.3 99.6 96.8 91.9 92.3 93.8 93.0 21.9 105.3 106.5 86.0 112.3 98.3 0100100100100100.0 100.0 94.8 100.0 100.0 86.8 100.0 124.6 .0 100.9 102.7 .8 0. 0. 0. 0. 100.0 100.0 100.0 100.0 100.0 8219517914389.2 114.3 117.9 109.5 88.2 0. 1. 2. 62.6 127.5 112.0 107.2 7 38.1 137.9 101.4 102.6 0 901748. 65.9 87.9 107.4 89.0 2. 0. 47.6 107.2 120.3 Table A2. wna6. 324. 1. 776.9 16.5 42.3 17.7 44.2 10.1 119.3 57.4 82.4 34.4 11.7 11.8 47.8 94.9 50.0 78.6 13.6 27.3 55.7 20.2 83.2 65.9 79.9 63.0 42.9 12.2 110.7 118.6 52.8 15.3 63.2 28.6 60.9 1.3 50.4 96.6 Rwanda 65.6 63.3 27.2 80.6 108.4 57.1 64.4 Nigeria 43.1 84.0 67.0 136.5 Morocco 52.1 52.3 52.5 124.8 65.1 66.3 12.3 Zambia 58.8 71.1 137.7 69.0 88.5 88.9 Cambodia 68.0 68.3 44.7 Liberia 102.1 83.1 67.6 34.4 71.4 10.6 Cameroon 69.9 20.7 70.2 94.5 74.5 25.0 74.6 Ghana 54.0 47.9 71.0 61.1 Uganda 53.1 70.9 63.2 102.7 60.5 Togo 115.6 72.7 97.4 101.9 48.7 Malawi 109.7 65.0 34.9 73.5 60.8 47.4 46.7 Lao P.D.R. 63.2 69.2 78.8 84.6 India 102.6 106.6 99.6 56.3 74.2 100.0 Kenya 16.2 79.0 92.0 104.1 61.5 79.5 69.2 Guatemala 72.7 79.8 89.5 65.0 79.8 Lesotho 103.3 92.4 75.7 21.2 Congo 64.9 80.0 112.6 95.5 81.7 24.4 Syria 103.9 80.8 89.3 87.4 Honduras 96.4 63.2 82.1 82.2 90.1 Nicaragua 125.2 82.9 109.2 Myanmar 112.8 83.1 89.4 83.9 Oman 81.9 Zimbabwe 98.2 Algeria 101.1 90.8 Saudi Arabia 91.0 Swaziland Namibia Skills, 2001 klsLtrc Enroll Literacy Skills 178. 401375. 13.6 54.8 113.7 64.0 80.2 71.7 92.2 99.0 67.9 87.4 94.2 90.7

(cont’d) etPiayScnayTertiary Secondary Primary ment Table 7. Networks, 2001 ie 852. 693. . 6.1 7.9 9.1 5.2 21.9 35.7 6.5 3.6 61.5 16.9 20.1 2.7 26.3 16.8 31.3 20.2 67.4 15.4 18.5 3.6 22.6 64.8 73.6 32.3 31.8 26.1 31.8 74.0 34.0 31.9 21.5 50.1 Niger 75.2 39.9 Burkina Faso 34.4 49.3 54.1 40.6 2.3 Mali 40.9 41.6 39.3 51.4 Guinea 23.4 14.8 15.0 42.0 17.4 Ethiopia 46.8 2.9 80.0 Chad 42.9 31.8 30.6 92.0 43.3 Angola 14.6 7.7 Senegal 96.0 8.4 83.4 6.8 37.8 Djibouti 35.2 48.4 52.8 63.4 43.9 55.3 Republic 41.3 28.1 74.8 Central African 76.7 45.7 47.2 82.7 24.8 49.8 Mozambique 46.7 59.8 47.8 43.1 42.1 46.8 Congo D.R. 51.8 27.1 93.0 Mauritania 39.1 53.8 55.3 48.0 Benin 46.2 48.2 66.8 69.3 Tanzania 43.0 48.9 44.2 52.1 100.8 8.9 Pakistan 19.0 71.9 Gambia 68.3 69.5 55.6 Eritrea 20.9 73.9 Côte d’Ivoire 49.6 79.6 103.7 Sudan 118.8 58.2 66.8 58.9 Bangladesh 44.6 75.4 58.3 NewGuinea 58.3 Papua 82.3 52.4 62.4 Haiti 60.6 62.9 Madagascar Yemen Nepal klsLtrc noletPiayScnayTertiary Secondary Primary Enrollment Literacy Skills 573. 184. 493.8 14.9 44.5 21.8 30.3 7.9 25.7 14.1 75.4 32.3 58.9 43.6 28.8 33.9 81.7 48.1 60.7 54.0 904. 423. 9.6 5.1 30.9 84.2 42.8 43.0 111.0 55.8 79.0 62.1 844. 685.8 26.8 47.1 28.4 153 153 153 153 153 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 154 154 154 154 154 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A3. ava12212919118963.0 100.0 87.7 128.9 85.6 100.0 76.5 119.1 67.4 71.9 96.9 100.0 100.7 112.9 134.4 149.4 100.0 112.1 136.9 102.2 147.0 149.0 100.0 57.1 140.0 148.6 HYPOTHETICA 104.0 108.2 108.3 103.6 138.0 111.9 Latvia 113.3 95.7 92.7 202.3 114.6 Argentina 115.0 Poland 130.1 123.3 155.8 91.2 129.2 Kuwait 68.3 1 175.4 120.0 141.0 Croatia 163.0 1 80.2 162.2 202.3 Darussalam 114.3 89.6 120.5 Brunei 135.9 158.8 147.8 Costa Rica 237.8 121.8 124.1 196.3 123.2 Qatar 146.4 123.8 106.1 Uruguay 144.5 150.3 124.6 Seychelles 138.6 129.6 Mauritius 169.9 145.8 177.1 159.1 Greece 128.4 118.3 129.4 124.6 Hungary 133.2 245.0 141.6 145.6 Slovak Republic 261.6 156.0 234.1 202.3 130.7 98.9 Chile 202.3 147.2 164.0 Czech Republic 146.8 189.4 149.9 219.9 200.8 Malaysia 147.1 202.3 240.8 207.5 161.5 Macau 193.3 161.7 151.3 138.5 147.8 207.0 Emirates 202.3 270.2 202.3 164.7 229.6 United Arab 167.4 184.0 147.4 202.3 196.0 Bahrain 140.9 261.8 276.6 203.0 Spain 132.4 185.0 194.1 336.9 232.1 186.6 Portugal 196.1 328.9 191.1 144.0 Estonia 195.7 282.3 202.3 143.6 Italy 202.3 196.2 459.4 Cyprus 148.9 146.7 205.6 142.3 374.6 Malta 202.3 147.0 206.8 Israel 209.4 356.5 334.4 Belgium 124.1 212.8 France 301.6 160.4 336.8 218.0 Slovenia 137.9 202.3 202.3 Ireland 267.2 147.8 325.5 148.2 358.3 Austria 142.9 150.1 233.1 453.2 226.8 Gibraltar 202.3 427.9 229.9 234.5 United Kingdom 235.2 427.0 323.3 202.3 150.1 Taiwan, 202.3 China 360.7 434.2 151.1 Finland 240.2 128.0 Germany 404.3 181.1 202.3 246.7 Japan 406.2 251.0 148.3 417.0 Hongkong 145.1 146.0 373.9 202.3 398.4 Switzerland 251.2 453.6 202.3 252.2 253.8 521.7 Singapore 455.9 150.9 202.3 404.7 New Zealand 449.5 144.0 352.0 262.9 202.3 254.0 150.4 Netherlands 202.3 472.4 264.7 Australia 202.3 146.8 264.9 436.6 Luxembourg 142.5 186.4 147.1 266.8 Norway 526.2 269.7 143.2 Bermuda 271.9 202.3 Canada 274.4 Denmark 149.5 S. Korea Iceland 288.7 Sweden United States ICT Uptake, 2001 Uptake 6. 0. 0. 1. 274.1 114.1 202.3 106.5 161.1 341912236. 89.1 61.5 81.3 202.3 143.3 149.1 131.9 126.8 13.4 18.2 households households 3. 0. 2. 403.8 427.6 202.3 136.7 VRes. TV hnsPsInternet PCs phones 2. 2. 109.1 125.2 127.7 121.2 123.5 139.0 286.9 303.9 308.3 306.6 202.3 202.3 401.5 330.5 202.3 2. 325.3 321.8 313.3 435.5 Table 7. Networks, 2001 lei 161016. . 5.6 16.6 6.0 14.5 9.3 12.0 63.9 12.3 14.9 9.3 10.3 21.2 9.0 100.1 3.1 35.7 10.8 9.3 82.6 31.0 21.6 11.9 15.7 43.1 13.7 37.1 72.8 22.8 16.5 26.3 31.5 29.6 22.9 12.5 87.5 61.2 11.9 23.0 Algeria 104.7 24.9 14.6 10.8 92.1 114.7 Indonesia 21.0 24.6 115.4 25.9 24.6 11.5 18.9 Mongolia 56.8 5.2 24.9 25.6 Honduras 22.3 8.1 25.8 32.6 Guatemala 34.7 17.3 22.3 28.0 73.9 Paraguay 90.6 11.9 116.1 Senegal 42.8 40.0 13.0 18.3 25.9 146.4 16.0 Syria 27.0 21.4 13.4 27.3 23.2 Cuba 71.5 30.1 80.4 30.1 7.8 Kyrgyzstan 10.4 46.1 84.8 8.1 136.2 30.3 Nicaragua 31.1 93.2 115.1 Morocco 15.4 22.3 96.1 41.0 32.8 Samoa 24.2 32.7 99.6 31.8 15.9 109.5 Botswana 16.0 57.5 33.8 22.5 Bolivia 112.8 147.1 34.5 18.4 Philippines 39.0 83.9 95.1 39.1 129.9 19.6 Egypt 40.1 81.2 Moldova 132.3 76.8 22.3 41.2 50.3 78.8 Armenia 92.8 44.7 13.5 95.4 Namibia 58.3 128.7 23.4 Ukraine 45.8 Georgia 58.6 46.2 56.5 46.0 36.0 El Salvador 53.8 134.0 66.7 127.9 China 51.4 148.5 31.9 48.9 Maldives 53.3 102.2 40.3 33.5 Ecuador 55.3 86.0 19.7 Fiji 28.7 56.8 55.5 42.1 Guyana 38.9 116.2 126.2 Tunisia 39.8 38.3 23.5 98.4 105.9 Thailand 120.4 30.1 58.2 Cape Verde 139.1 27.3 98.5 35.4 61.9 39.3 Iran 61.9 101.1 25.5 99.4 Saudi Arabia 98.8 134.2 61.9 27.6 131.3 Panama 40.7 31.5 41.9 62.4 142.8 Yugoslavia 148.6 63.5 118.0 62.8 Peru 40.5 44.4 57.9 109.8 63.2 63.2 Jamaica 112.7 131.3 Suriname 65.0 144.9 52.9 80.6 93.4 52.5 Romania 93.7 64.0 63.7 Colombia 64.2 27.0 121.0 130.0 123.8 34.3 53.3 Oman 59.1 137.8 South Africa 171.3 64.8 68.6 199.8 77.3 Jordan 59.5 139.5 77.8 Russia 67.6 148.0 Venezuela 145.4 48.7 80.5 Mexico 85.4 62.8 1 140.1 Belize 78.4 Brazil 133.3 89.8 92.0 Bulgaria 184.8 140.4 Turkey 125.0 PLANETIA 94.4 Lithuania 96.9 the St. Vincent and 129.4 Lebanon Barbados 97.6 Trinidad &Tobago Grenadines Uptake 391817. 8420.4 18.4 77.1 128.1 43.9 23.9 57.8 11.7 121.6 52.8 60.7 126.1 56.5 146.4 58.1 41.6 95.2 154.2 127.1 93.9 households households 795. 8656.5 58.6 50.8 97.9 511457. 72.7 74.3 104.5 15.1 VRes. TV hnsPsInternet PCs phones 4. 8380.4 58.3 149.7 592. 35.9 22.2 75.9 Table A3. aba643. . . 2.1 2.5 6.0 0.8 13.9 3.3 2.3 4.2 1.7 4.7 1.5 6.5 33.2 8.7 21.4 4.2 3.0 60.3 142.0 6.1 6.4 3.0 4.5 15.6 5.7 15.7 2.2 8.2 7.5 7.9 71.7 3.5 126.6 8.1 11.7Lao P.D.R. 9.2 Zambia 6.4 47.7 9.8 9.5 Cameroon 21.6 10.0 7.0 12.6 5.9 Yemen 37.0 3.1 7.6 113.4 Kenya 24.5 60.2 7.9 Mauritania 61.3 11.7 4.9 12.3 12.7 20.2 Sudan 13.6 14.6 13.3 26.5 Solomon Islands 13.3 10.7 28.0 11.0 32.8 10.8 8.8 Papua NewGuinea 14.1 13.2 31.2 Pakistan 35.2 65.1 21.7 Djibouti 7.3 33.6 30.1 14.6 Albania 16.8 15.8 10.1 Gabon 32.5 14.9 18.9 Côte d’Ivoire 16.5 29.9 120.7 Sri Lanka 20.4 Zimbabwe 20.7 20.6 India Gambia Kiribati Viet Nam Togo ICT Uptake, 2001 Uptake 06651. 314.0 33.1 14.8 3.7 6.5 6.1 10.6 25.5 70.4 14.2 households households 5476251.6 2.5 7.6 45.4 VRes. TV (cont’d) hnsPsInternet PCs phones Table 7. Networks, 2001 hd081206130.5 0.3 1.3 0.2 1.0 1.7 0.6 1.0 2.2 3.1 2.5 1.1 0.7 1.2 4.2 3.0 2.5 2.2 1.1 0.8 1.6 1.4 4.7 2. 1.3 2.2 0.8 1.7 1.9 1.7 2.0 1.4 1.2 6.3 1.3 6.9 13.2 Chad 1.7 1.2 4.3 3.6 1.4 2.2 Ethiopia 5.4 1.9 Myanmar 3. 2.9 2.3 1.1 2.6 3.4 1.7 1.6 2.1 Rep. Central African 1.5 1.8 9.3 2.3 Malawi 7.7 1.7 5.6 12.7 3.0 Cambodia 1.4 3.6 17.1 4.7 3.1 0.9 3.0 Uganda 3.4 36.5 Mali 3.6 36.0 2.8 2.8 8.2 3.4 29.0 Burkina Faso 14.2 5.8 Mozambique 4.1 4.0 15.7 10.0 7.0 4.1 Madagascar 5.8 3.8 Guinea 4.3 4.5 32.4 Bangladesh 4.9 69.7 Angola 5.7 Eritrea Tanzania 6.0 Benin Nepal Ghana Nigeria Uptake households households VRes. TV hnsPsInternet PCs phones . 0.6 1.3 1.5 3 3.3 5 155 155 155 155 155 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 156 156 156 156 156 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A4. yrs5. 299. 0. 2. 94.2 98.8 120.0 103.1 82.4 90.8 99.0 67.0 99.4 72.9 85.5 53.9 79.4 56.3 67.0 35.6 55.6 48.5 25.6 40.3 32.1 104.6 100.0 31.1 27.4 93.3 85.8 20.6 16.1 71.9 71.3 Cyprus 120.6 51.3 55.9 69.7 PLANETIA 41.0 45.4 70.8 Latvia 20.2 22.3 35.3 41.9 Argentina 11.7 134.6 Poland 37.7 115.2 123.0 HYPOTHETICA 136.2 38.8 111.3 92.9 111.2 New Caledonia 100.1 138.3 68.9 88.5 83.8 110.4 Uruguay 38.0 138.4 46.7 74.7 94.5 144.0 73.8 Slovak Republic 125.7 33.5 35.3 128.4 65.2 77.1 100.5 35.7 Emirates United Arab 112.6 79.6 45.8 62.2 84.2 Aruba 57.6 147.7 150.6 42.2 67.1 Cayman Islands 142.3 157.9 38.4 134.6 Brunei Darussalam 51.6 106.2 111.2 85.0 100.8 Greece 64.7 77.7 154.0 64.6 Andorra 136.8 52.2 56.9 45.8 115.2 Hungary 77.9 159.9 Estonia 160.4 167.1 137.9 141.3 61.7 Spain 131.2 123.1 119.0 43.5 95.8 Greenland 40.1 45.9 61.8 72.6 S. Korea 37.2 20.8 46.9 31.7 Italy 22.4 21.5 179.5 179.0 37.2 Virgin Islands(U.S.) 122.9 180.0 163.2 116.4 Slovenia 161.9 155.0 185.5 93.9 161.9 130.0 Faroe Islands 149.8 194.6 141.7 132.6 69.6 107.4 Guernsey 201.7 123.3 109.6 200.7 87.6 46.1 190.4 95.1 Jersey 176.8 129.7 46.2 Malta 144.1 99.8 120.6 208.1 47.8 Czech Republic 71.7 101.8 201.1 217.9 Bermuda 81.4 167.1 208.3 223.4 120.0 Portugal 163.0 242.7 107.7 135.4 Israel 188.4 75.2 106.1 225.1 144.2 Gibraltar 78.1 117.7 194.3 105.7 241.6 France 96.5 155.2 242.0 82.6 211.3 135.5 215.4 Japan 166.0 123.6 189.7 135.1 Singapore 104.4 150.6 117.3 New Zealand 126.0 97.5 152.1 100.7 Ireland 124.3 94.7 Austria 273.1 1 247.9 263.2 Australia 73.6 276.0 218.5 268.6 Hongkong 259.1 161.8 224.5 42.6 223.8 116.1 Iceland 176.0 131.9 189.6 84.2 155.8 Germany 282.8 112.5 166.4 311.5 103.2 255.8 143.9 Taiwan, 281.8 China 222.4 336.0 255.3 United Kingdom 189.5 313.2 208.7 168.2 183.5 289.0 Liechtenstein 181.7 341.6 138.1 128.6 245.6 146.8 344.4 Belgium 284.5 107.1 218.5 294.3 255.7 Canada 178.5 284.4 217.1 249.3 United States 174.7 194.9 379.8 140.4 Luxembourg 156.5 343.5 151.7 Finland 264.6 196.8 Sweden 141.1 Switzerland 111.2 Denmark Norway Netherlands Evolution ofNetworks 9619 9819 002001 2000 1999 1998 1997 1996 843. 436. 19112.5 81.9 66.5 54.3 37.5 132.9 18.4 103.3 70.1 59.0 41.4 36.1 156.7 137.2 101.8 86.4 65.4 174.5 43.8 141.3 104.6 77.4 55.2 36.1 9. 5. 8. 1. 344.5 318.5 281.7 250.4 194.7 235. 738. 97.2 84.0 67.3 52.2 42.3 2. 6. 8. 212.3 183.4 168.9 123.1 253.9 231.7 206.0 167.2 743. 20102.8 42.0 34.9 27.4 117.9 108.6 94.3 76.9 153.8 92.5 101.4 88.0 253.1 221.3 169.8 14.5 2. 3. 149.6 136.8 121.1 252.3 233.6 180.9 301.7 271.9 227.2 eu1. 401. 262. 28.4 28.1 24.7 20.9 29.7 22.6 14.8 23.5 31.5 11.4 17.2 17.1 32.3 27.5 8.3 14.0 12.9 24.9 32.4 15.8 12.3 8.9 7.1 22.6 21.1 9.6 36.2 17.7 3.8 17.3 30.6 7.4 9.4 15.7 36.3 21.9 4.9 36.6 10.9 24.0 14.7 6.2 Philippines 23.6 9.4 15.7 10.5 Peru 17.8 11.8 8.8 Georgia 37.2 12.4 8.8 Guatemala 32.4 37.9 8.7 Paraguay 22.7 6.1 38.8 39.5 5.6 Grenada 21.8 30.9 43.3 29.8 43.0 44.0 4.0 Islands 13.6 14.1 22.8 24.6 36.7 34.4 Northern Marianas 9.0 1.2 5.9 21.3 20.2 33.6 7.5 Fiji 44.1 45.5 20.7 18.7 0.5 25.6 Ukraine 33.5 5.0 32.5 17.9 14.9 Jordan 20.2 19.3 3.6 28.3 2.4 Saudi Arabia 15.0 45.7 19.4 23.0 46.2 Namibia 0.7 39.6 16.4 47.0 16.1 38.4 Botswana 34.1 10.2 90.2 6.7 20.7 Thailand 25.4 48.7 49.2 68.8 57.9 Colombia 18.4 42.9 39.0 21.3 Oman 44.2 13.4 23.3 31.2 36.9 Samoa 40.4 19.0 25.9 49.9 18.8 Russia 16.8 24.1 52.1 43.6 Tonga 14.8 5.9 40.8 32.4 Venezuela 55.0 5. 33.4 Bahamas 23.4 48.7 24.5 T.F.Y.R. 26.1 15.5 Macedonia 38.5 22.0 Qatar 20.0 8.3 27.2 58.5 Jamaica 60.3 17.9 17.0 69.0 3.6 Belize 54.6 7.5 32.6 61.4 Costa Rica 62.2 51.1 2.5 26.3 55.0 22.0 Yugoslavia 62.9 53.6 45.4 19.3 47.7 15.0 Barbados 53.4 50.8 41.2 10.6 39.9 French Guiana 46.1 53.2 41.7 63.8 30.9 Romania 32.8 46.1 69.9 55.8 21.7 Puerto Rico 24.5 40.6 70.4 61.4 33.6 Dominican Rep. 16.6 50.3 53.6 71.4 26.9 71.8 Panama 37.3 47.0 61.9 18.7 68.4 Macau 23.8 26.7 40.8 55.6 13.0 Lebanon 62.9 18.2 34.8 27.4 Kuwait 54.9 75.2 72.8 11.7 14.8 Turkey 40.9 58.2 65.9 77.4 77.4 South Africa 6.6 46.3 45.1 58.3 63.4 58.9 Mauritius 25.9 26.1 57.5 81.0 41.5 Malaysia 16.5 16.8 44.4 29.3 26.7 Bulgaria 87.5 13.2 3.4 34.7 18.1 93.0 19.7 Martinique 72.3 39.7 73.5 18.6 14.7 Guam 11 49.8 9.3 56.0 93.9 Trinidad and Tobago 38.6 63.4 42.5 Guadeloupe 7.5 32.6 50.1 29.2 Mexico 23.1 25.3 42.1 Bahrain 28.0 28.7 Brazil 14.5 16.4 Seychelles French Polynesia Croatia Chile Lithuania 9619 9819 002001 2000 1999 1998 1997 1996 502. 262. 2735.2 32.7 28.1 32.6 24.9 15.0 57.0 31.5 26.0 19.9 17.6 12.5 02. 843. 2572.2 62.5 39.5 28.4 20.9 .0 322. 913. 46.6 34.0 29.1 21.0 13.2 3 384. 995. 63.4 56.0 49.9 42.3 33.8 823. 1949.7 41.9 36.9 54.3 28.2 47.9 55.2 39.9 51.7 27.1 48.1 46.2 85.0 72.5 55.7 36.3 . 032. 36.8 23.4 20.3 8.7 Table A4. oo02203546659.1 9.1 6.5 6.7 4.6 9.1 9.2 3.5 3.5 6.3 7.1 3.3 9.8 2.0 5.6 4.5 11.2 1.8 4.6 0.2 4.7 7.5 2.5 0.2 11.1 0.4 3.2 5.5 2.0 10.2 0.3 11.2 2.1 4.0 8.1 1.5 35.2 0.1 3.0 13.2 5.6 26.3 0.1 Togo 11.7 13.5 1.8 18.6 1.7 Libya 13.6 7.1 9.4 14.5 13.9 0.2 Pakistan 12.5 4.4 14.5 4.6 7.6 4.5 5.6 Kenya 12.2 6.2 2.8 15.1 Mauritania 5.6 4.1 2.7 5.4 5.3 Turkmenistan 15.8 1.6 15.2 4.0 3.6 1.2 3.4 India 15.2 5.6 3.9 12.3 8.7 3.5 3.7 1.2 Maldives 13.9 6.8 9.0 3.3 16.3 Solomon Islands 10.7 1.8 16.0 7.9 5.7 11.6 Marshall Islands 2.8 5.1 13.6 Senegal 8.5 7.0 4.7 17.2 3.2 10.1 Honduras 5.1 16.2 17.4 2.7 4.5 17.2 7.0 0.3 Kiribati 7.6 7.9 4.2 12.5 Gambia 3.5 5.7 4.3 17.5 8.6 Tunisia 5.7 3.1 2.4 3.9 12.9 Guyana 18.2 2.5 3.7 4.3 18.3 10.5 Zimbabwe 14.4 1.8 19.0 0.5 3.3 18.7 16.9 Egypt 7.9 10.9 13.3 16.8 13.5 Côte d’Ivoire 0.2 8.1 2.3 6.6 20.9 6.1 13.2 10.5 Sri Lanka 6.4 17.0 5.6 15.3 Iran 7.8 5.2 11.3 5.8 23.4 Mongolia 9.2 3.1 4.3 8.4 Kyrgyzstan 15.1 7.8 11.8 2.6 Albania 9.7 6.7 23.9 3.3 24.3 Indonesia 20.8 8.3 4.4 21.4 Nicaragua 1.2 15.0 17.6 6.5 Armenia 8.6 0.5 18.9 25.4 13.3 24.7 Reunion 4.6 20.0 7.0 20.0 10.2 20.6 Gabon 15.7 7.0 Morocco 15.3 3.0 26.0 9.0 Cape Verde 9.4 22.1 Saint Lucia 7.2 26.2 1.1 12.1 Belarus 27.4 17.3 27.8 6.0 1.0 10.0 Azerbaijan 21.5 11.2 14.4 7.5 4.7 Bolivia 18.3 6.9 6.5 theGrenadines 5.8 15.0 1.4 St. 5.1 Vincent 4.9 and 11.3 Swaziland 4.4 3.9 7.6 China 3.9 El Salvador Moldova Kazakhstan Ecuador Suriname Evolution ofNetworks 9619 9819 002001 2000 1999 1998 1997 1996 . 461. 211. 24.6 16.5 12.1 10.0 14.6 7.3 091. 091. 12.1 10.7 10.9 10.2 10.9 (cont’d) 252. 7523.4 27.5 21.0 22.5 25.9 25.6 24.1 14.8 111. 11.9 12.4 11.1 . . 15.9 9.7 6.6 7722.2 17.7 yna . . . . . 0.8 0.9 1.3 0.9 1.4 1.3 0.6 1.6 0. 0.2 1.3 1.6 0.6 1.0 0.2 0.1 0.9 3. 1.3 0.5 4.0 0.2 1.7 0.1 0.1 1.6 2.0 1.6 0.6 2.5 0.9 2.3 0.1 0.1 0.8 2.0 0.2 0.6 1.5 0.1 2.2 0.0 0.5 1.3 0.6 0.3 Myanmar 2.6 0.2 1.6 0.5 1.0 Eritrea 0.6 1.9 0.1 1.3 Bangladesh 3.1 0.4 0.3 2.4 1.7 0.1 0.1 Chad 3.4 2.4 0.3 0.1 Ethiopia 2.0 1.4 4.1 0.2 2.9 1.1 Sudan 2.0 1.1 2.7 2.5 Burundi 0.8 4.4 1.5 2. Congo D.R. 0.7 1.4 1.3 4.6 4.6 0.7 Liberia 3.9 1.5 1.5 1. 0.7 0.5 3.8 2.8 Niger 0.6 4.7 3.8 1.4 0.2 0.8 4.8 0.6 Haiti 0.4 2.8 1.5 3.2 2.1 Rep. Central African 0.4 3.7 0.9 2.2 0.1 2.6 Angola 1.2 2.3 1.0 Mozambique 5.5 0.1 2.0 0.5 5.5 0.2 Malawi 0.8 2.7 0.1 1.6 Bhutan 3.9 0.2 4.4 Nigeria 1.0 5.7 0.3 0.1 1. Mali 3.6 5.8 0.2 Tajikistan 1.0 6.4 1. 3.2 Uganda 5.0 0.1 1.0 1.8 0.8 6.4 2.5 Syria 6.6 4.6 0.1 1.3 Yemen 0.5 2. 4.1 4.7 3.2 Nepal 6.7 1.0 6.9 1.9 6.5 2.3 Madagascar 4.8 2.9 5.2 7.0 6.9 1.0 4.3 0.6 6.3 1.8 Sierra Leone 2.5 2.1 Ghana 5.6 5.0 0.9 2.7 5.6 1.1 7.3 2.5 Guinea 7.4 0.7 3.6 2.5 0.6 4.1 0.3 Uzbekistan 4.8 2.2 7.9 3.3 0.6 2.7 1.9 0.1 Cambodia 8.0 3.0 3.4 6.8 4.5 Lao P.D.R. 0.5 8.7 2.2 1.0 0.1 4.2 1.0 9.0 2.2 Burkina Faso 5.0 2.5 7.8 1.7 0.4 1.9 Papua NewGuinea 1.5 0.8 3.7 2.1 5.1 Djibouti 1.3 2.1 0.9 1.4 Cameroon 1.6 3.3 1.2 1.6 Rwanda 1.0 2.5 1.0 Tanzania 1.5 Cuba 2.2 1.3 Viet Nam Algeria Lesotho Congo Zambia Benin 9619 9819 002001 2000 1999 1998 1997 1996 . . 0.9 1.2 0.9 5.7 0.3 4.6 2 0 3.0 3.6 2.7 3.5 4 1.8 1.6 3 5.0 5.2 3.9 5.6 9 4.8 6.0 4.2 4.7 3 3.2 3 157 157 157 157 157 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 158 158 158 158 158 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A5. abds4. 925. 507. 82.1 83.4 72.5 86.0 78.2 65.0 79.9 73.2 56.9 87.2 73.2 66.7 49.2 78.7 90.2 66.8 57.8 46.4 76.5 78.9 94.2 61.7 48.3 70.7 68.8 85.2 94.4 96.9 56.7 66.8 51.7 80.1 79.8 83.8 66.9 40.9 69.8 68.7 67.5 Barbados 36.4 61.7 54.6 52.1 Lebanon 100.4 66.3 46.8 43.5 Malaysia 91.2 37.3 104.5 36.9 Trinidad 100.0 and T 75.6 103.9 92.8 91.9 Macau 117.2 66.6 80.3 83.3 109.0 Mexico 60.8 54.7 100.5 69.5 73.1 111.6Bahrain 88.6 51.2 90.5 57.5 65.4 Bulgaria 77.5 114.3 79.2 52.6 57.4 Brazil 114.8 105.7 115.9 71.5 107.4 93.0 PLANETIA 104.4 94.0 58.5 92.9 77.2 HYPOTHETICA 78.4 116.1 43.8 81.2 Croatia 61.1 102.6 69.7 Cyprus 65.1 49.4 85.0 58.8 51.1 Chile 54.3 72.0 54.9 132.7 Emirates 122.1 United Arab 58.9 133.0 109.6 Lithuania 118.2 44.8 94.2 136.3 137.1 108.3 Argentina 130.9 77.0 121.3 96.5 Uruguay 115.9 140.4 103.5 66.3 87.1 142.4 102.4 85.0 Latvia 142.3 116.4 86.3 71.1 133.5 72.5 Slovak Republic 114.4 69.8 124.0 100.1 64.3 144.3 Poland 107.5 84.9 135.0 96.3 Brunei Darussalam 145.6 123.0 138.2 Greece 84.2 100.1 129.7 87.6 153.5 Hungary 108.5 144.8 93.4 72.9 155.5 Estonia 144.0 127.8 77.5 132.6 Malta 123.3 122.6 155.8 110.8 Italy 107.2 153.0 94.8 80.6 162.9 Spain 161.9 139.3 152.3 77.0 65.6 117.9 151.3 163.2 Slovenia 136.9 133.9 110.4 166.0 S. Korea 125.6 172.6 120.7 161.1 91.2 115.7 112.2 168.3 133.3 Czech Republic 174.3 102.8 149.3 101.9 116.8 181.1 Israel 136.5 99.0 158.6 119.3 Portugal 180.5 137.1 101.8 118.3 169.5 Singapore 158.0 104.6 Hongkong 140.1 Japan 132.0 127.4 186.5 112.9 128.4 141.5 France 116.1 172.4 194.6 128.4 Ireland 155.5 188.5 111.8 146.5 142.1 175.3 Austria 118.3 138.8 168.5 New Zealand 126.5 107.3 157.8 201.7 146.7 Iceland 199.5 192.1 189.2 180.4 Germany 215.9 177.8 210.6 155.4 154.2 Luxembourg 204.9 165.5 191.3 131.5 137.6 194.3 222.9 155.9 180.9 111.7 Australia 127.1 175.1 141.4 205.2 163.9 Canada 163.4 200.8 147.2 228.2 145.9 186.2 131.9 United Kingdom 219.9 164.9 211.5 United States 146.8 232.6 192.2 145.9 Belgium 220.2 177.7 Switzerland 192.4 159.2 166.5 Finland 141.1 Denmark 126.1 Norway Sweden Netherlands Evolution ofInfodensity obago 9619 9819 002001 2000 1999 1998 1997 1996 354. 426. 0586.8 80.5 64.1 54.2 46.1 33.5 116.1 98.6 88.2 124.1 79.0 109.1 89.0 66.4 78.6 46.4 64.1 60.3 6. 8. 0. 1. 223.8 214.8 201.6 180.8 166.6 327. 099. 98.5 91.0 80.9 70.7 63.2 399. 96104.6 99.6 92.6 83.9 146.0 129.8 110.2 94.3 5. 6. 174.8 161.9 155.0 181.8 175.0 183.1 153.9 173.9 158.6 196.2 187.1 176.3 iy . 391. 062. 32.8 28.3 20.6 32.8 18.7 35.5 27.2 13.9 36.4 34.0 37.6 24.4 4.3 32.3 30.3 33.4 20.9 25.9 28.5 21.3 17.5 38.4 22.6 16.8 21.7 38.3 16.4 32.4 20.0 4.7 19.5 33.9 28.1 16.0 14.6 29.2 40.4 21.9 Libya 25.2 41.3 19.7 15.4 40.9 Honduras 22.3 42.1 24.5 16.9 34.7 Zimbabwe 21.1 28.3 26.7 43.7 31.2 Morocco 23.7 23.9 43.9 28.5 42.3 Egypt 27.0 19.6 44.8 19.7 42.1 26.7 28.3 Tunisia 24.8 34.6 16.8 46.0 37.7 20.7 Nicaragua 22.6 22.7 47.3 13.9 38.7 33.4 15.6 Iran 20.8 43.1 31.6 29.1 48.5 13.5 Sri Lanka 48.3 19.3 37.2 19.9 18.1 42.9 Guyana 44.4 16.7 28.7 7.6 37.8 Indonesia 52.5 33.1 9.9 28.8 Albania 48.9 4.1 25.2 52.6 9.2 Mongolia 26.1 43.4 22.0 53.4 46.5 Armenia 23.6 36.1 55.7 17.9 49.5 42.7 Gabon 30.7 44.8 36.8 38.5 Kyrgyzstan 25.3 35.3 56.0 33.7 33.8 56.3 Swaziland 32.3 44.9 57.3 23.2 27.5 49.2 Guatemala 57.1 28.8 35.8 53.3 12.8 46.3 China 49.1 24.0 28.9 50.9 40.4 El Salvador 41.1 58.1 24.7 43.7 Bolivia 29.3 36.7 58.7 54.0 22.9 39.1 59.6 Ecuador 23.6 31.2 52.6 45.3 36.7 43.0 Moldova 28.5 44.3 60.7 45.1 41.3 Belarus 39.5 60.0 34.9 Kazakhstan 40.5 64.4 32.5 53.0 29.1 Paraguay 37.3 39.3 51.6 68.0 21.2 35.4 Philippines 13.6 33.5 44.2 57.9 10.2 68.1 68.1 Peru 30.5 36.0 51.8 6.7 62.8 60.2 Saudi Arabia 28.0 68.1 45.5 59.8 28.0 Namibia 22.2 55.4 43.2 51.8 22.2 Georgia 69.5 44.8 38.1 70.6 Oman 45.4 18.7 64.7 70.3 38.6 70.9 62.8 Botswana 38.6 8.2 59.6 65.7 33.1 98.6 55.6 Fiji 50.4 56.7 27.5 86.5 Jordan 49.5 42.0 48.1 71.2 46.5 Thailand 46.6 35.4 38.9 66.3 71.9 61.1 Colombia 75.5 23.0 28.5 48.4 65.4 43.6 Samoa 69.9 42.7 48.2 Ukraine 76.8 67.4 50.5 39.5 80.9 Venezuela 77.6 70.6 69.1 47.8 37.1 Yugoslavia 70.9 67.8 66.2 64.8 79.7 Belize 67.9 50.5 56.2 60.4 79.7 73.3 Qatar 49.5 48.7 81.4 74.2 56.4 Costa Rica 45.5 39.8 88.1 65.6 49.9 Jamaica 35.8 60.5 55.6 41.4 Bahamas 49.5 43.6 34.2 Kuwait 40.5 28.6 Dominican Rep. 33.8 Turkey Russia 54.4 Mauritius Romania Panama South Africa 9619 9819 002001 2000 1999 1998 1997 1996 142. 544. 8348.8 48.3 46.6 35.4 24.6 21.4 57.7 46.0 42.5 27.3 18.3 22.1 76.1 56.3 50.6 43.5 40.4 33.7 701. 222. 37.7 24.2 22.2 18.7 17.0 81.8 77.5 73.4 68.5 61.5 633. 6539.6 36.5 31.4 26.3 71.1 64.2 60.0 54.3 755. 61.1 56.1 47.5 Table A5. yi . . . 071. 19.1 18.2 14.9 15.4 10.7 10.8 19.4 3.1 9.5 20.8 13.5 3.0 8.3 17.0 6.9 19.7 15.2 20.9 3.0 9.5 4.5 17.1 14.1 19.5 16.5 13.8 8.3 21.0 12.4 19.1 12.8 12.3 17.4 7.8 17.9 5.9 21.4 11.2 16.4 15.1 6.9 14.6 6.4 2.1 Tanzania 21.4 14.9 12.7 4.3 Syria 18.1 2.1 12.1 21.9 7.2 Ghana 11.6 3.5 23.6 9.8 20.5 8.0 Papua NewGuinea 23.7 6.6 22.4 20.0 2.5 Cambodia 24.9 7.6 22.2 25.2 18.2 6.0 15.3 2.4 Benin 22.8 16.9 21.1 14.7 11.7 25.3 5.4 Rwanda 19.6 13.5 17.7 12.8 7.6 18.0 Pakistan 25.6 16.7 10.4 15.0 11.9 26.0 11.7 Lao P.D.R. 16.9 10.4 7.7 11.2 22.5 10.2 Mauritania 26.1 19.8 8.9 3.2 17.7 10.0 Cameroon 14.4 26.9 14.5 28.2 12.9 9.2 Tajikistan 13.7 21.8 13.1 28.5 24.9 11.4 Zambia 12.6 18.3 11.4 23.3 19.8 9.7 Senegal 12.4 14.3 19.4 16.5 Lesotho 8.6 8.5 16.6 15.2 Togo 9.1 14.4 13.3 Congo 11.1 Algeria Kenya Gambia Viet Nam Cuba India Côte d’Ivoire Evolution ofInfodensity 9619 9819 002001 2000 1999 1998 1997 1996 081. 511. 2429.2 22.4 18.2 15.1 13.1 10.8 . . 1221.3 11.2 9.4 8.4 (cont’d) ie . . . . . 6.1 5.9 7.3 4.7 7.5 7.4 4.1 2.4 8.1 6.9 3.1 2.2 2.1 8.5 6.0 2.8 1.4 2.0 8.7 8.5 8.2 1.8 9.4 2.7 1.7 6.2 18.0 6.7 1.7 7.6 10.5 16.0 4.5 6.8 10.7 1.0 14.9 8.7 11.4 12.3 7.6 11.3 4.8 6.4 9.0 8.7 8.6 Niger 11.5 8.6 2.9 4.3 Eritrea 6.4 11.3 7.3 3.2 4.0 Chad 2.7 9.5 3.8 4.2 6.6 Ethiopia 2.2 8.5 11.6 14.9 3.9 Myanmar 5.6 13.2 12.5 2.1 2.7 Bangladesh 14.9 4.6 7. 14.9 8.8 10.5 Congo D.R. 13.7 3.5 7.2 10.5 9.5 Sudan 7.0 12.2 9.9 16.9 Rep. 6 Central African 7.3 6.7 17.2 6.6 17.3 13.9 Angola 9.0 5.0 13.9 2.4 5.4 15.2 Liberia 6.0 14.3 12.5 6.7 2.7 11.6 Mali 10.2 4.2 10.5 17.9 3.5 17.4 Haiti 5.0 10.0 8.9 16.2 17.6 Mozambique 9.2 3.2 13.9 Burkina Faso 15.0 6.9 2.7 11.8 Malawi 10.4 7.1 Guinea 7.7 Nigeria 6.4 7.3 Djibouti Yemen Nepal Madagascar Uganda 9619 9819 002001 2000 1999 1998 1997 1996 . . . 10.2 9.2 8.9 7.6 5 6741. 12.8 10.1 7.4 .6 . . 6.6 6.6 3.8 159 159 159 159 159 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX 160 160 160 160 160 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Table A6. abds4. 777. 528. 101.4 87.3 100.0 101.8 92.6 93.4 75.2 102.9 82.9 76.3 71.9 97.6 73.2 56.3 57.7 91.8 63.6 43.6 49.3 82.2 104.3 53.5 36.1 93.9 73.6 105.3 60.0 77.5 57.1 103.6 HYPOTHETICA 107.4 68.9 99.7 92.2 Barbados 58.7 57.6 89.2 79.5 Argentina 47.4 49.3 109.6 70.7 67.9 110.7 PLANETIA 96.5 100.7 55.1 113.1 46.8 Kuwait 91.5 91.3 98.3 33.9 Brunei Darussalam 75.0 75.3 116.2 91.2 Costa Rica 106.2 117.3 63.9 65.1 80.3 98.6 61.4 109.2 Croatia 53.7 58.5 78.8 67.1 92.8 48.1 Uruguay 119.7 63.7 53.2 86.3 Mauritius 102.5 141.8 93.2 53.3 60.9 Slovak Republic 126.1 143.6 72.2 79.0 113.1 45.2 Seychelles 110.0 105.0 103.8 60.4 58.9 Greece 80.7 88.4 144.9 146.3 45.0 Hungary 62.1 137.6 74.0 132.2 Czech Republic 115.6 109.0 61.5 102.7 148.0 96.7 Qatar 150.0 85.1 140.7 127.1 84.8 Chile 120.0 157.2 110.6 70.6 109.0 Malaysia 70.5 144.7 102.4 154.1 104.2 92.2 127.3 88.5 Macau 130.6 82.1 92.7 157.6 124.3 80.8 64.6 Bahrain 146.7 50.5 114.5 164.6 78.0 132.5 Emirates United Arab 104.7 155.9 63.5 118.1 89.4 137.8 174.8 Estonia 97.9 126.8 152.8 178.7 Spain 104.2 135.3 61.3 163.6 86.7 122.0 Portugal 141.1 107.6 125.8 Malta 184.4 93.6 185.8 104.1 Slovenia 175.6 179.9 86.1 167.5 113.1 Italy 162.5 99.9 192.5 Cyprus 149.7 195.8 186.8 86.5 130.5 142.1 185.5 Israel 172.9 121.0 168.5 75.0 157.7 196.1 France 104.1 153.0 126.1 181.6 Ireland 135.3 100.8 166.2 118.5 United Kingdom 145.8 203.6 125.4 Finland 150.2 200.2 103.3 136.0 169.3 140.5 Gibraltar 116.2 124.0 149.9 211.6 Australia 211.9 134.3 102.3 202.2 118.2 210.2 Austria 194.3 190.4 144.5 177.4 New Zealand 173.3 160.5 Japan 156.8 141.2 216.1 136.9 Germany 206.9 232.0 223.1 154.7 Belgium 197.4 214.0 216.5 232.8 138.2 168.3 173.7 196.1 Norway 225.5 129.4 148.1 126.7 170.2 237.4 213.8 Bermuda 132.3 108.2 150.5 227.5 193.4 90.4 130.4 Iceland 207.3 171.0 187.9 Luxembourg 142.0 237.9 242.3 160.2 225.9 222.2 Netherlands 134.8 211.9 200.0 Switzerland 195.2 253.4 187.0 259.8 176.2 229.9 165.3 S. Korea 246.1 155.3 208.2 144.9 226.9 Sweden 143.8 182.2 203.6 122.4 167.8 Denmark 176.0 95.7 136.9 146.3 United States Singapore Taiwan, China Hongkong Canada Evolution ofInfo-use 9619 9819 002001 2000 1999 1998 1997 1996 528. 979. 97103.7 99.7 94.6 89.7 83.2 75.2 6. 7. 9. 0. 205.4 206.9 199.2 178.9 165.3 567. 479. 102.7 94.2 84.7 74.8 65.6 577. 09103.9 90.9 76.2 65.7 107.5 101.5 86.5 71.7 113.9 101.4 92.5 81.3 2. 3. 143.8 136.8 127.6 184.4 173.2 160.7 196.1 183.9 167.8 202.3 189.3 167.6 212.1 186.7 169.7 249.9 191.6 179.3 noei 381. 082. 9634.5 34.7 29.6 30.1 35.9 24.3 36.3 26.6 33.7 20.8 37.1 28.9 22.0 28.3 19.2 36.4 31.7 25.3 16.2 24.3 13.8 28.9 28.4 18.0 37.2 9.9 37.1 19.8 26.4 23.5 13.4 28.1 29.0 13.3 23.4 19.0 10.3 23.7 20.1 38.4 15.8 2.9 38.8 19.3 Indonesia 16.8 35.5 10.2 34.4 14.7 41.8 Honduras 42.0 12.4 29.8 25.9 12.1 35.9 Paraguay 37.2 9.4 26.6 42.9 22.7 43.5 Senegal 31.8 32.3 23.5 32.8 14.1 37.7 Guatemala 24.6 26.1 18.3 28.0 10.2 30.9 Syria 19.6 44.5 44.7 20.7 26.2 24.8 Kyrgyzstan 15.4 39.6 41.8 17.2 22.3 45.2 20.2 Cuba 34.9 31.5 19.0 49.6 39.6 Nicaragua 17.1 31.7 26.4 49.6 43.2 34.5 Morocco 19.6 15.7 44.0 50.9 28.6 21.7 54.5 Botswana 15.5 38.0 9.9 40.3 26.6 19.1 46.6 Bolivia 34.5 34.5 55.1 17.7 16.6 34.9 Samoa 55.2 30.4 25.2 48.1 10.7 23.4 Egypt 46.8 25.1 22.0 40.6 Philippines 14.7 40.1 19.4 33.7 Moldova 10.4 60.4 26.2 28.5 Armenia 41.2 49.5 24.4 24.8 Namibia 32.7 62.4 42.7 61.6 21.7 Ukraine 25.0 54.6 24.2 53.4 Georgia 43.6 20.6 46.3 China 31.9 64.7 17.3 38.6 Maldives 15.7 25.6 66.6 63.1 35.4 Ecuador 19.4 53.0 54.1 27.1 El Salvador 47.2 47.8 67.5 Fiji 40.7 67.6 38.9 61.8 Guyana 32.0 63.6 30.4 57.4 Tunisia 27.0 53.7 51.7 Thailand 68.6 67.9 48.1 Iran 42.9 60.1 62.3 69.0 33.2 69.4 Cape Verde 35.8 50.6 43.2 63.5 27.7 65.0 Panama 69.5 48.1 40.7 58.1 Saudi Arabia 55.9 60.6 41.2 37.1 70.3 40.0 Peru 55.0 44.9 54.3 34.3 30.2 66.3 Colombia 47.5 36.1 75.3 76.3 46.2 61.4 Romania 32.6 29.4 66.9 68.6 35.0 79.9 58.0 80.7 South Africa 64.2 58.8 19.2 74.2 41.4 77.3 Yugoslavia 83.1 53.4 51.6 61.4 86.6 31.6 69.9 Russia 71.1 43.7 43.5 52.8 71.2 Suriname 57.6 65.8 38.1 33.7 45.9 87.3 60.6 Oman 49.5 48.8 91.6 38.8 84.4 52.1 Jordan 42.6 42.2 86.9 70.7 94.7 43.7 Jamaica 33.3 77.9 64.1 91.7 36.3 Venezuela 99.0 65.4 49.8 75.6 Mexico 86.6 53.2 36.5 Bulgaria 65.7 79.7 30.9 Belize 54.3 70.0 Turkey 39.2 55.9 Brazil 47.1 Lithuania Lebanon Trinidad & T Latvia Poland theGrenadines St. Vincent and obago 9619 9819 002001 2000 1999 1998 1997 1996 992. 033. 6055.2 46.0 38.5 30.3 26.0 19.9 64.4 55.1 65.5 46.8 59.6 28.8 51.0 19.0 35.4 12.4 29.6 24.7 94.5 89.0 80.2 66.0 99.2 53.1 87.7 39.1 80.8 72.8 61.3 52.3 772. 315. 61.0 52.9 43.1 24.3 17.7 67.9 66.6 62.0 56.0 47.6 655. 55.9 52.4 46.5 Table A6. aba7172921. 4014.6 14.0 16.3 13.0 16.8 17.0 15.0 9.2 15.8 13.8 12.7 13.9 19.4 9.7 7.2 7.9 19.0 11.0 15.7 7.7 7.1 17.2 6.5 9.4 9.4 6.8 14.8 5.5 4.7 6.3 10.6 4.8 23.0 4.3 6.3 20.1 0.9 20.9 24.6 3.8 Zambia 15.1 20.6 18.0 Cameroon 13.8 25.0 Yemen 17.1 15.6 11.2 16.3 22.9 15.9 Kenya 10.6 13.1 6.9 15.8 16.1 Mauritania 13.7 8.5 10.3 12.7 13.5 27.4 25.6 Sudan 7.6 5.9 8.4 23.6 27.7 22.1 Solomon Islands 18.7 24.9 17.3 2.3 Papua NewGuinea 31.7 7.4 15.1 21.4 14.6 Pakistan 21.6 31.9 9.5 12.1 13.7 11.5 Djibouti 17.5 27.1 10.0 32.9 7.5 9.0 Albania 10.2 16.8 30.1 Côte d’Ivoire 20.7 6.2 7.3 12.4 34.1 24.8 Gabon 16.8 10.5 3.3 30.9 15.2 Sri Lanka 13.4 5.5 24.0 12.9 Zimbabwe 17.7 India 8.7 15.3 Gambia 10.0 Viet Nam Kiribati Togo Algeria Mongolia Evolution ofInfo-use 9619 9819 002001 2000 1999 1998 1997 1996 061. 792. 24.8 21.2 25.3 17.9 23.7 14.5 19.7 10.6 17.2 13.1 (cont’d) 691. 0620.7 20.6 19.3 16.9 711. 23.9 19.9 17.1 31.8 29.6 24.8 hd08132127343.7 3.4 5.1 2.7 5.2 3.8 2.1 6.1 4.7 3.2 1.3 5.1 2.8 2.7 7.4 8.6 0.8 6.0 4.4 1.6 2.2 7.1 7.6 5.7 3.0 0.9 1.6 5.7 6.1 5.4 2.2 0.7 8.9 4.1 5.3 4.4 1.6 7.4 3.2 3.2 3.5 Chad 3.6 6.7 2.4 Ethiopia 2.1 2.7 10.8 2.8 10.6 5.9 Myanmar 10.9 9.9 8.5 Rep. Central African 9.6 4.6 8.9 Malawi 11.2 6.5 3.8 11.5 4.0 5.9 2.3 Cambodia 9.1 4.5 6.6 12.1 10.2 3.9 2.4 Uganda 3.1 4.4 11.1 7.4 3.2 9.0 5.0 13.5 2.1 Mali 9.7 4.0 12.6 4.4 6.6 Burkina Faso 3.1 14.0 1.3 9.9 1.1 6.9 Mozambique 11.2 4.1 5.4 14.1 Madagascar 7.3 10.0 5.1 2.8 2.9 12.0 Guinea 8.8 6.2 3.4 9.4 Bangladesh 8.0 4.2 Angola 6.9 Eritrea 6.8 4.5 Tanzania Benin 2.2 Nepal Ghana Nigeria Lao P.D.R. 9619 9819 002001 2000 1999 1998 1997 1996 . . . 6.4 5.4 4.9 9.5 4.1 9.6 8.2 9.9 9.1 6.5 8.5 8.4 4.9 7.5 6.5 4.4 161 161 161 161 161 Monitoring the Digital Divide ...and beyond - STATISTICAL ANNEX Monitoring the Digital Divide… and beyond is a remarkable attempt to offer a global set of indicators… As the world prepares for the two parts of the World Summit on the Information Society (Geneva, 2003 and Tunis, 2005), the work produced by Orbicom to describe, measure and monitor the Digital Divide has a very distinct and important role to play. It offers a fresh and broad-ranging perspective of the ways in which ‘info-ready’ countries differ from ‘info-challenged’ ones. Governments, international organizations, business, non-governmental organizations, academia and civil society as a whole will be all the more interested in building a development-supportive, open and vibrant information society now that they will have at their disposal a reliable, action-oriented and diversified set of indicators to measure both the intensity of their efforts and the level of their impact. José-Maria Figueres CEO of the World Economic Forum and Chairman of the United Nations ICT Task Force Bruno Lanvin Manager of infoDev at the World Bank and Co-editor of the Global Information Technology Report (GITR)

This work comes at an opportune time. To measure the Digital Divide most attempts had concentrated on such aspects as connectivity measurements and e-readiness for a restricted number of countries. The Orbicom research breaks new ground with a conceptual framework that goes beyond infrastructure to examine the content and dimension of the Digital Divide through the inclusion of existing and reliable education data. In addition, it has the merit of covering a substantial part of the planet with an emphasis on developing countries. In this sense, Monitoring the Digital Divide …and beyond is an essential tool for policymakers, donors and other stakeholders concerned with access to information and the acquisition of knowledge and skills as a means to bridge the Digital Divide. It is also a very good example of innovative uses of data and their transformation into insightful synthesis. Dr. Abdul Waheed Khan Assistant Director-General for Communication and Information / UNESCO

Digital divides - both between countries and within countries - pose some of the most complex and difficult economic, social, and north-south policy questions that exist today. What is the situation? How is it evolving? What action is to be taken, where should it be directed, and why? Formulating, analyzing and monitoring policy requires measurement, and measurements require careful interpretation. Monitoring the Digital Divide …and beyond represents a big step forward in measuring and understanding many crucial aspects of the Digital Divide. Based on a coherent conceptual framework and the broadest possible coverage that existing data today allow, it offers a unique and authoritative perspective both on the magnitude and the evolution of the Digital Divide. The quantification of the situation in each country, component-by-component and over time, sets high standards in international benchmarking… John Dryden Deputy Director, Information, Communication and Computer Policy, DSTI, OECD

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