45 4 5 The Internet has opened a new world to us. Any kind of information is out there
a) to explain what is being done to help micro and small business by the EU Institutions b) to list the most important websites that are relevant to micro and small businesses We are not claiming to be all inclusive,
For more information on the SME definition, please visit the European commission website at: http://ec. europa. eu/enterprise/policies/sme/files/sme definition/sme user guide en. pdf 2. 2 Why is this relevant?
It consists of actions aimed at helping SMES to gain benefits from the digital economy. Innovation Union:
That is why the Commission has created a set of additional directives to complete these objectives. 3. 1. 3 Monitoring Progress Regarding SMES, the European commission monitors progress in the fields of research and development and innovation,
as well as the links to the relevant websites and points of contact. The European union has three key funding instruments to support SMES.
The Digital Agenda, one of the seven flagship initiatives of the Europe 2020 Strategy, has the goal of creating a flourishing digital economy by 2020.
which are published once a year by the European commission on FP7 website. Certain topics are dedicated to SMES
and to apply for grants you can visit the website of the European Research Council:
The European commission issues calls for project proposals, experts and competitive calls on the FP7 website. http://cordis. europa. eu/fp7/dc/index. cfm.
Several contact points are provided for each Member State and associated states on the website of the FP7 programme.
You can find a search engine of NCPS by visiting FP7 support webpage:.http://cordis. europa. eu/fp7/get-support en. html Lastly,
environment and risk prevention and access to transport and telecommunications services of general economic interest. http://ec. europa. eu/regional policy/thefunds/regional/index en. cfm#http
http://ec. europa. eu/regional policy/images/map/cooperat2007/crossborder/crossborder27 eu 07. pdf The following web link will give you information on transnational cooperation and eligible regions:
Calls for proposal are published on the Marco polo website at the beginning of each year. http://ec. europa. eu/transport/marcopolo/about/index en. htm 4. 4. 3 European Lifelong Learning Programme This programme,
http://eacea. ec. europa. eu/llp/leonardo/leonardo da vinci en. php http://ec. europa. eu/education/lifelong-learning-programme/doc1208 en. htm For inquiries
://www. erasmus-entrepreneurs. eu/page. php? pid=051 EU-Who do I call? 36 37 38 39 5. EU-Who do I call?
you are not likely to pick up the telephone to give Mr van Rompuy a call.
You can write to the EU SME Envoy at the following email address: entr-sme-envoy@ec. europa. eu In February of this year, the Small Business Act for Europe was reviewed thoroughly.
agencies and other bodies http://europa. eu/geninfo/mailbox/contact point en. htm This link provides an overview of telephone
and fax numbers and postal addresses, see the list of Contact points within the EU institutions, agencies and other bodies Europe Direct Tel:
://ec. europa. eu/solvit/site/index en. htm SOLVIT is designed to solve problems encountered by both citizens
EU information and assistance services http://ec. europa. eu/publications/booklets/others/83/index en. htm On this website you can find booklets issued by the Commission answering questions you may have both
or related to your business. 42 43 Managenergy http://www. managenergy. net/smes. html The website provides you with a guide with thematic and sectorial access to locally relevant energy information for SMES
Erasmus for Young Entrepreneurs NCP http://www. erasmus-entrepreneurs. eu/page. php? cid=05 Erasmus for Young Entrepreneurs is a grant providing promising European entrepreneurs with the skills necessary to start
National contact points can be found through the web link. European Documentation Centres http://europa. eu/europedirect/meet us/directory/index en. htm European Documentation Centres offer online access to EU sources for research
Each national representation's website provides the address of regional representations as well. http://europa. eu/whoiswho/public/index. cfm?
The following website helps you to find the representations of the EU institutions in your country. http://europa. eu/euinyourcountry/index en. htm 44 45 Understanding the processes
+32 2 639 62 31 Fax:++32 2 644 90 17e-mail: secretariat@esba-europe. orgwww. esba-europe. org
& Innovation Strategy for the Midwest Region of Ireland 2014-2018 Cluster Development Cluster development involves identifying the Region's core competence
/cosme/index en. htm 9 http://www. wheel. ie/sites/default/files/Consultation%20process%20on%20partnership%20agreement%202014%20-%202020. doc 17
which has been designated as a European TEN-T Core port. The ports of the Estuary are the third largest in Ireland by tonnage.
based on FÁS Regional Labour market Bulletin 2012 & CSO Figures 22 The available data indicates that
Dublin Southwest West Midwest Other Total Software & Services 38 7 2 2 8 57 Industrial & Life sciences 12 2 1 4
Subdivision of data into North & South Tipperary areas not available. 28 Case study: Benefi ts of participation in EU Projects Tyndall National Institute was established in 2004 under a formal agreement between University college Cork and the Minister for Enterprise, Trade and Innovation.
The irish Software engineering Research Institute (LERO), a global leader in software engineering research; and The Institute for the Study of Knowledge in Society (ISKS),
There are currently 15 industry-led research centres working in fields such as IT Innovation, Biorefining & Bioenergy, Data Analytics and Manufacturing Research.
c) Data pertaining to employment, turnover and exports in the Region from the Central Statistics Offi ce and;
identifying the Region's core competences and putting formal structures in place to maximise the potential of those competences.
A cluster must be based around the core competencies of the region. This is a critical element of their successful development.
Industry/businesses are at the core of every cluster; Public bodies/government agencies make policy decisions
A national clustering policy is essential to provide support and structure to cluster development. Additionally, there is a requirement for policies to ensure the creation of the type of environment that companies need
and public awareness in the areas of cluster structure, cluster development and the regional benefits of clustering;
and analysis in the Midwest Region to identify the core regional competence; To secure public funding to engage a Cluster Facilitator to identify regional opportunities for collaboration
By crowdsourcing innovation, both internally and externally, GE is improving customer value and driving advancements across industries.
To lobby nationally for policy guidance in relation to data protection, IP and competition policy to support an open innovation environment for all.
Baseline Data: The initial sections of this Strategy form a baseline assessment of the current research and innovation activities and strengths in the Region.
Priority Area A-Future Networks & Communications Priority Area B-Data Analytics, Management, Security & Privacy Priority Area C-Digital Platforms, Content & Applications
ICT/Electronics High tech Manufacturing/Engineering Logistics/Distribution High Value Food & Drink Life sciences Business and Professional Services Tourism, Sport & Leisure Secure investment
Priority Areas The Life sciences, Biotechnology and Medical Technology ICT including Software Logistics and Supply Chain Management Food Sector & Agribusiness Tourism and Leisure Renewable and Sustainable Energy
Abbey street, Nenagh, Co. Tipperary Email: info@mwra. ie Tel: 067 33197 www. mwra. ie
Measuring the Information Society Report 2014 Measuring the Information Society Report 2014 2014 ITU International Telecommunication Union Place des Nations CH-1211 Geneva
Switzerland Original language of publication: English. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system,
recording, or otherwise, without the prior permission of the International Telecommunication Union. ISBN 978-92-61-15291-8 ii Foreword iii I am pleased to present to you the 2014 edition of the Measuring the Information Society Report.
Its core feature is the ICT Development Index (IDI which ranks countries'performance with regard to ICT infrastructure, use and skills.
Over the past year, the world witnessed continued growth in the uptake of ICT and, by end 2014, almost 3 billion people will be using the Internet, up from 2. 7 billion at end 2013.
While the growth in mobile-cellular subscriptions is slowing as the market reaches saturation levels, mobile broadband remains the fastest growing market segment
Despite this encouraging progress, there are important digital divides that need to be addressed: 4. 3 billion people are still not online,
reinforcing the urban-rural digital divide. As this report finds, ICT performance is better in countries with higher shares of the population living in urban areas, where access to ICT infrastructure,
and mobile broadband is six times more affordable in developed countries than in developing countries. Income inequalities within countries are one of the reasons why broadband in particular fixed broadband remains unaffordable to large segments of the population.
The report finds that in 40 per cent of countries a basic fixed-broadband Brahima Sanou Director Telecommunication Development Bureau (BDT) International Telecommunication Union subscription still represents more than 5 per cent
An enabling telecommunication regulatory environment can significantly influence the affordability of services. The report finds that the price of ICT services falls with better market regulation and increased competition.
For example, in developing countries, fixed-broadband prices could be reduced by 10 per cent and mobile-cellular prices by 5 per cent if competition and/or the regulatory framework improved.
In this fast-changing digital era, one of the key challenges in measuring the information society is the lack of up-to-date data, in particular in developing countries.
ITU is joining the international statistical community in looking into ways of using new and emerging data sources such as those associated with big data to better provide timely and relevant evidence for policy-making.
Calls for a data revolution are prominent in the international debates around the post-2015 development agenda
store and analyse huge amounts of data, as well as being a major source of big data in their own right.
Big data from mobile operators, for example, are real-time and low-cost and have one of the greatest development potentials in view of the widespread use and availability of mobile networks and services.
This report provides the reader with a comprehensive and critical overview of the role of big data from the telecommunication sector,
for use in social and economic development policy and for monitoring the future information society.
I trust that the data and analysis contained in this report will be of great value to the ITU membership,
including policy-makers, the ICT industry and others working towards building an inclusive global information society.
Acknowledgements The 2014 edition of the Measuring the Information Society Report was prepared by the ICT Data and Statistics Division within the Telecommunication Development Bureau of ITU.
and Michael Minges to the compilation of data on international bandwidth, revenue and investment. Helpful inputs and suggestions were received from Joan Calzada Aymerich from the University of Barcelona (Chapter 4), Jake Kendall from the Gates Foundation, Anoush Tatevossian and Alex Rutherford from UN Global Pulse,
The work was carried out under the overall direction of Cosmas Zavazava, Chief, Project Support and Knowledge management Department, Telecommunication Development Bureau.
The report includes data from Eurostat, OECD, IMF, Informa, the UNESCO Institute for Statistics, the United nations Population Division and the World bank,
ITU also appreciates the cooperation of countries that have provided data included in this report. The report was edited by Anthony Pitt and Bruce Granger, ITU English Translation Section.
The desktop publishing was carried out by Nathalie Delmas and the cover was designed by Jesus Vicente. Administrative support was provided by Herawasih Yasandikusuma. v Table of contents Foreword...
4 1. 4 Revenue and investment in the telecommunication sector...13 1. 5 Use of ICTS...
35 2. 2 Global IDI analysis...41 2. 3 Monitoring the digital divide: Developed, developing and least connected countries...
107 4. 2 Fixed-telephone and mobile-cellular prices...108 4. 3 Broadband prices...114 4. 4 Income inequality and broadband prices...
140 4. 5 The impact of competition and regulation on telecommunication prices...152 Chapter 5. The role of big data for ICT monitoring and for development...
173 5. 1 Introduction...173 5. 2 Big data sources, trends and analytics...175 5. 3 Telecommunication data and their potential for big data analytics...
181 5. 4 Big data from mobile telecommunications for development and for better monitoring...185 5. 5 Challenges and the way forward...
195 Chapter 5 Annex...207 List of references...213 Annex 1. CT Development Index (IDI) methodology...
221 Annex 2. ICT price data methodology...231 Annex 3. Statistical tables of indicators used to compute the IDI...
241 Annex 4. Statistical tables of prices used to compute the ICT Price Basket...251 viii List of charts 1. 1 Fixed-telephone subscriptions by level of development, 2005-2014 (left) and by region, 2014 (right...
2 1. 2 Mobile-cellular subscriptions by level of development, 2005-2014 (left) and by region, 2014 (right...
3 1. 3 Fixed (wired)- broadband subscriptions by level of development, 2005-2014 (left) and by region, 2014 (right...
5 1. 4 Active mobile-broadband subscriptions by level of development, 2007-2014 (left) and by region, 2014 (right...
7 1. 6 Rural population covered by at least a 3g mobile network, 2009-2012.8 1. 7 Fibre and microwave routes,
and route metres per capita (right), selected regions, 2013.9 1. 8 Total International Internet bandwidth (Gbit/s), by level of development (left) and regional share (right
), 2004-2013.10 1. 9 International Internet bandwidth (bit/s) per Internet user, by region, 2004 and 2013.10 1. 10 Percentage of households with Internet access, by level of development
12 1. 12 Proportion of post offices providing public Internet access, by region, 2005-2007 vs 2010-2012.12 1. 13 Telecommunication revenues, world and by level
13 1. 14 Annual investment by telecommunication operators, world and by level of development, 2007-2012, total in USD (left) and annual growth (right...
13 1. 15 Individuals using the Internet, by level of development, 2005-2014 (left) and by region, 2014 (right...
14 1. 16 Growth in daily Google searches, 2007-2013.16 1. 17 Growth in Facebook monthly active users, 2004-2013 (millions of users...
17 1. 18 Wikipedia articles total and English language, 2003-2013 (thousands of articles...17 1. 19 Fixed-broadband access in enterprises using the Internet, selected countries, 2005-2012.19 1. 20 E-government Development Index (EGDI),
2003-2014.20 ix 1. 21 E-government services provided by countries (transactional services, left, and e-participation services, right)..
subscriptions per 100 inhabitants, top five IDI countries, 2013.46 2. 2 Wireless-broadband subscriptions per 100 inhabitants, top five IDI countries, 2010
-2013.47 2. 3 Wireless-broadband penetration, Bhutan, 2008-2013.50 2. 4 Proportion of households with a computer and proportion of households with Internet access, 2012-2013, Qatar...
2013.84 3. 2 IDI values compared with the global, regional and developing/developed-country averages, Africa, 2013.85 3. 3 Mobile-cellular subscriptions per 100 inhabitants
87 3. 4 IDI values compared with the global, regional and developing/developed-country averages, Arab States, 2013.89 x 3. 5 Wireless-broadband subscriptions
2012 and 2013.91 3. 6 IDI values compared with the global, regional and developing/developed-country averages, Asia and the Pacific, 2013.92 3. 7 Wireless-broadband penetration
regional and developing/developed-country averages, Europe, 2013.98 3. 11 Percentage of Individuals using the Internet,
and 2013.103 4. 1 Fixed-telephone basket (left) and mobile-cellular basket (right), in PPP$, world and by level of development, 2008-2013.109 4. 2 Fixed
-telephone basket (left) and mobile-cellular basket (right), as a percentage of GNI p. c.,
. c. in Asia and the Pacific, 2013.122 4. 10 Fixed-broadband prices as a percentage of GNI p. c. in Africa, 2013.123 4. 11 Availability of mobile-broadband
services by type of service, by level of development, 2013 and 2012.127 4. 12 Mobile-broadband prices, in PPP$, world and by level of development, 2013.217 4. 13 Mobile
2013.128 4. 14 Mobile-broadband prices as a percentage of GNI p. c.,world and by level of development, 2013.128 xi 4. 15 Mobile-broadband prices
as a percentage of GNI p. c.,by region, 2013.129 4. 16 Comparison of postpaid fixed-broadband and postpaid computer-based mobile-broadband prices, in USD, by region, 2013.130
2013.162 4. 24 Variation in mobile-cellular prices(%)explained by each variable, 2013.165 List of figures 2. 1 Three stages in the evolution towards an information society...
78 4. 1 Mobile-broadband services by type of device/plan...125 4. 2 Relationship between regulation, competition and prices...
156 5. 1 The five Vs of big data...176 5. 2 An overview of telecom network data...
182 5. 3 Customer profiling using telecom big data...184 xii List of boxes 1. 1 Final review of the WSIS targets:
Achievements, challenges and the way forward...26 1. 2 A decade of successful international cooperation on ICT measurement...
28 1. 4 What is a data revolution?..30 2. 1 ITU discussion forums on ICT statistics...
120 4. 2 Panel regression models for fixed-broadband and mobile-cellular prices...158 5. 1 How big data saves energy Vestas Wind Systems improves turbine performance...
177 5. 2 How Twitter helps understand key post-2015 development concerns...179 5. 3 How mobile operators currently use data to track service uptake, business performance and revenues...
183 5. 4 Using mobile data for development...187 5. 5 How mobile network data can track population displacements an example from the 2010 Haiti earthquake...
188 5. 6 Leveraging mobile network data for transportation and urban planning in Sri lanka...189 5. 7 Poverty mapping in Côte d'ivoire using mobile network data...
190 5. 8 Using mobile-phone data to track the creditworthiness of the unbanked...191 5. 9 Using mobile big data
and mobile networks for implementing surveys...193 List of tables 1. 1 Rural population covered by a mobile-cellular signal, 2012.4 1. 2 Total Internet domain registrations by world region, 2003,2008
and 2013.18 2. 1 IDI values and changes, 2012 and 2013.41 2. 2 ICT Development Index (IDI),
2012 and 2013.42 xiii xiv 2. 3 IDI access sub-index, 2012 and 2013.43 2. 4 IDI use sub-index, 2012 and 2013.44 2. 5 IDI skills sub-index, 2012 and 2013.45 2. 6 Most dynamic
countries-changes between IDI 2013 and 2012.49 2. 7 IDI by level of development, 2012-2013.55 2. 8 IDI by groups, 2012 and 2013.57 2. 9 Partial correlation analysis of IDI, population and geographic characteristics...
60 2. 10 Examples of contribution of ICTS towards the Millennium Development Goals...66 2. 11 Results of partial correlation analysis between IDI and MDG indicators...
67 2. 12 Simple correlation analysis between relative change in IDI values and MDG indicators 2002-2011, developing countries...
77 3. 1 IDI by region, 2013 and 2012.84 3. 2 The top five economies in each region and their ranking in the global IDI, 2013.85 3. 3
102 4. 1 Fixed-telephone sub-basket, 2013.112 4. 2 Mobile-cellular sub-basket, 2013.113 4. 3 Fixed-broadband prices
as a percentage of GNI p. c.,by region, 2013.116 4. 4 Fixed-broadband sub-basket, 2013.124 4. 5 Mobile-broadband prices, postpaid handset
-based 500 MB, 2013.132 4. 6 Mobile-broadband prices, prepaid handset-based 500 MB, 2013.134 4. 7 Mobile-broadband prices
, postpaid computer-based 1 GB, 2013.136 4. 8 Mobile-broadband prices, prepaid computer-based 1 GB, 2013.138 4. 9 Fixed
Panel regression results, fixed-broadband prices and regulation...160 4. 14 Panel regression results, mobile-cellular prices and regulation...
163 4. 15 ICT Price Basket and sub-baskets, 2013.166 5. 1 Sources of big data...
175 1 Measuring the Information Society Report 2014 Chapter 1. Recent information society developments 1. 1 Introduction The past year has been characterized by uninterrupted growth
While the global mobile-cellular market is approaching saturation levels, mobile-broadband uptake continues to grow at double-digit rates in all regions,
The data also show a continuous increase in Internet usage, with growth in the number of Internet users in all countries and increasing availability of online content,
much of which is created user through social media applications and platforms (e g. Twitter, Youtube, Whatsapp. With more and more applications now available through mobile platforms (mobile apps),
and the strong growth in mobile Internet uptake, an increasing number of people are joining,
and participating actively in, the information society. While the information society is growing worldwide, digital divides remain and are even widening in some segments.
In particular, there is a significant and persistent urban-rural digital divide, whereby urban citizens enjoy ubiquitous mobile network coverage,
affordable high-speed Internet services and the higher levels of skills required to make effective use of online content and services,
while the opposite is often the case in rural and remote areas of many developing countries.
This chapter will present and discuss key indicators for monitoring the global information society. It will first look at the uptake of ICT infrastructure and services,
covering the fixed and mobile (voice and data) market segments, and considering both subscriptions and household access data.
This will be followed by a presentation of the latest trends in terms of investment and revenue in the telecom sector.
Then, a number of key indicators will be presented concerning ICT uptake by individuals, businesses and public organizations (from the government and education sectors),
as Chapter 1. Recent information society developments 2 well as growth in online content and particularly social media.
in particular in the context of the post-2015 development debate and the WSIS+10 review, the demand for a data revolution,
and the role of big data for ICT monitoring. 1. 2 The voice market In line with developments in recent years,
there are only around a dozen countries where fixed-telephone uptake has increased actually over the past year. 1 Fixed-telephone penetration decreased by about 2 per cent globally in the past year,
The decline in fixed-telephone subscriptions over the past decade was accompanied by strong growth in the mobile-cellular market until 2010, at
there will be almost as many mobile-cellular subscriptions (6. 9 billion) as people On earth, more than three quarters of them (5. 4 billion) in the developing world and more than half (3. 6 billion) in the Asia-Pacific region.
While this does not mean that everyone has a mobile phone since many people have more than one subscription
or SIM CARD the total numbers and growth rates strongly point to market saturation. Whether this will change in the near future,
Africa and Asia and the Pacific are the regions with the strongest mobile-cellular growth,
Fixed-telephone subscriptions by level of development, 2005-2014 (left) and by region, 2014*(right) Note:*
ITU World Telecommunication/ICT Indicators database. 39.2 26.3 24.9 15.8 12.7 8. 7 1. 3 05 10 15 20 25
the fixed-telephone market is shrinking and the mobile-cellular market is tapering off. In addition, mobile-cellular population coverage has reached 93 per cent globally:
in other words, almost every person on the globe lives within reach of a mobile-cellular signal and,
at least theoretically, has access to mobile communication services. Closer examination and disaggregation of the data reveal,
however, that digital divides still exist and that some people are excluded still from access to communication networks.
First, there are populations living in rural areas that are covered not by a mobile-cellular signal (Table 1. 1)
. Even though rural population coverage is very high, at 87 per cent globally, at end 2012 around 450 million people worldwide still lived out of reach of a mobile signal.
Second high mobile-cellular penetration does not imply that everyone owns or is using a mobile Chart 1. 2:
Mobile-cellular subscriptions by level of development, 2005-2014 (left) and by region, 2014*(right) Note:*
ITU World Telecommunication/ICT Indicators database. 162.7 124.7 109.9 108.5 96.4 89.2 69.3 0 20 40 60 80 100 120
140 160 180 Per 100 inhabitants CIS phone. For countries where data are available, the number of mobile subscriptions far exceeds the number of mobile phone users (Partnership, 2014).
No regular pattern can be established, though, and the difference between mobile-phone user penetration and mobile-cellular subscription penetration ranges between 8 per cent (France) and 111 per cent (Panama) across countries.
According to GSMA estimates, unique mobile subscribers account for about half of mobilecellular subscriptions, which would translate into a penetration rate of around 48 per cent globally, 63 per cent in developed countries, 45 per cent in developing countries and 30 per cent in least developed countries (LDCS).
2 Third, household access to a telephone is still not the norm in many developing countries, in particular in LDCS (Partnership, 2014.
For example, according to the latest population and housing census carried out in India in 2011,63 per cent of households had a telephone (up from 9 per cent ten years earlier.
In addition, there were significant differences between urban and rural areas, with 82 per cent of Indian urban households having access to a telephone compared with 54 per cent of rural households. 3 Household telephone penetration in Malawi stood at 36 per cent in 2011 73
per cent in urban households 125.8 96.4 90.2 59.0 Per 100 inhabitants 0 20 40 60 80 100 120 140 Developed World Developing
LDCS 2014*200520062007200820092010201120122013 Chapter 1. Recent information society developments 4 and 29 per cent rural households.
The urban-rural gap in household telephone access prevails in many developing countries for which data are available,
but is closing with the availability of affordable mobile-phone services in rural areas. Further research and data would be necessary to determine people's access to,
and use of, voice communications and to identify other potential barriers, such as those related to poverty, literacy, education or lack of electricity,
and mobilebroadband markets Infrastructure deployment providing access to broadband Internet continues to be a priority for telecommunication service providers and governments in most countries.
Rural population covered by a mobile-cellular signal, 2012 Source: Partnership (2014) based on ITU data.
Overall mobile-cellular population coverage(%)Rural population covered(%)Rural population covered (millions) Rural population not covered (millions) Africa 88 79 498
129 Americas 99 96 171 9 Asia 92 87 2 017 309 Europe 99 98 196 3 Oceania 96 81
despite low penetration, coincides with a strong growth in mobile-broadband subscriptions in the developing world (see Chart 1. 3). A closer look at different regions shows that Africa,
whereas The americas region displays the lowest growth in fixed broadband, estimated at 2. 5 per cent and reaching a penetration rate of around 17 per cent by end 2014.
ITU World Telecommunication/ICT Indicators database. 27.7 16.7 14.3 9. 8 7. 7 3. 1 0. 4 05 10 15
In Saudi arabia, 30 per cent of all wireless-broadband subscriptions are fixedwireless and satellite subscriptions.
1. 4). Mobile broadband is growing fastest in developing countries, where growth rates over the last year are expected to be twice as high as in developed countries (26 per cent,
This is driven by the availability and uptake of more affordable devices (smartphones) and types of plan on offer in the market.
A closer look at the different mobile technologies and their market shares highlights the shift from lower-speed to higher-speed technologies over the past 15 years (Chart 1. 5). In developed countries,
3g subscriptions overtook 2g subscriptions in 2010 and 3g growth is flattening. In developing countries, the large majority of subscriptions are still 2g,
but 3g is growing rapidly and will overtake 2g subscriptions in a few years. 4g4 services came onto the market only recently
and 4g subscriptions still account for only a small market share in both developed and developing countries.
The data on fixed-and mobile-broadband uptake confirm what has been observed on the ground. In developed countries, fixedbroadband infrastructure and services were available much earlier than in most developing countries,
and before fast mobile-broadband services and smartphones entered the market. This has contributed to the higher uptake of fixed broadband in developed countries.
Active mobile-broadband subscriptions by level of development, 2007-2014 (left) and by region, 2014*(right) Note:*
ITU World Telecommunication/ICT Indicators database. 83.7 32.0 21.1 6. 3 0 10 20 30 40 50 60 70 80
for example when people use multiple devices (e g. smartphone, tablet) and SIM CARDS. Looking towards the future, the growth potential for mobile broadband looks promising,
as 7 Measuring the Information Society Report 2014 Chart 1. 5: Share of mobile subscriptions by technology, 2000-2015, developed countries (left) and developing countries (right) Source:
data based on ITU and Telecom Advisory Services calculations. more and more countries upgrade their mobile networks. As mentioned earlier, 2g population coverage stands at over 90 per cent worldwide.
Data on 3g population coverage are less available. According to ITU estimates, global 3g population coverage stood at around 50 per cent by end 2012,
and there were still sizeable ruralurban gaps. Rural population coverage ranged from 100 per cent in the Gulf countries of United arab emirates
and Bahrain to zero in some African countries (Chart 1. 6). These numbers are expected, however to change significantly in the near future,
as more and more countries are deploying 3g+technologies and services, and given the strong growth in mobile-broadband subscriptions.
At the same time, the issue of spectrum allocation will have to be addressed to ensure that the increasing demand for high-speed mobile access can be met,
including in rural areas, where the additional spectrum represented by the digital dividend could play a crucial role in universalizing mobile-broadband access.
Backbone and bandwidth The growth in broadband subscriptions is accompanied by continuous growth in national backbone capacities and international Internet bandwidth.
Indeed, without further deployment of backbone infrastructure service providers are unable to expand their markets to previously underserved regions.
New data collected by ITU on the deployment of fibre transmission capacity in countries shows that by end 2013,
a closer look at the data also reveals major disparities across regions: Asia and the Pacific (in particular China and India) accounts for more than 85 per cent of the total length of backbone networks (Chart 1. 7, left.
3g 4g%1g 2g 3g 4g%0 10 20 30 40 50 60 70 80 90 100 Developing countries Chapter
Rural population covered by at least a 3g mobile network, 2009-2012 Source: Partnership (2014) based on ITU data.
Percentage of rural population covered by at least a 3g mobile network 2012 or 2011 Percentage of rural population covered by at least a 3g mobile network 2010 Percentage of rural population covered by at least a 3g mobile network 2009 0000000000001 5 11 31 32 32343638 41
42 42 46 50 50 50 55 555860616365 68 69 69 77 77 7778818486 87 88 8889 90 9092 93
93 94 94 95 95 96 100 100 0 10 20 30 40 50 60 70 80 90 100 Antigua and barbuda
These numbers reveal significant digital divides between and within regions and point to opportunities for service providers to increase their subscriber base (ITU, 2014).
and speed of networks is the amount of international Internet bandwidth available in countries and regions,
such bandwidth being a key requirement for delivering data-intensive applications and services through high-speed networks.
Over the past decade, international Internet bandwidth has climbed sharply, from around 1 600 Gbit/s in 2001 to 60 400 Gbit/s in 2010 and more than 140 000 Gbit/s in 2013
ITU Trends in Telecommunication Reform, 2014. of the world. Growth in international bandwidth has been strong in all regions,
Europe leads by far in terms of international Internet bandwidth, accounting for more than 50 per cent of the world's total (2013),
Europe's leadership in international Internet bandwidth is explained by the advanced level of broadband adoption and usage in the region,
and depend on international connections to reach the global Internet. As a result, the Internet backbone network in the region is interlinked by means of several Internet exchange points (IXPS) that interconnect national networks
and give them access to the global Internet. Indeed, some of the world's largest IXPS are located in Europe
and have an international reach, such as for instance the German Commercial Internet Exchange (DE-CIX), the Amsterdam Internet Exchange or the London Internet Exchange. 6 The United kingdom stands out as a prominent global hub for international 3
%2%85%4%6%Africa Arab States Asia & Pacific CIS The americas 0. 4 0. 7 2. 6 1. 6 1
Total international Internet bandwidth (Gbit/s), by level of development (left) and regional share (right), 2004-2013 Source:
ITU World Telecommunication/ICT Indicators database. connectivity, because of the strong internal demand and also its location:
as do several cables linking Western Africa and the Arab States with Europe. 7 International Internet bandwidth in the UK accounts for almost twice as much as Africa, Arab States and CIS combined,
In order to understand better the impact of available international bandwidth on Internet 0 20'000 40'000 60'000 80'000 100'000 120'000 140'000
160'000 Africa Arab States CIS Asia & Pacific The americas Europe International Internet bandwidth (Gbit/s) 2004200520062007200820092010201120122013-20'000 40'000
60'000 80'000 100'000 120'000 140'000 160'000 World Developed Developing International Internet bandwidth (Gbit/s
which differs widely across regions and countries, Chart 1. 9 shows bandwidth per Internet user. This indicator has increased significantly between 2004 and 2013.
Households with Internet access Household access to the Internet is the ultimate way of guaranteeing an inclusive information society in which all people, irrespective of age, gender, employment status,
etc. or possible level of disability, can access the Internet within the privacy and proximity of their own home.
A policy aimed at universal access to broadband Internet will eventually ensure access for all households nationwide.
International Internet bandwidth (bit/s) per Internet user, by region, 2004 and 2013 Source: ITU World Telecommunication/ICT Indicators database. 221 420 1'213 702 4'384 11'572 8'074 19'037 21
'472 43'072 53'992-10'000 20'000 30'000 40'000 50'000 60'000 70'000 80
'000 2004 2013 161'027 International Internet bandwidth (bit/s per user) Africa Arab States Asia &pacific CIS The Americaseurope 11 Measuring
the Information Society Report 2014 The latest ITU data show that by end 2014, almost 44 per cent of the world's households will have Internet access at home, up from 40 per cent one year earlier and 30 per cent four years earlier (Chart 1. 10).
only 11 per cent of households in Africa have Internet, and growth remains at a high 18.4 per cent,
The Asia and the Pacific region boasts the highest number of households with Internet Chart 1. 10:
ITU World Telecommunication/ICT Indicators database. 78.0 57.4 53.0 43.6 36.0 35.9 11.1 0 10 20 30 40 50 60
and some two-thirds of the household in the region are connected not yet to the Internet.
In countries where data are available, rural household access falls far below urban household access,
with differences ranging from 4 per cent (meaning that household Internet penetration in urban areas is 4 per cent higher than in rural areas) in highly developed countries such as Japan and the Republic of korea to 35 per cent in developing countries
In Guatemala, urban households are 12 times more likely to be connected to the Internet than rural households (Partnership
2014). 8 Available data also show that Internet access in rural households is growing slowly, 78.4 43.6 31.2 5. 0 0 10 20 30 40 50 60 70
but data are not readily available for those countries. As has been illustrated earlier, network deployment is limited still
thus preventing rural households from purchasing Internet services. At the same time, the benefits brought by ICTS and the Internet are especially impactful in rural areas,
which often also lack access to other infrastructure and public services. Therefore, connecting rural households to broadband networks should remain a priority for policymakers in all countries.
public access to the Internet plays a greater role in those areas. Data on public access is collected by ITU through its household questionnaire
but only few countries report data on this indicator, let alone broken down by urban and rural populations.
Public access can be provided by commercial facilities, such as privately operated Internet cafes, as well as community-type facilities,
which typically provide Internet access free of charge. Schools also constitute an important location for Internet access, especially in rural areas,
and post offices can play a major role in terms of providing access to the Internet: they are open to the public,
For example, worldwide, only 10 per cent of post offices provide public access to the Internet, even though 31 per cent of post offices have a broadband Internet connection (Chart 1. 11), with major differences across regions (Chart 1. 12).
These numbers refer to 2012, and have increased most probably somewhat today. Nevertheless, there is huge potential
if all post offices were provided with broadband Internet and offered this as a service to the public.
2014 small towns had access to the Internet, while with 60 per cent coverage half of all rural areas would be connected. 9 The World Report series published by the International Federation of Library Associations
and Institutions Committee on Freedom of Access to Information and Freedom of Expression (IFLA/FAIFE) contains information about the extent and growth of public access to the Internet in public libraries from 2007 to 2009.10 While the results point
to improvements in providing public Internet access in public libraries, progress has not been visible everywhere. Significant differences exist between developed and developing countries in terms of the provision of public Internet access,
and there are still a number of countries reporting low rates of public access. 1. 4 Revenue and investment in the telecommunication sector In 2012,
total telecommunication revenue stagnated at around USD 1. 88 trillion, or 2. 7 per cent of world GDP (Chart 1. 13).
The evolution of telecommunication revenues in developed countries follows the overall pattern of their economies as a whole (in the European union, for instance,
and consumer spending on telecommunication services. In addition to the adverse economic context, the voice market in developed countries is declining
thus also exerting pressure on the revenues generated by the strongest growing market segments, such as mobile broadband.
In contrast to the situation in the developed world, developing countries saw a 4 per cent growth in telecommunication revenues in 2012,
This confirms the steady progress of telecommunication revenues seen Chart 1. 13: Telecommunication revenues, world and by level of development, 2007-2012, total in USD (left) and annual growth (right) Note:‘
‘World'includes 103 countries accounting for 96 per cent of world GDP.‘‘Developed'includes 40 developed countries accounting for 99 per cent of total GDP in the developed world.‘
As a result, the developing countries'share of total telecommunication revenues increased from 26 per cent in 2007 to 32 per cent in 2012,
This testifies to the growing importance of the telecommunication sector in the economic growth of the developing world.
For example, in the recent revision of Nigeria's GDP, it was found that the telecommunication industry accounted for more than a quarter of the upgrade in GDP. 12
Chart 1. 14 shows the evolution of investment in telecommunications, which is fundamental to supporting ICT uptake and innovation.
and the reduction in telecommunication investment persisted in 2009(-2 per cent). The overall economic environment of restricted access to capital markets and the efforts of some operators to reduce debt exposure explain the sluggish investment levels seen in 2011 and particularly in 2012.13 In developing countries,
investment in telecommunication infrastructure and services has been more stable, with a smaller drop in 2008(-4 per cent) and moderate growth in the following years (4 per cent compound annual growth rate between 2009 and 2012).
which is compared relatively high with the share of global telecommunication revenues generated in developing countries (32 per cent).
The investment-to-revenue ratio in the telecommunication sector stood at 17 per Chart 1. 14:
Annual investment by telecommunication operators, world and by level of development, 2007-2012, total in USD (left) and annual growth (right) Note:‘
This means that, on average, for each USD 100 generated globally by telecommunication services, USD 17 were reinvested in capital expenditure
and improve telecommunication services). The investment-to-revenue ratio was somewhat lower in developed countries (15 per cent) than in developing countries (22 per cent.
On the one hand, telecommunications is a capital-intensive industry and part of the capital investments are delivered by global equipment providers,
in order to provide incentives for operators to make the investments necessary to bridge the infrastructure gap between developing and developed countries. 1. 5 Use of ICTS Internet users ITU estimates that, by end 2014,
almost 3 billion people will be using the Internet, corresponding to a global penetration rate of 40.4 per cent (Chart 1. 15).
The numbers also show that there are still 4. 3 billion people worldwide who are not yet using the Internet, 90 per cent of
Nevertheless, Internet usage is growing steadily, at 6. 6 per cent in 2014 3. 3 per cent in developed countries and 8. 7 per cent in developing countries.
in developing countries, the number of Internet users will have doubled in five years (2009-2014), and two-thirds of today's Internet users live in the developing world.
Growth rates are highest in LDCS (13 per cent in 2014), but they are starting from low Chart 1. 15:
Individuals using the Internet, by level of development, 2005-2014 (left) and by region, 2014*(right) Note:*
ITU World Telecommunication/ICT Indicators database. 78.3 40.4 32.4 8. 0%0 10 20 30 40 50 60 70 80
Internet usage varies considerably across regions. In Europe, Internet usage on average is approaching saturation levels, with almost 75 per cent penetration and low growth of 2. 3 per cent during the past year.
In Africa, the region with the lowest Internet penetration rate (19 per cent), Internet usage is growing strongly at 13 per cent,
and almost twice as many people will be online by the end of this year compared with only four years earlier.
The Asia and the Pacific region includes the two most populous countries China and India.
Therefore, it comes as no surprise that 45 per cent of the world's Internet users live in this region.
The two countries combined are home to around 860 million Internet users, almost 30 per cent of the world's total and 66 per cent of Internet users in the Asia-Pacific region.
while the percentage of Internet users in China is 46 per cent, it is only 18 per cent in India.
Internet usage in The americas region is relatively high: with almost 66 per cent penetration, it is much higher than household Internet access (57 per cent.
with 19 per cent Internet penetration compared with 11 per cent of households with Internet access.
In view of infrastructure limitations and a lack of affordable services, people are more likely to use the Internet at locations outside the home,
Internet content and use of social media The growth in Internet users has witnessed a parallel, steep growth in the volume of Internet content.
and using social media and other Internet-based applications, covering a large range of topics and sectors.
While measuring online content and website use is a challenging task on account of the sheer volume of information available,
which includes an assessment of Internet content (Partnership, 2014). Some key findings featured in the report are presented below.
Over the past decade, the number of websites has been growing at exponential rates and, according to estimates by Netcraft, there were over 850 million hostnames and approximately 185 million active sites at the beginning of 2014.
Google remains the leading search engine in most countries, and accounts for around 90 per cent of the search market. 16 The number of daily Google searches reached almost 6 billion by end 2013 (Chart 1. 16)
and the total number of searches made through Google in 2013 exceeded 2 trillion. Social media sites have become the most accessed websites by users in both developed Chart 1. 16:
Growth in daily Google searches, 2007-2013 Source: Partnership (2014), based on http://www. statisticbrain. com/google-searches/.
/0 2'000 4'000 6'000 8'000 2013 2012 2011 2010 2009 2008 2007 Millions of searches 17 Measuring
the Information Society Report 2014 and developing countries. Since its creation in 2004, Facebook has grown to comprise 1. 3 billion active users by end 2013, a growth of 22 per cent over the past year (Chart 1. 17),
although a single user could be operating several accounts and therefore the numbers do not represent unique Internet users (ITU, 2011).
Twitter, the leading international microblogging service, founded in 2007, has grown to comprise 646 million active registered users by end 2013 (and 115 million active monthly users),
and some 58 million tweets were posted daily in the past year. 17 The Chinese microblog service Tencent Weibo accounts for a further 507 million subscribers, out of an estimated 582 million Chinese Internet
subscribers (Partnership, 2014). More than 6 billion hours of video are being watched each month and more than 100 hours of video content are uploaded every minute on Youtube, the leading international videofilesharing site with services in 61 countries.
As of early 2014, Youtube boasted more than 1 billion unique visitors monthly. Other top popular websites include Amazon,
Wikipedia and Linkedin as well as various news and online e-market sites at the national level (see below on e-business).
Wikipedia, the largest and most widely used online encyclopaedia, featured more than 30 million articles by end 2013 (Chart 1. 18).
Articles are now available in 287 languages across 30 million pages of content. By February 2014, Wikipedia registered more than 20 billion page views per month by Internet users.
At the same time, the proportion of articles in English has decreased significantly from 46 per cent in 2003 to 15 per cent in 2013
while those in other languages have increased accordingly, although pages viewed are still predominantly in English
While these numbers illustrate the huge increase in Internet content and usage overall a more nuanced analysis needs to be carried out to identify digital divides.
Table 1. 2 shows that, for example, domain-name registrations are still dominated by content providers in developed countries,
The data include both global top-level domain (gtld) and country code top-level domain (cctld) registrations,
Growth in Facebook monthly active users, 2004-2013 (millions of users) Source: Partnership (2014), based on http://www. theguardian. com/news/datablog/2014/feb/04/Facebook-in-numbers-statistics, accessed 6 march 2014.
Data sourced from Facebook. 0 500 1'000 1'500 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 Millions
of users Chart 1. 18: Wikipedia articles total and English language, 2003-2013 (thousands of articles) Source:
Wikipedia statistics at http://stats. wikimedia. org/EN/Tablesarticlestotal. htm. 0 5'000 10'000 15'000 20'000 25'000
30'000 35'000 Total English Chapter 1. Recent information society developments 18 content generated by countries and regions and highlight the differences.
International data on ICT access and use by enterprises are collected annually by the United nations Conference on Trade and Development (UNCTAD),
The proportion of businesses with websites was lower, accounting on average for 71 per cent and ranging from 36 per cent in Romania to 91 per cent in Finland.
a recent Eurostat survey revealed that more and more enterprises are making use of social media. In 2013
around 30 per cent of European enterprises used social media, ranging from 15 per cent of enterprises in Latvia to 55 per cent in Malta. 18 Activities are mostly related to social networks (e g.
Facebook), followed by multimedia content sharing websites (e g. Youtube) and blogs (e g. Twitter. In the developing world, data on ICTS in enterprises are scarce
and only collected by few countries. The proportion of businesses with Internet access varies between 48 per cent in Azerbaijan and 97 per cent in Lebanon. 19 Of these,
not all have broadband access, which is essential to enable businesses to engage in, and take full advantage of, the potential of e-business (Chart 1. 19).
Total Internet domain registrations by world region, 2003,2008 and 2013 Source: Partnership (2014. Data supplied by Zooknic,
compiled from cctld and other sources. Figures exclude fifteen cctlds which act as virtual gtlds. 2003 2008 2013 Millions%Millions%Millions%World 59.7 100.0 173.4 100.0 245.2 100.0 Developed 49.6
backbone connectivity and international Internet bandwidth is still lacking in many regions of the developing world.
but governments are also increasingly using the Internet to provide services to their citizens. E-government contributes to increased efficiency
There is little data on the use of ICTS by government organizations and those countries that do have data are usually the more advanced ones with high levels of connectivity in general.
More information is available about government services provided online, tracked by the United nations through its E-government Survey,
The latest data show that, today, governments of all countries have established central websites and that more than 50 per cent of countries provide links to local and/or regional government agencies'sites (UNDESA, 2014).
Efforts are needed still to connect lower-tier administrations in countries. When it comes to the provision of e-services,
the results from the latest UN survey show that considerable progress has been made over the past decade.
For example, online information and services on government website portals increased threefold, with 70 per cent of countries providing a one-stop shop portal in 2012,
By 2014, all countries had a government web presence, and almost all countries in Europe and the majority of countries in The americas and Asia provided online information on education,
Fixed-broadband access in enterprises using the Internet, selected countries, 2005-2012 Source: UNCTAD Information Economy Database, 2014, available at unctadstat. unctad. org.
Percentage of enterprises 92 85 78 72 43 31 30 50 18 14 0 10 20 30 40 50 60 70
Data from United nations E-government Survey (2014. Chart 1. 21: E-government services provided by countries (transactional services, left,
Data from United nations E-government Survey (2014). 101 73 60 46 44 42 41 40 39 33 29 27 76 0
ICT use in schools Providing schools with Internet access (in particular broadband Internet) is a basic infrastructure requirement in today's information society.
Access to high-speed Internet is necessary to enable students to use the Internet for educational purposes,
and helps enhance education administration through the electronic exchange of forms, data and other information.
and reducing expenses associated with the printing and distribution of textbooks. The benefits are particularly attractive for remote schools
in remote and rural areas schools are indeed often the only place where young people can use the Internet (see section 1. 3 above).
The latest available data from the UNESCO Institute for Statistics (UIS) 22 show that, in developed countries, the vast majority of schools have Internet access,
which data are available. In developing countries, school access to Internet is lower on average, although much progress has been made in recent years.
There are significant differences across countries, even within the same region and with similar income levels.
and given the growth of mobile-broadband services, it may be expected that more and more schools will have broadband access in the near future
Data on broadband in secondary schools in Bangladesh are not available. Data for Nicaragua, Philippines and Indonesia do not include upper secondary.
Data for European countries and Costa rica refer to lower secondary. Data for Guyana, Nicaragua and Indonesia refer to primary and lower secondary.
Data for Cambodia include pre-primary schools. Data for Morocco, Tunisia, Guyana, Montserrat, Dominican republic, Nicaragua, Colombia, Trinidad and tobago, Bangladesh, Philippines, Sri lanka, Azerbaijan, Bhutan, Cambodia, Kazakhstan, Malaysia, Maldives, Singapore, Belarus
and the Russian Federation refer to public schools. In Suriname, there are no private schools in upper secondary.
Data for Palestine refer to West bank schools only. Source: UIS database, Partnership on Measuring ICT for Development WSIS Targets Questionnaire, 2013.
Percentage of schools Americas 0 10 20 30 40 50 60 70 80 90 100 Internet Fixed broadband Internet Percentage of schools
0 10 20 30 40 50 60 70 80 90 100 Asia and Africa Bangladeshnepal Kyrgyzstan Cambodiaphilippinessri Lanka Azerbaijanpalestine Indonesiabhutan Qatar
Saudi Arabiajordanturkey Iran I. R. Omanmalaysia Mongoliakazakhstan Thailandjapanarmeniageorgia Maldives Bahrain Brunei Darussalamchina, Hong kong Korea (Rep.)Singapore Ethiopiamorocco Sudansenegal Lesothobotwanaegypt Algeriatunisia Mauritius
Percentage of schools 0 10 20 30 40 50 60 70 80 90 100 Europe and Oceania Belarus Russian Federationpolandalbaniaestoniahungary Slovakiafinlandlithuaniaandorra Bosnia
In Uruguay, the El Ceibal initiative has driven ICT usage in schools, in partnership with the One Laptop per Child (OLCP) project.
resulting in 78 per cent of schools being connected to the Internet in 2013, compared to just 44 per cent in 2009 (Partnership, 2014).
and the country's more recent One Tablet per Child (OTPC) initiative has helped increase the learner-to-computer ratio in schools.
Internet connectivity in schools also depends on the development of the national telecommunication infrastructure and on whether service providers have reached out to rural and sometimes geographically difficult areas with low population density (Partnership, 2014).
rural areas often suffer from much lower network coverage and hence ICT uptake compared with urban areas.
While connecting schools to the Internet and other ICTS is essential in order to foster e-education,
In some cases, computers have been introduced in schools without Internet access, which effectively Chart 1. 23: Proportion of ICT-qualified teachers versus proportion of teachers trained to teach subjects using ICTS, by region, 2009-2012 Note:
Data for Philippines refer to primary and lower secondary. Data for Venezuela refer to primary only.
Data for Montserrat and Saint lucia refer to secondary only. Data for Palestine refer to West bank schools only.
Data for Bahrain, Belarus, Morocco and Tunisia refer to 2008. For Morocco, ICTQUALIFIED teachers figures refer to 2008.
Data for Azerbaijan, Barbados, Jordan, Saint lucia, Singapore, Trinidad and tobago, Uruguay, Philippines and Sri lanka refer to public schools only.
Source: UIS database, Partnership Questionnaire on WSIS Indicators, 2014. Proportion of teachers trained to teach basic computer skills
(or computing)(%Proportion of teachers trained to teach subjects using ICT(%)Anguilla Argentina Azerbaijan Bahrain Barbados Belarus Cayman islands Chile Montserrat Cuba Egypt Jordan Lithuania Malaysia China
, Hong kong Morocco Nauru Oman Palestine Qatar Saint kitts and nevis Saint lucia St vincent and the Grenadines Singapore Thailand Trinidad and tobago Tunisia Turks and Caicos Isl.
Uruguay Colombia Philippines Sri lanka Venezuela 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30
40 50 60 70 80 90 100 Asia Europe Africa Americas Chapter 1. Recent information society developments 24 limits their use
Available data collected by UIS at the international level shows that education systems in countries seem to put more emphasis on training teachers to teach subjects using ICTS than on training teachers to teach basic computer skills or computing (i e.
The document also includes two targets on data and monitoring and stresses the need to take urgent 25 Measuring the Information Society Report 2014 steps to improve the quality,
coverage and availability of disaggregated data to ensure that no one is left behind. 26 The role of ICTS as a key development enabler,
and encourages Member States to collect ICT data at the national level. Monitoring progress towards achievement of the WSIS outcomes has been an integral component of the WSIS process.
it referred to the Partnership on Measuring ICT for Development and its contribution towards developing indicators and collecting data and statistics on ICT.
to collect national-level data on the indicators identified to measure the WSIS targets. The results of the survey are featured in the report
along with other data sources. The Final review of the WSIS targets: Achievements, challenges and the way forward report is available at:
Using different data sources and big data analytics An important element in the discussion related to the post-2015 development agenda and the setting of measurable goals and targets has been need the to take a fresh look at the way data
and statistics are collected, analysed and disseminated, in view of the large data gaps prevailing in many developing countries in basic statistics in the areas of the economy, health education, labour, etc.,
all of which are crucial to monitoring the MDGS, and the future SDGS. This call for a data revolution was enunciated first in the report to the UN Secretary-general of the High-level Panel of Eminent Persons on the Post-2015 Development Agenda published in 2013 (see Box 1. 4 on the data revolution.
Since then, it has received considerable attention within the statistical community, as well as in other circles concerned with the lack of official statistics and development data.
According to the discussions, a data revolution considers new data sources, in addition to existing official sources: besides governments, other stakeholders such as the private sector, civil 27 Measuring the Information Society Report 2014 Box 1. 2:
A decade of successful international cooperation on ICT measurement In 2004, ICT measurement was still in its infancy,
with little attention paid to collecting official ICT statistics outside the member countries of the Organisation for Economic Cooperation and Development (OECD), with the exception of telecommunication statistics
calls surfaced for reliable and comparable data in order to take stock of the emerging information society, identify digital divides and measure the developmental impacts of ICTS.
In this context the Partnership on Measuring ICT for Development was launched during the UNCTAD XI conference in June 2014,
Its core list of 57 ICT indicators, covering many aspects of the information society and economy, is used widely by countries in the course of their national ICT data collection.
The methodological work developed by the Partnership has contributed significantly to the collection of evidence on ICT developments worldwide, based on internationally comparable statistical indicators.
Data availability has increased also significantly over the past decade. In particular there are more comparable data on ICT infrastructure, household access and Internet users.
For example, at the beginning of the century, only around a dozen developing countries collected data on Internet users, while today there are almost 50 developing countries collecting this indicator through official surveys (Chart Box 1. 2). Data on household access to the Internet
or a computer are now being collected by more than 100 economies worldwide, and data on Internet use in businesses by almost 70 countries,
although not on a regular basis (Partnership UNSC 2011). Similarly, whereas no data were available on ICT access
and use in schools, they have started now to be compiled in many developing countries (see section 1. 5). At the same time,
major data gaps remain, in particular in developing countries and LDCS. This concerns, notably, statistics on ICT use by individuals, businesses, governments and other public-sector organizations, ICT-related employment data,
as well as data related to online security and cybercrime, gender and youth, and cultural and environmental aspects.
The growing information society will increasingly require more and better statistics to assess the social,
economic and environmental impacts of ICTS. The Partnership, in close collaboration with national statistical systems and the international donor community, will continue its efforts to address these challenges.
Chart Box 1. 2: Number of countries collecting Internet user data through official surveys, by level of development Note:
Chart shows countries that have collected data on the number of Internet users through official national surveys.
Data are presented in three-year intervals and include countries that have collected data for at least one year within those intervals.
Source: ITU. 0 2 11 24 36 49 1 3 27 37 41 40 1 5 38 61 77 89 0
20 40 60 80 100 Developing Developed World 1997 1998-002001-03 2007-09 2004-06 2010-12 (and before) Chapter 1
and foster access to and increased use of telecommunications/ICTS Target 1. 1: Worldwide, 55%of households should have access to the Internet by 2020 Target 1. 2:
Worldwide, 60%of individuals should be using the Internet by 2020 Target 1. 3: Worldwide, telecommunication/ICTS should be 40%more affordable by 2020a Goal 2. Inclusiveness Bridge the digital divide
and provide broadband for allb Target 2. 1. A: In the developing world, 50%of households should have access to the Internet by 2020 Target 2. 1. B:
In the least developed countries (LDCS), 15%of households should have access to the Internet by 2020 Target 2. 2. A:
In the developing world, 50%of individuals should be using the Internet by 2020 Target 2. 2. B:
In the least developed countries (LDCS), 20%of individuals should be using the Internet by 2020 Target 2. 3. A:
The affordability gap between developed and developing countries should be reduced by 40%by 2020 Target 2. 3. B:
Broadband services should cost no more than 5%of average monthly income in developing countries by 2020 Target 2. 4:
Gender equality among Internet users should be reached by 2020 Target 2. 5. B: Enabling environments ensuring accessible telecommunications/ICTS for persons with disabilities should be established in all countries by 2020 Goal 3. Sustainability Manage challenges resulting from telecommunication/ICT development Target 3. 1:
Cybersecurity readiness should be improved by 40%by 2020d Target 3. 2: Volume of redundant e-waste to be reduced by 50%by 2020 Target 3. 3:
Greenhouse gas emissions generated by the telecommunication/ICT sector to be decreased per device by 30%by 2020 Goal 4. Innovation
and adapt to the changing telecommunication/ICT environment Target 4. 1: Telecommunication/ICT environment conducive to innovation Target 4. 2:
Effective partnerships of stakeholders in the telecommunication/ICT environment society and international organizations should be involved. New data sources could include big data (mostly provided by private-sector companies)
which could help improve the timeliness and completeness of data, without compromising the relevance, impartiality and methodological soundness of the statistics (UNSC, 2014).
The topic of big data is gaining momentum in the statistical community. Chief statisticians gathering at the UNSC meetings in 2013 recognized that big data constitute a source of information that cannot be ignored by official statisticians
and that official statisticians must organize and take urgent action to exploit the possibilities and harness the challenges effectively (UNSC, 2014). 30 In view of declining responses to national household and business surveys in a number of countries,
big data could provide important sources of more timely and relevant information, thus complementing official statistics on the economy, society and environment.
Furthermore administrative records, which are used widely by official Note: acost of ICT services to be 60 per cent of the 2012 value. bexisting
and additional targets for Goal 2 are being reviewed and adjusted, based on contributions from Member States. c Due to data limitations,
currently mobile-broadband signal coverage is considering in determining this target. d Data being compiled by the Global Cybersecurity Index (GCI).
Source: ITU. 29 Measuring the Information Society Report 2014 statisticians, could be explored further, thus potentially becoming big data sources as well.
At the UNSC meeting in 2014, the commission reiterated its call for the global statistical community to take action,
and supported the proposal to create a global working group on the use of big data for official statistics.
31 To make an inventory of ongoing activities and concrete examples regarding the use of big data for official statistics at regional,
access to data and legislation related to big data To address the issue of obtaining access at no cost to big data from the private sector for official statistical purposes,
as well as the issue of access to transborder data or access to data on transboundary phenomena To develop guidelines to classify the various types of big data sources
and approaches To develop methodological guidelines related to big data, including guidelines for all the legal aspects To formulate an adequate communication strategy for data providers
and users on the issue of use of big data for official statistics To reach out to other communities, especially those more experienced in IT issues or in the use of open data platforms.
The UN Global Working group on Big data for Official Statistics was launched formally in June 2014, under the auspices of the UN Statistics Division.
The mandate of the group, of which ITU is a member, includes: provide strategic vision, direction and coordination of a global programme on big data for official statistics;
promote practical use of sources of big data for official statistics; provide solutions for methodological, legal and privacy issues;
promote capacity building; foster communication and advocacy of the use of big data for policy applications;
and build public trust in the use of private-sector big data for official statistics. ICTS are part of the debate on the data revolution, big data and, more broadly,
emerging data issues in the post-2015 development debate. First, the ICT sector in itself represents a new source of data,
provided by, for example, Internet and telecommunication companies. Second, the spread and use of ICTS allow public and private entities across all economic sectors to produce,
store and analyse huge amounts of data. At the same time, however, monitoring access to and use of ICTS by people
public entities and private enterprises will be essential in order to identify the extent to which stakeholders in the ICT sector can be used as an alternative data source.
Without ICTS, no ICT-driven data revolution will take place. In view of the link between big data and ICTS, work is under way in ITU with a view to contributing to the debate
and identifying new ways and means of exploiting the potential of big data. The focus is primarily on the telecommunication/ICT sector as a source of big data,
including players such as operators and service providers, in the fixed, mobile and Internet sectors. Delegates attending the eleventh World Telecommunication/ICT Indicators Symposium (WTIS) in Mexico city in December 2014 recommended that ITU should further examine the challenges and opportunities of big data,
in particular data coming from ICT companies; that regulatory authorities should explore the development of guidelines on how big data could be produced,
exploited and stored; and that national statistical offices, in cooperation with other relevant agencies, should look into the opportunities for big data and address current challenges in terms of big data quality,
Chapter 1. Recent information society developments 30 veracity and privacy within the framework of the fundamental principles of official statistics. 33 The big data approach taken by ITU so far focuses on the following areas and questions:
Standardization: 34 Which standards are required to facilitate interoperability and allow technology integration in the big data value chain?
Which definitions, taxonomies, secure architectures and technology roadmaps need to be developed for big data analytics and technology infrastructures?
What is the relationship between cloud computing and big data in view of security frameworks? Which techniques are needed for data anonymization for aggregated datasets such as mobile-phone records?
How is exploited big data in different industries; what are the specific challenges faced; and how can these challenges be addressed through international standards?
Regulation: 35 What are the key regulatory issues at stake and how can and should big data be regulated?
How does big data impact on the regulation of privacy, copyright and intellectual property rights (IPR), transparency and digital security issues?
Box 1. 4: What is a data revolution? The report of the High-level Panel of Eminent Persons on the Post-2015 Development Agenda to the UN Secretary-general,
which was published in May 2013, has called for, inter alia, a data revolution taking advantage of new technology and improved connectivity:
We also call for a data revolution for sustainable development, with a new international initiative to improve the quality of statistics and information available to people and governments.
This has prompted a revolution in the debates taking place in the statistical communities at both the international and national levels on
what such a data revolution could entail and how it could be implemented. While no internationally agreed concept has thus far been defined,
the following elements seem to be part of a data revolution: 32 In view of the ubiquitous availability of communication networks, the use of new information technologies (e g. mobile technologies) should be leveraged for improving the collection
and dissemination of data Data should be further disaggregated (by gender, income, age, geography, etc.)
to ensure that no one is left out; in this regard, traditional statistical processes should be made more efficient Sustained investment in national statistical capacity,
both technical and institutional, is essential and needs to receive a major push from the international donor community The focus should go beyond data dissemination
and also include investment in the development of concepts, measurement frameworks, classifications and standards New,
nontraditional data sources should be explored and leveraged to complement existing ones and satisfy the demand for data needs in new areas, such as big data,
geospatial information and geographical information systems Open data policies should be envisaged to ensure accountability and promote transparency The role of data,
statistics and monitoring for policymaking and decision-making should be increased. 31 Measuring the Information Society Report 2014
What is the link between big data and open data (crowdsourcing, cloud computing, etc.)?is there need a to regulate data management and service providers?
How can market dominance in the area of big data be prevented and the rights of the data owners protected?
ICT data collection and analysis: How can big data complement existing ICT statistics to better monitor information-society developments?
Which type of data from ICT companies are most useful and for which purposes? Which new ICT indicators could be produced from big data sources?
What are the key issues that need to be addressed, and by whom, in terms of collecting and disseminating big data in telecommunications?
What is the role of national statistical offices and how can big data complement official ICT data?
How can big data from telecommunications inform not only ICT but broader development policy in real time, leading to prompt and more effective action?
Chapter 5 of this report addresses some of these questions and provides suggestions and recommendations for the way forward.
Chapter 1. Recent information society developments 32 1 Refers to countries where fixed-telephone penetration increased by more than 1 per cent in 2014.2 See https://gsmaintelligence. com/.3 http
://www. censusindia. gov. in/2011census/hlo/Data sheet/India/Communication. pdf. 4 4g refers to fourth-generation mobile network or service.
It is a mobile-broadband standard offering both mobility and very high bandwidth, such as long-term evolution (LTE) networks (ITU Trends 2014). 5 Data collection on Europe and North america will follow in 2014.6 For a list of IXPS,
see for instance http://www. datacentermap. com/ixps. html. 7 For more details on international submarine fibre-optic links,
see Telegeography's Submarine cable Map 2014, available at: http://submarine-cable-map-2014. telegeography. com. 8 For further discussion on progress made towards connecting rural households to the Internet,
see Partnership (2014), Chapter on Target 1. 9 Universal Postal Union (forthcoming 2014). Development strategies for the postal sector:
An economic perspective. 10 See http://www. ifla. org/faife/world-report. 11 Source: IMF World Economic Outlook Database, April 2014.12 Source:
The Economist, April 12 2014, Nigeria's GDP step change. 13 Telefónica, for instance, reduced its net debt by EUR 4 819 million in 2012 after several years of sustained increases in borrowings.
Source: Telefónica Financial Report 2012, p. 18, available at: http://annualreport2012. telefonica. com/pdf/FINANCIERO 2012 ENG. pdf. 14 For example, the cost of buying a mobile cell tower in Europe
and in Africa may not be very different, because only a limited group of large global equipment vendors can deliver it,
and equipment is traded usually in USD. On-site setup expenditure may however differ because of varying labour costs across countries and regions,
but that is only a part of the total CAPEX of telecommunication operators. 15 For instance, the average revenue per user per month for GSM services in India was less than USD 2 in March 2012,
almost unchanged from March 2011. Source: TRAI Annual Report 2011-12, p. 2, available at:
http://www. trai. gov. in/Writereaddata/Miscelleneus/Document/201301150318386780062annual%20report%20english%202012. pdf. 16 Exceptions include China, Russian Federation, Japan and Republic of korea. 17
http://www. statisticbrain. com/twitter-statistics/./18 Eurostat news release of 16 december 2013 and http://epp. eurostat. ec. europa. eu/statistics explained/index. php/Social media -statistics on the use by enterprises. 19 Data refer mostly to the year
2011.20 The UN E-government Development Index is a composite benchmarking indicator based on a direct assessment of the state of national online services, telecommunication infrastructure and human capital in all countries.
See http://unpan3. un. org/egovkb/global reports/index. htm. 21 See ITU Connect a School,
Connect a Community Toolkit of Best Practices and Policy Advice, available at: http://connectaschool. org/itu-module-list. 22 See Partnership (2014),
Chapter on Target 2. 23 For further information, see http://www. itu. int/wsis/index. html
and http://www. broadbandcommission. org. 24 Information on the post-2015 development agenda process is available at:
http://sustainabledevelopment. un. org/index. php? menu=1561.25 See http://unstats. un. org/unsd/broaderprogress/progress. html. 26 Outcome Document Open Working group on Sustainable Development Goals, available at http
://sustainabledevelopment. un. org/focussdgs. html. 27 Available at: http://unctad. org/meetings/en/Sessionaldocuments/CSTD 2014 DRAFTRES WSIS. pdf. 28 See ECOSOC Resolutions 2008/3, 2009/7, 2011/16,
2012/5 and 2013/9. 29 The ITU strategic goals are under discussion and have to be examined
and approved by the 2014 ITU Plenipotentiary Conference. Endnotes 33 Measuring the Information Society Report 2014 30 E/CN. 3/2014/11 31 E/2014/24 and E/CN. 3/2014/35 (UNSC
highlighted a number of elements that should be part of a data revolution. 33 See final report of WTIS-13, available at:
http://www. itu. int/en/ITU-D/Statistics/Pages/events/wtis2013/default. aspx. 34 For further information on the work on big data carried out by the ITU Telecommunication
see http://www. itu. int/en/ITU-T/techwatch/Pages/big data-standards. aspx. 35 A background document on big data that was prepared for GSR-14 is available at http
the digital divide, i e. differences between countries in terms of their levels of ICT development; the development potential of ICTS or the extent to which countries can make use of ICTS to enhance growth and development, based on available capabilities and skills.
and includes five infrastructure and access indicators (fixedtelephone subscriptions, mobile-cellular telephone subscriptions, international Internet bandwidth per Internet user, households with a computer,
and usage indicators (individuals using the Internet, fixed (wired)- broadband subscriptions, and wireless-broadband subscriptions).
and is given therefore less weight in the computation of the IDI compared with the other two sub-indices. 2 The choice of indicators included in the subindices reflects the corresponding stage of transformation to the information society.
and as more and better data become available. For example what was considered basic infrastructure in the past such as fixed-telephone lines is fast becoming less relevant in the light of increasing fixed-mobile substitution.
characterizing intense Internet use, and is included therefore in stage 2 (as an indicator in the use subindex).
Data availability and quality. Data are required for a large number of countries, as the IDI is a global index.
There is relative paucity of ICT-related data, especially on ICT usage, in the majority of developing countries.
In particular, the three indicators included in the skills sub-index should be considered as proxies until data directly relating to ICT skills become available for more countries.
The results of various statistical analyses. The statistical associations between various indicators were examined, and principal components analysis (PCA) was used to examine the underlying nature of the data
and to explore whether the different dimensions are statistically well-balanced. While the basic methodology has remained the same
Given the dynamic nature of the ICT sector and related data availability, the indicators included in the IDI
Indicator definitions and the IDI methodology are discussed in the ITU Expert Group on Telecommunication/ICT Indicators (EGTI)
The definitions of the following core indicators of the Partnership on Measuring ICT for Development included in the IDI were revised at a meeting of EGH held in Brazil in June 2013.3 Percentage of individuals using the Internet:
The suggested reference period for latest Internet usage was changed from the last twelve months to the last three months.
considering that Internet usage is now sufficiently frequent that the majority of users will be captured with the shorter time-frame.
the Expert Group on Telecommunication/ICT Indicators (EGTI) and the Expert Group on ICT Household Indicators (EGH.
and to experts in the field of ICT statistics and data collection, work through online discussion forums and annual face-to-face meetings.
They periodically report back to the World Telecommunication/ICT Indicators Symposium (WTIS), ITU's main forum on ICT statistics.
Percentage of households with a computer: The definition of computer was revised to include tablet or similar handheld computers in addition to desktop and laptop computers,
so as to reflect the uptake of these devices. The definition of household access was refined so that, in order for a household to have access to ICT equipment or services,
agreed that the reference values for the indicators international Internet bandwidth per Internet user and mobile-cellular subscriptions per 100 inhabitants would be reviewed.
For international Internet bandwidth per Internet user the methodology used in previous IDI calculations was kept, as there is no limit to the maximum value that could be achieved by a country.
The reference value employed for this indicator is used to screen outlier values. There were three economies that were identified as outliers, namely Hong kong (China), Luxembourg and Malta.
The reference value for mobile-cellular subscriptions was lowered to 120. This value was derived from examining the distribution of countries based on their mobile-cellular subscriptions per 100 inhabitants in 2013.
In order to determine the reference value, prepaid and postpaid mobile markets were examined separately, with the former making up the majority of cases.
For those countries, a mobile-cellular penetration of 120 per cent is the maximum value that was reached by the largest group of countries (23 countries with a mobilecellular penetration between 110 and 120 per cent),
by end 2014, the number of mobile-cellular subscriptions will have reached close to 7 billion, which almost corresponds to the figure for the world's population.
Multi-SIM ownership is driving up mobile-cellular subscription numbers, which is an issue in prepaid and, to a lesser extent, also in postpaid mobile markets.
one possibility would be to replace the subscription-based (supply-side) data with data based on national household surveys (demand-side indicators).
therefore provide a more accurate picture of the actual uptake, use and distribution of mobile-cellular services.
still only 42 countries reported these data to ITU for at least one year between 2011 and 2013.
It is therefore too early to substitute the current mobile-cellular subscription data in the IDI with mobile-phone user data.
In view of the methodological difficulties in collecting harmonized data on international Internet bandwidth a review of the definition of the indicator is currently under discussion in EGTI.
Preparation of the complete data set. This step includes filling in missing values using various statistical techniques.
Normalization of data. This is necessary in order to transform the values of the IDI indicators into the same unit of measurement.
Rescaling of data. The data were rescaled on a scale from 0 to 10 in order to compare the values of the indicators and the sub-indices.
Weighting of indicators and sub-indices. The indicator weights were chosen based on the principal components analysis (PCA) results.
This chapter presents the IDI based on data from 2013 in comparison with 2012. It should be noted that IDI 2012 values have changed from those published in the previous edition of this report as a result of:
Country data revisions. As more accurate data become available, countries provide ITU with revised statistics for previous years,
which have been taken into consideration. This also allows ITU to identify inconsistencies and revise previous estimates.
Revision of the definitions of the indicators percentage of individuals using the Internet (changing the reference period to the last three months)
and percentage of households with a computer (updating the definition of computer to include tablet
and similar handheld computers but excluding smartphones). Differences among countries included in the IDI. The calculation of the IDI ranking Chapter 2. The ICT Development Index (IDI) 40 ICT access Reference value(%)1. Fixed-telephone subscriptions per 100 inhabitants
2. Mobile-cellular telephone subscriptions per 100 inhabitants 3. International Internet bandwidth (bit/s) per Internet user 4. Percentage of households with a computer 5. Percentage of households
with Internet access 60 120 787'260*100 100 20 20 20 20 20 ICT use Reference value(%)6. Percentage of individuals using the Internet
7. Fixed (wired)- broadband subscriptions per 100 inhab. itants 8. Wireless-broadband subscriptions per 100 inhabitants 100 60 100 33 33 33
ICT skills Reference value(%)9. Adult literacy rate 10. Secondary gross enrolment ratio 11. Tertiary gross enrolment ratio 100 100 100 33 33 33 ICT Development Index 40 40 20 Figure 2. 2:
In each new edition, some countries are excluded and others added based on data availability. Overall, this version of the IDI includes 166 countries/economies as compared with 157 in last year's edition.
Section 2. 3 analyses the global digital divide by looking at the IDI results by level of development as well as by groups of countries with different IDI levels.
The use sub-index also displays the widest range and the lowest average value (3. 19.
According to data from the European union (EU), 85 per cent of Danes have some level of computer skills (compared to the EU average of 67 per cent)
and 42 per cent of the population have high computer skills. 7 In 2010, the digital economy accounted for more than 5. 8 per cent of GDP,
and it continues to grow. Having regard to the general economic downturn, Denmark's government sees ICTS as a major driver for growth, innovation and economic development (Government of Denmark, 2012).
Household ICT connectivity is extremely high, among the highest in Europe, with 93 per cent of households with Internet access and households with a computer by end 2013.
Denmark's national target even exceeds the EU's Digital Agenda objective of 100 per cent coverage of households with broadband speeds of 30 Mbit/s
The Danish Internet service provider (ISP) TDC is making investments to provide access to ultra-fast speeds for over half a million households. 9 Denmark enjoys abundant international Internet bandwidth of more than 260 000 bit
/s per Internet user in 2013. Denmark tops the IDI use sub-index. The country's broadband market is advanced particularly well.
At 107 per cent, it has one of the highest wireless-broadband penetration rates in the world,
In both indicators although Sweden has a slightly higher wireless-broadband penetration Denmark surpasses the other top five IDI countries (see Chart 2. 1). In terms of LTE population coverage
ITU World Telecommunication/ICT Indicators database. 33 35 36 38 40 110 75 87 105 107 0 50 100 150
Sweden Iceland United kingdom Korea (Rep.)Denmark Per 100 inhabitants Wireless-broadband subscriptions Fixed (wired)- broadband subscriptions 47 Measuring the Information Society Report 2014
ITU World Telecommunication/ICT Indicators database. have access to mobile broadband at speeds of at least 10 Mbit/s. 10 The Republic of korea ranks second in the IDI 2013.
Fixedbroadband and wireless-broadband penetration stand at top levels at 38 per cent and 105 per cent, respectively (see Chart 2. 1). The Republic of korea was the first country to offer 3g services
and LTE was offered first in 2011. Two years after commercializing the first LTE network, leading operator SK TELECOM reported that it passed the 10 million LTE subscriber mark in April 2013;
this represents 37 per cent of its total mobile subscriber base. Full coverage having being achieved (by April 2012,
LTE was available nationwide), the wirelessbroadband market is showing signs of saturation, with little growth over the past years.
From 2012 to 2013, there was only a minimal increase in penetration, from 105.1 per cent in 2012 to 105.3 in 2013 (see Chart 2. 2). The focus of operators
and policy-makers has shifted from access to wireless services to improving quality and speed. In July 2013, SK TELECOM launched the world`s first LTE-Advanced Network
with speeds of up to 150 Mbit/s. In 2014, the Vice-president of the European commission for the Digital Agenda and the Republic of korea's Minister of Science, ICT and Future Planning signed an agreement to work towards a global definition of 5g
and to cooperate in 5g research. 11 In regard to fixed broadband, there is still more potential for growth,
and from 2012 to 2013 more fixed-broadband than wireless-broadband subscriptions were added (around 485 000 fixedbroadband compared with 370 000 wirelessbroadband subscriptions).
Data also show that the Republic of korea achieves the highest advertised fixed-broadband speeds, with all subscriptions providing at least 10 Mbit/s. This compares with 75 per cent of fixed-broadband subscriptions at advertised speeds of at least 10 0 20 40 60 80 100 120
A somewhat lower proportion of 81 per cent of households have a computer. International Internet bandwidth is relatively low
compared to other top IDI countries, at just over one Tbit/s in 2012. There is however a sizeable domestic demand for data driven by the high volume of local content,
and domestic Internet bandwidth was compared ten times higher with international bandwidth. Third-placed Sweden records an IDI value of 8. 67 in 2013.
Like the remaining EU countries in the top ten, namely United kingdom (fifth), Netherlands (seventh), Finland (eighth) and Luxembourg (tenth), Sweden has an excellent ICT infrastructure, a skilled population and high
Luxembourg ranks first in the IDI access sub-index with its state-of-the-art infrastructure and large amounts of international Chapter 2. The ICT Development Index (IDI) 48 Internet bandwidth.
The growth in wireless-broadband subscriptions is having a major impact on ICT markets and European top performers have been at the forefront of this trend.
Norway has a wireless-broadband penetration of 89 per cent, followed by the United kingdom (87 per cent), Luxembourg (80.5 per cent), Iceland (75 per cent) and The netherlands (62 per cent).
By early 2013, virtually all (96 per cent) of EU citizens were covered by a 3g signal and 26 per cent of the population was covered by an LTE network. 12 Denmark,
Sweden and Finland are the countries with the highest LTE coverage in the European region (European commission, 2014a).
The European commission is partnering with the Republic of korea to work towards a definition of 5g. Furthermore, it launched a publicprivate partnership on 5g (5g PPP) in late 2013 that aims to deliver solutions, architectures, technologies and standards for the ubiquitous next-generation communication infrastructures of the coming decade.
A total of EUR 3 billion have been pledged over the next seven years, with EUR 700 million coming from the European commission and the private sector set to match this investment by up to five times. 13 European top performers stand out
Data from the EU confirm that household access is correlated highly with regular use of the Internet
which underlines the importance of household access. 14 All European countries included in the top ten of the IDI have a household ICT penetration (both households with a computer and households with Internet) of at least 88 per cent.
Iceland and The netherlands display the highest levels of households with a computer, at 97 and 95 per cent, respectively. 15 In Luxembourg and Norway,
and sets ambitious targets to have 50 per cent of homes subscribed to ultra-fast broadband (at least 100 Mbit/s) and coverage of all households by broadband speeds of at least 30 Mbit/s by 2020.16
The UK government aims to achieve coverage of at least 90 per cent in 2016, and has made GBP 530 million of funding available for the roll out of networks in sparsely populated underserved areas.
Data from the European commission's Digital Agenda underline the competitiveness of the European fixedbroadband market:
of Internet connectivity at home and the availability of mobile Internet translate into high degrees of Internet usage in the IDI's top ten countries.
The Nordic countries stand out with the highest percentage of Internet users globally. In Iceland, 97 per cent of the (in-scope) population is using the Internet and 95 per cent of Norwegians,
Swedes and Danes are online. 19 The availability of international Internet bandwidth is critical for ICT development.
All IDI top performers benefit from the abundant availability of international Internet bandwidth. Bandwidth is highest per Internet user) in such hubs as Luxembourg, Iceland,
Sweden and the United kingdom. Hong kong (China) made its entry into the top ten of the IDI 2013, up from 11th position in 2012.
The economy ranks in ninth position, with an IDI value of 8. 28. Hong kong (China) is particularly strong on the access sub-index of the IDI,
in which it ranks fourth. As an international financial hub, the regulator has made the provision of international Internet bandwidth a policy priority
in order to secure reliable and low-latency Internet connectivity (see MIS 2013). In 2013, international Internet bandwidth stood at 1. 7 million bit/s per Internet user,
which is the second highest value after Luxembourg's. Hong kong (China) has the second highest fixed-telephone penetration globally, at 63 per cent,
and relatively high levels of household ICT connectivity, at 80 per cent of households with Internet and 82 per cent with a computer.
Both fixed-broadband (31 per cent) and wirelessbroadband (95 per cent) penetration are very high in Hong kong (China.
Dynamic IDI countries Even though most countries do not dramatically increase in IDI rank within a year,
Globally, the number of mobile-broadband subscriptions20 grew by 24 per cent from 2012 to 2013.
High increases in wireless-broadband subscriptions can be seen in countries that were late adopters of 3g technology.
Other most dynamic countries have seen a significant increase in the number of wireless-broadband subscriptions from 2012 to 2013 due to a rise in competition (i e. the awarding of further licences),
In the Republic of the Congo, 3g was launched in late 2011 by Airtel Congo, and the entry of a second operator (MTN Congo) is reflected in a significant increase in penetration rate from 2 per cent in 2012 to 11 per cent in 2013 (Agence de Régulation des Postes
which added significant amounts of international Internet bandwidth and increased international Internet bandwidth per Internet user from around 6 000 bit/s in 2012 to close to 12 000 bit/s per user
It provides international Internet connectivity, which is of particular importance for enabling an island state such as Cape verde to join the information society.
Increases in the use sub-index are driven mostly by the impressive growth in the number of wireless-broadband subscriptions.
and by 2013 operator CVMOVEL had expanded 3g services to all the islands of the archipelago. 23 Bhutan is one of the most dynamic countries in the use sub-index, moving up eight places.
While mobilebroadband services were introduced as early as 2008 by state-owned operator Bhutan Telecom (under its B-Mobile brand),
major developments took place in 2013 that helped to boost penetration from only 2. 5 per cent in 2012 to 16 per cent in 2013 (see Chart 2. 3). Bhutan Telecom expanded its 3g services,
and expand the operator's mobile network. 24 Furthermore, Bhutan Telecom launched its high-speed 4g services in Thimphu Chart 2. 3:
Wireless-broadband penetration, Bhutan, 2008-2013 Source: ITU World Telecommunication/ICT Indicators database. 0. 0 0. 1 0. 3 1. 0 2. 5 15.6 02468 10 12
14 16 18 2008%2009 2010 2011 2012 2013 51 Measuring the Information Society Report 2014 in late 2013.
The launch of mobile-broadband services by the country's only private-owned operator Tashi Cell in late 2013 has helped to increase coverage and competition in the market,
which in turn has led to higher adoption rates. 25 Apart from those major improvements in access to wireless-broadband services,
uptake is ascribed also to the growing popularity of social media and increased availability of smartphones. 26 Bolivia is among the most dynamic countries on the access sub-index(+4 ranks),
and also shows good progress in the generally more dynamic use sub-index(+5 ranks.
The country reported an important increase in international Internet bandwidth. International Internet bandwidth per Internet user27 almost doubled between 2012 and 2013, climbing to 9 000 bit/s per user.
While this is still one of the lowest absolute figures in The americas region (only Cuba has a lower bandwidth per Internet user),
it indicates that good progress in being made in improving connectivity in the country. Bolivia has seen an important increase in wireless-broadband penetration, from 7 per cent in 2012 to 14 per cent in 2013.
In order to increase rural connectivity, landlocked Bolivia launched its first telecommunication satellite Tupac Katari in late 2013.28 ENTEL Bolivia's state-owned operator is contracting capacity from Tupac Katari
which it will use to connect more rural areas of the country through the establishment of 3 000 telecentres by end 2014.29 Georgia made remarkable progress in terms of ICT development over the period 2012-2013.
and a high amount of international Internet bandwidth (close to 82 000 bit/s per Internet user). 30 It is well-connected to its neighbouring countries in the CIS region
In particular, the country was very successful in connecting households to the Internet penetration increased from 27 per cent in 2012 to 35 per cent in 2013
and in increasing the proportion of households with a computer from 33 per cent in 2012 to close to 40 per cent in 2013.
At the same time, both wireless-and fixedbroadband penetration went up significantly. Wireless-broadband penetration almost doubled, to 17 per cent,
The proportion of households with Internet stands at 96 per cent and the proportion of households with a computer at 97 per cent in 2013 (see Chart 2. 4). A report by ICTQATAR shows that Qataris are not only almost all very well-connected at home,
Over the period 2012-2013, the ownership of devices such as mobile phones and laptops increased significantly within the country's mainstream population. 32 The report also highlights discrepancies in ICT connectivity between Qataris and westerners
While virtually all Qataris and westerners have an Internet connection at home, penetration stands at 85 per cent for the overall population.
Smartphone penetration is also much lower among transient labourers (24 per cent. Therefore, increasing the penetration of newer devices such as smartphones and tablets,
particularly in specific demographic segments like the transient labour population is one of the policy recommendations brought forward by the report (ICTQATAR 2014).
Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone subscriptions Fixed-broadband Internet users subscriptions Active mobilesubscriptions
Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband
Secondary enrolment Tertiary enrolmentliteracy Bolivia 2012 2013 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Mobile
-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed
-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Bosnia and herzegovina 2012 2013 0. 0 0. 2 0
. 4 0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone
subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Burkina faso 2012 2013 0. 0 0. 2 0. 4
0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone subscriptionsfixed
-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Cape verde 2012 2013 0. 0 0. 2 0. 4 0
. 6 0. 8 1 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a
computer Households with Internet Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Estonia 2012 2013 53
. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone subscriptions Fixed-broadband Internet users subscriptions Active
. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions
Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet
Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Gambia 2012 2013 0. 0 0. 2
0. 4 0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed
-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Mali 2012 2013 0. 0 0. 2 0
. 4 0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with a computer Households with Internet Fixed-telephone
subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Oman 2012 2013 0. 0 0. 2 0. 4
0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user Households with
a computer Households with Internet Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Qatar 2012 2013 Chapter
One of the core projects of the Qatar National Broadband Network is the deployment of a fibre-optic network infrastructure. 33 Chart 2. 1:
.)Denmark Per 100 inhabitants Wireless-broadband subscriptions Fixed (wired)- broadband subscriptions 2. 4: Proportion of households with a computer and proportion of households with Internet access, 2012-2013, Qatar ITU World Telecommunication/ICT Indicators database. 91.5 88.1 97.2 96.4 0
10 20 30 40 50 60 70 80 90 100 Households with a computer Household with Internet access 2012 2013 Per 100 households Thailand is one
of the most dynamic countries on the use sub-index(+34 ranks), which led to an improvement in its overall IDI ranking from 91st in 2012 to 81st in 2013.
more than 7 million new mobile-cellular subscriptions and close to 28 million new wireless-broadband subscriptions were added within one year.
Penetration rates stand at 138 per cent for mobile-cellular and 52 per cent for wireless-broadband services by end 2013.
The launch of 3g was anticipated much in Thailand, following the long delay in the auctioning of 3g licences.
In December 2012, licences were awarded finally to three Thai operators, 34 providing high-speed mobile Internet connectivity to users.
The rapid uptake of mobile-broadband services was spurred by heated competition among operators offering subsidized smartphones
and promotions on mobile data plans. 35 During 2013, operators further extended their wireless infrastructure and services throughout Thailand,
and are planning to provide further network updates. 36 Figure 2. 3: IDI spider charts, selected dynamic countries, 2012 and 2013 (continued) Note:
ITU. 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user
Households with a computer Households with Internet Fixed-telephone subscriptions Fixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracy Thailand 2012
2013 0. 0 0. 2 0. 4 0. 6 0. 8 1. 0 Mobile-cellular subscriptions International Internet bandwidth per Internet user
Households with a computer Households with Internet Fixed-telephone subscriptionsfixed-broadband Internet users subscriptions Active mobilesubscriptions broadband Secondary enrolment Tertiary enrolmentliteracyunited Arab Emirates 2012
2013 55 Measuring the Information Society Report 2014 2. 3 Monitoring the digital divide: Developed, developing and least connected countries Tracking the global digital divide is one of the main objectives of the IDI.
The digital divide can be understood as the difference in ICT development, within and between countries, regions or other groupings.
In this section, IDI performance will be analysed and compared with regard to levels of (economic) development, and on the basis of IDI groupings (based on IDI values).
The digital divide is measured by looking at differences in IDI values between these different groups. As a composite index that consolidates several ICT indicators into one single value,
Based on the 2013 and 2012 data presented in this chapter, the current (2013) global divide is measured
whether the digital divide has been increasing or decreasing over the past year. Special emphasis is placed on those countries that lie at the bottom of the IDI the socalled least connected countries (LCCS.
Fixed-telephone penetration is decreasing in developing and developed countries. Mobile-cellular subscription growth rates are slowing down,
indicating that mobile-cellular penetration is nearing saturation, including in developing countries. The growth in household ICT connectivity is much higher in developing countries,
where around threequarters of households are connected not yet to the Internet, leaving ample room for growth.
This also holds true for international Internet bandwidth which is still at very low levels in many developing countries.
the availability and uptake of wireless-broadband and fixedbroadband services in particular is limited still relatively. On average
While many developing countries saw important increases in their use sub-index value following the introduction of 3g services in 2012/2013,
In 2014, close to 4. 3 billion people, most of them living in the developing world, were not using the Internet.
Data change very little over time and advances in skills do not show immediate effects.
Therefore, for the purpose of comparing levels of ICT development and analysing the digital divide, countries are grouped also on the basis of their IDI value.
International Internet bandwidth availability is limited very, thus constraining Internet connectivity and driving up ICT prices,
which in turn hampers usage of ICTS. Few households (less than 5 per cent in the majority of LCCS) are connected to the Internet
and fewer than 5 per cent of households in all LCCS have a computer. Basic voice services are more widely available although LCCS like Eritrea (6 per cent),
Myanmar (13 per cent) and Cuba (18 per cent) still have very low mobile-cellular penetration levels.
The increased availability of wireless broadband could help bring more people online in LCCS. Today, in the majority of LCCS, few people use the Internet:
an estimated less than 2 per cent the population is online in Eritrea, Myanmar, Guinea, Niger and Ethiopia.
Above 2. 78 LCC (2. 78 and below) Data not available 59 Measuring the Information Society Report 2014 the profitability of various kinds of economic activities (Sachs, 2012.
Apart from the potential relationship of these variables with ICT developments, they were selected for their high data availability for a large number of countries.
ICT skills and fixed telecommunication infrastructure. Table 2. 9: Partial correlation analysis of IDI, population and geographic characteristics Control Variable Correlations IDI Population size Population density Land area Urban population GNI per capita IDI
Data on urban population, population density, land area and GNI p. c. are sourced from the World bank.
Data on population size are sourced from UNPD.**Correlation is significant at 0. 01 level.
Chapter 1 of this report drew attention to the significant and persistent urban-rural digital divide.
mobile-cellular coverage for rural populations has reached very high levels, with almost 90 per cent of the world's rural inhabitants covered by a 2g mobile-cellular signal by 2013.
On the other hand, 3g mobilecellular coverage was comparatively low for 0123456789 10 0 10000 20000 30000 40000 50000 60000 GNI p. c. USD) r=0. 775 IDI
when it comes to data on Internet access and use. Access to the Internet (be it narrowband
or broadband, fixed or wireless) is extremely low for rural households in developing countries, while rural households in developed countries enjoy comparable access to their urban counterparts,
albeit with slight variations in type of access and (usually) a small lag in levels of penetration.
The results confirm the strong need to address the urban-rural digital divide that prevails in many developing countries.
People living in rural areas, particularly in developing countries, are disadvantaged compared to their urban counterparts because of lower service coverage;
they also often lack the economic means to pay for broadband Internet services, as well as the skills to make effective use of ICTS.
a correlation analysis between IDI values and MDG indicators was conducted for 2011 where data are available for both sets of indicators.
of which data pertaining to both the MDG indicators and the IDI are available for at least 16 countries. 41 The MDG indicators measuring Goal 2 (literacy rate
and enrolment in primary education) and Goal 8 (fixed-telephone and mobile-cellular penetration and percentage of Internet users) were considered not,
support the development of multilingualism on the Internet; and ensure access to ICTS for more than half of world's inhabitants.
A comparison of IDI values of the 147 economies for which data were available for 2002 and 2013 shows that the global IDI value has doubled almost from 2. 52 in 2002 to 4. 88 in 2013
this would require different sets of data (including micro data) collected from official surveys. Therefore, the analysis should be considered as a first step in an attempt to quantify the relationship between ICT performance and MDG progress.
which sufficient data are available Almost all MDG indicators that are included under MDG 1, MDG 4, MDG 5,
and women using appropriate technologies MDG4 Reduce child mortality) Data collected through ICT applications allow health professionals to assess child health
SMS reminders for appointments or medical treatment) Improve access to medical databases and electronic health records (EHRS) Link community health workers to the national health system/specialist support MDG7 (Ensure environmental sustainability) Reduce greenhouse gas (GHG) emissions
Smart planning and reliable access to real-time data for climate monitoring, as well as the implementation of early warning systems Reduce energy and water consumption through smart transportation and logistics, dematerialization and other technologies.
Achieve, by 2015, universal access to reproductive health 5. 3 Contraceptive prevalence rate 5. 4 Adolescent birth rate 5. 5 Antenatal care coverage (at least one
In cooperation with the private sector, make available the benefits of new technologies, especially information and communications 8. 14 Fixed-telephone subscriptions per 100 inhabitants 8. 15 Mobile-cellular
subscriptions per 100 inhabitants 8. 16 Internet users per 100 inhabitants+++Note:****Significant at 0. 01 level.*
Insufficient data.++The indicator or similar indicator is included in the IDI. Source: UN for the list of MDG indicators and ITU for the correlation results.
5. 5 Antenatal care coverage (at least one visit and at least four visits) IDI Goal 7 7. 2 CO2 emissions,
through the analysis of health data collected through public health applications and by serving as a platform for exchange and advocacy.
120 140 160 180-1 2 3 4 5 IDI r=0. 529 Antenatal care coverage, at least four visits, percentage 0
but no relationship exists between antiretroviral therapy coverage among people with advanced HIV and IDI (see Chart 2. 16).
Halve, by 2015, the proportion of the population without sustainable access to safe drinking water and basic sanitation also displays a significant Chapter 2. The ICT Development Index (IDI) 74 Box 2. 4:
The use of smartphones to capture essential data on the patients and monitor their treatment has accelerated progress.
An electronic malaria information system (e-MIS) uploaded on the health workers'mobile devices shows malaria volunteers where to find patients and the status of their treatment,
Furthermore, ICTS such as mobile phones and the Internet can help inform people and allow them to share information on the use
where 2002 and 2011 data are available for both sets of indicators. The following steps were performed:
which data are available for 2002 and 2011 for both the MDG indicators and the IDI The number of countries included in the analysis ranges from 16 to 101.
and this indicator in the earlier section where 2011 data were analysed, improvements in the level of ICT access
and facilitating the monitoring of health via SMS and increased availability of information thanks to the Internet.
This type of analysis will require different data sets, including micro data on ICT usage collected from official surveys.
Micro data offer analysts and researchers ample information and considerable flexibility to apply quantitative models that identify relationships
and interactions between indicators and topics covered in a survey, thereby fostering the diversity and quality of research and analysis. Chapter 2. The ICT Development Index (IDI) 80 1 This section is based on the 2013 edition of Measuring the Information Society.
as well as the 2009 edition of Measuring the Information Society (ITU, 2009), which describe the methodology in more detail. 2 Data on the indicators included in the skills sub-index are sourced from the UNESCO Institute for Statistics (UIS).
http://data. worldbank. org/about/country-classifications/country-and-lending-groups#High income. 7 Based on 2011 data:
https://ec. europa. eu/digital-agenda/sites/digital-agenda/files/DAE%20scoreboard%202013%20-%20 3-INTERNET%20use%20and%20skills
. eu/digital-agenda/sites/digital-agenda/files/DK%20%20-%20broadband%20markets. pdf. 10 http://www. gsma. com/spectrum/wp
/digital-agenda/sites/digital-agenda/files/DAE%20scoreboard%202013%20-%202-BROADBAND%20 MARKETS%20. pdf. 13 http://europa. eu
/pdf/ppp/5g factsheet. pdf. 14 https://ec. europa. eu/digital-agenda/sites/digital-agenda/files/DAE%20scoreboard%202013%20-%203
-INTERNET%20 USE%20and%20skills. pdf. 15 Qatar (ranked 34th) has 97 per cent of households with a computer by end 2013.15 https://ec. europa. eu
and the European union define superfast services as those delivering download speeds of 30 Mbit/s or more. 18 https://ec. europa. eu/digital-agenda/en/pillar-4-fast-and-ultra
the in-scope population for data on Internet users is aged individuals 16-74.20 Refers to the indicator active mobile-broadband subscriptions.
which also includes terrestrial (fixed) wireless and satellite broadband subscriptions. 21 http://www. itnewsafrica. com/2013/05/airtel-launches-first-3-75-g-service-in-burkina-faso
/and http://news. aouaga. com/documents/docs/Rapportarcep. pdf. 22 http://www. thisdaylive. com/articles/a-year-after-wacs-is-faster-more-affordable-mobile-broadband
/and http://wacscable. com/index. jsp. 23 http://www. cvmovel. cv/nacional-gsm-3g-edge-e-gprs. 24 http://www. telecomasia
. net/content/bhutan-telecom-expand-3g-network. 25 http://www. tashigroup. bt/?/p=1058 and http://www. kuenselonline. com/tashicell-goes-3g/#.
/#U4rlg3ksx8e. 26 http://www. kuenselonline. com/530-increase-in-mobile-broadband-users/#./#U4rsghksx8e. 27 Purchased capacity.
Endnotes 81 Measuring the Information Society Report 2014 28 http://www. bbc. com/news/world-latin-america-26850393.29 http://www. entel. bo
/inicio3. 0/index. php/sala-de-prensa/item/309-contrato-entel-abe and http://www. entel. bo/inicio3. 0/index. php/sala
as well as the household access data, excludes transient labourers, which account for a significant proportion of residents in Qatar.
According to data from ICTQATAR transient labourers make up 27 per cent of the overall population. 33 http://qnbn. qa/qatar-vision-2030/34 http://www. nbtc. go. th/wps/portal/NTC/!
/WCM GLOBAL CONTEXT=/wps/wcm/connect/library+ntc/internetsite/eng/en interesting articles/en interesting articles detail/ae185900400633288ac5ceabcb3fbcab. 35 http://www. telecompaper. com/news/thai-operators-reduce-prices-of-smartphone
-data-plans--900198.36 http://www. telegeography. com/products/commsupdate/articles/2013/05/09/true-4g-launch-trumps-rivals-ais-claims-800000
World bank, see http://data. worldbank. org/indicator/NY. GNP. PCAP. CD. 39 See: Czernich, N.,Falck, O.,Kretschmer, T. and Woessmann, L. 2009), Broadband Infrastructure and Economic growth, http://papers. ssrn. com/sol3/papers. cfm?
if there is available data for at least one year between 2010 and 2012. The number of countries included in the analysis varies from 16 to 101 developing countries.
since it is the closest year to available MDG data. 42 Broadband Commission. Transformative Solutions for 2015 and Beyond a Report of the Broadband Commission Task force on Sustainable Development and http://www. broadbandcommission. org/Documents/Climate/BD-bbcomm-climate. pdf and The State
It presents IDI results separately for each of the six ITU Telecommunication Development Bureau (BDT) regions (Africa, Americas, Arab States, Asia and the Pacific, Commonwealth of independent states (CIS) and Europe), 1
Europe displays by far the highest average IDI value of 7. 14. The regional IDI values of the CIS (5. 33), The americas (4. 86), Asia and the Pacific (4. 57) and Arab States (4. 55) are relatively close to each other.
Improving coverage is particularly challenging in vast rural areas and where the reach of basic infrastructure,
Significant increases in mobile-cellular penetration from 2012 to 2013 were registered also in Guinea (from 49 per cent to 63 per cent),
Mozambique (from 35 per cent to 48 per cent) and the Republic of the Congo (from 31 per cent to 44 per cent)( see Chart 3. 3). A lack of international Internet bandwidth is seriously hampering ICT development
African countries are lacking international Internet connectivity. Chart 3. 3: Mobile-cellular subscriptions per 100 inhabitants, 2012 and 2013, Africa Note:
ITU World Telecommunication/ICT Indicators database. 0 20 40 60 80 100 120 140 160 180 2012 2013 Per 100 inhabitants
Rep.),Chad and Nigeria have less than 1 000 bit/s of international Internet bandwidth per Internet user at their disposal.
Kenya has the highest amount of international Internet bandwidth, both in total and per Internet user,
Seychelles (24 000 bit/s) and Mauritius (24 500 bit/s) also have relatively high amounts of bandwidth per Internet user, partly because of their very small populations and hence small number of Internet users.
by end 2013, on average, less than 10 per cent of households in the region had access to the Internet at home,
While 3g networks are continuing to be built and expanded across the region, numerous countries saw some important increases in penetration from 2012 to 2013.
In Burkina faso, 3g was launched finally in 2013, reaching a penetration of 9 per cent by end 2013.
at 43 per cent (after Botswana with 74 per cent) following an expansion of network coverage throughout the archipelago. 3 Large-scale infrastructure rollout also helped to increase uptake of wireless broadband in Nigeria4 (from 5 per cent in 2012
Data from South african operators show that not only is wirelessbroadband penetration reaching higher levels 29per cent by end 2013
but customers are consuming more data, indicating an increase in the intensity of usage. MTN reported a growth of 63 per cent in data volumes in the first half of 2013
and Vodacom reported that on average users were generating 75 per cent more data traffic per device than a year ago. 5 Wireless broadband is of particular importance in the region
because fixed-broadband infrastructure is lacking. The vast majority of African countries 32 out of 38 had fixed a-broadband penetration of less than 1 per cent by end 2013.
Africa was home to 150 million Internet users by end 2013. This corresponds to around 17 per cent of the population in the region.
Only Djibouti (30 per cent) and Syria (56 per cent) still had a very low mobile-cellular penetration in 2013.
and high levels of multi-SIM ownership (GSMA and Deloitte, 2013). Furthermore, the very high mobile-cellular penetration rates reached in the GCC countries are driven by large transient worker and expatriate populations.
Data from household surveys show that the actual number of people using a mobile-cellular phone is much lower than the number of subscriptions.
In Tunisia, 72 per cent of individuals were using a mobile-cellular phone, compared with a mobile-cellular penetration of 118 per cent in 2012.
Egypt reported a penetration of 120 per cent by end 2012, compared with 74 per cent of individuals using a mobile-cellular phone.
Fixed-telephone penetration is extremely low in the Arab States region with a regional average of 9 per cent in 2013.
This further highlights the importance of mobile networks in the region. The Arab States region and in particular the GCC countries are well-connected to submarine Internet cables.
The United arab emirates boasts the highest amount of international Internet bandwidth per Internet user (around 52 000 bit/s per user) in the region.
Furthermore, the country almost doubled its Internet bandwidth between 2012 and 2013. Oman, too, saw a significant increase in total international Internet bandwidth, up from 17 792 Mbit/s in 2012 to 82 010 Mbit/s in 2013.
In 2013 the Europe-Persia Express Gateway that connects the United arab emirates and Oman to Germany via the Islamic Republic of Iran went live,
increasing the region's international Internet connectivity. 6 Furthermore, the Gulf Bridge International (GBI) system completed its North Route terrestrial link in 2013,
which connects the Gulf region to Europe. 7 Chart 3. 4: IDI values compared with the global, regional and developing/developed-country averages, Arab States, 2013 Source:
ITU. IDI Developed Developing Arab States World 012345678 Bahrain Saudi arabia United Arab Emiratesqatar Omanlebanon Morocco Jordanegypt Tunisiapalestine Sudan Syriaalgeria Yemendjiboutimauritania Chapter
3. Regional IDI analysis 90 In terms of ICT household connectivity, Qatar stands out not only in the region but in international comparison with more than 96 per cent of households with Internet access and with a computer.
The remaining GCC states all reach high household ICT penetration rates of 70 per cent and above.
Morocco was able to connect a significant number of households to the Internet in 2013,
Oman saw high increases in terms of both households with Internet access and households with a computer as a result of the National PC Initiative.
secondary school or higher education studies) in Oman are offered one free computer per student. Furthermore Omantel provides discounted broadband Internet offers for eligible customers. 8 Wireless-broadband penetration levels vary considerably across the region.
The number of subscriptions exceeds the population in Bahrain, the United arab emirates has a penetration of 89 per cent,
and upgrading their mobilebroadband networks in Qatar LTE is available throughout the entire country9 Algeria
as 3g licences were awarded finally to three Algerian operators by end 2013.10 Penetration is also extremely low in Yemen (0. 3 per cent),
and also reflect the fact that mobile-broadband services in the lower-income countries are much less affordable than in the high-income Arab States (see Chapter 3). Fixed-broadband penetration is generally low in the Arab States
also reach Internet user penetration rates of 50 and 56 per cent, respectively. On the other hand, in the LCCS Mauritania and Djibouti, less than 10 per cent of the population are online. 3. 3 Asia
ITU World Telecommunication/ICT Indicators database. 2012 2013 Per 100 inhabitants Bahrain United Arab Emiratesqatar 0 20 40 60 80 100
and the Pacific countries have reached a mobile-cellular penetration of 100 per cent or above by end 2013.
Nepal achieved a mobile-cellular penetration rate of 71 per cent in 2013 up from 60 per cent in 2012.
In China, more than 100 million new mobile-cellular subscriptions were added in 2013, taking the penetration rate up to 89 per cent.
Hong kong (China) has the highest amount of international Internet bandwidth in the region, and indeed one of the highest volumes in the world, at close to 9. 5 million Mbit/s12 by end 2013.
As a regional hub and international financial centre, Hong kong (China) relies upon a secure and low-latency Internet connection,
and the telecommunication regulator has made the attraction of international submarine cables a policy priority13 (see MIS 2013,
international Internet connectivity was boosted in these countries. Additional international Internet bandwidth is of particular importance for sustaining ICT growth
and ensuring Internet connectivity for an increasing number of users in populous countries such as China (with an estimated 600 million Internet users) and the Philippines (with an estimated 36.5 million Internet users by end 2013).
Within the Philippines, domestic connectivity was improved further by connecting some of the Chart 3. 6:
which went live in the summer of 2013.15 Regional Internet connectivity was enhanced further when the Tonga Cable, connecting Fiji and Tonga,
a number of countries in Asia and the Pacific have very low levels of international Internet connectivity;
these include, in particular, the landlocked and least connected countries Afghanistan, Bhutan and Nepal, with less than 4 000 bit/s per Internet user.
By end 2013,29 per cent of households had a computer. Data from the annual ICT household survey show that,
since 2008, computers have replaced telephones as the most commonly available ICT device in Thai homes.
Furthermore, the majority of households Table 3. 5: IDI Asia and the Pacific Note:**Simple averages.
In Thailand, where 3g was launched very late, wireless-broadband penetration went up from 11 per cent in 2012 to more than 50 per cent in 2013.
Both countries feature among the most dynamic of the IDI 2013, mostly because of the considerable, above-average increases in wireless-broadband penetration.
All of these with the exception of the Islamic Republic of Iran are LCCS that could greatly benefit from the extension of wireless broadband to connect more people with ICTS.
with China Mobile entering the fixed-line market. 17 China's broadband strategy, published in August 2013, underlines the importance of broadband as a strategic public infrastructure for China's economic and social development in the new age.
ITU World Telecommunication/ICT Indicators database. 2012 2013 Per 100 inhabitants 0 20 40 60 80 100 120 140 Macao, Chinasingapore
(I. R.)Lao P. D. R. Afghanistan Myanmarpakistan Bangladesh 95 Measuring the Information Society Report 2014 household penetration of 50 per cent and a 3g penetration
This includes around 600 million Chinese and 200 million Indian Internet users. Comparing the two the proportion of the population using the Internet is much higher in China (44 per cent) than India (15 per cent.
India has one of the lowest rates in the region (and globally: only Afghanistan, Bangladesh, Cambodia, Lao P. D. R.,Myanmar, Nepal, Pakistan and Solomon islands recorded a lower proportion of Internet users.
Japan (86 per cent), the Republic of korea (85 per cent) and Australia and New zealand (both 83 per cent) exhibit the highest rates in the Asia
the CIS region had the highest mobile-cellular penetration of all regions, at Chart 3. 8:
The mobile markets in the CIS are predominately prepaid, with typically high rates of multi-SIM ownership.
In the majority of CIS countries, at least four mobile operators are active in the market.
Data from household surveys collected in a number of CIS countries underline that mobilecellular penetration,
measured as the number of mobile-cellular subscriptions, can give no more than an indication of the actual number of subscribers
(i e. a mobile-cellular penetration of above 100 per cent does not mean that every inhabitant has a mobile-cellular subscriptions).
In Georgia, 18 per cent of households did not have access to a mobilecellular telephone in 2012,
The Russian Federation had the highest proportion of households with Internet access and households with a computer in the region by end 2013, at 67 per cent and 70 per cent, respectively.
In Azerbaijan, Belarus and Kazakhstan, more than half of households have Internet access at home and a computer.
with less than 10 per cent of households in the country having access to the Internet.
Total international Internet bandwidth is by far highest in the Russian Federation, which is connected through a number of terrestrial links to both Europe
in terms of bandwidth per Internet user the country is below most other CIS countries. International Internet bandwidth per Internet user is highest in Moldova (115 845 bit/s per user),
followed by Belarus (94 797 bit/s per user) and Georgia (82 094 bit/s per user).
which hampers Internet connectivity and hence further development of the ICT sector in those countries.
By end 2013, half of CIS countries had reached a wireless-broadband penetration of more than 45 per cent.
The Russian Federation was one of the first countries in the region to launch 3g services in 2007.21 Since then,
further Internet connectivity. LTE services were launched in the Russian Federation in 2012.22 The highest growth in wirelessbroadband penetration from 2012 to 2013 took place in Georgia from 9 per cent in 2012 to 17 per cent in 2013 placing it among the most dynamic
countries in the region and indeed on the global IDI 2013. Ukraine has the lowest penetration in the region, at 7 per cent by end 2013.
The slow growth in wireless-broadband penetration in Ukraine explains why the country is falling back in international comparison.
ITU World Telecommunication/ICT Indicators database. 2012 2013 Per 100 inhabitants Belarus 05 10 15 20 25 30 35 Azerbaijanrussian Federationmoldova
Data from the Eurobarometer underlines this finding: on average, 92 per cent of European union citizens (the majority of countries in the region are members of the EU) had access to a mobile phone in 2013 (European commission, 2014b.
The region benefits from an abundant supply of international Internet bandwidth. The highest levels are reached in international hubs such as Germany, Luxembourg and the United kingdom. High amounts of bandwidth per Internet user,
as registered in most European countries, ensure that a large number of Internet users can go online at high speeds.
Around three-quarters of European households have access to the Internet at home. The highest proportions of households connected to the Chart 3. 10:
IDI values compared with the global, regional and developing/developed-country averages, Europe, 2013 Source:
ITU. IDI Developing Europe World Developed Denmark 0123456789 10 Sweden Iceland United Kingdomnorway Netherlandsfinlandluxembourg Switzerlandmonacogermany Franceandorra Estoniaaustriabelgiumirelandspainisraelmalta Slovenia Latviaitaly Croatiagreece Lithuania
Czech Republicportugal Polandslovakia Hungary Bulgariaserbiacyprusromaniatfyr Macedoniamontenegro Turkey Albania Bosnia and herzegovina 99 Measuring the Information Society Report 2014 Internet are found in Iceland (96 per cent), Luxembourg
Those countries also display an equally high level of households with a computer. In the majority of countries in Europe (25 out of 40), 70 per cent of households have Internet access;
and in an even higher number of countries (28 out of 40), 70 per cent of households have a computer.
Albania ranks last in the region also in terms of household ICT penetration, with 22 per cent of households with a computer and 24.5 per cent with Internet access by end 2013.
Among the countries that made the most progress in connecting households to the Internet from 2012 to 2013 are Italy (from 63 to 69 per cent), Czech republic (from 65 to 73 per cent) and Estonia (from 75 to 80 per cent.
Fast broadband coverage (defined at 30 Mbit/s) should be available throughout the entire EU
Growth in wireless-broadband penetration continued at double-digit rates from 2012 to 2013 in the majority of European countries.
In Albania, the incumbent operator launched its 3g services in early 2013, increasing competition in the market. 25 Operators in Slovakia and Romania have extended
and started to offer LTE services to customers. The top five countries in the world in terms of fixed-broadband penetration (Monaco, Switzerland, Denmark, Netherlands and France) are all European.
Percentage of Individuals using the Internet, Europe compared to global and developedcountry average, 2013 Note:
Data on Individuals using the Internet for Eurostat members are sourced from Eurostat. Eurostat collects data for Internet users aged 16-74 years old.
Source: ITU World Telecommunication/ICT Indicators database. World Developed 0 10 20 30 40 50 60 70 80 90 100%101 Measuring the Information Society Report 2014 penetration stands
at 6 per cent, all European countries exceed the global average penetration. A well-developed ICT infrastructure and the availability of high-speed broadband Internet access and relevant content are reflected in a higher proportion of Internet users in the region.
Close to half a billion Europeans were online in 2013, which corresponds to 73 per cent of the population.
Iceland has the highest proportion of Internet users globally at 96.5 per cent, followed by three other Nordic countries Norway,
Sweden and Denmark with 95 per cent of the population using the Internet. Turkey has the lowest proportion of Internet users, at below 50 per cent.
In Romania, too, less than half of the population are online (Chart 3. 11). 3. 6 The americas The United states
ranking 125th in the IDI 2013 as against 122nd in 2012 (see Table 3. 8). The country's mobile-cellular Chart 3. 12:
The country's Empresa de Telecomunicaciones de Cuba has one of the last state telecommunication-sector monopolies in the world.
International Internet connectivity, measured in bit/s per Internet user, is ample in the United states and Canada,
Colombia managed to quadruple its amount of international Internet bandwidth from around 20 000 bit/s per Internet user in 2012 to close to 80 000 bit/s in 2013.
The americas region has a relatively high household ICT penetration. By end 2013, on Table 3. 8:
per cent of households had Internet, which is the second highest regional average after Europe (76 per cent).
In addition, however, some of their Latin american neighbours boast a significant proportion of households connected to the Internet:
and at least 50 per cent had a computer. Household Internet access remains very low in the LCC Cuba (3 per cent),
and by negotiating agreements with telecom operators to offer discounted price plans (Galperin, 2012). Through national broadband plans, governments in The americas are recognizing the potential of ICTS to support economic development.
while Mexico and the Dominican republic both improved by five percentage points to 31 per cent and 19 per cent of households with Internet by end 2013, respectively.
reaching 42 per cent and 36 per cent of households with Internet by end 2013,
Furthermore, a number of countries awarded LTE licences or further extended 3g coverage in 2013, spurring growth in the mobile sector.
The United states has the highest wirelessbroadband penetration, at 94 per cent by end Chart 3. 13:
ITU World Telecommunication/ICT Indicators database. 0 10 20 30 40 50 60 70 80 90 2012 2013 Per 100 households
It was an early adopter of LTE technology, and coverage was extended massively throughout the country in 2013.
The operator Verizon had achieved 97 per cent LTE population coverage, and the majority of all data traffic is carried by the LTE network. 27 Very high increases were reported by Brazil,
where 40 million new wireless-broadband subscriptions were added within a year, resulting in a penetration of 52 per cent by end 2013.
LTE services were launched first in the country in early 2013.28 Antigua and barbuda (from 23 per cent to 49 per cent),
Colombia from 15 per cent to 25 per cent) and Saint lucia (from 19 per cent to 33 per cent) also show very good progress in terms of wirelessbroadband penetration from 2012 to 2013.
While the majority of countries in The americas region are making remarkable progress in extending their wireless-broadband networks,
services were still not available in Cuba, Dominica, Guyana and St vincent and the Grenadines by end 2013.
particularly those that were late adopters of mobile-broadband technology such as Dominica, Grenada and St vincent and the Grenadines, have significantly higher fixedbroadband than wireless-broadband penetration rates.
Close to 1 billion people are using the Internet in The americas region. While the highest proportion of individuals using the Internet is to be found in the United states and Canada,
more than half of the population is online in countries such as Argentina, Brazil, Chile, Colombia, Uruguay and Venezuela. 105 Measuring the Information Society Report 2014 1 See:
http://www. itu. int/ITU-D/ict/definitions/regions/index. html. 2 The standard deviation (Stdev) shows the average distance of a value to the mean.
The higher the CV, the greater the dispersion in the variable. 3 http://www. cvmovel. cv/nacional-gsm-3g-edge-e-gprs. 4 http://www. telegeography
/airtel-widens-3-5g-footprint/./5 http://www. gsmamobileeconomyafrica. com/Sub-Saharan%20africa me report english 2013. pdf. 6 http://www. epegcable. com/.7 http://www. gbiinc. com/Sitepages
itmid=110.8 http://www. omantel. om/Omanweblib/Individual/Internet/pc initiative. aspx? linkid=3&menuid=420 and http://www. ita. gov. om/ITAPORTAL/Pages/Page. aspx?
n=4392ef47-A715-496f-BF1D-A0ee8b74d0e7&d=20140128.10 http://www. telecompaper. com/news/algeria-awards-3g-licences-to-all-three-bidders--972965.11
Internet user data from Gulf countries are not comparable, as they refer to different populations. Data from Bahrain and Qatar refer to the overall population,
i e. including expatriate/transient workers. Data from United arab emirates are estimated by ITU based on base data excluding the transient worker population. 12 Reported in activated external capacity. 13 http://www. ofca. gov. hk
/en/industry focus/telecommunications/facility based/infrastructures/submarine cables/index. html. 14 http://submarinenetworks. com/systems/intra-asia/sjc/sjc-cable-system. 15 http
://www. submarinecablemap. com/#/submarine-cable/boracay-palawan-submarine-cable-system. 16 http://web. nso. go. th/en/survey/data survey/560619 2012 information-pdf. 17 http
://www. digitimes. com/news/a20131227pd215. html and http://www. eurobiz. com. cn/chinas-broadband-strategy/.
/18 http://file. eu-chinapdsf. org/Internet/PUB/Activity4/Results%203/Broadband%20china%20introduction yu%20xiaohui. pdf. 19 Belarus, Moldova, Russian
/the-eagle-has landed-incumbent-swoopsinto-3g-sector/./26 Data reported by the country refer to 2012.27 http://www. verizonwireless. com/wcms/consumer/4g lte. html
and http://www. telecompaper. com/news/verizon-wirelesslte-reaches-500-markets--952458.28 http://www. rcrwireless. com/article/20121214/carriers/claro-first-launch
-lte-services-brazil/./Endnotes 107 Measuring the Information Society Report 2014 Chapter 4. ICT prices
and the role of competition 4. 1 Introduction The price of ICT services constitutes a determining factor for ICT uptake and,
In Brazil, for instance, 44 per cent of all households with a computer did not have Internet in 2013
although not having Internet at home may be more attributable to other factors, such as lack of interest,
In the European union, around one in five households without Internet cite cost as the reason,
and seven out of ten of those who have Internet state that price is the most important factor
over half of EU citizens limit their national mobile phone calls because of concerns about cost (European commission, 2014.
In response to the demand for global benchmarks on ICT prices, ITU has been collecting ICT price data following a harmonized methodology since 2008.
Since 2012, the data collection has been extended to include mobilebroadband prices. These data have proved to be useful for the international comparison of ICT prices across more than 160 countries,
and for identifying those cases where prices constitute a barrier to ICT uptake. This year's analysis of ICT prices goes beyond simply measuring affordability,
The effects of competition in driving prices down and fostering innovation have been most apparent in the mobile-cellular market,
where low prepaid prices became a key enabler for the mass uptake of mobile-cellular services observed in the developing world in the last decade;
but they also apply to other telecommunication markets. Regulation sets the framework for competition, and is thus the lever
which telecommunication administrations may exert more direct control. They therefore merit particular attention. This chapter will present
They include end-2013 data for each of the three price sets contained in the IPB (fixed-telephone
The methodological details of the IPB and the collection of mobile-broadband prices can be found in Annex 2. 4. 2 Fixed-telephone
and mobile-cellular prices Traditional voice services4 and SMS have become the most ubiquitous ICT services,
For instance, only 9 per cent of households had a telephone in India in 2001 compared with 32 per cent of households with a TV and 35 per cent of 109 Measuring the Information Society Report 2014 households with a radio.
63 per cent of households had a telephone, 47 per cent a TV and 20 per cent a radio. 5 Despite the decline in fixed-telephone subscriptions over the last decade,
fixed telephony remains the most widespread ICT service based on fixed (wired) telecommunication networks. Global fixedtelephone penetration stood at 16 per cent by end 2013,
The coverage gaps in the fixed-telephone network have been filled by the mobile-cellular network, which covers 93 per cent of the global population.
This compares with a global 3g coverage of around 50 per cent by end 2012
highlighting that mobile-broadband services are likewise less available than mobile voice and SMS (see Chapter 1). Subscription figures confirm this:
despite double-digit mobile-broadband growth rates, there are three times as many mobilecellular voice subscriptions as mobile-broadband subscriptions, with almost as many mobilecellular subscriptions as people on earth.
the bulk of national voice traffic corresponds to calls made from mobile networks, thus confirming the shift from fixed to mobile voice.
International voice traffic is also predominantly mobile in most countries, although the number of international fixed-telephone minutes still exceeds international mobile voice minutes in one in four countries.
This situation occurs more frequently in the developed world: there is more fixed than mobile international telephone traffic in 36 per cent of the developed countries,
as against only 18 per cent of the developing countries. These differences are consistent with the higher fixed-telephone penetration rates recorded in developed countries almost four times higher than in developing countries;
differences in mobile-cellular penetration are smaller. These findings highlight that fixed telephony is used still more than mobile telephony in some countries for making international calls.
Such a ubiquitous uptake of voice services would not have been possible without affordable prices. Chart 4. 1 shows the evolution of fixed-telephone and mobile-cellular prices in the period 2008-2013.
A basic fixed-telephone service costs on average PPP$ 18.7 (or USD 13.9) per month by end 2013;
Fixed-telephone basket (left) and mobile-cellular basket (right), in PPP$, world and by level of development, 2008-2013 Note:
Based on 140 economies for which 2008-2013 data on fixed-telephone and mobile-cellular prices were available.
and PPP$ 28.4 (or USD 19.5) per month for a prepaid mobilebroadband service with a 500 MB monthly data allowance. 7 Despite the limitations of comparing such different services,
the results roughly confirm that fixed-telephone and mobile-cellular prices are the cheapest among ICT services,
suggesting that low prices have contributed to the widespread adoption of traditional voice and SMS services.
Fixed-telephone prices have followed an almost flat evolution, with a small decrease in prices observed during the period in developing countries(-1. 3 per cent compound annual growth rate (CAGR) in the developing world in the period 2008-2013).
The fixed-telephone market is the most mature segment of those included in the ITU price data collection exercise.
78 per cent of the countries with price data had already fully or partially liberalized their fixed-telephone market in 2008,
compared with 88 per cent in 2013. Moreover, in some cases liberalization has signalled the end of cross-subsidies for fixed-telephone services,
and the deregulation of retail fixed-telephone prices. The cheapest fixed-telephone prices are found in countries where there is still strong government control over the main fixed-telephone operator,
such as the Islamic Republic of Iran, Cuba and Moldova, where basic fixed-telephone services cost less than USD 0. 5 per month (Table 4. 1). Mobile-cellular prices have declined in the period
2008-2013, with a CAGR of-5. 7 per cent globally. The decrease in prices has affected developed and developing countries alike, with-4. 3 and-6. 4 per cent CAGR, respectively.
In developing countries, the number of mobile-cellular subscriptions almost doubled in the period 2008-2013.
The top countries with the cheapest prepaid mobile-cellular plans are all from the developing world,
including 15 countries where a low-usage monthly mobile-cellular plan costs less than USD 5 per month (Table 4. 2). Most countries with the cheapest prepaid mobile
with Sri lanka (USD 0. 95 or PPP$ 2. 6 per month) and Bangladesh (USD 1. 41 or PPP$ 4. 0 per month) standing out with the lowest prepaid mobile-cellular
. so as to provide an insight into the affordability of fixed-telephone and mobile-cellular services from a demand-side perspective (Chart 4. 2). From this perspective,
Fixed-telephone basket (left) and mobile-cellular basket (right), as a percentage of GNI p. c.,world and by level of development, 2008-2013 Note:
Based on 140 economies for which 2008-2013 data on fixed-telephone and mobile-cellular prices were available.
2010 2011 2012 2013 Developing World Developed As a%of GNI p. c. average in developing countries is explained by the large dispersion of fixed-telephone prices in the developing world:
Malawi and Madagascar (Table 4. 1). If the affordability target set by the Broadband Commission for Digital Development for broadband prices (less than 5 per cent of monthly GNI p. c. by 2015) were applied to fixed-telephone prices,
By end 2013, a low-usage prepaid mobile-cellular service cost on average 1. 6 per cent of GNI p. c. in developed countries,
The affordability of basic mobile-cellular services remains a major barrier to further adoption in several African countries:
of the 20 countries with the least affordable mobile-cellular prices in 2013,16 were from Africa.
Despite Kenya being one of the most dynamic mobile markets in Africa and having the twelfth cheapest prices in USD in the world (USD 3. 8 for a low-user basket in 2013),
. on account of the country's low-income level. 10 Further reductions in mobile-cellular prices could be achieved by combining regulatory actions to promote competition such as fostering inter-operator competition
Fixed-telephone sub-basket, 2013 Note:**Data correspond to the GNI per capita (Atlas method) in 2013
or latest available year adjusted with the international inflation rates.****Country not ranked because data on GNI p. c. are not available for the last five years.
Source: ITU. GNI p. c. and PPP$ values are based on World bank data. Rank Economy Fixed-telephone sub-basket GNI p. c.,USD, 2013*Rank Economy Fixed-telephone sub-basket GNI p. c
.,USD, 2013*as%of GNI p. c. USD PPP$ as%of GNI p. c. USD PPP$ 1 Iran (I. R.)0. 03 0. 12 0
. 26 5'780 85 Romania 1. 57 11.88 16.27 9'060 2 Cuba 0. 05 0. 24-6'014 86
*Data correspond to the GNI per capita (Atlas method) in 2013 or latest available year adjusted with the international inflation rates.**
because data on GNI p. c. are not available for the last five years. Source:
GNI p. c. and PPP$ values are based on World bank data. Rank Economy Mobile-cellular sub-basket GNI p. c.,USD, 2013*Rank Economy Mobile-cellular sub-basket GNI p. c
.,USD, 2013*as%of GNI p. c. USD PPP$ as%of GNI p. c. USD PPP$ 1 Macao, China 0. 11 5. 68 7
and the role of competition 114 Mobile-cellular services are very affordable in most developed countries,
This suggests that cost may be a barrier for further uptake of mobile-cellular services in these countries,
high-capacity and reliable Internet services. Despite the growth of mobile-broadband subscriptions, less than 3 per cent of global IP traffic corresponded to mobile networks by end 2013 according to CISCO estimates (CISCO, 2013.
Until deployments of advanced mobile-broadband technologies13 become more widespread, fixed broadband remains the de facto option for accessing high-volume Internet applications such as file sharing (less than 1 per cent of total filesharing traffic was transmitted through mobile networks in 2013)
and Internet video (2 per cent of total Internet video traffic was transmitted through mobile networks in 2013).
Therefore, some of the potential benefits of broadband as a development enabler, such as for instance its use in education (see Featured Insight 10 in Broadband Commission,
2013a), depend on fixed-broadband uptake in the near future. In addition, CISCO estimates that 45 per cent of total mobile data traffic was offloaded onto fixed networks in 2013 (CISCO,
2014), highlighting the role that fixed broadband plays in supporting the growth in mobile-broadband networks.
In the period 2008-2013, the price of an entrylevel fixed-broadband plan decreased by almost 70 per cent globally:
Based on 143 economies for which 2008-2013 data on fixed-broadband prices were available. Source:
On the other hand, there was an upgrade of entry-level fixed-broadband speeds in developing countries in 2013,
This is in line with the findings on bundle adoption from household surveys (European commission, 2014) and data on fixed (wired)- broadband subscriptions by speed
Based on 143 economies for which 2008-2013 data on fixed-broadband prices were available. Source:
Based on 165 economies for which 2013 data on fixed-broadband prices were available. Source: ITU.
GNI p. c. values are based on World bank data. 0. 0 0. 5 1. 0 1. 5 2. 0 2. 5 3
GNI p. c. values are based on World bank data. 02468 10 12 As a%of GNI p. c. Russian Federationkazakhstan Belarus Ukraineazerbaijan Armeniageorgia*Uzbekistanmoldova Kyrgyzstan
including the incumbent Rostelecom, Mobile Telesystems OJSC (MTS) and ER-Telecom. The national fixed-broadband market in the Russian Federation is
where there was only 20 000 Mbit/s of international Internet bandwidth to share among more than 300 000 fixed (wired)- broadband subscriptions in 2013.
The scarcity of international Internet bandwidth is confirmed further by the fact that the entry-level plan in Uzbekistan is capped at 1. 2 GB of usage per month,
and is concentrated relatively, dominated by Kyrgyz Telecom. These factors suggest that regulatory measures to promote competition
along with Tunisia (Chart 4. 7). Tunisie Telecom offers regular ongoing promotions for ADSL services with some of the most advantageous prices in the region:
USD 6. 1, by far the cheapest price offered by an incumbent operator in the Arab States for an Internet service at speeds above 512 kbit/s. The relatively low fixed (wired)- broadband 119
This may be explained by the limited coverage of Qualitynet, which suggests the need for more investment in broadband network equipment
GNI p. c. values are based on World bank data. for fixed-broadband uptake in the country.
international Internet bandwidth is limited very in the country: 620 Mbit/s in 2013, more than 40 times less than any other Arab country included in the comparison of fixed-broadband prices.
This means that if one-third of fixed (wired)- broadband subscriptions in Mauritania try to access the international Internet at the same time,
GNI p. c. values are based on World bank data. 05 10 15 20 As a%of GNI p. c. 85.8 66.1 United states Trinidad & Tobago Venezuela
which include specific entry-level fixed-broadband plans offered by the state-owned telecom operator ANTEL (ITU,
As a result, dial-up (narrowband) Internet remains the de facto technology for Internet access by residential customers in the island. 19 Asia
GNI p. c. values are based on World bank data. There are 13 countries in the Asia and the Pacific region where entry-level fixed-broadband plans cost more than 10 per cent of GNI p. c. These include some large countries, such as Pakistan and the Philippines,
Because of their geographic situation, one of the main challenges facing these countries is international Internet bandwidth.
the latest data on international connectivity show that this may remain an issue in Kiribati (45 Mbit/s), Marshall islands (45 Mbit/s), Micronesia (45 Mbit/s), Samoa (135
e g. by concentrating international traffic in a regional Internet exchange point and sharing the cost of building a high-capacity international link from there.
GNI p. c. values are based on World bank data. However, a comparison with other regions shows that it is feasible to improve the affordability of fixed-broadband prices in Africa significantly
*Data correspond to the GNI per capita (Atlas method) in 2013 or latest available year adjusted with the international inflation rates.**
because data on GNI p. c. are not available for the last five years. Source:
GNI p. c. and PPP$ values are based on World bank data. Rank Economy Fixed-broadband sub-basket Speed in Mbit/s Cap per month in GB GNI p. c.,USD, 2013*Rank Economy Fixed
Mobile broadband Mobile broadband is the most dynamic telecommunication market, the only one displaying sustained double-digit growth rates since 2008 (Chapter 1). According to ITU estimates,
around 50 per cent of the global population are covered by a 3g network, and this figure will grow as more and more mobilebroadband networks are deployed, until eventually 3g coverage approaches mobilecellular coverage (93 per cent).
As 3g networks become ubiquitous and therefore accessible to most of the population, affordability will be one of the most decisive factors for actual uptake of mobile broadband.
The dynamism of the mobile-broadband market is reflected also in prices. Unlike the fixedbroadband market, where price structures are fairly homogenous across countries and stable in time,
mobile-broadband prices vary and operators are continuously launching innovative offers to try to attract new customers.
On top of the main types of mobile-broadband plans for which ITU collects data on prices (Figure 4. 1),
operators are adding plans for specific devices, such as tablets, Mifi dongles, Blackberry, etc. Some operators, like for instance Rogers in Canada
and Verizon in the United states, are allowing customers to pool the data consumed by different devices in a single subscription. 23 In addition,
handset-based mobile-broadband Figure 4. 1: Mobile-broadband services by type of device/plan Source:
Mobile broadband Handset-based Computer-based Prepaid Postpaid Prepaid Postpaid Chapter 4. ICT prices and the role of competition 126 plans are bundled often with mobile voice
and SMS. This makes it difficult to isolate the prices of mobile-broadband services, particularly for postpaid plans where it is sometimes cheaper to buy a package including Internet,
voice and SMS than to contract only Internet. Fixed-broadband and mobile-broadband Internet prices follow different pricing structures,
and therefore the analysis of mobile-broadband prices cannot be based on the same parameters used for fixed broadband.
In the case of fixed Internet access the progress from narrowband (dial-up to broadband brought not only a change in speed but also in pricing.
Dial-up was priced on the basis of usage (usually billed per minute/hour), whereas fixed-broadband Internet usually follows a flat-rate arrangement,
whereby the customer pays a monthly fee and has unlimited access to the Internet at a given speed, with neither time nor data volume constraints.
This is the common scheme in a vast majority of countries, where fixed-broadband plans are unlimited
and the differentiating factor is speed the of the connection (Table 4. 4). Mobile-broadband plans are seldom based on flat-rate schemes,
and almost all of them include data volume caps, e g. USD 10 for 50 MB per month.
Several operators also offer advantageous plans based on a combination of time and data volume limitations, e g.
This reflects the stricter bandwidth constraints of mobile-broadband networks, and particularly the spectrum limitations in the access network.
if new spectrum is allocated for mobile broadband (for instance, part of the digital dividend) and mobile-broadband networks are upgraded to advanced technologies (such as LTE-Advanced
and Wirelessman-Advanced) that allow more efficient use of spectrum. Currently most mobile-broadband plans are priced on the basis of the data allowance (i e. the data volume in MB included in the plan) and not the speed.
Many operators do not even advertise the speed of the mobile-broadband service, but confine themselves to a generic mention of the technology deployed
(which provides only an indication of the speed, since the definition of‘3. 5g'or‘4g'may vary across operators).
This may also change in the future, as some operators are starting to offer premium plans (at a higher cost) for mobile-broadband services based on high-speed networks.
These plans are labelled often as‘4g 'and may include some indication of the theoretical speeds that can be achieved.
This is the case, for instance, of the operator Tigo, which offers premium‘4g'plans in Bolivia, Colombia, Guatemala and Paraguay. 24 In any case,
mobile-broadband speeds depend on several external factors, such as distance from the base station, location (e g. inside a building or outside),
Chart 4. 11 shows that mobile-broadband plans are becoming more and more available, particularly in developing countries,
Globally, the mobile-broadband service available in the most countries is based prepaid handset, which was offered in 153 countries in 2013.
There are far fewer countries (121 in 2013) where all four modalities of mobile-broadband services are offered.
Availability of mobile-broadband services by type of service, by level of development, 2013 and 2012 Note:
A mobile-broadband service is counted as available if it was advertised on the website of the dominant operator
or prices were provided to ITU through the ICT Price Basket Questionnaire. 25 Source: ITU. analysis of the 2013 prices, without comparing them with the 2012 figures.
A comparison of mobile-broadband prices across time would reflect the changes in pricing structures (changes in data allowances,
rather than actual differences in prices for the same mobile-broadband service. The global average price for a computer-based mobile-broadband service with 1 GB monthly data allowance was PPP$ 36.6 (or USD 24.4) for prepaid plans and PPP$ 30.0 (or USD 19.2
) for postpaid plans in 2013 (Chart 4. 12. The price difference between postpaid and prepaid plans is also found in respect of regular mobile-cellular services,
where postpaid computer-based mobile-broadband plans cost 37 per cent less than the corresponding prepaid plans in PPP terms.
Differences between prepaid and postpaid computer-based mobilebroadband plans are marked less in developing countries, suggesting that operators differentiate less between postpaid and prepaid offers for the time being.
The average cost for a handset-based mobilebroadband service with 500 MB monthly data allowance was PPP$ 25.3 (or USD 16.9) for prepaid plans and PPP$ 25.7 (or USD 17.6) for postpaid
Prices were compared cheaper with computer-based plans because the monthly data allowance was half as large.
Nevertheless, the reduction in price was not proportional to the reduction in the data allowance,
confirming that the price per GB is lower for larger data allowances, the equivalent of a volume discount.
Unlike in the case of computer-based mobile-broadband services, the prices for postpaid and prepaid handsetbased mobile-broadband plans were similar,
which means that operators are in most cases offering the same rates to postpaid and prepaid smartphone customers.
A feature of postpaid handset-based mobilebroadband plans is that they are in some cases bundled with voice minutes
where in one in four countries the cheapest postpaid handset-based Internet plans included free minutes and SMS in 2013.
It is much less 0 20 40 60 80 100 120 140 Number of countries 2013 handset-based (500mb) Prepaid handset-based (500mb) Postpaid computer
-based (1gb) Prepaid computer-based (1gb) developing developed 160 2012 Postpaid 2013 2012 2013 2012 2013 2012 Chart 4. 12:
Based on 119 economies for which data on mobilebroadband prices were available for the four types of plans.
The existence of different levels of bundling in mobile-broadband plans makes it difficult to compare prices on a like-forlike basis. Mobile-broadband prices in PPP$ are more expensive in developing countries than in developed countries
This suggests that operators in developing countries still have ample room to streamline their mobile-broadband services and offer cheaper prices.
for fixed-telephone and mobile-cellular services. 26 The fact that this is not fully happening for fixed
These differences in mobile-broadband prices between developed and developing countries are even more apparent when looking at the affordability of the service.
Indeed, handsetbased mobile-broadband plans with a monthly data allowance of 500 MB are about eight times more affordable in developed countries than in developing countries, on average (Chart 4. 14.
Computer-based services with a monthly allowance of 1 GB are about six times more affordable in developed countries, on average.
The average price for a computer-based mobile-broadband service with 1 GB monthly data allowance corresponded to more than 20 per cent of GNI p. c. in Africa,
Based on 119 economies for which data on mobilebroadband prices were available for the four types of plans.
Based on 119 economies for which data on mobilebroadband prices were available for the four types of plans.
Indeed, it is estimated that mobile-broadband penetration will reach 19 per cent in Africa by end 2014,
with mobile-broadband subscribers consuming much less than 500 MB of Internet data per month, supported by the fact that several African operators offer discount plans for occasional use.
However, such low-volume short-validity plans allow only limited use of the Internet, and therefore restrict the benefits that can be obtained from broadband.
For instance, Internet video cannot be consumed on the basis of such limited data allowances, and even Internet radio would need to be limited.
This suggests that, if mobile broadband is to bridge the broadband gap between Africa and the other regions,
mobile-broadband services will have to become more affordable in Africa so that most applications enabled by a broadband connection are within the means of a majority of the population.
Europe stands out as having the most affordable mobile-broadband plans, corresponding to less than 2 per cent of GNI p. c. for all services.
and prices are just slightly above that value in the case of computer-Chart 4. 15:
Based on 119 economies for which data on mobilebroadband prices were available for the four types of plans.
prepaid computer-based plans being the only ones clearly above that threshold. Country data for The americas reveal that there are a number of countries
which have high prepaid computerbased prices because minimum packages include a monthly data allowance much larger than 1 GB (Table 4. 8). This is the case of Chile (USD 73 for 14 GB), Antigua and barbuda (USD 63
for 10 GB), Haiti (USD 23 for 10 GB), El salvador (USD 28 for 8 GB) and Argentina (USD 46 for 7 GB).
Such high monthly data allowances for prepaid mobile-broadband dongles suggest that these services target high-end customers, rather than the average user.
Postpaid mobilebroadband dongles include much lower monthly data allowances in The americas (Table 3. 7), suggesting that postpaid rather than prepaid is the base offer for regular computer-based mobile-broadband customers.
Average prices for computer-based mobilebroadband plans with a monthly data allowance of 1 GB suggest that mobile broadband could be a cheaper alternative to fixed broadband in many Chapter 4. ICT prices and the role
of competition 130chart 4. 16: Comparison of postpaid fixed-broadband and postpaid computer-based mobile-broadband prices, in USD, by region, 2013 Note:
Percentages are calculated on the basis of the total number of countries with data available in each region:
27 countries in Africa, 14 countries in the Arab States, 29 economies in Asia and the Pacific, 10 countries in the CIS, 41 countries in Europe and 27 countries in The americas.
Percentage of countries Africa Cheaper mobile broadband Cheaper fixed broadband Almost no difference 0 10 20 30 40 50 60 70<5 5
-10 10-20 20-30>30 Difference in prices (USD) Percentage of countries Arab States Cheaper mobile broadband Cheaper fixed broadband Almost no difference
Cheaper mobile broadband Cheaper fixed broadband Almost no difference 0 10 20 30 40 50 60 70<5 5-10 10-20
20-30>30 Difference in prices (USD) Percentage of countries CIS Cheaper mobile broadband Cheaper fixed broadband Almost no difference 0 10 20 30
-20 20-30>30 Difference in prices (USD) Percentage of countries Europe Cheaper mobile broadband Cheaper fixed broadband Almost no difference 0 10 20
mobile broadband Cheaper fixed broadband Almost no difference 0 10 20 30 40 50 60 70<5 5-10 10-20 20
There are qualitative differences that make mobile-broadband and fixed-broadband plans not strictly comparable (see Box 3. 4 in ITU
Chart 4. 16 shows a comparison of prices for fixed-broadband and postpaid computer-based mobile-broadband plans with a 1 GB monthly allowance.
This latter mobile-broadband plan is chosen because it is the best match for fixed-broadband services:
are based computer and include a monthly data allowance of at least 1 GB. Although the minimum data allowance is the same,
in practice most fixed-broadband plans allow unlimited data use (Table 4. 4), whereas most computerbased mobile-broadband plans with a minimum monthly data allowance of 1 GB really do have a cap of 1 GB (Tables 4. 7 and 4. 8). In almost half
of the African countries included in the price benchmark, mobile-broadband prices were more than USD 10 cheaper per month than fixed-broadband prices.
Taking into account the GNI p. c. levels in Africa such price savings could make the difference between a service being affordable or not.
This is particularly so in Namibia, where an entrylevel fixed-broadband plan corresponds to 10.6 per cent of GNI p. c as against 3. 2 of GNI p. c. for a computer-based mobile-broadband plan.
In other African countries, mobile broadband may be a more affordable alternative to fixed broadband for instance, in the Democratic republic of the congo and Zambia,
where mobile broadband is more than USD 50 cheaper per month but mobile-broadband prices still correspond to more than 5 per cent of GNI p. c. This reinforces the idea that mobile operators
and policy-makers in Africa share the common challenge of achieving lower mobile-broadband prices in order to unlock the real potential of broadband in the region.
In the Arab States and the CIS, there are almost as many countries where mobile broadband is fixed cheaper than broadband as vice versa.
computer-based plans). In many countries in Asia and the Pacific, there is little difference between fixed-broadband and mobile-broadband prices.
Of those countries where mobile broadband is significantly cheaper, Indonesia and Thailand are the only ones in
which the 5 per cent affordability target for broadband services is achieved, thanks to affordable mobile-broadband plans.
despite mobile broadband being more than USD 20 cheaper per month than entry-level fixed broadband,
such as the lack of international Internet bandwidth, also constrain mobile-broadband services. There are four countries in The americas that attain the 5 per cent affordability target by virtue of cheaper mobile-broadband prices:
Belize, El salvador, Paraguay and Suriname. In these countries, mobile broadband is an affordable alternative to entry-level fixed-broadband plans.
However, the mobile-broadband market is still in its early stages, with penetration rates below 5 per cent in Belize, El salvador and Paraguay,
Therefore, the extent to which Internet users turn to mobile broadband as an affordable alternative to fixed broadband will only be seen in the coming years.
Mobile-broadband prices, postpaid handset-based 500 MB, 2013 Rank Economy Mobile-broadband, postpaid handset-based (500 MB) GNI p
. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p. c. USD PPP$ 1 Austria 0. 13 5. 31 4. 62
Mobile-broadband prices, postpaid handset-based 500 MB, 2013 (continued) Rank Economy Mobile-broadband, postpaid handset-based (500 MB) GNI
p. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p. c. USD PPP$ 80 Maldives 2. 08 9. 7 12.6
*Data correspond to the GNI per capita (Atlas method) in 2013 or latest available year adjusted with the international inflation rates.**
because data on GNI p. c. are not available for the last five years. Bundles include:
GNI p. c. and PPP$ values are based on World bank data. Chapter 4. ICT prices and the role of competition 134 Table 4. 6:
Mobile-broadband prices, prepaid handset-based 500 MB, 2013 Rank Economy Mobile-broadband, prepaid handset-based (500 MB) GNI p
. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p. c. USD PPP$ 1 Norway 0. 1 8. 34 5 102
*Data correspond to the GNI per capita (Atlas method) in 2013 or latest available year adjusted with the international inflation rates.**
because data on GNI p. c. are not available for the last five years. Bundles include:
GNI p. c. and PPP$ values are based on World bank data. Rank Economy Mobile-broadband, prepaid handset-based (500 MB) GNI p. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p
. c. USD PPP$ 80 Antigua & Barbuda 2. 58 27.78 33.8 12'910 1'024 81 India 2. 58 3. 38
Mobile-broadband prices, postpaid computer-based 1 GB, 2013 Rank Economy Mobile-broadband, postpaid computer-based (1 GB) GNI p
. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p. c. USD PPP$ 1 Austria 0. 13 5. 31 4. 62
Mobile-broadband prices, postpaid computer-based 1 GB, 2013 (continued) Note:**Data correspond to the GNI per capita (Atlas method) in 2013
or latest available year adjusted with the international inflation rates.****Country not ranked because data on GNI p. c. are not available for the last five years.
Source: ITU. GNI p. c. and PPP$ values are based on World bank data. Rank Economy Mobile-broadband, postpaid computer-based (1 GB) GNI p. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p
. c. USD PPP$ 76 Montenegro 2. 89 17.49 28.93 7'260 3 77 China 2. 95 16.14 26.60 6'560
2 78 Seychelles 2. 98 31.10 44.01 12'530 1 79 Georgia 3. 03 9. 02 18.72 3'570 1
Mobile-broadband prices, prepaid computer-based 1 GB, 2013 Rank Economy Mobile-broadband, prepaid computer-based (1 GB) GNI p
. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p. c. USD PPP$ 1 Austria 0. 13 5. 31 4. 62
Mobile-broadband prices, prepaid computer-based 1 GB, 2013 (continued) Note:**Data correspond to the GNI per capita (Atlas method) in 2013
or latest available year adjusted with the international inflation rates.****Country not ranked because data on GNI p. c. are not available for the last five years.
Source: ITU. GNI p. c. and PPP$ values are based on World bank data. Rank Economy Mobile-broadband, prepaid computer-based (1 GB) GNI p. c.,USD, 2013*Monthly data allowance (MB) as%of GNI p
. c. USD PPP$ 76 South africa 4. 82 28.90 51.17 7'190 1 77 Suriname 4. 91 37.88 64.17 9'260
5 78 Namibia 5. 09 24.75 42.88 5'840 1 79 Paraguay 5. 52 18.59 33.70 4'040 1 80
and the role of competition 140 the region mobile broadband is more than USD 10 cheaper per month.
This reflects the early launch of 3g services in Europe28 and the maturity achieved in the mobilebroadband market, with a mobile-broadband penetration of 57 per cent by end 2013, the highest of all regions.
European countries dominate the global top ten of most affordable mobile-broadband plans with Austria, Finland and Iceland featuring in the top ten for all categories of mobile-broadband services (Tables 4. 5 to 4. 8). 4. 4 Income inequality
and broadband prices The affordability of ICT services depends as much on the price of the service itself as on the economic means of the specific customer.
In this and previous Measuring the Information Society Reports, affordability has been measured in terms of prices as a percentage of GNI per capita,
and hunger have contributed to making available more data on household and individual economic welfare, as well as its distribution.
which data on the distribution of household income or consumption expenditure are available. The objective is to explore how factors such as income inequality may affect people's access to,
as a way of classifying countries by income levels for instance by the World bank. 30 Data on household income, on the other hand, measure only people's economic welfare,
Data are collected by national statistical offices by means of household income and expenditure surveys (HIES)
In order to classify income data by deciles, individuals are placed in ascending order according to the household income attributed to them,
such as access and availability of products, may be relevant. 34 Data on household consumption expenditure are obtained also from household surveys.
when making international comparisons based on data on household economic welfare because there are several methodological issues that limit comparability, notably:
householdlevel economic data provide information (not available from macroeconomic indicators) on the actual income and expenditure capacity of households in a country and the differences across households.
these data can be used to obtain a finer-grain indication of the affordability of broadband services for households from different economic levels.
Data for the United states and Sweden are sourced from the OECD Database on Income Distribution
Data for South africa and Viet nam are sourced from the World bank's Povcalnet and refer to 2008.0 20 40 60 80 100 10 20 30 40 50 60 70 80 90 100%population Sweden United states%of total
Chart 4. 18 uses data on income inequality to reveal differences in the affordability of fixed-broadband services between these two countries,
Household disposable income based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household. would need to spend 8. 5 per cent of their household disposable income,
economic data at the household level also make it possible to determine more precisely the affordability of residential fixed-broadband services.
Household consumption based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household.
and applying this threshold to both household disposable income and expenditure data, the following conclusions can be drawn:
In more than 85 per cent of countries for which data are available, the richest 20 per cent of the population can afford an entry-level fixed-broadband plan.
In 40 per cent of countries for which data are available, a basic fixedbroadband subscription still represents more than 5 per cent of household income/consumption for over half of the population.
As a result, in most developing countries for which data on household income or expenditure distribution are fixed available
Data on household disposable income refer to 2011 or latest year available.*‘*‘Lowest 20%'refers to the price divided by the average income of the first and second income/consumption deciles.‘
Household disposable income for OECD countries and the Russian Federation based on data from the OECD Database on Income Distribution adjusted with ITU estimates on average persons per household.
Household disposable income and consumption expenditure for other countries based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household.
Data on household consumption expenditure refer to 2011 or latest year available.*‘*‘Lowest 20%'refers to the price divided by the average expenditure of the first and second consumption expenditure deciles.‘
Household consumption expenditure based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household.
In the Arab States, the comparison is limited by lack of data on the distribution of household income/expenditure.
Available data suggest that basic fixedbroadband plans are affordable for 90 per cent of the population in Jordan and Tunisia
Mobile broadband The same approach is used to analyse how income inequalities within countries determine the affordability of mobile-broadband services.
Table 4. 11 and Table 4. 12 show the price of prepaid handset-based mobile-broadband plans (with a 500 MB monthly data allowance) as a percentage of disposable household
because prepaid handset-based is on average the cheapest of the four mobile-broadband services for
which ITU collects price data (Chart 4. 13), and is currently the mobile-broadband service that is available in most countries (Chart 4. 11). 147 Measuring the Information Society Report 2014 Handset-based mobile-broadband services are affordable
for the large majority of the population in all developed countries except Ukraine, where the cost of the service represents more than 5 per cent of household expenditure for more than half of the population.
which suggests that the unaffordability of handset-based mobile-broadband services for low-and middle-income households is holding back mobile-broadband adoption in the country.
which data are available. This is also the situation in Armenia, Dominican republic and Egypt. In these countries, a prepaid handset-based mobile-broadband plan represents
on average, more than 5 per cent of household income or expenditure, suggesting that mobile-broadband affordability is an issue irrespective of income/expenditure distribution.
This suggests that neither handset-based mobile-broadband prices nor income/expenditure inequalities are a barrier to mobile-broadband adoption in these countries.
A comparison of fixed-broadband and prepaid handset-based mobile-broadband prices shows that mobile broadband may be the only affordable alternative for low-income households in several developing countries.
but which could afford a mobile-broadband plan. This might be the case in countries such as Albania, Azerbaijan, Kazakhstan, Sri lanka and TFYR Macedonia.
However, subscription data show that, in developed countries, handset-based mobile-broadband subscriptions are individual,
Data on household disposable income refer to 2011 or latest year available.*‘*‘Lowest 20%'refers to the price divided by the average income of the first and second income deciles.‘
Household disposable income for OECD countries and the Russian Federation based on data from the OECD Database on Income Distribution adjusted with ITU estimates on average persons per household.
Household disposable income for other countries based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household.
Data on household consumption expenditure refer to 2011 or latest year available.*‘*‘Lowest 20%'refers to the price divided by the average expenditure of the first and second expenditure deciles.‘
Household consumption expenditure based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household.
and the role of competition 150 Sweden, there is on average more than one mobile-broadband subscription per person.
On the one hand, there are many more mobile-broadband subscriptions than households in countries such as Botswana, Bahrain, Costa rica, Qatar and the United arab emirates, suggesting that handset-based mobilebroadband subscriptions are also individual in these countries.
On the other hand, there are fewer mobile-broadband subscriptions than households in most African countries and in several developing countries in the Asia and the Pacific and Americas regions.
This is consistent with the early stages of development of the mobile-broadband market in these countries
Chart 4. 20 complements the previous analysis by showing the affordability of handset-based mobilebroadband plans assuming that each member of the household has his/her own SIM CARD with a mobile-broadband plan.
or consumption per person. 39 Data show that handset-based mobilebroadband prices (with a 500 MB monthly allowance) are affordable for a majority of the population in developed countries,
in Armenia, the richest 10 per cent of the population could afford to pay for one mobile-broadband plan with 500 MB/month for each person in the household;
for the remaining 80 per cent of the population, such a mobile-broadband plan would be somewhat unaffordable
Data on equivalized household income and consumption expenditure per decile refer 2011 or latest available year.
Equivalized household disposable income for OECD countries and the Russian Federation based on data from the OECD Database on Income Distribution.
Equivalized household disposable income and consumption for other countries based on World bank's Povcalnet data adjusted with ITU estimates on average persons per household. high-income economies (Australia and New zealand) to unaffordable for a majority
This tallies with the variations in mobile-broadband penetration in the region, which ranges from more than 100 per cent in Australia to less than 1 per cent in Bangladesh.
Considering the high mobile-cellular penetration in both countries, this suggests they are in a good position to see an increase in mobile-broadband adoption in the coming years.
Data for the Arab States are limited to four countries. In Tunisia and Jordan, individual handset-based mobile-broadband subscriptions are affordable for a majority of the population.
who may thus need to share a mobile-broadband plan with other members of their household.
In Egypt and Sudan, the cost of a mobile-broadband plan corresponds to more than 10 per cent of equivalized household expenditure for more than half of the population.
and expenditure constitute major barriers for increasing mobile-broadband adoption in many African countries. On the basis of the data presented, it can be concluded that income inequality does
not only have an impact on the proportion of households within a country that have access to mobilebroadband services,
but also by affordability. 4. 5 The impact of competition and regulation on telecommunication prices The impact of ICTS as development enablers depends on access to ICT services
or granting 153 Measuring the Information Society Report 2014 a licence to a new entrant in the mobile-cellular market.
This section presents a quantitative analysis of the role of competition and regulation in shaping prices for mobile-cellular (voice and SMS) and fixed-broadband services.
Among all ICT services mobile cellular and fixed broadband have been selected for the analysis because of the availability of comprehensive data series on the prices for these two services,
and is limited often in scope because of lack of data for developing countries. Quantitative studies are in several cases restricted to samples of EU and OECD countries
by using ITU price and regulatory data for up to 144 countries in the period 2008-2013.
most of them from the developing world, makes it possible to formulate some genuine global conclusions on the links between competition, regulation and telecommunication prices, based on a worldwide representative sample,
and to check to what extent the quantitative results based on telecommunication data from EU, OECD and specific countries hold true in a global context.
since data availability and comparability for such a large number of countries is limited more. Therefore, more specific quantitative results,
such as the impact of a specific regulatory intervention on telecommunication prices (e g. mandating infrastructure sharing
competition and prices The fall in telecommunication prices in the last decade, and in the period analysed in this chapter (2008-2013),
In recent decades, there has been a global trend towards the liberalization of telecommunication services and the privatization of incumbent operators.
and monitor the liberalized electronic communication markets (ITU, 2013b). ) Regulators have thus become the custodians of competition in telecommunication services at the country level.
From the literature on cross-national institutional analysis, there is a broad consensus on the importance of institutional soundness
and its links to the performance of capital-intensive industries like telecommunications. 43 A country's institutional endowment determines the scope for arbitrary administrative discretion, the legal certainty necessary for investors and, through this,
such as the majority of mobile-cellular and fixed-broadband markets. Regulatory and policy action can also have a direct impact on retail prices,
such as for instance by regulating mobile termination rates, as happens in most countries. 47 Regulation also affects the level of competition in each market,
In addition, regulation is a significant part of the institutional framework that affects telecommunication markets. Thus, it can contribute to creating legal certainty and a level playing field,
Market competition is one of the main drivers of affordable prices in telecommunication services. Chart 4. 21 shows the evolution of average entry-level fixed-broadband prices and competition.
Chart 4. 22 shows the evolution of entrylevel prices and competition in mobile-cellular markets,
Simple averages for 140 economies with available data on fixed-broadband prices and competition for the period 2008-2013.
Herfindahl-Hirschman Index (HHI) data sourced from Informa. Chart 4. 22: Competition in mobile markets and mobilecellular prices as a percentage of GNI p. c.,2008-2013 Note:
Simple averages for 140 economies with available data on mobile-cellular prices and competition for the period 2008-2013.
Herfindahl-Hirschman Index (HHI) data sourced from Informa. competition and the openness of the market to private and foreign investment.
the following section presents an econometric model based on panel data regression. This enables us to go beyond descriptive statistics and draw some robust conclusions on the link between competition and prices.
the regulatory mandate in the different segments of the telecommunication sector; Cluster 3: the regulatory regime in the different areas covered by the regulatory authority,
)+-HHI fixed broadband (competition) Fixed-broadband basket as%of GNI p. c.+-HHI mobile (competition) Mobile-cellular basket as%of GNI p. c. 0123456789
2010 2011 2012 2013 Mobile-cellular basket as a%%of GNI p. c. HHI mobile (competition) Chapter 4. ICT prices and the role of competition 156 the value, the more conducive the regulatory environment to ICT developments.
For the analyses in this section, data from the Regulatory Tracker have been extracted for clusters 1, 2 and 3,
The combined value of clusters 1 to 3 is used to test the link between regulation and prices of mobile-cellular and fixed-broadband services.
Choice of the model The analysis was conducted through econometric modelling using panel regressions for up to 144 countries based on data for the five-year period from 2008 to 2013.
Panel data regression is a statistical technique which is used to assess how variations in a set of variables over a given time period relate to Figure 4. 3:
Panel regressions minimize problems of omitted variable bias (the omission of important variables) and multicollinearity (the co-variations of variables modelled as independent).
In addition, panel regressions have the advantage of discounting known and unknown region fixed effects. These are structural geographic conditions
and common institutional frameworks that go beyond telecommunication regulation (e g. the European union acquis). 49 Such background fixed effects may be important for each region,
Secondary trading allowed 9. Unbundled access to the local loop required 8. Co-location/site sharing mandated 7. Infrastructure sharing mandated 6. Infrastructure sharing for mobile operators allowed 5. Qos
mandatory before decisions 4. Percentage of diversified funding 3. Accountability 2. Autonomy in decision making 1. Separate telecom/ICT regulator Regulatory authority 11.
IT 9. Internet content 8. Broadcasting content 7. Broadcasting (radio and TV transmission) 6. Universal service/access 5. Spectrum monitoring
and enforcement 4. Radio frequency allocation and assignment 3. Interconnection rates and price regulation 2. Licencing 1. Qos measures
and mobile-cellular prices and competition and regulation metrics, using panel regressions with fixed effects.
Final prices reflect a number of parameters that characterize a telecommunication market and are often the result of the simultaneous effects of technology choices, competition and regulation.
It is established well that prices of telecommunication services vary with levels of economic development. Therefore, gross national income per capita (GNI p. c is included in the model to control for the differences in economic resources that play a role in shaping prices.
The deployment of telecommunication networks requires large investments that operators evaluate depending on the demand for the service and the specific business case in each geographic area.
The degree of competition for the fixedbroadband and mobile-cellular markets is captured through the Herfindahl-Hirschman Index (HHI),
For instance, a mobile-cellular market with three players with one-third market share each would be more competitive
and 1 GB of data allowance included per month. However, entry-level fixed-broadband plans in several countries offer higher speeds and larger data allowances.
In order to measure how these enhanced features affect prices, a variable on fixedbroadband speed and another one on capped data allowances are included as controls in the model for fixed-broadband prices.
Chapter 4. ICT prices and the role of competition 158box 4. 2: Panel regression models for fixed-broadband and mobile-cellular prices Two models are used for the regressions:
one for fixed-broadband prices and another for mobile-cellular prices (voice and SMS). Both models test variations between prices and a number of variables for up to 144 countries
Data collected by ITU, see Annex 2 for more details on the methodology for the collection of fixed-broadband prices.
and represents the price by country and year for a low-user basket of mobile-cellular calls and SMS in current USD.
Data collected by ITU, see Annex 2 for more details on the methodology for the collection of mobile-cellular prices.
Descriptive statistics of the dependent variables: Average Standard deviation Minimum Maximum 2008 2013 2008 2013 2008 2013 2008 2013 Fixed-broadband prices 77.4 26.1 204.8 16.7 4
. 0 2. 9 1718.8 108.5 Mobile-cellular prices 22.1 16.4 13.9 10.4 2. 8 0. 9 75.4 48.3 2. Explanatory
Data sourced from Informa. Herfindahl-Hirschman Index for mobile cellular (voice and SMS: with Li being the number of mobile-cellular subscriptions of firm i,
and TL he total number of mobile-cellular subscriptions in the country. It is the sum of the squared market shares of each mobile-cellular service provider calculated in terms of subscriptions.
As in the case of the HHI for fixed broadband, this ranges from to 1, where k is the total number of mobile-cellular service providers in the market.
Data sourced from Informa. 159 Measuring the Information Society Report 2014 Box 4. 2: Panel regression models for fixed-broadband and mobile-cellular prices (continued) Regulatory variable:
The combined values of clusters 1 to 3 of the ITU ICT Regulatory Tracker. The Regulatory Tracker is an aggregate benchmark of each country's legal and regulatory frameworks using as a reference internationally recognized regulatory best practices.
The following three clusters are used for the analysis: Score of cluster 1: Regulatory authority Score of cluster 2:
Regulatory mandate Score of cluster 3: Regulatory regime The scores of each cluster are combined into a single value for the regulatory variable,
Data collected by ITU, see www. itu. int/tracker for more information. Fixed-broadband speed:
Speed of the entry-level fixed broadband plan in Mbit/s. Data collected by ITU.
if the fixed-broadband plan includes a cap in the monthly data allowance, and 0 otherwise.
Data collected by ITU. Descriptive statistics of the dependent variables: Average Standard deviation Minimum Maximum 2008 2013 2008 2013 2008 2013 2008 2013 GNI p. c. 14'707 16'439 18
0. 57 0. 53 0. 29 0. 28 0. 13 0. 13 1. 00 1. 00 HHI mobile cellular 0
Descriptive statistics calculated for 124 economies that have complete data for the two models. Source: ITU.
Results for fixed broadband The panel regression model for fixed-broadband prices has a medium explanatory power (an R-squared value of 0. 41,
Panel regression results, fixed-broadband prices and regulation Variable Coefficient Statistical significance Interpretation GNI p. c. 0. 217 (0. 034) Highly significant
plans with data caps are linked to prices 31%lower than unlimited plans Fixed-broadband speed 0. 016 (0. 023) Not significant Constant 4. 304 (0
For instance, a country with a separate telecommunication/ICT regulator that has autonomy in decision-making, enforcement power,
The model also suggests that the application of data caps by fixed-broadband service providers is correlated with cheaper entry-level fixedbroadband plans,
The ITU data collection considers a minimum of 1 GB Chapter 4. ICT prices and the role of competition 162 monthly consumption for fixed-broadband plans.
whether it includes a data cap of 1 GB or more, or allows unlimited traffic.
In 2013, entry-level fixed-broadband plans included a data cap in 35 per cent of the countries included in the fixed-broadband model, a higher percentage than in 2008 (24 per cent.
fixedbroadband prices are cheaper in countries where data caps were enforced in entry-level fixedbroadband plans.
This suggests that operators can offer cheaper prices in exchange for reduced data consumption, thus indicating that capacity in fixed-broadband networks is still an issue in several countries,
i e. the marginal cost of additional Internet data beyond 1 GB is still nonnegligible in many countries.
which offered automatic upgrades of base speeds once networks are upgraded. 54 Chart 4. 23 provides an approximation of the explanatory power of each factor in the variations in fixed-broadband prices observed across countries
such as operators'strategies on data caps, competition in the fixed-broadband market and the ICT regulatory environment, may together be a greater determinant for fixed-broadband prices than the price difference explained by GNI
Results for mobile cellular The results of the panel regression for mobilecellular prices (voice and SMS) indicate that the model constructed has a medium explanatory power (an R-squared value of 0. 41,
For example, the model predicts that in a market with two mobile-cellular operators sharing the market equally,
Competition in mobile-cellular markets is stronger than in fixed-broadband markets, and differences in competition levels across mobile-cellular markets are on average smaller.
Panel regression results, mobile-cellular prices and regulation Variable Coefficient Statistical significance Interpretation GNI p. c. 0. 147 (0. 024) Highly significant
. 284 (0. 071) Highly significant (1%level) A country with 55%urban population is linked to prices 2. 7%cheaper than a country with 50%urban population HHI mobile
when compared with the reduction in mobile-cellular prices that could be achieved in those developing countries with highly concentrated markets,
Urbanization is significantly related to final prices for mobile-cellular services: a 5 per cent increase in the percentage of the population in urban areas is correlated with prices 2. 7 per cent cheaper.
Chart 4. 24 provides an estimation of the explanatory power of each factor in the variations in mobile-cellular prices observed across countries in 2013.
Since the effects of both factors on mobile-cellular prices are opposite, the impact of these variables may almost balance out
GNI p. c. has a weaker effect on the final price in the case of mobile-cellular services.
56 this finding highlights the importance of competition as a driver for lower prices in mobile-cellular markets.
suggesting that regulation is less of an issue in mobile-cellular markets. This may be because the regulation in place in most countries (e g. regulation of mobile termination rates) already supports the development of competition in the market
In addition, the deployment of mobile networks tends to be less capital-intensive than the deployment of fixed-broadband networks,
and why light-touch regulation and a liberal spectrum assignment approach may already be conducive to competition and lower prices in mobile-cellular services,
Variation in mobile-cellular prices(%)explained by each variable, 2013 Note: Calculated taking as a reference the average of each variable and adding a standard deviation.
In each case, the percentage displayed is the relative difference in mobile-cellular prices that would be obtained keeping all other variables constant.
ITU. impact of competition and regulation on fixedbroadband and mobile-cellular prices: Fixed broadband: Different regulation may account for almost 10 per cent of the differences in prices observed across countries.
Another factor that is found to be fixed relevant in-broadband prices is the existence of data caps
ICT Price Basket and sub-baskets, 2013 Rank Economy IPB 2013 Fixed telephone subbasket as a%of GNI per capita, 2013 Mobile-cellular subbasket as a%of GNI
ICT Price Basket and sub-baskets, 2013 (continued) Rank Economy IPB 2013 Fixed telephone subbasket as a%of GNI per capita, 2013 Mobile-cellular subbasket
*Data correspond to the GNI per capita (Atlas method) in 2013 or latest available year adjusted with the international inflation rates.
GNI p. c. and PPP$ values are based on World bank data. Chapter 4. ICT prices and the role of competition 168 and that policy-makers and regulators may need to look at the actions that can be taken to ease capacity bottlenecks.
Based on the econometric model, it can be concluded that factors that are purely attributable to the telecommunication sector, such as operators'strategies on data caps,
Different competition levels largely explain the differences in mobile-cellular prices observed across countries (an estimated 7 per cent),
For more information on the PPP methodology and data, see http://icp. worldbank. org. 3 GNI takes into account all production in the domestic economy (i e.
http://data. worldbank. org/indicator/NY. GNP. PCAP. CD. 4 Voice over internet services, such as Skype or Voipbuster, are excluded from the analysis in this section
because they require an Internet connection and do not have guaranteed a quality of service. They are considered under broadband services. 5 Source:
Census of India 2011. Mode of communication 2001-2011. Available at: http://censusindia. gov. in/2011census/hlo/Data sheet/India/Communication. pdf. 6 Prices for each service are calculated on the basis of a low-user basket that defines the number of calls,
minutes and SMS (in the case of mobile-cellular plans) that are consumed per month. For more information on the baskets and the pricecollection methodology, see Annex 2. 7 Prepaid handset-based mobile-broadband plans were only available in 122 out of the 140 countries included in the comparison with the other
telecommunication services. Therefore, the average for handset-based mobile-broadband prices provides only an indication
the entry of Bharti Airtel as the fifth mobile network operator in the market led to an aggressive price war.
and a floor rate for national mobile calls with the aim of protecting mobile operators'margins.
At present, Sri lanka continues to have some of the cheapest mobile-cellular prices in the world
see Galpaya (2011) and the presentation of the Telecommunications Regulatory Commission of Sri lanka on the impact of the floor rate, available at:
ITU data for countries accounting for 97 per cent of global fixed (wired)- broadband subscriptions,
see http://data. worldbank. org/about/country-classifications/country -and-lending-groups. 11 The Communications Commission of Kenya (CCK) issued mobile virtual network operator licences to three operators in April 2014.
http://www. cck. go. ke/news/downloads/MVNO. pdf. 12 Although four international operators compete in the Kenyan mobile-cellular market,
the dominant mobile operator (Safaricom) holds a market share of almost 70 per cent, and on-net traffic accounts for 87 per cent of the total.
These data suggest there is limited competition among operators. 13 Advanced mobile technologies refer to standards agreed by the ITU Radiocommunication Assembly for next-generation mobile technologies IMT-Advanced such as
LTE-Advanced and Wirelessman-Advanced. For more details on these standards, see http://www. itu. int/net/pressoffice/press releases/2012/02. aspx. 14 Cuba is excluded from the world and developing averages of fixed-broadband prices,
see http://www. eircom. net/efibreinfo/map. 16 The most visited websites in Tunisia by December 2011 were predominantly in English.
and the role of competition 170 18 Mauritel reported 7 352 fixed Internet subscriptions by end 2013,97 per cent of which trhough ADSL (source:
Maroc Telecom, http://www. iam. ma/Groupe/Institutionnel/Qui-Sommes-Nous/Filiales participations/Pages/Mauritel. aspx), out of a total of 7 532 fixed
/page=internet conectividad&sub=internet. 20 For instance, the latest Computer literacy Survey in Sri lanka (2009) found that only 20 per cent of the household population (aged 5-69) could use a computer on their own (Department of Census
TEAMS'website (http://www. teams. co. ke) and EASSY's website (http://www. eassy. org.
see http://www. rogers. com/web/content/share-everything? asc refid=shareeverything. For the details of Verizon's MORE Everything plan, see http://www. verizonwireless. com/wcms/consumer/shop/shop-data-plans/more-everything. html. 24 The details of the different‘4g'plans
offered by Tigo can be found on the following websites: http://www. tigo. com. bo/personas/planes-y-promociones/Internet-movil-en-tu-modem, http://www. tigo. com. co/4g
, http://www. tigo. com. gt/personas/internet-movil/internet-movil-tigo-4g and https://www. tigo. com. py/contenido/para-navegar
-con-el-modem. 25 Data for mobile-broadband services have been collected since 2012 through the ITU ICT Price Basket Questionnaire,
which is sent out annually to all ITU Member States/national statistical contacts. 26 In 2013,
the average price in USD for an entry-level fixed-telephone service was 49 per cent cheaper in developing countries than in developed countries,
and for Orange Côte d'ivoire mobilebroadband plans, see http://www. orange. ci/menu-mobile-3g/pass-internet-3g. html. 28 The UMTS auctions took place in 2000 and 2001
where 3g licences were awarded in 2000. In most developing countries on the other hand, 3g licences were granted not until much later.
In large emerging countries such as China and India, for instance, 3g licences were awarded in 2009 and 2010, respectively.
See for instance Xia (2011) and India's Department of Telecommunication press release: http://www. dot. gov. in/as/Auction%20of%20spectrum%20for3g%20&%20bwa/Auction%20results/3g auction -Final results. pdf. 29 For example, outflows of profits generated by a multinational operating
in country A and transferred back to the country of ownership of the multinational would count in country A's GDP,
http://data. worldbank. org/about/country-classifications/country-and-lending-groups. 31 Household incomes include wages, salaries, self employment incomes, capital and property income,
and consumption expenditure as welfare indicators, see for instance the World bank's website on measuring poverty:
http://go. worldbank. org/W3hl5gd710. 35 Differences in the equivalence scales of the source data used in this chapter are corrected roughly using ITU estimates on the number of inhabitants per household for each country.
World bank Povcalnet data on income distribution are published per capita, and figures from the OECD Database on Income Distribution are equivalized using the square root scale. 36 For OECD countries,
household income is estimated by multiplying the equivalized household income by the square root of the average number of inhabitants per household in the country.
According to 2008/9 survey data, mean household consumption in Angola was almost the same as in Uganda,
rather than to an overall higher level of income and consumption expenditure by most households. 38 Data on income distribution are aggregated per household
data on income distribution are averaged per decile. If the price of a fixed-broadband plan represents less than 5 per cent of the average income of the first decile in a country,
In conclusion, the data presented do not determine whether 100 per cent of a population within a country can afford a fixed-broadband plan,
The World bank's Povcalnet data are adjusted using ITU estimates on the average number of inhabitants per household. 40 See Footnote 10.41 For examples regarding broadband markets
2013a) have set specific entry-level fixed-broadband plans that are offered by the main operators in these countries. 47 Mobile termination rates are regulated in more than 120 countries.
ITU Tariff Policies Database 2013 (ICTEYE, http://www. itu. int/net4/ITU-D/icteye. 48 For a detailed description of the ITU ICT Regulatory Tracker, see www. itu. int/tracker. 49 The EU acquis is the body of rights
which price and market share data were available. This includes 95 economies from the developing world
which price and market share data were available for 2013. This includes 99 economies from the developing world
of the regulatory framework through the indicators Separate telecom/ICT regulator, Separate telecom/ICT regulator, Enforcement power,
http://www. omantel. om/Omanweblib/Mediacenter/Press release. aspx. 55 The mean HHI for mobile cellular is calculated as the simple average of the HHI of 140 economies for
which price and market share data were available. This includes 96 economies from the developing world
whereas dispersion in mobile-cellular prices is of 60 per cent around the mean. 173 Measuring the Information Society Report 2014 Chapter 5. The role of big data for ICT monitoring
and for development 5. 1 Introduction One of the key challenges in measuring the information society has been the lack of upto-date and reliable data, in particular from developing countries.
and quality of those statistics and identify new data sources. In this context the emergence of big data holds great promise,
and there is an opportunity to explore their use in order to complement the existing, but often limited, ICT data.
There is no unique definition of the relatively new phenomenon known as big data. At the most basic level it is understood as being data sets whose volume,
velocity or variety is compared very high to the kinds of datasets that have traditionally been used.
The emergence of big data is linked closely to advances in ICTS. In today's hyper-connected digital world, people and things leave digital footprints in many different forms
and through ever-increasing data flows originating from, among other things, commercial transactions, private and public records that companies and governments collect
and store about their clients and citizens, usergenerated online content such as photos, videos, tweets and other messages,
but also traces left by the Internet of things (Iot), i e. by those uniquely identifiable objects that can be tracked.
Big data have great potential to help produce new and insightful information, and there is a growing debate on how businesses,
governments and citizens can maximize the benefits of big data. Although it was the private sector that first used big data to enhance efficiency and increase revenues
the practice has expanded to the global statistical community. The United nations Statistical commission (UNSC) and national statistical organizations (NSOS) are looking into ways of using big data sources to complement official statistics
and better meet their objectives for providing timely and accurate evidence for policy-making. 1 So far,
there is limited evidence as to the value added by big data in the context of monitoring of the information society,
and Chapter 5. The role of big data for ICT monitoring and for development 174 there is a need to explore its potential as a new data source.
While existing data can provide a relatively accurate picture of the spread of telecommunication networks and services
there are significant data gaps when it comes to understanding the development of the information society.
While an increasing number of countries currently collect data on the individual use of ICTS, many developing countries do not produce such information (collected through household surveys
Consequently, not enough data are available about the types of activity that the Internet is used for,
and little is known about the Internet user in terms of age, gender, educational or income level, and so on. In other areas, such as education, health or public services, even fewer data are available to show developments over time
and enable informed policy decisions. The recently published Final WSIS Targets Review report (Partnership, 2014),
and that big data have the potential to help realize those efforts. In addition to the data produced
and held by telecommunication operators, the broader ICT sector, which includes not just telecommunication companies but also over-the-top (OTT) service providers such as Google, Twitter, Facebook, Whatsapp, Netflix, Amazon and many others, captures a wide array of behavioural
data. Together, these data sources hold great promise for ICT monitoring, and this chapter will explore the potential of today's hyper-connected digital world to expand on existing access and infrastructure indicators and move towards indicators on use, quality and equality of use.
At the same time, there is a growing debate on the role and potential of big data when it comes to providing new insights for broader social and economic development.
Big data are already being leveraged to understand socioeconomic well-being forecast unemployment and analyse societal ties. Big data from the ICT industry play a particularly important role
because they are the only stream of big data with global socioeconomic coverage. In particular, mobile telephone access is quasiubiquitous,
and ITU estimates that by the end of 2014 the number of global mobile subscriptions will be approaching 7 billion.
At the same time, almost 3 billion people 40 per cent of the world's population will be using the Internet.
In recent years, moreover, the strongest growth in telecommunication access and use has been recorded in the developing economies,
where ICT penetration levels have increased and where big data hold great promise for development. However, while there are a growing number of research collaborations and promising proofof-concept studies,
no significant project has yet been brought to a replicable scale in the development sphere. Future efforts will have to overcome a number of barriers,
including the development of models to protect user privacy while at the same time allowing for the extraction of insights that can improve service delivery to low-income populations.
To this end, this chapter will contribute to the debate on big data for development highlight advances, point to some best practices
and identify challenges, including in regard to the production and sharing of big data for development. The chapter will first (in Section 5. 2) describe some of the current big data trends and definitions,
highlight the technological developments that have facilitated the emergence of big data, and identify the main sources
and uses of big data, including the use of big data for development and ICT monitoring. Section 5. 3 will examine the range and type of data that telecommunication companies,
in particular mobile-cellular operators, produce, and how those data are 175 Measuring the Information Society Report 2014 currently being used to track ICT developments
and improve their business. Section 5. 4 looks at the ways in which telecom big data may be used to complement official ICT statistics and assist in the provision of new evidence for a host of policy domains,
while Section 5. 5 discusses the challenges of leveraging big data for ICT monitoring and broader development, including in terms of standardization and privacy.
It will also make some recommendations for mainstreaming and fully exploiting telecom big data for monitoring and for social and economic development,
in particular with regard to the different stakeholders involved in the area of big data from the ICT industry. 5. 2 Big data sources,
trends and analytics With the origins of the term big data being shared between academic circles, industry and the media,
the term itself is amorphous, with no single definition (Ward and Barker, 2013). At the most basic level of understanding, it usually refers to large and complex datasets, Table 5. 1:
Sources of big data Sources Some examples Administrative data Electronic medical records Insurance records Tax records Commercial transactions Bank transactions (inter-bank as well as personal) Credit card
transactions Supermarket purchases Online purchases Sensors and tracking devices Road and traffic sensors Climate sensors Equipment and infrastructure sensors Mobile phones Satellite/GPS devices
Online activities/social media Online search activities Online page views Blogs and posts and other authored and unauthored online content and social media activities Audio/images/videos Source:
ITU, adapted from UNSC (2013. and reflects advances in technology that make it possible to capture,
store and process increasing amounts of data from different data sources. Indeed, one of the key trends fostering the emergence of big data is the massive datafication
and digitization, including of human activity, into digital breadcrumbs or footprints. In an increasingly digitized world,
big data are generated in digital form from a number of sources. They include administrative records (for example, bank or electronic medical records), commercial transactions between two entities (such as online purchases or credit card transactions), sensors and tracking devices (for example
mobile phones and GPS devices), and activities carried out by users on the Internet (including searches
and social media content)( Table 5. 1). Big data is not just about the volume of the data.
One of the earliest definitions, introduced by the Gartner consultancy firm, describes big data characteristics such as velocity and variety,
in addition to volume (Laney, 2001). Velocity refers to the speed at which data are generated, assessed and analysed,
while the Chapter 5. The role of big data for ICT monitoring and for development 176 term variety encompasses the fact that data can exist as different media (text,
audio and video) and come in different formats (structured and unstructured). The three-Vs definition has caught on
and been expanded upon. A fourth V veracity was introduced to capture aspects relating to data quality and provenance,
and the uncertainty that may exist in their analysis (IBM, 2013). A fifth V value is included by some to acknowledge the potentially high socioeconomic value that may be generated by big data (Jones,
2012)( Figure 5. 1). Included within the scope of big data is the category of transaction-generated data (TGD),
2 also sometimes described as data exhaust or trace data. These are digital records or traces that have been generated as by-products of doing things (such as processing payments,
making a phone call and so on) that leave behind bits of information. The value of this subset of big data is that it is connected directly to human behaviour
and its accuracy is generally high. Most of the data captured by telecommunication companies can be classified as TGD.
As is often the case with technological innovation, it is the private sector that has been Figure 5. 1:
The five Vs of big data Source: ITU. at the forefront of extracting value from this data deluge.
Encouraged by promising results but also reduced budgets, the public sector is turning towards big data to improve its service delivery and increase operational efficiency.
In addition, there are uses for big data in broader development and monitoring, and there is an increasing focus on big data's role in producing timely (even real-time) information,
as well as new insights that can be used to drive social and economic well-being. Big data uses by the private and public sectors Marketing professionals,
whose constant aim is to understand their customers, are now increasingly shifting from conventional methods, such as surveys, to the extraction of customer preferences from the analysis of big data.
Walmart, the world's biggest retailer, has been one of the largest and earliest users of big data.
In 2004, it discovered that the snack food known as Pop Tarts was purchased heavily by United states citizens preparing for serious weather events such as hurricanes.
The correlation analysis revealed a behaviour associated with VOLUME VARIETY VERACITY Vast amounts of data generated through large-scale datafication and digitization of information Different types and forms of data
including large amounts of unstructured data Level of quality, accuracy and uncertainty of data and data sources VALUE Potential of big data for socioeconomic development VELOCITY Speed at
which data are generated and analyzed 177 Measuring the Information Society Report 2014 a specific condition that then led Walmart to improve its production chain in this case,
by increasing the supply of Pop Tarts to areas likely to be affected by a disaster.
Walmart has also made use of predictive analytics, which uses personal information and purchasing patterns to extrapolate to a likely future behaviour,
and to better target and address customer needs. Together large-scale automated correlation analysis and predictive analytics are two of the key techniques that have helped unleash the value of big data.
Nor is the private sector's use of big data techniques restricted solely to market research. Companies and whole industries (healthcare, energy and utilities, transport, etc.
are using such techniques to optimize supply chains and production (see Box 5. 1 for an example from the energy industry).
New value is extracted by being able to link new information on customers to the production process in a way that enables companies to tailor
and segment their products at low cost. Firms that are highly proficient in their use of data-driven decision making have been found to have productivity levels up to 6 per cent higher than firms making minimal to no use of data for decision-making (Brynjolfsson, Hitt and Kim, 2011.
Significantly, industries now have the ability to conduct controlled experiments at a scale and with Box 5. 1:
How big data saves energy Vestas Wind Systems improves turbine performance Vestas, a global energy company dedicated to wind energy,
with installations in over 70 countries, has used big data platforms to improve the modelling of wind energy production
and identify the optimal placement for turbines. Wind turbines represent a major investment and have a typical lifespan of 20 to 30 years.
By using big data techniques based on a large set of factors and an extended set of structured and unstructured data
Big data have enabled the creation of a new information environment and allowed the company to manage
and location data in ways that were previously not possible. These new insights have led to improved decisions relating not only to wind turbine placement and operation,
while increasing the accuracy of the customer's return-on-investment estimates. Source: ITU, based on IBM (2012.
a speed that are unprecedented. Google, for example, is running about a thousand experiments at any given point in time (Varian, 2013a.
Telecom network operators make extensive use of such techniques when rolling out new services, among other things for the purpose of pricing.
Telecom operators also use big data techniques to understand and control churn, optimize their management of customer relations
and manage their network quality and performance. These fundamental shifts in data exploitation to generate new socioeconomic value,
coupled with the simultaneous emergence of new rich data sources that can potentially be linked together
and analysed with ease, have sparked also the interest of governments, researchers and development agencies. Encouraged by the potential of big data to produce new insights and slimmer budgets
governments (at all levels) are now looking to exploit big data and increase the application of data analytics to a range of activities, including monitoring and improvement of tax compliance and revenues, crime detection and prediction,
and improvement of public service delivery (Giles, 2012; Lazer et al. 2009). ) To this end, governments, in addition to the data they collect
and generate themselves, Chapter 5. The role of big data for ICT monitoring and for development 178 complement their official statistics by leveraging data from new sources, including crowd-sourced data generated by the public.
In the United states, for example, Boston City hall released the mobile app Street Bump, which uses a phone's accelerometer to detect potholes
while the app user is driving around Boston and notifies City hall. 3 Some of the richest data sources for enabling governments
and development agencies to improve service delivery are actually external. Such external data include those captured
and/or collected by the private sector, as well as the digital breadcrumbs left behind by citizens as they go about their daily lives.
According to a recently published White house report, United states government agencies can make use of public and private databases
and big data analytics to improve public administration, from land management to the administration of benefits. The Department of the treasury has set up a Do Not Pay portal,
which links various databases and identifies ineligible recipients to avoid wrong payments and reduce waste and fraud4 (The White house, 2014).
Big data for development and ICT monitoring One of the richest sources of big data is captured the data by the use of ICTS.
This broadly includes data captured directly by telecommunication operators as well as by Internet companies and by content providers such as Google, Facebook, Twitter, etc.
Big data from the ICT services industry are already helping to produce large-scale development insights of relevance to public policy.
Collectively, they can provide rich and potentially real-time insights to a host of policy domains.
It should be noted that in some countries and regions the use of big data including big data from the ICT industry,
is subject to national regulation. In the EU, for example, a number of directives require data producers to obtain users'consent before gathering any of their personal data. 5 One of the best-known examples of leveraging the online population's digital breadcrumbs for development purposes is Google Flu Trends (GFT.
Following its launch in 2008, GFT was remarkably accurate in tracking the spread of influenza in the United states,
doing so more rapidly than the Centers for Disease Control and Prevention (CDC), with a lag time of only one day as opposed to one week.
GFT was held up as an outstanding example of big data in action and of the great potential of big data for broader development and monitoring (Mayer-Schönberger and Cukier, 2013;
Mcafee and Brynjolfsson, 2012. GFT worked by monitoring health-seeking behaviour expressed through online searches,
) This proved to be so successful that it spawned similar efforts focusing on the use of search-engine data to understand dengue fever outbreaks,
6 monitor prescription drug use (Simmering, Polgreen and Polgreen, 2014), predict unemployment claims in the United states (Choi and Varian, 2009) and Germany (Askitas and Zimmermann, 2009),
The Internet has also been a rich source of big data beyond the realm of user search terms.
Online job-posting data are being used to supplement traditional labour statistics in the United States7 and other countries.
In another effort, an academic project at MIT known as the Billion Prices Project collects high-frequency price data from hundreds of online retailers. 8 The data are used then by researchers to understand a whole host of macroeconomic
a UN initiative to use big data for sustainable development and humanitarian action, has been mining Twitter data from Indonesia (where Twitter usage is high) 9 to understand food price crises.
Global Pulse was able to identify a consistent pattern among specific food-related tweets and the daily food price index.
In fact, it was able to use predictive analytics on the Twitter data to forecast the consumer price index several weeks in advance (Byrne, 2013.
UN Global Pulse is also using Twitter data to understand and compare the relevance of different development topics among countries (Box 5. 2). Box 5. 2:
How Twitter helps understand key post-2015 development concerns As the process of formulating the post-2015 development agenda continues,
UN Global Pulse and the Millennium Campaign are using big data and visual analytics to identify the most pressing development topics that people around the world are concerned about
Users can select a country to see the number of tweets generated by its Twitter users in regard to the highlighted topic,
Also highly ranked, in 7th position, was phone and Internet access. By clicking on any of the data points in the chart,
Using Twitter to visualize trends in global development topics In fact, the ICT sector is itself using the Internet as a source of big data for monitoring purposes.
Regulators and others are now using the Internet to crowdsource quality of service (Qos) data on broadband quality.
For example, the United states Federal Communications Commission (FCC) has released mobile apps that enable consumers to check their broadband quality.
and address coverage and quality issues in different areas. 10-200'000 400'000 600'000 800'000 1'000'000 1'200'000
and oceans Phone and Internet access Equality between men and women Chapter 5. The role of big data for ICT monitoring and for development 180 Mobile data Despite the rapid growth in Internet access,
60 per cent of the world's population is still not using the Internet. Household Internet penetration in developing economies is expected to reach 31 per cent by the end of 2014,
as against almost 80 per cent in developed economies. In addition as Internet penetration rates remain limited,
Internet users are not yet) representative of the population at large. For example, Internet users tend to be younger, relatively well educated,
with men still more likely to be online than women, especially in developing countries11 (ITU, 2013).
Depending on the source of Internet data, results may also be biased more or less. A 2013 study into the characteristics and behaviour of Facebook users, for example, revealed that
while in many ways Facebook users have real-life behaviour and characteristics, in many ways the social network fails as a representation of society.
On the one hand, for example, the American Facebook user's relationship status of married on Facebook is very similar to real life (census) data on the average age
when American people get married. On the other hand, however, the average American Facebook user is much younger than the average citizen. 12 This is just one example
but it highlights the need to take account of particular characteristics and the limitations of producing representative results
when extracting information from online users'behaviour. Given the popularity of mobile-cellular services, non-Internet-related mobilenetwork big data seems to have the widest socioeconomic coverage in the near term,
and the greatest potential to produce relatively representative information globally, particularly in developing countries. By the end of 2014, the number of mobilecellular subscriptions is expected to be nearing 7 billion,
and the number of mobilecellular subscriptions per 100 inhabitants is expected to reach 90 per cent.
Mobile data are already being utilized for research and policy-making not only in developed but also in developing economies.
There are various examples of how mobile phone records have been used to identify socioeconomic patterns and migration patterns, describe local, national and international societal ties,
and forecast economic developments. 13 Data are also being used to improve responsiveness in the event of natural disasters or disease outbreaks.
Lu, Bengtsson and Holme (2012) used mobile call records to study the population displacements following Haiti's 2010 earthquake,
with a view to using such methods to improve the effectiveness of humanitarian relief operations immediately after a disaster.
Call records have also been merged with epidemiological data to understand the spread of malaria in Kenya (Pindolia et al.
Mobile network big data have been utilized to great effect in the area of transportation, helping to measure and model people's movements (even in real time) and understand traffic flows (Wu et al.,
) It is evident from the examples given that big data from the ICT sector, and especially those available to telecommunication operators, have wide applicability for informing multiple public policy domains.
Leveraging such data to complement official statistics and facilitate broader development will enable governments as well as development agencies to better serve their citizens and beneficiaries.
Less use has thus far been made of telecommunication big data with a view to understanding its potential for producing additional information and statistics on the information society.
In assessing that potential, including the potential for providing complementary 181 Measuring the Information Society Report 2014 information on the development of the information society,
it is first important to better understand the type of data that can be made available. 5. 3 Telecommunication data
and their potential for big data analytics Fixed and mobile telecommunication network operators, including Internet service providers (ISPS), are an important source of data and for the purpose of this chapter, all forms of telecommunication big data (either volume,
velocity or variety) are being considered. Most telecommunication data can be considered as TGD, 14 that is, the result of an action undertaken (such as making a call,
sending an SMS, accessing the Internet or recharging a prepaid card). Since the service with the widest coverage and greatest uptake and popularity is the mobilecellular service,
data from mobile operators have the greatest potential to produce representative results and reveal developmental insights on the population,
including in developing countries and, increasingly, low-income areas. Not surprisingly, the big data for development initiatives (outlined in Section 2. 2) have drawn mainly on mobile network big data rather than on those from fixed-telephone operators or ISPS.
Figure 5. 2 illustrates some of the similarities and differences in the type of information that mobile network operators,
as opposed to fixed-telephone operators and ISPS, produce, and shows some of the additional insights,
in particular in terms of the location and mobility information that mobile networks and services generate. Telecommunication data The mobile telecommunication data that operators possess can be classified into different types,
depending on the nature of the information they produce. They include traffic data, service access detail records, location and movement data, device characteristics, customer details and tariff data.
For a more detailed overview of these types of data, see Chapter 5 Annex. To collect traffic data,
operators use a range of metrics to understand and manage the traffic flowing through their networks,
including the measurement of Internet data volumes, call, SMS and MMS volumes, and value-added service (VAS) volumes.
Internet service providers can also use deep packet inspection (DPI), 15 which is a special process for scanning data packages transiting the network.
Service access detail records, including call detail records (CDRS), are collected by operators whenever clients use a service.
They are used to manage the infrastructure and for billing purposes, and include information on the time
and duration of services used and the technology used, for example, for the mobile network (2g, 3g, etc.).
These data are potentially also very useful for building a rich profile of customers, as outlined in this section.
Mobile networks capture a range of movement and location variables to identify user location and movement patterns.
The degree of accuracy of this information depends on a number of factors, including the network used and device generation,
and can be classified broadly into two different types: passive and active positioning data, with the latter providing more detailed and precise location information.
Since mobile user devices used to access mobile telecommunication services come with an international mobile station equipment identity (IMEI) number,
operators can identify some device characteristics, including the handset make and model and type of technology (2g, 3g, LTE) employed.
Mobile network operators can use the IMEI number to identify the specific mobile handset being used by a subscriber,
which in turn can provide some insight as to that Chapter 5. The role of big data for ICT monitoring and for development 182figure 5. 2:
An overview of telecom network data Source: ITU, adapted from Naef et al. 2014). ) Traffic data Fixed operator Mobile operator ISP Data volume Call volume SMS/MMS volume Erlang DPI data Timestamp of use Contact
network Duration of use Applicable charges Handset type Technology utilized (2g, 3g, DSL/ADSL, etc.
Billing address Passive positioning data (e g. cell ID) IMEI Active positioning data (e g. cell triangulation, GPS) MAC address Customer demographics (e g. age, gender
, national ID card number) Payment history (postpaid) Billing address Recharge history (prepaid) Service order history Tariff sheet Service access detail records Location data Device
characteristics Customer details Tariff data Transaction generated data Stored warehouse data 183 Measuring the Information Society Report 2014 subscriber's purchasing power (see below for more details.
In addition, telecommunication operators hold various customer details that were captured during the customer registration process. These can include the customer's name, age,
gender, billing address and, in some cases, national identity card number. Customer details may also include a history of the services accessed,
service option preferences as well as other details (as referred to in Chapter 5 Annex). Finally, operators maintain tariff data in the form of billing records for their current and past services,
from which information on a customer's usage patterns and preferences can be extracted. The information outlined above is used at the aggregate level to derive a range of indicators to provide operators with information on the uptake of different services
How mobile operators currently use data to track service uptake, business performance and revenues Operators use their TGD to monitor the uptake
to determine the number of active mobile-cellular and active mobile-broadband subscriptions. On the basis of the detailed service-usage data collected, telecommunication operators can produce a range of detailed indicators relating to service consumption.
For each customer it is possible to determine the minutes of use (Mou), number of originating
data upload volumes, data download volumes, level of use of different VAS, and level of use of different OTT services.
These data can be reported as averages (over time or for different categories of user), as well as at various levels of aggregation (again over time or for different categories of user).
Finally, service consumption data are used to produce revenue data and projections at various levels of disaggregation or aggregation.
often associate revenue data with resource allocation to ensure that Qos at the base stations used by their premium customers is maintained at the highest possible level.
but also consultancy firms and others, use aggregated revenue data to track and benchmark countries'ICT developments, monitor the evolution of the information society
and identify digital divides. The telecom industry's use of big data Telecommunication companies are actively seeking to intensify their use of big data analytics
in order to improve existing services and create new ones. For operators, big data open up opportunities for better understanding of their customers,
which in turn leads to improved sales and marketing opportunities. At the same time big data can help optimize network operations
and create new revenue streams and business lines, for example when selling data. Customer profiling Telecom operators capture a range of behavioural data about their customers.
Chapter 5. The role of big data for ICT monitoring and for development 184 Customer profiles include details about customers'mobility patterns, social networks and consumption preferences.
Collectively, these digital breadcrumbs enable operators to profile and segment their customers based on a variety of metrics (Figure 5. 3). Depending on the country or region,
there may be different privacy and data regulations governing the manner in which operators may keep and/or use such data.
This being the case the extent to which behavioural profiling is used by operators may vary greatly.
Customer interests: these can be captured, or in some cases inferred, on the basis of usage levels (time spent and/or volume) for different VAS and OTT services.
DPI can also be used to categorize interests based on sites visited (as opposed to content accessed.
Big data, on the other hand, can help to enhance that classification by enabling analysis of the levels of consumption of different services,
Big data techniques can help operators understand churn better by enabling them to model the likelihood of customers leaving the network
This often calls for an understanding of the level of influence of each subscriber's social networks, both on-network (i e. within the same operator) as well as off-network (i e.
Customer profiling using telecom big data Source: ITU. CUSTOMER INTERESTS SOCIOECONOMIC CLASS LEVEL OF INFLUENCE OF CUSTOMERS LIKELIHOOD OF CHURN MOBILITY PROFILE 185 Measuring the Information Society Report 2014 in competitor networks.
For example, by understanding their customers'relationships to their social networks (and their relative importance within them),
Furthermore, social network insights can be used by an operator to market its services to the off-network contacts that are connected to its customers
In the Republic of korea, for example, SK Planet, a subsidiary of SK TELECOM, uses big data to help its parent company to cut churn
and has used data mining to achieve a fourfold improvement in churn forecasting. The operator found that customers planning to quit their current package tend to use specific search phrases, such as data plan or operator benefits, at least three to seven days before taking action.
When operators suspect that customers may be looking elsewhere, they may try to keep them by providing them with a tailored offer. 18 Network planning
operators may also seek to monetize the data they hold. The simplest way of doing this is to sell (anonymized) data to third parties.
The customer insights obtained through the analysis of usage data can also help create new business lines,
either through innovation (e g. new types of VAS) or by partnering with other businesses, including credit-scoring and related financial services.
One example is based the US big data startup Cignifi, 19 which obtains data from mobile operators
and financial institutions to build credit profiles and evaluate customer creditworthiness (see Box 5. 8). Cross-promotions with brick-andmortar businesses are a potentially high-growth area in
which the detailed mobility profiles available to operators are leveraged. 5. 4 Big data from mobile telecommunications for development and for better monitoring In 2013, the United nations High-level Panel of Eminent Persons on the Post-2015
Development Agenda called for a data revolution that draws on existing and new sources of data for the post-2015 development agenda (United nations, 2013).
In March 2014, the forty-fifth session of UNSC, the highest decision-making body for international statistical activities, presented a report on big data and modernization of Chapter 5. The role of big data for ICT
and proposed the creation of a big data working group at the global level (UNSC, 2013). 20 Current uses of big data to complement official statistics are still exploratory,
but there is a growing interest in this topic, as evidenced by the numerous initiatives being pursued by the United nations, as well as by others,
There are many big data sources that can be used to monitor and assess development results. In a world where mobile telephony is increasingly ubiquitous,
it is not surprising that mobile telecommunication big data have unique potential as a new data source,
with high mobilecellular penetration levels and the increasing use of mobile phones, even among the poorest and most deprived, making them particularly valuable by comparison with other types of telecommunication data.
Indeed, when referring to the data revolution, the United nations High-level Panel cited the example of mobile technology
and other advances to enable realtime monitoring of development results. This section will present some of the existing (and growing) evidence for the role of mobile big data in achieving development goals in various policy areas,
including disaster management and sustainable and economic development. In addition to their use for development, telecommunication big data have potential as a source to enable monitoring of the information society,
although they have yet to assume a critical role in complementing the official ICT statistics that are collected
and used for that purpose. As the lead agency on global telecommunication and ICT statistics, however, ITU is exploring the potential of big data to complement its existing,
and often limited, set of ICT statistics. This section presents a first attempt to help identify some of the areas in which mobile telecommunication big data could complement existing ICT indicators to provide a more complete
comprehensive and up-to-date picture of the state of today's information society. Mobile phone big data for development Mobile data offer a view of an individual's behaviour in a low-cost, high-resolution, realtime manner.
Each time a user interacts with a mobile operator, many details of the interaction are captured, creating a rich dataset relating to the consumer.
Topping up airtime, making calls and sending SMSS, downloading applications or using value-added services are all examples of interactions for which the time, location, device,
In addition to the fact that these data are uniquely detailed and tractable, the information captured cannot easily be derived from other sources on such a scale.
The fact that the format of the data is relatively similar across different operators and countries creates a huge potential for the global scaling of any application found to have significant benefits.
Box 5. 4 illustrates the potential of mobile data for development in a number of different areas.
Big data for disaster management and syndromic surveillance21 Mobility data collected immediately after a disaster can in many cases help emergency responders to locate affected populations
One application of such mobility data is for syndromic surveillance, especially to model the spread of vector-borne22 and 187 Measuring the Information Society Report 2014 Box 5. 4:
Using mobile data for development A recently published report (Cartesian, 2014) explores the potential of mobile data for development.
It points to three primary types of analysis-ex-post evaluation, real-time measurement, and future predictions and planning-in a number of areas (including health, agriculture and economic development),
and assessment Measurement and real-time feedback Prediction and planning Financial services Economic development Health Agriculture Commercial Other High Medium Low Mobile agent placement Algorithmic
fraud detection Social network analysis marketing Agent monitoring Enhanced credit Algorithmic liquidity needs prediction Income and poverty assessment Mapping social divides GDP estimates
through mobile data Migration monitoring Text analysis economic downturn prediction Text analysis commodity fluctuation prediction Assessment of mobility restrictions Disease containment targeting Migratory p
opulation tracking Predicting outbreak spread Mobile data to track food assistance delivery Geo-targeted links between Ag suppliers/purchasers Pests,
bad harvest Ag yield/shock predictions Campaign effectiveness Social network delineated market areas Predictive algorithms to anticipate prod. churn Social network targeted marketing Post-disaster refugee reunification
Sentiment analysis of public campaigns Urban planning Mobile disaster relief targeting High frequency surveys Crime detection Social unrest prediction Ex-post Current Future Pilot
Pioneering research in Kenya combined passive mobile positioning data with malaria prevalence data to identify the source and spread of infections (Wesolowski et al.
) Similar work in Haiti showed how mobile phone data was used to track the spread of cholera after the 2010 earthquake (Bengtsson et al.
2011, see Box 5. 5). The integration of mobility data from mobile networks with geographic information frameworks,
This highlights the need to ensure that the response plan implemented after any disaster includes ensuring that any damaged mobile network infrastructure is repaired as rapidly as possible.
Chapter 5. The role of big data for ICT monitoring and for development 188 Big data for better transportation planning A data-centric approach to transportation management is already a reality in many developed economies.
Transportation systems are being fed with sensor data from a multitude of sources such as loop detectors axle counters, parking occupancy monitors, CCTV, integrated public transport card readers and GPS data derived not only from phones but also from public transport and private vehicles (Amini, Bouillet, Calabrese, Gasparini
and Verscheure, 2011. One advantage of mobile networks is that even the least developed mobile network infrastructure generates passive positioning data,
which, despite its limited spatial accuracy (cell ID), has great potential for transportation planning. For example, IBM researchers used CDR data from mobile operator Orange to map out citizens'travel routes in Abidjan, the largest city in Côte d'ivoire,
and show how data-driven insights could be used to improve the planning and management of transportation services, thereby reducing congestion (Berlingerio et al.,
2013). ) By simply extending one bus route and adding four new ones, overall travel time was reduced by ten per cent.
Passive mobile positioning data has also been used for transportation planning and management in Estonia (Ahas and Mark, 2005),
and has provided reliable results in Sri lanka (Lokanathan et al.,2014, see Box 5. 6). Box 5. 5:
How mobile network data can track population displacements an example from the 2010 Haiti earthquake The Figure below shows the number of people estimated to have been in Port-au-prince (Pap) on the day of the 2010 Haiti earthquake,
This map was produced on the basis of mobile network data to show the potential of big data in tracking population movements.
Tracking mobility through mobile phones Port-au-prince (Pap) Number of people displaced after earthquake 189 Measuring the Information Society Report 2014 Box 5. 6:
Leveraging mobile network data for transportation and urban planning in Sri lanka Very similar findings between the results of an official household survey assessing mobility patterns (right-hand map) with the results of a big data analysis using mobile-phone
data (left-hand map) underscore the merits of big data. The image on the left, based on mobile-phone data, depicts the relative population density in Colombo city
and its surrounding regions at 1300 hours on a weekday in 2013, compared to midnight the previous day.
Mobile big data (left) versus official survey data (right) Both passive and active positioning data are used to analyse traffic conditions, particularly in urban areas with higher base-station density.
Active positioning data (especially GPS) produce higher precision in location data and are therefore the most useful.
Operators may offer such specialized services (based on passive or active location data) either directly, or by providing data to third parties.
Mobile network data are less expensive, are in real time and are less time-consuming to produce than survey data,
particularly in urban and peri-urban areas where base-station density tends to be high. In another example
the analysis of mobility flows between two Spanish cities derived from three different data sources mobilephone data,
geolocated Twitter messages and the census showed very similar results, and although the representativeness of the Twitter geolocated data was lower than the (real-time) mobile-phone and census data,
the degrees of consistency between the population density profiles and mobility patterns detected by means of the three datasets were significant (Lenormand et al, 2014).
Chapter 5. The role of big data for ICT monitoring and for development 190 Big data for socioeconomic analysis Data from mobile operators can provide insights in the areas of economic development and socioeconomic status
, often in near real time. Big data techniques can therefore complement official statistics in the intervals between official surveys,
which are usually relatively expensive and time-consuming and therefore carried out infrequently. In many cases, insights derived from big data sources may help to fill in the gaps,
rather than replace official surveys. It should also be noted that mobile network big data are one of the few big data sources
(and often the only one) in developing economies that contain behavioural information on low-income population groups Frias-Martinez et al.
2012) developed a mathematical model to map human mobility variables derived from mobile network data to people's socioeconomic and income levels.
and income-related data derived from official household surveys, and the results showed that populations with higher socioeconomic levels are associated more strongly with larger mobility ranges than populations from lower socioeconomic levels.
the study suggested that it was possible to create a model to estimate income levels based on data from mobile network operators.
used two types of mobile network data, namely subscriber communication data and airtime credit purchase records,
Poverty mapping in Côte d'ivoire using mobile network data In Côte d'ivoire, researchers used mobile network data (specifically communication patterns,
They combined this analysis with a study of users'social networks with two users being considered as connected
One of the challenges has to do with operator sensitivity regarding revenue data and the difficulty this poses for outside parties wishing to obtain such data.
The use of mobile-operator TGD can also foster financial inclusion by facilitating the provision of credit to the unbanked.
In 2012, the Consultative Group to Assist the Poor (CGAP) and GSM Association (GSMA) estimated that close to 2 billion people had a mobile phone but no bank account.
A compelling example of how mobile big data can be used for the unbanked is Cignifi, a big data startup that uses the mobile phone records of poor people to assess their creditworthiness
when they apply for a loan (Box 5. 8). Big data for understanding societal structures Social-network studies relying on self-reporting relational data typically involve both a limited number of people
and limited number of time points (usually one). As a result, social-network analysis has generally been confined to the examination of small population groups through a small number of snapshots of interaction patterns.
By examining social communication patterns based on telecommunication data, it has become possible to obtain insights into societal structures on a scale that was previously unavailable.
Using mobile-phone data to track the creditworthiness of the unbanked Cignifi, a big data startup, has developed an analytic platform to provide credit
and marketing scores for consumers, based on their mobile-phone data. The Cignifi business model is founded on the idea that Mobile phone usage is not random it is highly predictive of an individual consumer's lifestyle and risk.
Based on the behavioural analysis of each mobile-phone user phone calls, text messages, data usage and, extrapolating from these,
lifestyles the company identifies patterns and uses them to generate individual credit risk profiles. This information could help many of the world's unbanked to have access to insurance
credit cards and loans. Scores are dynamic and respond to changes in customer activity as the data are refreshed, usually every two weeks.
In addition to updating a person's creditworthiness, the system also helps to identify a customer's appetite for different products and inclination to churn.
and verified the findings from the model against historical lending data from approximately 40 000 borrowers using the mobile operator Oi's lending business,
Chapter 5. The role of big data for ICT monitoring and for development 192 have been used to study the geographic dispersion
) However, telecommunication data are also revolutionizing the study of societal structures at the micro level.
2009) show that it is possible to assess friendship using data from mobile network operators, and that the accuracy is compared high
when with self-reported data. Leveraging these behavioural signatures to obtain an accurate characterization of relationships in the absence of survey data could also enable the quantification
and prediction of macro and micro social-network structures that have thus far been unobservable. Big data to monitor the information society There is a case to be made for analysing data captured by telecommunication operators in the interests of improving the current range of indicators used for monitoring the information society.
An internationally-accepted and widely-adopted list of indicators is the core list of ICT indicators developed by the Partnership on Measuring ICT for Development,
a multistakeholder initiative launched in 2004.24 This list includes, among others, the key-infrastructure, access and individual-use indicators that ITU collects
and disseminates. Some of these indicators are amenable for augmentation using big data analytics. 25 The core indicators on ICT infrastructure
and access include indicators on mobile-cellular and mobile-broadband subscriptions, which remain some of the most widely used
and internationally comparable telecommunication indicators produced for tracking the information society. One of the main issues with mobile-cellular and mobile-broadband subscription data is that they do not refer to unique subscriptions
or mobile users. Since one person can have multiple subscriptions, or share a subscription with another person,
it is not possible to determine how many individuals subscribe to, or use, the mobile service. It is often the case that countries with large numbers of prepaid subscriptions display relatively high penetration rates
making it important for operators to monitor the time during which a SIM CARD remains inactive.
the number of unique mobile subscriptions was just over 50 percent, whereas the number of connections per 100 population far exceeded 100 per cent. 26 Survey-based data,
for example on Internet users and mobile-phone users, do not entail the same issues as subscription data.
They are collected through household surveys, directly from citizens, and their level of reliability is relatively high.
one of the core indicators reflects the types of online activity pursued by Internet users, and includes response categories such as seeking health information,
or participating in social networks. Survey-based data can also be broken down by individual characteristics, including gender, age, educational level and occupation,
which substantially increase the data's added value. However, the main challenge with these data is that they are not widely available (in particular,
many developing countries do not yet collect data on individual use of, or household access to, ICTS), are relatively expensive to produce,
and are much less timely than subscription data (often with a time lag of one year.
Consequently, data on users of the information society and the types of online service they consume are limited,
and in many 193 Measuring the Information Society Report 2014 cases outdated. Against this background, mobile networks and mobile big data could be used to identify alternative,
less costly and faster ways of carrying out representative surveys (Box 5. 9). Given the shortcomings of existing administrative data from operators and survey data collected by NSOS,
it is particularly interesting to assess some of the ways in which big data can be used to overcome the shortcomings of existing key ICT indicators
and to provide additional insights into ICT access and use, user behaviour, activities and also the individual user.
Big data could help in obtaining more granular information in several areas, and big data techniques could be applied to existing data to produce new insights.
In particular, operators'big data could produce information in the following areas: Individual subscriber characteristics: Additional categorization across both time and space are possible for subscription indicators,
and big data could provide additional information on gender, socioeconomic status and user location. Information on gender or age, for example, could be derived from customer registration information (notwithstanding a number of challenges and privacy issues,
as discussed later in Section 5. 5). The socioeconomic status of the person linked to a subscription could be derived from big data techniques applied to users'consumption information,
as well as other data contained in customer registration information. In addition, the analysis of customers'mobility patterns will often allow for an understanding of important locations (work
and home being the two most important) and of the use of mobile services in rural versus urban areas.
It would thus be possible to gain a more reliable and more granular understanding of service penetration across space on the basis of actual behaviour/activity, rather than of
All subscription data could provide information as to location. In the case of fixed-telephone and fixed-broadband subscriptions,
which are linked to an address through the billing information, it is possible to obtain information on the administrative division of subscribers,
or to link those characteristics to other (administrative) databases in order to Box 5. 9: Using mobile big data
and mobile networks for implementing surveys An important measurement for assessing the development of the information society is the extent to
which households have access to ICTS. Given the need for continued recourse to surveys for collecting the corresponding data,
and the declining response rates where traditional surveys are concerned (Groves, 2011), mobile operators could develop platforms to facilitate the collection of survey data.
This could include targeting a wide variety of respondents covering the full spectrum of appropriate demographic profiles,
followed by a process of extrapolation using big data analytics. In 2011 for example, UN Global Pulse partnered with Jana, 27 a mobile-technology company,
to explore the feasibility of using mobile phones for the deployment of rapid global surveys on well-being. 28 This requires,
To that end, the World bank has experimented with the use of mobile phones to conduct statistically representative monthly household surveys in Latin america and the Caribbean. 29 Source:
Chapter 5. The role of big data for ICT monitoring and for development 194 create new information.
Particularly rich possibilities exist where data from mobile-cellular and mobile-broadband subscriptions are concerned, since they are linked to mobility profiles.
The indicators for such subscriptions could be broken further down to understand the utilization of services including voice, data and VAS over time,
Mobile operators are able to provide information not only on the different technologies (3g LTE-Advanced, etc.
but also on the types of service that subscribers are using, and the frequency and intensity of that use.
therefore, potentially identify Internet and VAS usage patterns between rural and urban areas, and identify the kinds of application or webpage that mobile-Internet users access.
Combined with individual subscriber characteristics, this information could provide new and rich insights into the digital divide
and help understand usage patterns, including intensity of use, by gender, socioeconomic status and also location.
and intensity of use with respect to different Internet activities carried out by individuals. This information is collected currently only by countries that carry out household ICT surveys.
In addition, mobile-operator data could be combined with customer information from popular online services, such as Facebook, Google or other, local (financial, social etc.
This could be done by using probabilistic analyses to match the profiles developed using data from online services with customer profiles generated from analyses of mobile-operator data.
This would require telecommunication operators, OTT providers and other Internet content providers to work together and share information.
This technique is, currently, probably the least developed one, also because of the lack of a good ontology and of privacy issues.
In addition, if websites could be classified individually in terms of the information they provide, then Internetuser activities, including their frequency
By applying big data techniques to survey data and administrative data from operators, new insights could be derived, in particular, in respect of the following:
Big data techniques could help extrapolate the actual number of unique mobile subscribers or users, rather than just subscriptions,
and by taking into account usage patterns or data from popular Internet companies such as Google or Facebook.
By linking data collected from different sources and combining subscription data and usage patterns, a correlation algorithm could be developed to reverse engineer approximate values for these indicators,
in order to estimate user numbers in between surveys, and possibly in real time. This could be pursued in a similar way to the work done by Frias-Martinez
and Virseda (2012) on estimating socioeconomic variables using mobile-phone usage data, as described in greater detail at the beginning of this section.
big data methods only complement existing surveys rather than replacing them completely (see Section 5. 5 for a further discussion of this).
In sum, relatively simple big data techniques can help analyse and provide complementary information on existing ICT data,
and provide new insights into the measurement of the information society. This includes information on the use of different services and applications, intensity, frequency,
Given multiple SIM usage and the fact that users will in many cases be using ICT services from more than one operator or device
Such techniques will often include combining data from surveys with big data to build new correlation and predictive analytic techniques.
and for other big data for development projects, big data analysis cannot replace survey data, which is needed to build
and test correlations and to validate big data results. While the opportunities discussed above present what is analytically possible,
data access and privacy considerations are nuanced complex and, and therefore place constraints on what is practically feasible or advisable.
and the way forward Attempting to extract value from an exponentially growing data deluge of varying structure
The most pressing concerns are associated those with the standardization and interoperability of big data analytics as well as with privacy and security.
Addressing such privacy and other concerns with respect to data sharing and use is critical, and it is important for big data producers
and users to collaborate closely in that regard. This includes raising awareness about the importance and potential of producing new insights,
and the establishment of public-private partnerships to exploit fully the potential of big data for development.
Data curation, standardization and continuity Data curation and data preparation help to structure, archive, document
and preserve data in a framework that will facilitate human understanding and decision-making. Traditional curation approaches do not scale with big data
and require automation, especially since 85 per cent of big data are estimated to be unstructured (Techamerica Foundation, 2012.
Dealing with large heterogeneous data sets calls for algorithms that can understand the data shape
while also providing analysts with some understanding of what the curation is doing to the data (Weber, Palmer and Chao, 2012).
Telecom network operators themselves have to contend with interoperability issues arising from the different systems (often from different vendors) they employ.
It is not uncommon for operators to write customized mediation software to overcome potential inter-comparability issues among data from different systems.
The problems are compounded when one has to take account of secondary third-party users that may seek to leverage the data.
The framework used by an NSO to organize data would be different from that used by a network engineer or a marketing or business intelligence specialist.
Naturally, telecom network operators have curated their data based on their needs. To be able to use telecom big data for development and monitoring
and to guarantee its continuity, the creation of a semantic framework would require greater consensus among the many diverse stakeholders involved (telecom operators, network equipment manufacturers, system developers, developmental practitioners and researchers, NSOS, etc.).
Chapter 5. The role of big data for ICT monitoring and for development 196 Accessing and storing data,
and data philanthropy Big data for development is still in its nascent stages and, as such, comes with its share of challenges, not least
of which is obtaining access to what is essentially private data. Private corporations would hesitate to share information on their clients
and their business processes in case such sharing is illegal, precipitates a loss of user confidence and/or accidentally reveals competitive business processes.
More importantly, companies will not share until there are incentives to do so. Until holders of big data become more comfortable about their release
it is going to be difficult for third-party research entities to gain access. Researchers (mainly from developed countries, with some exceptions such as LIRNEASIA) have succeeded recently in obtaining mobile network big data,
but it has taken them considerable time to build and leverage the necessary relationships with operators.
Such privileged access is conditioned for the most part by lengthy legal agreements whose preparation requires major investments of time.
All the parties to such agreements have to address the necessary parameters as to how data are to be used,
both researchers and operators face costs arising from the technical challenges associated with extraction of the data,
Some mobile operators are taking tepid steps towards sharing data more publicly. Orange, for example, hosted a Data for Development Challenge,
releasing an aggregated anonymized mobile dataset from Côte d'ivoire to researchers and convening a conference at MIT in early 2013,
this time using Orange data from Senegal, is planned for 2015.30 In 2014, Telecom italia initiated a similar challenge,
making data from the territories of Milan and the Autonomous Province of Trento available to researchers for analysis. 31 It has gone,
however one step further: in addition to releasing some of its own telecom datasets, it partnered with other data providers to curate
and release additional big datasets containing weather, public and private transport, energy, event and social network data.
In both the Orange and Telecom italia cases, researchers had to go through an approval process in order to gain access.
Organizations such as UN Global Pulse are seeking to popularize the concept of data philanthropy, aimed at systematizing the regular and safe sharing of data by building on the precedents being created by the ad hoc activities outlined earlier.
Such efforts by UN Global Pulse as well as by other organizations such as LIRNEASIA, that seek to bring different stakeholders to the same table,
remain critical to the efforts being made to open up private-data stores in order to obtain actionable development insights.
There is a gap that needs to be addressed if large-scale pooling and sharing of such data are to become a reality.
Cross-sector and crossdomain collaboration would benefit greatly from facilitators or intermediaries capable of addressing issues related to standardization and data-curation practices when pooling data from multiple sources.
This facilitatory role may even be played by a third-party organization able to subsume regulatory and privacy burdens faced by data providers,
effectively acting as a gatekeeper to ensure that data are used transparently and in a way that contributes to overall scientific knowledge generation,
while ensuring that any safeguards that may be applicable in respect of private information are applied. Such an approach was taken recently by the pharmaceutical company Johnson and Johnson,
which decided to share all of its clinical trial data. To facilitate the process, they hired Yale university's Open Data Access (YODA) Project 197 Measuring the Information Society Report 2014 to act as gatekeepers (Krumholz, 2014).
YODA undertakes the necessary scientific review of any proposals (from scientists around the world) to make use of the data
and ensures that necessary privacy and data usage guidelines are followed. 32 The question remains as to who is placed best to act as gatekeepers
and standard-bearers when it comes to telecom network big data. Some have argued that NSOS are placed well to ensure that best practices are followed in the collection and representation of big data,
and to provide a stamp of trust for potential third-party data seekers. Telecom operators, for their part
are regulated mostly by sectorspecific regulators who can also have purview and dictate terms governing the privacy
and data-reporting responsibilities of operators. Ultimately, however, the decision as to who takes on the gatekeeper
and standardization function requires the confluence of multiple actors. It is here that organizations such as ITU,
UN Global Pulse and others have a greater role to play in building an institutional model for data sharing and collaboration, in consultation with all stakeholders.
The sharing (subject to appropriate privacy protocols) of privately held data such as mobilephone records can be mutually beneficial to both government and private sector.
For example mobile network operators monitor and forecast their revenue at the cell-tower level. Emerging research in Africa shows how reductions in revenues,
including airtime top-ups, could presage declines in income in specific regions. This could allow for targeted
and timely policy actions by government to address the underlying problems, which would not be possible with the delayed insights provided by traditional statistics.
Such a collaborative early-warning and earlyaction system shows how data sharing could be considered a business risk mitigation strategy for operators in emerging markets.
it should be noted that the emergence of big data is linked closely to advances in the ICT sphere,
including the falling cost of data storage. Depending on the data volume, storage can still be costly,
especially where privacy considerations preempt the use of specialized third-party cloud-based services. But as storage prices continue to fall,
Privacy and security As social scientists look towards private data sources, privacy and security concerns become paramount.
all stakeholders must see tangible benefits from such data sharing. These stakeholders include not just the public and private sectors
but also, significantly, the general public, who in many cases are the primary producers of such data through their activities.
It is also the public that must ultimately decide on how the data they produce may be used.
The World Economic Forum's Rethinking Personal data project has identified key trust challenges facing the personal data economy,
and the private and public sectors in today's new data ecosystem. 33 Discussions must address the individual's privacy expectations,
or explicit existence of personal data that needs to be protected. OECD, for example, defines personal data as any information relating to an identified or identifiable individual (data subject)( OECD, 2013.
The result of such an approach has been the policy of inform and consent practised by most companies to inform users of what data are Chapter 5. The role of big data for ICT monitoring
and for development 198 being collected and how they will be used. It has been argued, however, that in a big data world the inform
and consent approach is woefully inadequate and impractical, and that a new approach is needed (Mayer-Schönberger and Cukier, 2013;
Secondly, in the big data paradigm, the greatest potential often lies in secondary uses, which may well manifest long after the data was collected originally.
It is thus impractical for companies to have a priori knowledge of all the potential uses and to seek permission from the user every time such a new use is found.
Given the volumes of data that individuals are now generating, companies would find themselves struggling to maintain meaningful control.
when data from one source is combined with data from other sources to reveal/infer new data and insights.
This blurs the lines between personal and nonpersonal information, allowing seemingly nonpersonal data to be linked to an actual individual (Ohm, 2010.
Digitized behavioural data crumbs may in fact greatly diminish personal privacy. The use of DPI, for example can technically reveal all of a user's online activity.
For instance, a recent study showed how Facebook likes could accurately predict a range of behavioural attributes such as, inter alia, sexual orientation, ethnicity, religious and political views,
Data anonymization35 (i e. methods designed to strip data of personal information), employed by computational social scientists,
the authors point out that the data could in fact be de-anonymized completely by cross-referencing them with other data sources.
SIM resellers may pre-register the SIMS they sell under their own name, and SIMS that are registered by one family member may be used by other members of the same family.
Sri lankan operators, for example, see a great mismatch between the person registering a subscription and the person using it.
These safeguards must also ensure that data are kept secure. Data breaches undermine consumer confidence and hinder efforts to exploit big data for the greater social good.
Encryption, virtual private networks (VPNS), firewalls, threat monitoring and auditing are some potential technical solutions that are employed currently,
199 Measuring the Information Society Report 2014 but they need to be mainstreamed (Adolph, 2013).
The paradigmatic shift required to address privacy has started, but it will be some time before a consensus is achieved on the most appropriate method (s). In response to the growing trend to unlock socioeconomic value from the rising tide of big data,
the World Economic Forum (WEF) initiated a global multi-stakeholder dialogue on personal data that advocated a principle-based approach,
with the principles arising from a new approach that shifts governance from the data per se to its use;
acknowledges the importance of context rather that treating privacy as a binary concept; and acknowledges the need for new tools to actively engage users,
Given the complexity of the questions related to privacy and data protection in a big data world, the danger is that these questions may take too long to resolve
and further delay the potential use of big data for broader development. Hence, a balanced risk-based approach may be required in the context of
i e. the use of telecom big data for monitoring and development. This does still require the confluence of appropriate stakeholders.
research into the use of big data for development can be sandboxed, with appropriate privacy protections imposed on researchers,
and worked out. 37 Veracity in data analysis and results Garbage in, garbage out, or GIGO for short, is a computer science concept that refers to the fact that the veracity of the output of any logical process depends on the veracity of the input data.
In the big data paradigm, it is easy to overlook that concept, given the expectation that when dealing with vast volumes of (often unstructured) data from a multitude of sources,
messiness is to be expected. As Mayer-Schönberger and Cukier (2013) note, What we lose in accuracy at the micro level we gain in insight at the macro level.
This common conception can often be misleading. Data quality and their provenance do matter, and the question is important in establishing the generalizability of the big data findings.
Data provenance and data cleaning Understanding data provenance involves tracing the pathways taken by data from the originating source through all the processes that may have mutated,
replicated or combined the data that feed into the big data analyses. This is no simple feat.
Nor given the varied sources of data that are utilized, is it always as feasible as the scientific community would wish.
However, at the very least it is important to understand some aspects of the origin of data.
For example, the fact that some mobile network operators choose to include the complete routing of a call that has been forwarded means that there may be multiple records in the CDRS for the same call.
If that is not taken into consideration, the subsequent social network analysis could contain errors (overstating or understating tie strength, for example).
While it may not be possible to establish data provenance as envisaged by scientists, it is at the very least important to understand the underlying processes that may have created the data.
Data cleaning remains a key part of the process to ensure data quality. It is important to verify that the quantitative and qualitative
(i e. categorical) variables have been recorded as expected. In a subsequent step, outliers must be removed, using decision-tree algorithms or other techniques.
However, data cleaning itself is a subjective process (for example, one has to decide which variables to consider)
and not a truly agnostic one as would be desirable, and is thus open to philosophical debate (Bollier, 2010.
Are the data representative? Related to the question of data provenance is the issue of understanding the underlying Chapter 5. The role of big data for ICT monitoring
and for development 200 population whose behaviour has been captured. The large data sizes may make the sampling rate irrelevant,
but they do not necessarily make it representative. Not everyone uses Twitter, Facebook or Google. For example, ITU estimates suggest that 40 per cent of the world's population uses the Internet.
In other words, more than four billion people globally are not yet using the Internet, and 90 per cent of them are from the developing world.
Of the world's three billion Internet users two-thirds are from the developing countries. Even though mobile-cellular penetration is close to 100 per cent,
this does not mean that every person in the world is using a mobile phone. This issue of representativeness is of high relevance
when considering how telecommunication data may be used for monitoring and development. While the potential benefits to be gained from leveraging mobile network operator data for monitoring
and development purposes hinges on the large coverage, close to the actual population size, it is nevertheless not the whole population.
Questions such as the extent of coverage of the poor or the levels of gender representation among telecom users, are all valid considerations.
While the registration information might provide answers, the reality is that the demographic information on telecom subscribers,
for example, is not always accurate. With prepaid subscriptions being the norm in most of the developing world,
the demographic information contained in mobileoperator records is practically useless, even with mandatory registration as discussed above.
The issue of sampling bias is illustrated best by the case of Street Bump, a mobile app developed by Boston City hall.
Street Bump uses a phone's accelerometer to detect potholes while users of the app are driving around Boston
and notifies City hall. The app however, introduces a selection bias since it is slanted towards the demographics of app users,
who often hail from affluent areas with greater smartphone ownership (Harford, 2014). Hence, the big in big data does not automatically mean that issues such as measurement bias and methodology,
internal and external data validity and data interdependencies can be ignored. These are fundamental issues not just for small data but also for big data (Boyd and Crawford, 2012.
Behavioural change Digitized online behaviour can be subject to self-censorship and the creation of multiple personas,
so studying people's data exhaust may not always give us insights into real-world dynamics. This may be less of an issue with TGD,
where in essence the data artefact is itself a by-product of another activity. Telecom network big data,
which mostly fall under this category, may be less susceptible to self-censorship and persona development, but the possibility of these phenomena cannot be ruled out.
Nor is it inconceivable that users may stop using their mobiles or even turn them off,
in areas where they do not wish their digital footprint to be left behind. In a way, big data analyses of behavioural data are subject to a form of the Heisenberg uncertainty principle,
whereby as soon as the basic process of an analysis is known, there may be concerted efforts to exhibit different behaviour
and/or actions to change the outcomes (Bollier, 2010). For example, the famous Google pagerank algorithm has spawned an entire industry of organizations that claim to enhance website page rankings,
and search-engine optimization (SEO) 38 is established now an part of website development. Changes in behaviour could also partially explain the declining veracity of Google Flu Trends (GFT),
researchers having found influenza-like illness rates as reflected by Google searches to be no longer necessarily correlating with actual influenza virus infections (Ortiz et al.,
2011). ) Recent research has shown that since 2009 (when GFT failed to reflect the nonseasonal influenza outbreak),
infrequent updates have not improved the results and GFT has overestimated in fact persistently flu prevalence (Lazer, Kennedy, King and Vespignani, 2014).
GFT does not and cannot know what factors contributed to the strong correlations found in its initial 201 Measuring the Information Society Report 2014 work.
The point is that the underlying real-world actions of the population that turned to Google with its health queries,
and which contributed to the original correlations identified by GFT, may in fact have changed over time,
diminishing the robustness of the original algorithm. For example, the enthusiasm surrounding GFT may well have created rebound effects,
with more and more people turning to Google with their broader health questions, thereby introducing additional search terms (due to different cultural norms
and understanding the real-world context therefore remains important when considering big data analyses for monitoring purposes.
Dr Nathan Eagle, a pioneer in the use of cellphone records to understand phenomena related to social development and public health,
when CDR data from Rwanda showed low mobility in the wake of flooding, he theorized that this was due to an outbreak of cholera.
when it comes to the generalizability of telecomdata analyses based on big data. For example, prior research had established a power-law distribution between the frequency of airtime recharges
When researchers working with Sri lankan mobile datasets attempted to use these findings to help them segregate their analyses for different socioeconomic groups,
Causation versus correlation It is easy to confuse correlation with causation in the big data paradigm
As Google's Chief Economist, Hal Varian, notes, there are often more police in precincts with high crime,
Big data draws many of its techniques from machine learning, which is primarily about correlation and predictions. 40 Big data are by their very nature observational
and can measure only correlation and not causality. Supporters of big data have predicted the end of theory and hypothesis-testing, with correlation trumping causality as the most relevant method (Anderson, 2008;
Mayer-Schönberger and Cukier, 2013. However, such predictions may be premature. The behavioural economist Sendhil Mullainathan notes that inductive science
(i e. the algorithmic mining of big data sources) will not drown out traditional deductive science (i e. hypothesis testing), even in a big data paradigm.
Among the three Vs in the traditional big data definition, volume and variety produce countervailing forces.
More volume makes big data induction techniques easier and more effective, while more variety makes them harder and less effective.
(i e. deductive science) Chapter 5. The role of big data for ICT monitoring and for development 202 rather than merely predicting it (Mullainathan,
) Causal modelling is possible in a big data paradigm by conducting experiments. Telecom network operators themselves use such techniques when rolling out new services or, for that matter, for pricing purposes.
The question, then, is how third-party researchers will be able to leverage operators'systems in order to conduct such experiments.
The role of traditional small data in verification The documented failures of GFT also point to the importance of traditional statistics as corroborating evidence.
the true value of GFT is realized only through its pairing with small data, in this case the statistics collected by the Centers for Disease Control and Prevention (CDC).
when combined with small data, Greater value can be obtained by combining GFT with other near real time health data.
Where data from mobile network operators are used for syndromic surveillance, as in the case of malaria in Kenya (Wesolowski et al.,
2012a), big data are most useful as a basis for encouraging timely investigation, rather than as a replacement for existing measures of disease activity.
Even when engaging with the broader question of how telecommunication network data could be used for monitoring,
surveys and supplemental datasets will remain important to sharpen the analyses and especially to verify the underlying assumptions.
For instance, Blumenstock and Eagle (2012) ran a basic household survey against a randomized set of phone numbers prior to data anonymization to build a training dataset.
social networks and consumption among men and women, and between different socioeconomic groups, which would not have been possible using only the call records.
Similarly, Frias-Martinez and Virseda (2012) needed census data to build their algorithms and provide training data for their algorithms to reverse engineer approximate survey maps.
Official statistics will thus continue to be important to building the big data models and for periodic benchmarking
so that the models can be tuned fine to reflect ground realities. Transparency and replicability The issues with GFT also illustrate transparency and replicability problems with big data research.
The fact that the original private data may in many cases not be available to everyone underscores the importance of opening up such private-data sources (in a manner that addresses potential privacy concerns)
or of peer reviews that can hone and improve the analyses. Instead, consumers of such research have no option
and extracting value from big data calls for a combination of specialized skills in the areas of data mining, statistics and domain expertise,
as well as data preparation, cleaning and visualization. NSOS may have deep statistical skills in house, but this is not enough
when it comes to working with large volumes of big data calling for computer science and decision-analysis skills that are emphasized not in traditional statistical courses (Mcafee and Brynjolfsson,
2012). ) NSOS recognize this shortcoming. In a recent global survey of NSOS from 200 economies, conducted by UNSC,
and identified intensive training and capacity development of their staff as a prerequisite to being able to exploit new big data sources (UNSC, 2013).
i e. data scientists. Mckinsey predicts that by 2018 the demand for data-savvy managers and analysts in the United states will amount to 450 000,
whereas the supply will fall far short of this, at only 160 000 (Manyika et al.,2011).
) This suggests that organizations wishing to leverage big data for development will face competition from the private sector
which stand to benefit the most from the use of telecommunication big data to complement official statistics,
The way forward Current research suggests that new big data sources have great potential to complement official statistics and produce insightful information to foster development.
Future efforts to mainstream and derive full benefit from the use of big data will have to overcome a number of barriers.
Very limited information is available on opportunities for using big data to complement official ICT statistics.
Although this report highlights some of the big data sources and techniques that could be used, further research is needed to understand
and confirm the usefulness of big data sources for monitoring the information society. As with other official statistics, it is paramount for big data producers
and big data users to collaborate and to initiate a dialogue to identify opportunities and understand needs and constraints.
Since many of the big data sources lie within the private sector close cooperation between NSOS, on the one hand,
and telecommunication operators and Internet companies, including search engines and social networks, on the other, is necessary
and could be institutionalized through public-private partnerships. Operators and Internet companies Business interests will naturally provide operators and Internet companies with the incentive to talk to commercial vendors of big data analytics.
In addition, operators and Internet companies can benefit greatly from engagement with academia and researchers to understand how to leverage big data for different purposes.
Such engagement will also broaden their understanding of the limitations and assist them in the development of new methodologies
algorithms and software techniques that can be repurposed for business-use cases. Indeed, where the applications of data use for development are concerned,
operators also have an interest in maximizing the economic well-being of their customer base. Operators and Internet companies need to take advantage of their existing customer relationships to elicit a greater understanding of consumer concerns
and needs in relation to privacy. They are placed well to develop a Chapter 5. The role of big data for ICT monitoring and for development 204 privacy framework, in consultation with other stakeholders.
Given their business concerns operators and Internet companies may hesitate to pool and share their data with those from other sources (including from competitors),
but this is something that is worth exploring. Combining big data sources has great potential to increase added value
and produce new insights. There is scope for exploring established models for such pooling for example, the sharing by banks of some of their customer data with credit bureaux.
Governments Governments have different opportunities and different roles to play in the exploitation of big data for monitoring and development.
They can use big data to identify areas where rapid intervention may be necessary to track progress
and make sure their decisions are based evidence, and to strengthen accountability. More and more governments are recognizing the importance of big data
and have set up communities of practice and working groups to study their use and potential impact (UNSC 2013).
Governments should also facilitate the legislative changes that are required and take a lead in setting big data standards.
To this end, national regulatory authorities (NRAS) and NSOS, in consultation with other national stakeholders, are placed best to lead the corresponding discussions
and bring together the relevant stakeholders. In particular, NSOS, given their legal mandate to collect and disseminate official statistics
and big data clearing houses that promote analytical best practices in relation to the use of big data for complementing official statistics and for development.
would also have to encompass best practices in relation to data curation and metadata standards. To this end, NSOS must also prioritize the upgrading of the in-house technical skills they require
in order to handle big data, while at the same time investing in the necessary computational infrastructure. As the main regulatory interface to the telecom sector, NRAS are placed well to co-champion the national discussion on how telecommunication big data may be leveraged for social good.
Regulators have a role to play in facilitating the introduction of legislation that addresses privacy concerns while encouraging data sharing in a secure manner.
The following recommendations were made in a recently published ITU draft paper (ITU 2014): ) Establishing mechanisms to protect privacy:
Regulators could develop a regulatory mechanism that would shift the focus of privacy protection from informed consent at the point of collecting personal data to accountable and responsible uses of personal data.
In return, data users would be permitted to reuse personal data for novel purposes where a privacy assessment indicates minimal privacy risks.
While the use of big data can help better decision-making through probabilistic predictions, this information should not be used against citizens.
which government agencies and others can utilize big data predictions. Fostering big data competition and openness: Regulators could foster big data competition in increasingly concentrated big data markets,
including by ensuring that data holders allow others to access their data under fair and reasonable terms. 205 Measuring the Information Society Report 2014 International stakeholders International stakeholders including UN AGENCIES and initiatives (such as
ITU and UN Global Pulse), the Partnership on Measuring ICT for Development, ICT industry associations and producers of big data (Google, Facebook, etc.)
have an important role globally. More work is needed to understand fully the potential of big data
and examine the challenges and opportunities related to big data in the ICT sector. To this end, the key international stakeholders have to work together to facilitate the global discussion on the use of big data.
UN Global Pulse, as one of the main UN initiatives exploring the use of big data,
can do much to inform and motivate the discussion on global best practices and the use of big data for development.
Where using big data for monitoring the information society is concerned new partnerships, including public-private partnerships between data providers
and the ICT statistical community, including ITU, could be formed to explore new opportunities and address challenges, including in the area of international data comparability and standards.
As one of the main international bodies working on issues related to the telecommunication sector, ITU could leverage its position to facilitate global discussion on the use of telecom big data for monitoring the information society.
Together, ITU and UN Global Pulse could facilitate the work that needs to be done by NRAS and NSOS, through awareness raising and engagement on privacy frameworks
data sharing, and analytical global best practices. ITU could help reduce the transaction costs associated with obtaining telecommunication big data,
for example by facilitating the standards-setting process. Standardized contracts for obtaining data access as well as standards on how the data are stored,
collated and curated can collectively reduce the overall transaction costs of accessing and leveraging telecommunication big data for social good.
Academia, research institutes and development practitioners The research into how telecom data may be used to aid broader development is being done mainly by academia, public and private research institutes and, to a lesser degree,
development practitioners. This makes them important stakeholders in defining the state of the art with respect to leveraging big data for development.
They, more than others, have been the first to engage with telecommunication operators with a view to using their data for development.
They therefore understand the potential and challenges from multiple perspectives. Their collective experiences will be valuable as big data for development becomes mainstreamed. 207 Measuring the Information Society Report 2014 Chapter 5 Annex The mobile-telecommunication data that operators possess can be classified into different types,
depending on the nature of the information they produce. They include traffic data service access detail records, location and movement data, device characteristics, customer details and tariff data.
Traffic data Operators use a range of metrics to understand and manage the traffic flowing through their networks.
These include: Data volume: both uplink and downlink volumes for Internet traffic can be captured at various levels of disaggregation down to the individual subscriber,
or even to the level of a base station (in the case of a mobile operator) or local switch (in PSTN networks).
These can be analysed to understand subscriber demand for data at both an individual level and at aggregate levels,
and the understanding thus gained can be used for billing purposes and for network management. Erlang:
a dimensionless metric used by mobile network operators to understand the offered and utilized network load. 41 Erlang data are used to understand the load on a base station at any given time.
Call, SMS and MMS volumes are used for a variety of purposes from billing to customer relationship management, as well for network planning.
Deep packet inspection42 (DPI) is used to scan the information that goes over a network. Operators employ DPI to varying degrees,
and it is not always feasible for the entire data stream to be captured and stored, owing to the storage requirements that would be needed and also to privacy concerns.
Often only the header information which includes originating and recipient Internet protocols (IPS), is captured for a variety of purposes,
including to manage the network and understand the demand for particular applications and websites. Service access detail records
Whenever a user utilizes a telecommunication service, each access is recorded not only for infrastructure management but also for billing purposes.
Depending on the type of service, the resulting records may be referred to as call detail records (CDRS), SMS/MMS detail records, Internet access detail records, etc.,
and may include the following information: A timestamp of when the service was accessed. The duration of use of the service (for example, duration of a call.
The numbers of all parties communicating (for example, a CDR would include the numbers of the originating
and terminating parties). Applicable charges for the access. The type of handset used. The technology used (2g, 3g, etc..
The most common use of such data is for basic billing purposes, in addition to which they can be used to build a rich profile of customers,
as outlined in Section 5. 3. Chapter 5. The role of big data for ICT monitoring
and for development 208 Location and movement data Mobile networks can, depending on their sophistication, capture a range of movement and location variables,
which can be classified broadly into two different types: passive and active positioning data (see Annex Box 1). Annex Box 1:
Active versus passive positioning data Passive positioning transaction generated data (TGD) is generated automatically by the network
and captured in the operator's logs for billing and network management purposes, to understand network load
and to keep track of the handset in relation to its network elements. Active positioning data (which is of relevance only to mobile networks) is location
and movement data that is captured in response to a specially initiated network query to locate a handset using either network or handset-based positioning methods.
GPS location data can also be considered as active positioning data. Active positioning data Active positioning data can be generated using either devicecentric or network-centric methods
as well as via satellite (i e. GPS. The use of these methods has developed either in response to national regulations requiring operators to capture higher-precision location data,
and/or to a business case for providing location-based services. The large-scale capture of such higher-resolution data is undertaken mainly by operators in developed economies.
Operators in developing economies use some of these methods, but often on a case-by-case basis,
and not for their entire subscriber base. 43 However, this trend is currently changing, and an increasing number of regulators are
Passive positioning data Passive location data are contained in the subscriber's registration information which includes some form of mailing address (billing address in the case of postpaid).
While fixed-telephone network operators have access only to static location data, mobile networks have much richer and dynamic location data.
CDRS, SMS detail records and Internet access records are the main sources of passive positioning data for mobile operators
and reside in their data warehouses. These records include the ID of the antenna (cell ID),
which in turn has a geolocation, an azimuth (i e. antenna orientation information) and an angular tilt. 45 It is also possible to obtain such data in real time through data-mediation services,
but these are implemented not universally. Passive location data from the billing records are obviously sparse
and generated only when the phone is used and when the network knows which cell a particular handset is connected currently to.
However, many operators choose not to archive these data if they do not have a business case to justify the additional storage costs.
Where they are archived, such cellhandoff data provide a time-stamped sequence of cells that the phone was attached to,
and provides for a rich mobility profile as compared to the event-based billing records.
Passive positioning data based on cell IDS is compared inexpensive when to active positioning data, but the tradeoff lies in their lower precision, usually at the level of network cells.
This lower-resolution location estimate can range from a few hundred metres in urban areas with a higher-density base station coverage
to a few kilometres, especially in rural areas with sparse coverage. Furthermore, handsets are served not always by the nearest antenna,
for a variety of reasons associated with signal strength, topography and saturation loads at the nearest antenna during peak times.
but the user has chosen to connect only to 3g networks, the handset will always connect to an antenna that supports 3g,
even if it is further away. Despite these location errors or limitations that can occur in analyses using such passive location data
at an aggregate level (temporal and/or spatial) these data remain very valuable. Source: ITU.
Device characteristics All mobile user devices used to access mobile telecommunication services come with an international mobile station equipment identity (IMEI) number.
This 15 or 16 digit number is captured whenever a device is used to access 209 Measuring the Information Society Report 2014 Table Annex Box 1:
Active positioning methods Method Description Cellular triangulation using angle of arrival (Aoa) The Aoa method uses data from base stations that have been augmented with arrays of smart antennas.
so it is suited more for rural and semi-urban areas with sparser antenna coverage. GSM-GPS and assisted GPS (A-GPS) Both of these utilize the network (mainly via triangulation from multiple base stations) to augment the satellite signal.
They are particularly useful in locations and situations where there is interference in the satellite signal (e g. inside buildings).
Such location data have high spatial resolution, but are costly for operators to implement. Enhanced observed time difference (E-OTD) E-OTD is a device-centric positioning technique requiring the handset to make the necessary location calculation, based on the signals from three or more synchronized
The accuracy of such techniques range from 50 to 125m Note/Source: For more information regarding these methods, refer to CGALIES (2002.
telecommunication services. In addition to serving as a unique serial number for the handset, parts of it can reveal information on the handset make and model, type of technology (e g. 2g, 3g, LTE),
and it can be used for the collective categorization of handsets. Furthermore, devices used to access the Internet (mobile handsets, routers,
modems) also have a unique identifier known as a media access control46 (MAC) address. Such identifiers can provide details of the device used to access the network.
Mobile network operators can use the IMEI number to identify the specific mobile handset being used by a subscriber
Customer details Telecom network operators capture various items of demographic data during the customer registration process.
and usage profile that the operator can leverage for a variety of purposes (see Section 5. 3). Tariff data Operators maintain the complete tariff sheet
Mobile operators can associate such data with traffic data to understand the revenue that is being generated by specific network elements (e g. base stations),
Chapter 5. The role of big data for ICT monitoring and for development 210 1 The report of the UN Secretary-general's High-level Panel of Eminent Persons on the post-2015 Development Agenda
calls for a data revolution that would draw on existing and new sources of data to fully integrate statistics into decision-making
and use of, data and ensure increased support for statistical systems. This suggests that efforts to improve the availability of,
and complement, official statistics have turned to the search for new data sources, including big data. In addition, the European Statistical System Committee (ESSC) in 2013 adopted the Scheveningen Memorandum on Big data and Official Statistics,
which acknowledges that Big data represents new opportunities and challenges for Official Statistics, and which encourages the European Statistical System
and its partners to effectively examine the potential of Big data sources, see: http://epp. eurostat. ec. europa. eu/portal/page/portal/pgp ess/0 docs/estat/SCHEVENINGEN MEMORANDUM%20final%20version. pdf. 2 This term was discussed first
in 1991, although the term then used was generated transaction information (Mcmanus, 1990) 3 For more information, see http://www. cityofboston. gov/doit/apps/streetbump. asp. 4 See http://www. donotpay. treas. gov/About. htm. 5 In Europe,
the collection and processing of personal data or information is regulated currently by Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data (Data protection Directive) 1 and Directive
2002/58/EC, as amended by Directive 2009/136/EC, on privacy and electronic communications (the eprivacy Directive),
which focuses more specifically on the processing of personal data in the electronic communications sector. Article 7 of the Data protection directive establishes the principle of opt-in, according to
which personal data cannot legitimately be processed without the consent of the data subject, except if necessary to preserve public order or morality,
as well as to further the general interest of society or individuals. Building on this principle, Article 5 of the eprivacy Directive further provides that the processing of personal data can be effected only with the consent of the data subject
who should be given clear and comprehensive information as to the manner and purpose of such processing, except where it is directly instrumental to the provision of a service
http://policyreview. info/articles/analysis/big data-big-responsibilities. 6 See https://www. google. org/denguetrends/.
/7 A good example of this is the Conference Board Help Wanted Online (HWOL) data series that measures the number of new,
first time online jobs and jobs reposted from the previous month for over 16 000 Internet job boards,
corporate boards and smaller job sites in the United states. More information can be found at http://www. conference-board. org/data/helpwantedonline. cfm. 8 See http://bpp. mit. edu
/.9 According to Peerreach. com, 20 per cent of Indonesia's online population uses Twitter, the second highest ratio in the world.
See http://www. ibtimes. com/twitter-usage-statistics-which-country-has-most-active-twitter-population-1474852.10 See http://www. broadband. gov/qualitytest/about/.
/11 ITU World Telecommunication/ICT Indicators database, 17th edition, 2014, available at: http://www. itu. int/en/ITU-D/Statistics/Pages/publications/wtid. aspx. 12 See http://blog. stephenwolfram. com/2013/04/data science-of-the-facebook-world/.
/13 Mobile phone records have been used to infer locations of economic activity within, and patterns of migration to, slum areas in Kenya (Wesolowski and Eagle, 2010) and internal migration in Rwanda (Joshua E. Blumenstock, 2012).
Other work has sought to understand international societal ties in Rwanda (Joshua E. Blumenstock, 2011) and the effects of migration on societal ties in Portugal (Phithakkitnukoon, Calabrese, Smoreda and Ratti, 2011.
Exploratory research in Latin america used mobile phone records to forecast the socioeconomic levels of localities, thereby yielding approximate census maps (Frias-Martinez, Virseda-Jerez and Frias-Martinez, 2012.
Mao, Shuai, Ahn and Bollen (2013) investigated the relationship between mobile phone usage and regional economic development in Côte d'ivoire. 14 The term metadata is used also quite extensively to refer to TGD from telecommunication operators. 15 Deep packet inspection (DPI) is a process that utilizes specialized software to scan all of the data
packets traversing a particular IP network. It can be employed by network operators (especially ISPS) to filter for malicious content (e g. spam)
For further information, see http://en. wikipedia. org/wiki/Deep packet inspection. 16 It should be noted that there is not a harmonized methodology for the allocation of revenues from bundled packages to each specific service Taking into account the increasing trend towards the bundling
of telecommunication services (e g. double-and triple-play offers), revenue figures disaggregated per service are in most cases not comparable across operators and countries.
in order to understand which sites were accessed, in what order and how much time was spent at each. Endnotes 211 Measuring the Information Society Report 2014 18 Comments by SK TELECOM CEO Jinwu So to Mobile Asia Expo attendees-http://www. lightreading. com
/document. asp? doc id=703298.19 For more information on Cignifi, see their website at http://www. cignifi. com/.20 The full report can be accessed at http://unstats. un. org/unsd/statcom/doc14/2014
-11-Bigdata-E. pdf. 21 Syndromic surveillance refers to the collection and analysis of health data about a clinical syndrome that has a significant impact on public health,
with the data in question being used to drive decisions about health policy and health education. 22 A vector-borne disease is a disease that is transmitted through an agent (person, animal or microorganism).
23 A geographic information framework is a representation framework that codes the components of geospatial data in a standardized manner so as to facilitate analyses
and data exchange. 24 See http://www. itu. int/en/ITU-D/Statistics/Pages/intlcoop/partnership/default. aspx for more information regarding the partnership. 25 For the latest list
of core ICT indicators refer to 2014 edition of the Manual for Measuring ICT Access
and Use by Households and Individuals available at http://www. itu. int/dms pub/itu-d/opb/ind/D-IND-ITCMEAS-2014-PDF-E. pdf
. 26 See https://gsmaintelligence. com. 27 Jana has integrated its systems with 237 mobile operators worldwide,
see http://www. unglobalpulse. org/projects/global-snapshot-wellbeing-mobile-survey. 29 For more information regarding this project,
see http://web. worldbank. org/WBSITE/EXTERNAL/EXTABOUTUS/ORGANIZATION/EXTHDNETWORK/0,,contentmdk: 23154296 menupk: 2880846 pagepk:
514426,00. html. 30 More information about the Data for Development (D4d) challenges using Orange data can be found at http://www. d4d. orange. com/home. 31 See http://www. telecomitalia. com
/tit/en/bigdatachallenge/contest. html. 32 More information about the Yale university Open Data (YODA) project can be found at http://medicine. yale. edu/core/projects/yodap/index
see http://www. weforum. org/issues/rethinking-personal data. 34 It should be noted that there is no single ITU definition of privacy,
http://www. unglobalpulse. org/privacy-and-data protection for an understanding of the privacy protections UN Global Pulse imposes on its researchers. 38 SEO is established an marketing strategy
whereby a website's structure and content are optimized to make the site more visible to the webpage-indexing process of one or more search engines,
thereby ensuring that the website and/or webpage appears higher up in the results of a search query. 39 In a power-law distribution,
or two people talking for 30 minutes each. 42 DPI is a process that utilizes specialized software to scan all of the data packets traversing a particular IP network.
For further information, see http://en. wikipedia. org/wiki/Deep packet inspection. 43 Based on interviews between LIRNEASIA and operators in South and Southeast asia. 44 For example
and would have to have an accuracy estimate of up to 50m within three years of the new licence coming into effect.
of the sample regulation on the Indian Department of Telecommunications website (http://dot. gov. in/sites/default/files/Unified%20licence 0. pdf). Chapter 5. The role of big data for ICT monitoring and for development
For more information, see http://en. wikipedia. org/wiki/Sector antenna. 46 A media access control (MAC) address is a unique identifier that is assigned to network interfaces mostly by a hardware manufacturer.
For example, the telecom operator captures the MAC address from a modem or router or handset that accesses its network and maintains the mapping of this network interface to a particular customer.
For more information, see http://en. wikipedia. org/wiki/MAC ADDRESS. 47 An international mobile subscriber identity (IMSI) number is a 15-digit number unique to the particular SIM in a subscriber's handset.
The mobile operator's system retains a mapping between an IMSI number and the particular mobile number assigned to a user.
The IMSI conforms to Recommendation ITU-T E. 212. For more information, see http://en. wikipedia. org/wiki/International mobile subscriber identity. 213 Measuring the Information Society Report 2014 List of references Arpce (2011), Agence de Régulation
des Postes et des Communications Electroniques: Rapport d'activité 2011. Ahas, R. and Mark, Ü. 2005), Location based services new challenges for planning and public administration?
the data deluge makes the scientific method obsolete. Wired Magazine, 16 (7). Askitas, N. and Zimmermann, K. F. 2009), Google econometrics and unemployment forecasting.
Retrieved from: http://ftp. iza. org/dp4201. pdf. Azman, A s.,Urquhart, E. A.,Zaitchik, B. and Lessler, J. 2013), Using Mobile phone Data to Supercharge Epidemic Models
of Cholera Transmission in Africa: A Case study of Côte d'ivoire. Data for Development: Net Mobi 2013.
Bengtsson, L.,Lu, X.,Thorson, A.,Garfield, R. and von Schreeb, J. 2011), Improved response to disasters and outbreaks by tracking population movements with mobile phone network data:
a post-earthquake geospatial study in Haiti. PLOS Medicine, 8 (8), e1001083. doi: 10.1371/journal. pmed. 1001083.
and optimizing public transport using cellphone data. Data for Development: Net Mobi 2013. Blumenstock, J. E. 2011), Using mobile phone data to measure the ties between nations.
Proceedings of the 2011 iconference (pp. 195 202. New york, USA: ACM Press. doi: 10.1145/1940761.1940788.
Blumenstock, J. E. 2012), Inferring patterns of internal migration from mobile phone call records: evidence from Rwanda.
Information technology for Development, 18 (2), 107 125. doi: 10.1080/02681102.2011.643209. Blumenstock, J. E. and Eagle, N. 2012), Divided we call:
Disparities in Access and Use of Mobile phones in Rwanda. Information technologies & International Development, 8 (2), 1 16.
Bollier, D. 2010), The Promise and Peril of Big data. WASHINGTON DC. Bouckaert, J.,Van dijk, T. and Verboven, F. 2010), Access regulation, competition, and broadband penetration:
An international study. Telecommunications Policy 34.11 (2010): 661-671. Boyd, D. and Crawford, K. 2012), Critical questions for big data.
Information, Communication & Society, 15 (5), 662 679. doi: 10.1080/1369118x.2012.678878 Broadband Commission (2013a), The State of Broadband 2013:
Universalizing Broadband. Available at: http://www. broadbandcommission. org/Documents/bb-annualreport2013. pdf. Broadband Commission (2013b), Planning for Progress:
Why National Broadband Plans matter. Available at: http://www. broadbandcommission. org/documents/reportnbp2013. pdf. References 214 Broadband Commission for Digital Development (2011), Broadband Targets for 2015.
How Does driven Data Decisionmaking Affect Firm Performance? Retrieved from: http://papers. ssrn. com/sol3/papers. cfm?
Byrne, C. 2013), How The UN's New Data Lab in Indonesia Uses Twitter to Preempt Disaster, Fast Company.
http://www. fastcolabs. com/3007178/open-company/how-uns-new-data-lab-indonesia-uses-twitterpreempt-disaster.
Caceres, N.,Wideberg, J. P. and Benitez, F. G. 2007), Deriving origin destination data from a mobile phone network.
Calabrese, F.,Di Lorenzo, G.,Liu, L. and Ratti, C. 2011), Estimating origin-destination flows using mobile phone location data.
IEEE Pervasive Computing, 10 (4), 36 44. doi: 10.1109/MPRV. 2011.41. CGALIES (2002), Report on implementation issues related to access to location information by emergency services (E112) in the European union, Coordination group on access to Location Information for Emergency Services.
http://static. googleusercontent. com/media/research. google. com/en//archive/papers/initialclaimsus. pdf. Choi, H. and Varian, H. 2012), Predicting the present with Google trends.
Global Mobile Data Traffic Forecast Update, 2013-2018, White paper, February 2014. Available at: http://www. cisco. com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white paper c11-520862. pdf. CCK (2010), Communications
http://www. cck. go. ke/regulations/downloads/interconnection determination no2 2010. pdf. CCK (2013), Communications Commission of Kenya:
http://www. cck. go. ke/resc/downloads/Sector statistics report q2 201314. pdf. David, T. 2013), Big data from Cheap Phones.
Department of Census and Statistics Sri lanka (2009), Computer literacy Survey 2009, December 2009. Available at: http://www. statistics. gov. lk/CLS/Buletincomputerliteracy 2009. pdf. 215 Measuring the Information Society Report 2014 Eagle, N.,Pentland, A s. and Lazer, D
. 2009), Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences of the United states of america, 106 (36), 15274 8. doi:
European commission (2014), E-Communications and Telecom Single Market Household Survey. Special Eurobarometer 414, March 2014.
Fink, C.,Mattoo, A. and Rathindran, R. 2002), An assessment of telecommunications reform in developing countries.
Information technology for Development, 18 (2), 91 106. doi: 10.1080/02681102.2011.630312. Frias-Martinez, V.,Virseda-Jerez, J. and Frias-Martinez, E. 2012b), On the relation between socioeconomic status and physical mobility.
Information technology for Development, 18 (2), 91 106. doi: 10.1080/02681102.2011.630312. Giles, J. 2012), Computational social science:
, M. S. and Brilliant, L. 2009), Detecting influenza epidemics using search engine query data. Nature, 457 (7232), 1012 4. doi:
Groves, R. M. 2011), A possible data future for the observational social sciences. COSSA 30th Anniversary meeting, Washington, D c. Gruber, H. and Koutroumpis, P. 2013), Competition enhancing regulation and diffusion of innovation:
Gruber, H. and Verboven, F. 2001), The diffusion of mobile telecommunication services in the European union.
GSMA and Deloitte (2013), Arab States Mobile Observatory 2013. Available at: http://www. gsma. com/publicpolicy/wpcontent/uploads/2012/03/GSMA MOBILEOBSERVATORY ARABSTATES2013. pdf. GSMA (2013a), Country overview:
https://wiki. gsmaintelligence. com/gsma kb/images/e/e0/Mobile for development country overview sri lanka. pdf. GSMA (2013b), The Mandatory Registration of Prepaid SIM CARD Users, White paper.
), Telecommunications policies: Determinants and impacts. CEPR Discussion Paper No. 4578. Gutierrez, L. 2003), Regulatory governance in the Latin american telecommunications sector.
Utilities Policy, 11,225 240. Gutierrez, L. and Berg, S. 2000), Telecommunications liberalization and regulatory governance:
Lessons from Latin america. Telecommunications Policy, 24 (10 11), 865 884. Gutierrez, T.,Krings, G. and Blondel, V. D. 2013), Evaluating socioeconomic state of a country analyzing airtime credit and mobile phone datasets,
1 6. Retrieved from: http://arxiv. org/abs/1309.4496. Harford, T. 2014), Big data: are we making a big mistake?
Financial times. pp. 7 11. Retrieved 28 march 2014 from: http://www. ft com/intl/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0. html#axzz2xkbwrrlj.
IBM (2012), Analytics: The real-world use of big data. How innovative enterprises extract value from uncertain data.
Retrieved from: http://public. dhe. ibm. com/common/ssi/ecm/en/gbe03519usen/GBE03519USEN. PDF. IBM (2013), The Four V's of Big data.
Retrieved from: http://www. ibmbigdatahub. com/sites/default/files/infographic file/4-Vs-of-big data. jpg.
ICT Qatar (2014), Qatar's ICT landscape 2014 households and individuals. Available at: http://www. ictqatar. qa/en/documents/download/Qatar's%20ict%20landscape%20report%202014-Household%20and%20individuals 12. pdf. ITU (2006), Security in Telecommunications
and Information technology: An overview of issues and the deployment of existing ITU-T Recommendations for secure telecommunications.
Retrieved from: http://www. itu. int/dms pub/itu-t/opb/hdb/T-HDB-SEC. 03-2006-PDF-E. pdf. ITU (2009), Measuring
the Information Society 2009. Available at: http://www. itu. int/ITU-D/ict/publications/idi/material/2009/MIS2009 W5. pdf. ITU (2010), Monitoring the WSIS Targets a mid-term review.
Available at: http://www. itu. int/ITU-D/ict/publications/wtdr 10/material/WTDR2010 EXECSUM-en. pdf. ITU (2011), Handbook for the collection of administrative data on Telecommunications/ICT.
Available at: http://www. itu. int/en/ITU-D/Statistics/Pages/publications/handbook. aspx. ITU (2011), Measuring the Information Society 2011.
Available at: http://www. itu. int/ITU-D/ict/publications/idi/material/2011/MIS 2011 WITHOUT ANNEX 5. pdf. ITU (2012a), ICT adoption and prospects in the Arab region.
http://www. itu. int/en/ITU-D/Statistics/Documents/publications/mis2012/MIS2012 WITHOUT ANNEX 4. pdf. ITU (2013), Big data:
http://www. itu. int/en/ITU-D/Statistics/Documents/publications/mis2013/MIS2013 WITHOUT ANNEX 4. pdf. ITU (2013b), Trends in Telecommunication Reform 2013:
Report 2014 ITU GSR discussion paper (2014), Big data-Opportunity or Threat. Retrieved from: http://www. itu. int/en/ITU-D/Conferences/GSR/Documents/GSR2014/Discussion%20papers%20and%20presentations%20-%20gsr14/Session3 gsr14-Discussionpaper-Bigdata
. pdf. Jones, S. 2012), Why Big data is the fourth factor of production, Financial times. Retrieved 27 december 2012 from:
Krumholz, H. M. 2014), Give the data to the People, New york times. Retrieved 2 february 2014 from:
http://www. nytimes. com/2014/02/03/opinion/give-the-data-to-the-people. html? hp&rref=opinion& r=1. Kumar, K. and Muhota, K. 2012), Can Digital Footprints Lead to Greater Financial Inclusion?(
Laney, D. 2001), 3d data management: Controlling data volume, velocity and variety. Gartner, 6. Lazer, D.,Kennedy, R.,King, G. and Vespignani, A. 2014), Big data.
The parable of Google Flu: traps in big data analysis. Science (New york, N y.),343 (6176), 1203 5. doi:
10.1126/science. 1248506. Lazer, D.,Pentland, A.,Adamic, L.,Aral, S.,Barabasi, A l.,Brewer, D.,Van alstyne, M. 2009), Computational social science.
Science (New york, N y.),323 (5915), 721 3. doi: 10.1126/science. 1167742. Lenormand, M, Picornell, M.,Cantu-Ros, O.,Tugores, A.,Louail, T.,Herranz, R.,Barthelemy, M.,Frias-Martinez, E.,Ramasco, J. Cross
-checking different sources of mobility information, 2014. Levy, B. and Spiller, P. 1994), The institutional foundations of regulatory commitment:
A comparative analysis of telecommunications regulation. Journal of Law, Economics and Organization, 10 (2), 201 246.
Comparative studies of telecommunications, Cambridge, UK: Cambridge university Press. Lokanathan, S.,Silva, N. de, Kreindler, G.,Miyauchi, Y. and Dhananjaya, D. 2014), Using mobile network big data for informing transportation and urban planning in Colombo.
Lu, X.,Bengtsson, L. and Holme, P. 2012a), Predictability of population displacement after the 2010 Haiti earthquake.
Manyika, J.,Chui, M.,Brown, B.,Bughin, J.,Dobbs, R.,Roxburgh, C. and Byers, A. H. 2011), Big data:
Mao, H.,Shuai, X.,Ahn, Y.-Y. and Bollen, J. 2013), Mobile Communications Reveal the Regional Economy in Cote d'ivoire.
Data for Development: Net Mobi 2013. Mayer-Schönberger, V. and Cukier, K. 2013), Big data: A revolution that will transform how we live,
work, and think. Houghton Mifflin Harcourt. Mcafee, A. and Brynjolfsson, E. 2012), Big data: the management revolution.
Harvard Business Review, 90 (10), 60 6, 68,128. Retrieved from: http://www. ncbi. nlm. nih. gov/pubmed/23074865.
References 218 Mcmanus, T. E. 1990), Telephone transaction generated information: Rights and restrictions. Cambridge, MA.
What big data means for social science. Headcon'13 Part I. Retrieved from: http://edge. org/panel/headcon-13-part-i. Naef, E.,Muelbert, P.,Raza, S.,Frederick, R.,Kendall, J. and Gupta, N. 2014
), Using mobile data for development. Gates Foundation Whitepaper. Narayanan, A. and Shmatikov, V. 2008), Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy (sp 2008)( pp. 111 125.
IEEE. doi: 10.1109/SP. 2008.33. Nardotto, M.,Valletti T. M. and Verboven F. 2012), Unbundling the incumbent:
http://www. oecd. org/sti/ieconomy/oecd privacy framework. pdf. Ofcom (2011), Measuring Mobile Broadband in the UK:
http://stakeholders. ofcom. org. uk/binaries/research/telecoms-research/bbspeeds2010/Mobile bb performance. pdf. Ofcom (2013), Ofcom Communications Market Report 2013, August 2013.
a comparison of traditional surveillance systems with Google Flu Trends. Plos One, 6 (4), e18687. doi:
. 2011), Out of sight out of Mind-How Our Mobile Social network Changes during Migration. 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int
'l Conference on Social Computing (pp. 515 520. IEEE. doi: 10.1109/PASSAT/Socialcom. 2011.11. Pindolia, D. K.,Garcia, A j.,Wesolowski, A.,Smith, D. L.,Buckee, C. O.,Noor, A m.,Tatem, A j. 2012), Human movement data
for malaria control and elimination strategic planning. Malaria Journal, 11 (1), 205. doi: 10.1186/1475-2875-11-205.
The effects of telecommunications reform on network expansion and efficiency. Journal of Regulatory Economics, 1 (15), 65 92.219 Measuring the Information Society Report 2014 Ros, A. 2003), The impact of the regulatory process and price cap regulation in Latin american
telecommunications markets. Review of Network Economics, 2 (3), 270 286. Simmering, J. E.,Polgreen, L a. and Polgreen, P m. 2014),
Web search query volume as a measure of pharmaceutical utilization and changes in prescribing patterns.
2012), Demystifying Big data: A Practical Guide To Transforming The Business of Government. Retrieved from: http://www. techamerica. org/Docs/filemanager. cfm?
f=techamerica-bigdatareport-final. pdf. The White house (2014), Big data: Seizing Opportunities, Preserving values. Executive Office of the President.
http://www. whitehouse. gov/sites/default/files/docs/big data privacy report 5. 1. 14 final print. pdf. UN Global Pulse (2012), Taking the Global Pulse:
Using New Data to Understand Emerging Vulnerability in Real-time. UN High-level Panel (2013), A new global Partnership:
Eradicate Poverty and Transform Economies through Sustainable Development. The Report of the High-level Panel of Eminent Persons on the Post-2015 Development Agenda.
Available at: http://www. post2015hlp. org/wp-content/uploads/2013/05/UN-Report. pdf. UN-OHRLLS (2013), State of the Least Developed Countries 2013.
http://unohrlls. org/custom-content/uploads/2013/10/State-of-the-LDCS-2013. pdf. UNSC (2013), Report of the Secretary general on Big data and modernization of statistical systems.
UNSC (2014), Big data and modernization of statistical systems, December 2013. Available at: http://unstats. un. org/unsd/statcom/doc14/2014-11-Bigdata-E. pdf. Van damme, E. 2002), The European UMTS-auctions, European Economic
Review, Volume 46, Issues 4-5 may 2002, pp. 846-858. doi: 10.1016/S0014-2921 (01) 00219-7. Varian, H r. 2013a), Beyond Big data.
NABE Annual Meeting. San francisco, CA. Varian, H r. 2013b), Big data: New Tricks for Econometrics. Wallsten, S. 2001), An econometric analysis of telecom competition, privatization,
and regulation in Africa and Latin america. Journal of Industrial Economics, 49 (1), 1 19. Wang, H.,Calabrese, F.,Di Lorenzo, G. and Ratti, C. 2010), Transportation mode inference from anonymized and aggregated mobile phone call detail records. 13th International IEEE Conference
on Intelligent Transportation systems (pp. 318 323. IEEE. doi: 10.1109/ITSC. 2010.5625188. Ward, J. S. and Barker,
A. 2013), Undefined By Data: A Survey of Big data Definitions, 2. Databases. Retrieved from: http://arxiv. org/abs/1309.5821.
Waverman, L. and Koutroumpis, P. 2011), Benchmarking telecoms regulation: The Telecommunications Regulatory Governance Index (TRGI.
Telecommunications Policy, 35,450-468. Weber, N m.,Palmer, C. L. and Chao, T. C. 2012), Current Trends and Future Directions in Data Curation Research and Education.
Journal of Web Librarianship, 6 (4), 305 320. doi: 10.1080/19322909.2012.730358. WEF (2013), Unlocking the Value of Personal data:
From Collection to Usage. Geneva, Switzerland. Retrieved from: http://www3. weforum. org/docs/WEF IT UNLOCKINGVALUEPERSONALDATA COLLECTIONUSAGE REPORT 2013. pdf. WEF (2014), The Global Information technology Report 2014:
Rewards and Risks of Big data (B. Bilbao-Osorio, S. Dutta and B. Lanvin, Eds..References 220 Wesolowski, A. and Eagle, N. 2010), Parameterizing the Dynamics of Slums.
AAAI Symposium on Aritificial Intelligence and Developmen (pp. 103 108. Wesolowski, A.,Eagle, N.,Tatem, A j.,Smith, D. L.,Noor, A m.,Snow, R. W. and Buckee, C. O. 2012a), Quantifying the impact of human mobility
on malaria. Science (New york, N y.),338 (6104), 267 70. Doi: 10.1126/science. 1223467. Wesolowski, A.,Eagle, N.,Tatem, A j.,Smith, D. L.,Noor, A m.,Snow, R. W. and Buckee, C. O. 2012b), Quantifying the impact of human mobility
on malaria. Science (New york, N y.),338 (6104), 267 70. doi: 10.1126/science. 1223467. World bank (2014), Ending poverty requires more than growth, Press release No. 2 014/434/DEC. Available at:
http://www. worldbank. org/en/news/press-release/2014/04/10/ending-poverty-requires-more than-growth-says-wbg.
Wu, W.,Cheu, E. Y.,Feng, Y.,Le, D. N.,Yap, G. E. and Li, X. 2013), Studying Intercity Travels and Traffic
Using Cellular network Data. Data for Development: Net Mobi 2013. Xia, J. 2011), The third-generation-mobile (3g) policy and deployment in China:
Current status, challenges, and prospects, Telecommunications Policy, Volume 35, Issue 1 february 2011, pp. 51-63. doi:
10.1016/j. telpol. 2010.12.008.221 Measuring the Information Society Report 2014 Annex 1. ICT Development Index (IDI) methodology This annex outlines the methodology used to compute the IDI,
and provides more details on various steps involved, such as the indicators included in the index and their definition, the imputation of missing values, the normalization procedure,
and the results of the sensitivity analysis. 1. Indicators included in the IDI The selection of indicators was based on certain criteria,
including relevance for the index objectives, data availability and the results of various statistical analyses such as the principal component analysis (PCA).
Data for all of these indicators are collected by ITU. 2 1. Fixed-telephone subscriptions per 100 inhabitants Fixed-telephone subscriptions refers to the sum of active analogue fixed-telephone lines,
voice services using Internet Protocol (IP) delivered over fixed (wired)- broadband infrastructure (e g. DSL fibre optic), and voice services provided over coaxial-cable television networks (cable modem.
It also includes fixed wireless local loop (WLL) connections, which are defined as services provided by licensed fixed-line telephone operators that provide last-mile access to the subscriber using radio technology,
when the call is routed then over a fixed-line telephone network (and not a mobilecellular network).
and do not require a computer. Voip is also known as voice-overbroadband (Vob), and includes subscriptions through fixed-wireless,
DSL, cable, fibre-optic and other fixed-broadband platforms that provide fixed telephony using IP. 2. Mobile-cellular telephone subscriptions per 100 inhabitants Mobile-cellular telephone subscriptions refers to the number of subscriptions
to a public mobiletelephone service which provides access to the public switched telephone network (PSTN) using cellular technology.
It includes both the number of postpaid subscriptions and the number of active prepaid accounts (i e. that have been Annex 1. ICT Development Index (IDI) methodology 222 active during the past three months).
It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services,
radio paging and telemetry services. 3. International Internet bandwidth (bit/s) per Internet user International Internet bandwidth refers to the total used capacity of international Internet bandwidth,
in megabits per second (Mbit/s). It is measured as the sum of used capacity of all Internet exchanges offering international bandwidth.
International Internet bandwidth (bit/s) per Internet user is calculated by converting to bits per second and dividing by the total number of Internet users. 4. Percentage of households with a computer A computer refers to a desktop computer, a laptop (portable computer or a tablet or similar handheld computer.
It does not include equipment with some embedded computing abilities, such as smart TV SETS, and devices with telephony as a main function, such as mobile phones or smartphones.
Household with a computer means that the computer is available for use by all members of the household at any time.
The computer may or may not be owned by the household, but should be considered a household asset. 3 Data are obtained by countries through national household surveys
and are provided either directly to ITU by national statistical offices (NSO), or ITU carries out the necessary research to obtain them, for example from NSO websites.
There are certain data limits to this indicator, insofar as estimates have to be calculated for many developing countries
which do not yet collect ICT household statistics. Over time, as more data become available, the quality of the indicator will improve. 5. Percentage of households with Internet access The Internet is a worldwide public computer network.
It provides access to a number of communication services, including the World wide web, and carries e-mail, news, entertainment and data files,
irrespective of the device used (not assumed to be only a computer it may also be a mobile telephone,
tablet, PDA, games machine, digital TV, etc..Access can be fixed via a or mobile network. Household with Internet access means that the Internet is available for use by all members of the household at any time.
Data are obtained by countries through national household surveys and are provided either directly to ITU by national statistical offices (NSO),
or ITU carries out the necessary research to obtain them, for example from NSO websites. There are certain data limits to this indicator,
insofar as estimates have to be calculated for many developing countries which do not yet collect ICT household statistics.
Over time, as more data become available, the quality of the indicator will improve. b) ICT use indicators The indicators included in this group capture ICT intensity and usage.
Data for all of these indicators are collected by ITU. 4 1. Percentage of individuals using the Internet Individuals using the Internet refers to people who used the Internet from any location and for any purpose, irrespective of the device and network used, in the last three months.
It can be via a computer (i e. desktop computer, laptop computer or tablet or similar handheld 223 Measuring the Information Society Report 2014 computer), mobile phone
games machine, digital TV, etc..Access can be fixed via a or mobile network. Data are obtained by countries through national household surveys
and are provided either directly to ITU by national statistical offices (NSO), or ITU carries out the necessary research to obtain them, for example from NSO websites.
There are certain data limits to this indicator, insofar as estimates have to be calculated for many developing countries
which do not yet collect ICT household statistics. Over time, as more data become available, the quality of the indicator will improve. 2. Fixed (wired)- broadband subscriptions per 100 inhabitants Fixed (wired)- broadband subscriptions refers to the number of subscriptions for high-speed access to the public Internet (a TCP IP connection).
Highspeed access is defined as downstream speeds equal to, or greater than, 256 kbit/s. Fixed (wired) broadband includes cable modem, DSL, fibre and other fixed (wired)- broadband technologies (such as Ethernet LAN,
and broadband-overpowerline (BPL) communications). Subscriptions with access to data communications (including the Internet) via mobile-cellular networks are excluded. 3. Wireless-broadband subscriptions per 100 inhabitants Wireless-broadband subscriptions refers to the sum of satellite broadband, terrestrial fixed
wireless broadband and active mobile-broadband subscriptions to the public Internet. Satellite broadband subscriptions refers to the number of satellite Internet subscriptions with an advertised download speed of at least 256 kbit/s. It refers to the retail subscription technology and not the backbone technology.
Terrestrial fixed wireless broadband subscriptions refers to the number of terrestrial fixed Wireless internet subscriptions with an advertised download speed of at least 256 kbit/s. This includes fixed Wimax and fixed wireless subscriptions
but excludes occasional users at hotspots and Wi-fi hotspot subscribers. It also excludes mobilebroadband subscriptions where users can access a service throughout the country wherever coverage is available.
Active mobile-broadband subscriptions refers to the sum of standard mobilebroadband subscriptions and dedicated mobile-broadband data subscriptions to the public Internet.
It covers actual subscribers, not potential subscribers, even though the latter may have enabled broadband handsets. Standard mobile-broadband subscriptions refers to active mobile-cellular subscriptions with advertised data speeds of 256 kbit/s
or greater that allow access to the greater Internet via HTTP and which have been used to set up an Internet data connection using Internet Protocol (IP) in the past three months.
Standard SMS and MMS messaging do not count as an active Internet data connection, even if the messages are delivered via IP.
Dedicated mobile-broadband data subscriptions refers to subscriptions to dedicated data services (over a mobile network) that allow access to the greater Internet
and which are purchased separately from voice services, either as a standalone service (e g. using a data card such as a USB modem/dongle) or as an add-on data package to voice services
which requires an additional subscription. All dedicated mobile-broadband subscriptions with recurring subscription fees are included regardless of actual use.
Prepaid mobilebroadband plans require use if there is no monthly subscription. This indicator could also include mobile Wimax subscriptions.
Annex 1. ICT Development Index (IDI) methodology 224 c) ICT skills indicators Data on adult literacy rates and gross secondary and tertiary enrolment
ratios are collected by the UNESCO Institute for Statistics (UIS. 1. Adult literacy rate According to UIS, the Adult literacy rate is defined as the percentage of population aged 15 years
and over who can both read and write with understanding a short simple statement on his/her everyday life.
Generally,‘literacy'also encompasses‘numeracy, 'the ability to make simple arithmetic calculations. The main purpose of this indicator is to show the accumulated achievement of primary education and literacy programmes in imparting basic literacy skills to the population,
thereby enabling them to apply such skills in daily life and to continue learning and communicating using the written word.
Literacy represents a potential for further intellectual growth and contribution to economic-sociocultural development of society. 5 2. Gross enrolment ratio (secondary and tertiary level) According to UIS,
the Gross enrolment ratio is the total enrolment in a specific level of education, regardless of age, expressed as a percentage of the eligible official school age population corresponding to the same level of education in a given school-year. 2. Imputation of missing data A critical step in the construction of the index is to create a complete
data set, without missing values. There are several imputation techniques that can be applied to estimate missing data. 6 Each of the imputation techniques,
like any other method employed in the process, has its own strengths and weaknesses. The most important consideration is to ensure that the imputed data will reflect a country's actual level of ICT access
usage and skills. Given that ICT access and usage are correlated both with national income, hot-deck imputation was chosen as the method for imputing the missing data,
where previous year data are not available to calculate the growth rates. Hotdeck imputation uses data from countries with similar characteristics, such as GNI per capita and geographic location.
For example, missing data for country A were estimated for a certain indicator by first identifying the countries that have similar levels of GNI per capita
and that are from the same region and an indicator that has known a relationship to the indicator to be estimated.
For instance Internet use data of country A was estimated by using Internet use data of country B from the same region with similar level of GNI per capita and similar level of fixed Internet and wireless-broadband subscriptions.
The same logic was applied to estimate missing data for all indicators included in the index. 3. Normalization of data Normalization of the data is necessary before any aggregation can be made
in order to ensure that the data set uses the same unit of measurement. For the indicators selected for the construction of the IDI,
it is important to transform the values to the same unit of measurement, since some values are expressed as a percentage of the population/of households,
whereby the maximum value is 100, while other indicators can have values exceeding 100, such as mobilecellular and wireless-broadband penetration or international Internet bandwidth (expressed as bit/s per user).
There are certain particularities that need to be taken into consideration when selecting the normalization method for the IDI.
For example, in 225 Measuring the Information Society Report 2014 order to identify the digital divide, it is important to measure the relative performance of countries (i e. the divide among countries).
Second, the normalization procedure should produce index results that allow countries to track progress of their evolution towards an information society over time.
A further important criterion for the selection of the normalization method was to choose one that can be replicated by countries.
Indeed, some countries have shown a strong interest in applying the index methodology at the national or regional level.
International Internet bandwidth per Internet user, which in 2013 ranges from 136 (bits/s/user) to almost 6 445 759.
the data were transformed first to a logarithmic (log) scale. Outliers were identified then using a cut off value calculated by adding two standard deviations to the mean of the rescaled values, resulting in a log value of 5. 90.
The reference value for mobile-cellular subscriptions was reviewed and lowered to 120. This value (120) was derived by examining the distribution of countries based on their mobile-cellular subscriptions per 100 inhabitants value in 2013.
For countries where postpaid is the predominant mode of subscriptions, 120 is the maximum value achieved,
Fixed-telephone subscriptions per 100 inhabitants, which in 2013 range from zero to 124. The same methodology was used to compute the reference value
In line with fixed-telephone subscriptions, the ideal value was defined at 60 per 100 inhabitants. After normalizing the data,
the individual series were rescaled all to identical ranges, from 1 to 10. This was necessary
Weights (Indicators) Weights (Sub-indices) ICT access 0. 40 Fixed-telephone subscriptions per 100 inhabitants 0. 20 Mobile-cellular telephone subscriptions per 100
inhabitants 0. 20 International Internet bandwidth per Internet user 0. 20 Percentage of households with a computer 0. 20 Percentage of households with Internet access 0. 20 ICT
use 0. 40 Percentage of individuals using the Internet 0. 33 Fixed (wired)- broadband Internet subscriptions per 100 inhabitants 0. 33 Wireless-broadband subscriptions per 100
enrolment ratio 0. 33 ICT access is measured by fixed-telephone subscriptions per 100 inhabitants mobilecellular subscriptions per 100 inhabitants, international Internet bandwidth per Internet user, percentage of households with a computer and percentage of households with Internet access.
ICT use is measured by percentage of individuals using the Internet, fixed (wired)- broadband Internet subscriptions per 100 inhabitants and wirelessbroadband subscriptions per 100 inhabitants.
ICT skills are approximated by adult literacy rate, secondary gross enrolment ratio and tertiary gross enrolment ratio.
The values of the sub-indices were calculated first by normalizing the indicators included in each sub-index
For computation of the final index, the ICT access and ICT use sub-indices were given 40 per cent weight each,
(which tops the IDI 2013). 6. Sensitivity analysis Sensitivity analysis was carried out to investigate the robustness of the index results,
the data were transformed first to a logarithmic (log) scale. The ideal value of 787'260 bit/s per Internet user is equivalent to 5. 90 if transformed to a log scale.
Source: ITU. DENMARK Indicators 2013 ICT access Ideal value*a Fixed-telephone subscriptions per 100 inhabitants 60 37.4 b Mobile-cellular telephone subscriptions per 100 inhabitants 120
127.5 c International Internet bandwidth per Internet user**787'260 261'221 d Percentage of households with a computer 100 93.1 e Percentage of households with Internet access
100 92.7 ICT use f Percentage of individuals using the Internet 100 94.6 g Fixed (wired)- broadband Internet subscriptions per 100 inhabitants 60 40.2 h Wireless
-broadband subscriptions per 100 inhabitants 100 107.5 ICT skills i Adult literary rate 100 99.0 j Secondary gross enrolment ratio 100 124.7 k Tertiary gross enrolment ratio 100 79.6 Normalized values
Formula Weight ICT access z1 Fixed-telephone subscriptions per 100 inhabitants a/60 0. 20 0. 62
z2 Mobile-cellular telephone subscriptions per 100 inhabitants b/120 0. 20 1. 00 z3 International Internet bandwidth per Internet user log (c)/ 5
. 90 0. 20 0. 92 z4 Percentage of households with a computer d/100 0. 20 0. 93 z5 Percentage of households with Internet access e
/100 0. 20 0. 93 ICT use z6 Percentage of individuals using the Internet f/100 0. 33 0. 95 z7 Fixed (wired)- broadband
Internet subscriptions per 100 inhabitants g/60 0. 33 0. 67 z8 Wireless-broadband subscriptions per 100 inhabitants h/100 0. 33
. 88 y1 Fixed-telephone subsriptions per 100 inhabitants z1*.*20 0. 12 y2 Mobile-cellular telephone subscriptions per 100 inhabitants z2*.
*20 0. 20 y3 International Internet bandwidth per Internet user z3*.*20 0. 18 y4 Percentage of households with a computer z4*.
*20 0. 19 y5 Percentage of households with Internet access z5*.*20 0. 19 ICT use sub-index (M) y6+y7+y8 0. 40 0. 87 y6 Percentage of individuals using the Internet z6*.
*33 0. 32 y7 Fixed (wired)- broadband Internet subscriptions per 100 inhabitants z7*.*33 0. 22 y8 Wireless-broadband subscriptions per 100 inhabitants z8*.
*33 0. 33 ICT skills sub-index (N) y9+y10+y11 0. 20 0. 93 y9 Adult literary rate z9*.
*33 0. 33 y10 Secondary gross enrolment ratio z10*.*33 0. 27 y11 Tertiary gross enrolment ratio z11*.
*33 0. 33 IDI ICT Development Index((L*.40)+(M*.40)+(N*.20))*10 8. 86 Annex 1. ICT Development
Index (IDI) methodology 228 Potential sources of variation or uncertainty can be attributed to different processes employed in the computation of the index,
including the selection of individual indicators, the imputation of missing values and the normalization, weighting and aggregation of the data.
and the countries in this group ranked low in all index computations using different methodologies. This confirms the results conveyed by the IDI. 229 Measuring the Information Society Report 2014 1 Principal component analysis was used to examine the underlying nature of the data.
A more detailed description of the analysis is available in the Annex 1 to the 2009‘Measuring the Information Society.
The ICT Development Index'report (ITU, 2009). 2 More information about the indicators is available in the ITU‘Handbook for the collection of administrative data on telecommunications/ICT'2011,
on 4-6 june 2013, see http://www. itu. int/en/ITU-D/Statistics/Documents/events/brazil2013/Final report EGH. pdf). As some of the data used in the calculation of the IDI
however, the data may not necessarily reflect these revisions. 4 More information about the indicators is available in the ITU Handbook for the collection of administrative data on telecommunications/ICT'2011,
Technical Guidelines',see http://www. uis. unesco. org/ev. php? ID=5202 201&id2=DO TOPIC. 6 See OECD
Endnotes 231 Measuring the Information Society Report 2014 Annex 2. ICT price data methodology 1. Price data collection and sources The price data
The data were collected through the ITU ICT Price Basket questionnaire, which was sent to the administrations and statistical contacts of all 193 ITU Member States in October 2013.
Through the questionnaire, contacts were requested to provide 2013 data for fixed-telephone, mobile-cellular, fixed-broadband and mobile-broadband prices;
prices were collected directly from operators'websites and/or through direct correspondence. Prices were collected from the operator with the largest market share,
preference was given to prices offered by the (former) incumbent telecommunication operator. In some cases, especially when prices were advertised not clearly
the fixed-telephone, mobile-cellular and fixed-broadband sub-baskets. The IPB is calculated the value from the sum of the price of each sub-basket (in USD) as a percentage of a country's monthly GNI per capita,
The collection of price data from ITU Member States and the methodology applied for the IPB was agreed upon by the ITU Expert Group on Telecommunication/ICT Indicators (EGTI) 1 and endorsed by the eighth World
Telecommunication/ICT Indicators meeting held in November 2010 in Geneva, Switzerland. The fixed-telephone sub-basket The fixed-telephone sub-basket refers to the monthly price charged for subscribing to the public switched telephone network (PSTN
plus the cost of 30 three-minute local calls to the same (fixed) network (15 peak and 15 offpeak calls.
Annex 2. ICT price data methodology 232 The fixed-telephone sub-basket does not take into consideration the onetime connection charge.
then these are taken into consideration and deducted from the total cost of the fixed-telephone sub-basket.
The cost of a three-minute local call refers to the cost of a three-minute call within the same exchange area (local call) using the subscriber's equipment (i e. not from a public telephone).
because fixed-telephone access remains an important access technology in its own right in a large number of countries.
Since the IPB does not include dialup (but only broadband) Internet prices and since dial-up Internet access requires users to subscribe to a fixed-telephone line,
The mobile-cellular sub-basket The mobile-cellular sub-basket refers to the price of a standard basket of mobile monthly usage for 30 outgoing calls per month (on-net, off-net
The mobile-cellular sub-basket is based on prepaid prices, although postpaid prices are used for countries where prepaid subscriptions make up less than 2 per cent of all mobile-cellular subscriptions.
The mobile-cellular sub-basket is largely based on, but does not entirely follow, the 2009 methodology of the OECD low-user basket,
which is the entry-level basket with the smallest number of calls included (OECD, 2010b).
which is based on the prices of the two largest mobile operators, the ITU mobile sub-basket uses only the largest mobile operator's prices.
Additionally, the ITU mobile-cellular sub-basket does not take into account calls to voicemail (which in the OECD basket represent 4 per cent of all calls),
such as the onetime charge for a SIM CARD. The basket gives the price of a standard basket of mobile monthly usage in USD determined by OECD for 30 outgoing calls per month in predetermined ratios plus 100 SMS messages. 4 The cost
Rules applied in collecting fixed-telephone prices 1. The prices of the operator with the largest market share (measured by the number of fixed-telephone subscriptions) should be used. 2. Prices should be collected in national currency,
The selected city should be mentioned in a note in the monthly subscription indicator. 4. From all fixed-telephone plans meeting the above-mentioned criteria
this should be indicated in a note. 6. The same price plan should be used for collecting all the data specified.
This can present a challenge for data collection, since it may not be possible to isolate the prices for one service.
the mobilecellular sub-basket therefore corresponds to a basic, representative (low-usage) package Annex 2. ICT price data methodology 234 Annex Table 2. 1:
OECD mobile-cellular low-user call distribution (2009 methodology) Note: N/A: Not applicable. Source:
For comparability reasons, the fixedbroadband sub-basket is based on a monthly data usage of (a minimum of) 1 Gigabyte (GB.
For plans that limit the monthly amount of data transferred by including data volume caps below 1 GB,
the cost for the additional bytes is added to the sub-basket. The minimum speed of a broadband connection is 256 kbit/s. 235 Measuring the Information Society Report 2014 Annex Box 2. 2:
In this case, the monthly subscription fee, plus any free minutes, will be taken into consideration for the calculation of the mobile-cellular sub-basket. 4
then this is taken into consideration in the formula for the mobile-cellular sub-basket, based on 30 calls. 11.
the mobile-cellular sub-basket formula will be calculated on the basis of 30 calls or 50.9 minutes.
Annex 2. ICT price data methodology 236annex Box 2. 3: Rules applied in collecting fixed-broadband Internet prices 1. The prices of the operator with the largest market share (measured by the number of subscriptions) should be used. 2. Prices should be collected in national currency,
including taxes. 7 3. Only residential, single-user prices are collected. If prices vary between different regions of the country,
the cheapest one on the basis of a 1 GB monthly usage and an advertised download speed of at least 256 kbit/s should be selected.
7. The same price plan should be used for collecting all the data specified. For example, if a given Plan A is selected for the fixedbroadband service,
the volume of data that can be downloaded, etc. 8. Prices should be collected for regular (non-promotional) plan
This can present a challenge for price data collection, since it may not be possible to isolate the prices for one service.
preference is given to the cheapest available connection that offers a speed of at least 256 kbit/s and 1 GB of data volume.
If providers set a limit of less than 1 GB on the amount of data that can be transferred within a month,
then the price per additional byte is added to the monthly price so as to calculate the cost of 1 GB of data per month.
Preference should be given to the most widely used fixed (wired)- broadband technology (DSL, cable, etc.).
(i e. in terms of the price per Mbit/s)( see Annex Box 2. 3). 3. Mobile-broadband prices In 2012,
for the first time, ITU collected mobilebroadband prices through its annual ICT Price Basket Questionnaire. 8 The collection of mobilebroadband price data from ITU Member States was agreed upon by the ITU Expert Group
on Telecommunication/ICT Indicators (EGTI) 9 in 2012, and revised in 2013 by EGTI in view of the lessons learned from the first data collection exercise.
The revised methodology was endorsed by the eleventh World Telecommunication/ICT Indicators Symposium held in December 2013 in Mexico city, Mexico.
To capture the price of different data packages, covering prepaid and postpaid services, and supported by different devices (handset and computer),
mobile-broadband prices were collected for two different data thresholds, based on a set of rules (see Annex Box 2. 4). For plans that were limited in terms of validity (less than 30 days),
the price of the additional days was calculated and added to the base package in order to obtain the final price.
Two possibilities exist, depending on the operator, for extending a plan limited in terms of data allowance (or validity).
The customer:(i) continues to use the service and pays an excess usage charge for additional data10 or (ii) purchases an additional (add-on) package.
The plans selected represent the least expensive offers that include the minimum amount of data for each respective mobile-broadband plan.
and could purchase given the data allowance and validity of each respective plan. Annex 2. ICT price data methodology 238annex Box 2. 4:
Rules applied in collecting mobile-broadband prices11 1. Prices should be collected based on one of the following technologies:
UMTS, HSDPA+/HSDPA, CDMA2000, and IEEE 802. 16e. Prices applying to Wifi or hotspots should be excluded. 2. Prices should be collected in national currency,
including taxes. 3. Only residential, single-user prices should be collected. If prices vary between different regions of the country,
and b) computer-based mobile-broadband subscriptions. 5. Mobile-broadband prices should be collected from the operator with the largest market share in the country, measured by the number of mobile-broadband subscriptions.
mobile-broadband prices should be collected from the mobile-cellular operator with the largest market share (measured by the number of mobile-cellular subscriptions) in the country. 6. Different operators can be chosen, for a different mobile-broadband service, if:
a) there are differing market leaders for specific segments (postpaid, prepaid, computer-based, handset-based; b) there is no offer available for a specific sub-basket. 7. Prices should be collected for prepaid and postpaid services, for both handset and computer-based plans.
If there are several plans, the plan satisfying the indicated data volume requirement should be used. 8. Where operators propose different commitment periods for postpaid mobile-broadband plans,
the 12-month plan (or the closest to this commitment period) should be selected. A note should be added in case only longer commitment periods are offered. 9. Price data should be collected for the cheapest plan with a data volume allowance of a minimum of:
i. 1 GB for USB/dongle (computer-based) subscriptions ii. 500 MB for handset-based subscriptions.
The selected plan should not be the one with the cap closest to 500 MB or 1 GB,
Data volumes should refer to both upload and download data volumes. If prices are linked to‘hours of use'and not to data volumes
this information should be added in a separate note. Note: ITU will most likely not be able to include these cases in a comparison. 10.
A validity period of 30 days should be chosen. If this is not available, 15 days should be used.
Preference should be given to packages (including a certain data volume. Pay-as-you-go offers should be used
since most often there are limits in the data volumes, either applied by throttling (limiting the speed)
If the plan chosen includes other services besides mobile-broadband access, these should be specified in a note. 15.
Special prices that apply to a certain type of phone (iphone/Blackberry, ipad) should be excluded.
Allowances during the night are included not..Source: ITU. 239 Measuring the Information Society Report 2014 1 The Expert Group on Telecommunication/ICT Indicators (EGTI) was created in May 2009 with the mandate to revise the list of ITU supply-side indicators
(i e. data collected from operators), as well as to discuss outstanding methodological issues and new indicators. EGTI is open to all ITU members and experts in the field of ICT statistics and data collection.
It works through an online discussion forum (http://www. itu. int/ITU-D/ict/Expertgroup/default. asp) and face-to-face meetings.
EGTI reports to the World Telecommunication/ICT Indicators Symposium (WTIS. 2 In some cases, it is not clear
whether taxes are included or not and it was not possible to obtain this information from country contacts or operators;
5 On-net refers to a call made to the same mobile network, while off-net and fixed-line refer to calls made to other (competing) mobile networks and to a fixed-telephone line, respectively. 6 In some cases,
it is not clear whether taxes are included or not and it was not possible to obtain this information from country contacts or operators;
in such cases, the advertised price is used. 8 Data for fixed-telephone, mobile-cellular and fixed-broadband have been collected since 2008 through the ITU ICT Price Basket Questionnaire,
which is sent out annually to all ITU Member States/national statistical contacts. 9 See footnote 1. 10 Some operators throttle speeds after the data allowance included in the base package has been reached.
11 These rules were presented to the Expert Group on Telecommunication/ICT Indicators (EGTI) in September 2012.
In the 2013 revision, EGTI agreed that ITU should collect prepaid and postpaid prices, for both handset-and computer-based services, with the following volume allowances:
1 GB for computer-based and 500 MB for handset-based usage. The EGTI proposals to measure mobile-broadband prices were endorsed by the eleventh World Telecommunication/ICT Indicators Symposium held in December 2013 in Mexico city, Mexico.
Endnotes 241 Measuring the Information Society Report 2014 Annex 3. Statistical tables of indicators used to compute the IDI Annex 3. Statistical tables of indicators used to compute de IDI 242 Fixed
-telephone subscriptions per 100 inhabitants Mobile-cellular subscriptions per 100 inhabitants International Internet bandwidth Bit/s per Internet user Percentage of households with computer Percentage of households
3'876 3'858 17.6 20.0 7. 2 7. 7 Access indicators 243 Measuring the Information Society Report 2014 Fixed-telephone
subscriptions per 100 inhabitants Mobile-cellular subscriptions per 100 inhabitants International Internet bandwidth Bit/s per Internet user Percentage of households with computer Percentage of households with Internet access
Data in italics refer to ITU estimates. For further notes, see p. 248. Source: ITU World Telecommunication/ICT Indicators database.
Annex 3. Statistical tables of indicators used to compute de IDI 244 Percentage of individuals using the Internet Fixed (wired)- broadband subscriptions per 100 inhabitants Wireless-broadband subscriptions per 100 inhabitants
Economy 2012 2013 2012 2013 2012 2013 1 Afghanistan 5. 5 5. 9 0. 0 0. 0 0. 4 1
Society Report 2014 Percentage of individuals using the Internet Fixed (wired)- broadband subscriptions per 100 inhabitants Wireless-broadband subscriptions per 100 inhabitants Economy 2012 2013 2012
Data in italics refer to ITU estimates. For further notes, see p. 248. Source: ITU World Telecommunication/ICT Indicators database.
Annex 3. Statistical tables of indicators used to compute de IDI 246 Skills indicators Gross enrolment ratio Adult Seconday Tertiary literacy rate Economy 2012 2013
Data in italics refer to ITU estimates. Source: ITU World Telecommunication/ICT Indicators database. Annex 3. Statistical tables of indicators used to compute de IDI 248 Access indicators Fixed-telephone subscriptions per 100 inhabitants, 2012: 1) Incl. 524 958 WLL
subscriptions. 2) Incl. payphone, excl. VOIP. 3) Incl. ISDN channels measured in ISDNB channels equivalents. 4) Incl.
Voip. 5) Bhutan Telecom is the only service provider as of now in Bhutan. 6) By December 7) Fixed
and WLL. 8) Total access lines. 9) First trimester 2012.5 431 registered subscriptions. 10) Estimate. 11) Incl. public payphones. 12) Decrease is caused by change in tariff
policy of the biggest WLL operator. 13) Data excluding own (NRA) consumption. 14) Excl. voice-over-IP (Voip) subscriptions,
Telecom italia access lines, ULL, Virtual ULL, Naked DSL, Wholesale line Rental, Fiber, Public Telephony. 17) The number of fixed public payphones is as of March
2012.18) Fixed Wireless Local Loop. 19) Including digital lines. Without ISDN channels. 20) Excl. ISDN channels and fixed wireless subscriptions. 21) Incl. inactive subscriptions. 22) Preliminary. 23) Refers to active Fixed Wired/Wireless lines. 24) POTS,
ISDN BRA & ISDN PRA. 25) Decrease due to cleaning out of inactive accounts. 26) Excluding fixed wireless. 27) Excl. internal lines and WLR of incumbent.
Fixed-telephone subscriptions per 100 inhabitants 2013 1) Incl. 420 000 WLL subscriptions. 2) Incl. payphone, excl.
VOIP. 3) Incl. ISDN channels measured in ISDNB channels equivalents. 4) Incl. Voip. 5) Estimate. 6) Bhutan Telecom is the only service provider as of now in Bhutan. 7) By December 2013.8) Excl. voice-over-IP (Voip) subscriptions,
fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents. 9) Incl. PSTN lines, ISDN paths, FWA subscriptions, public payphones and VOIP subscriptions. 10) Total number of access paths. 11) Incl.
Telecom italia access lines, ULL, Virtual ULL, Naked DSL, Wholesale line Rental, Fiber, Public Telephony. 12) The number of fixed public payphones is as of March
ISDN channels and fixed wireless subscriptions. 16) Break in comparability. Only active subscriptions. Inactive subscriptions are:
45 609.17) February 2013 NTA MIS. 18) Based on 2013q3 data. 19) Refers to active Fixed Wired/Wireless lines. 20) Per June 2013.21) Operators'data
Definitive data (annual report) may change because quarterly reports use a smaller sample of operators than annual report. 24) Fixed and fixed-wireless subscriptions. 25) Excl. internal lines
Data for the third quarter of 2013. Mobile-cellular subscriptions per 100 inhabitants, 2012: 1) Numbers are down due to data cleanse. 2) ACMA Communications Report 2011-12.2) incl. payphone, excl.
Voip. 3) Active subscriptions. 4) Bhutan Telecom and Tashi Cell are the only two service providers in Bhutan. 5) Activity criteria:
voice or data communication in the last month. 6 december 2012.7) Total number of subscriptions (including non-active:
2 082 589.8) Excl. 2 720 698 prepaid cards that are used to provide Travel SIM/World Mobile service. 9) Excl. data-only SIM CARDS
and M2m cards. 10) Decrease due to merge of second and third operators of the mobile market. 11) Incl. fixed wireless local loop (WLL) subscriptions.
Including PHS and data cards, undividable. 15) Decrease was due to registration of SIMS. 16) Figure obtained from all five mobile (GSM
& CDMA) operators currently providing service in the country. 17) Incl. inactive subscriptions. 18) Preliminary. 19) Active subscriptions (87.85%total).
20) Measured using subscriptions active in the last 90 days. 21) Estimate. Incl. inactive. 22) Incl. data-only subscriptions (not possible to disaggregate the information at this point.
23) Break in comparability: excl. 226 827 M2m subscriptions. 24) Incl. active (in the last 6 months) prepaid accounts. 25) Registered SIM CARDS (incl. inactive:
261 887 751.26) Break in comparability: from this year only counting prepaid subscriptions used in the last 90 days. 27) No differentiation between active and non-active subscriptions. 28) Incl. data dedicated subscriptions. 29) Decrease due to the closing
of MTS. Mobile-cellular subscriptions per 100 inhabitants, 2013: 1) Preliminary. 2) Active subscriptions. 3) Bhutan Telecom and Tashi Cell are the two service providers in Bhutan
. 4) Activity criteria: voice or data communication in the last month. 5) By December 2013.6) Incl. all mobile cellular subscriptions that offer voice communications,
but excl. mobile data subscriptions (via data cards, USB modems and M2m cards). 7) Estimate. 8) Incl. data-only subscriptions. 9) Data based on NRA estimates.
Excl. data-only SIM CARDS and M2m cards. 10) Incl. fixed wireless local loop (WLL) subscriptions. 11) Incl.
PHS and data cards, undividable. 12) Active subscriptions (after clearing simcards that became inactive. 13) Figure obtained from all four mobile (GSM
& CDMA) operators currently providing service in the country. 14) Break in comparability. Incl. only active subscriptions. 15) Active subscriptions (83.44%total.
16 february 2013 NTA MIS. 17) Based on 2013q3 data. 18) Measured using subscriptions active in the last 90 days. 19) Per June 2013.20) Excl. 463 646
M2m subscriptions. 21) Incl. active (in the last 6 months) prepaid accounts. 22) Registered SIM CARDS (incl. inactive:
277 744 809.23) Prepaid: 43.9 million; post-paid: 6. 94 million. 24) No differentiation between active and non-active subscriptions. 25) Q4 report.
Definitive data (annual report) may change because quarterly reports use a smaller sample of operators than annual report. 26) Data for the fourth quarter of 2013.27) Incl. data dedicated subscriptions
. 28) Reduction is due to implementation of sim cards registration. International Internet bandwidth Bit/s per Internet user, 2012: 1) Refers to a survey conducted with the following companies:
Global crossing, TIWS, Embratel e Globenet. 2) This is from one ISP only. No response received from other ISPS. 3) Symmetric. 4) Total installed capacity. 5) Purchased capacity. 6) By December 2012.7) Estimate. 8) Activated external capacity. 9) Only
ETL, LTC and LANIC. 10) Source: MOT. 11) Data obtained from nine service operators. 12 may 2012 purchased capacity.
Lit capacity: 43 096 471 Mbit/s. 13) Incoming capacity; peak Notes 249 Measuring the Information Society Report 2014 weekly incoming capacity, averaged over 4 weeks in December 14) Preliminary. 15) SLT Data. 16) Refers to the total
capacity of the international bandwidth. 17) Potential (installed) capacity. International Internet bandwidth Bit/s per Internet user, 2013: 1) Purchased capacity. 2) As at December 2013.3) Total installed capacity.
4 may be revised with comprehensive data from mobile broadbnad providers. 5) Estimate. 6 june. 7) Sum of incoming capacity of all ISPS in the country. 8) Activated external capacity. 9) By September 2013.10) Data obtained from eight service operators. 11
) 1st april 2013 purchased capacity. Lit capacity: 70 464 304 Mbit/s. 12) Incoming capacity, average peak incoming capacity for December 13) Not update for 2013.14) Refers to the total capacity of the international bandwidth. 15) Potential (installed) capacity.
Percentage of households with computer, 2012: 1) Estimated based on 2011 proportion of households with a computer and using annual growth rate of 3%.2) Preliminary. 3) Refers to PC
or laptop. 4) Data correspond to dwellings (not households). 5) Ghana Living Standards Survey 2012/2013.
The estimate is based on households who own and/or have access to a desktop, laptop or TABLET PCS.
Sample weights have been applied. 6) Personal computer included desktop computer, laptop/notebook/netbook/tablet and palm top/Personal digital assistant (PDA),
but excluded digital diary and electronic dictionary. 7) Estimate. 8) From Household Socioeconomic survey-2012.9) Census data. 10) Computer includes the number of personal computer, Notebook,
and PDA. 11) U s. Census bureau, table 4: http://www. census. gov/hhes/computer/publications/2012. html. Percentage of households with computer, 2013: 1) Labour force Survey 2013.2) Cambodia Inter-censal
Population Survey. 3) Refers to PC, laptop or a tablet. 4) Data correspond to dwellings (not households).
5) Ghana Living Standards Survey 2012/2013. The estimate is based on households who own and/or have access to a desktop, laptop or TABLET PCS.
Sample weights have been applied. 6) Preliminary. 7) Estimated. Percentage of households with Internet access, 2012: 1) Estimated based on 2011 proportion of households with internet and using estimated annual growth rate of 2. 8%.2) Preliminary. 3) Data
correspond to dwellings (not households. 4) Ghana Living Standards Survey 2012/2013. The estimate is based on households who own
and/or have access to internet. Sample weights have been applied. Not restricted to access at home. 5) Incl. desktop computer, laptop/notebook/netbook/tablet,
but excluded palm top/Personal digital assistant (PDA) and other devices for Internet connection (e g. smartphone, game console and e-book reader).
6) Accessing from personal computers. 7) Estimate based on 2011 Census Population Household Projection Estimates. 8) From Household Socioeconomic survey-2012.9) Break in comparability:
Refers to access at home, on cell phone or other mobile device and via mobile modem. 10) Census data. 11) Excl. households
which didn't know type of internet access 172 346 households. 12) U s. Census bureau, table 3:
http://www. census. gov/hhes/computer/publications/2012. htm. Percentage of households with Internet access, 2013: 1) Labour force Survey 2013.2) Corresponds to all type of internet connections
. 3) Data correspond to dwellings (not households. 4) Ghana Living Standards Survey 2012/2013. The estimate is based on households who own
and/or have access to internet. Sample weights have been applied. Not restricted to access at home. 5) Included desktop computer, laptop/notebook/netbook/tablet,
but excluded palm top/Personal digital assistant (PDA) and other devices for Internet connection (e g. smartphone, game console and e-book reader).
Use indicators Percentage of individuals using the Internet, 2012: 1) 15 years and older. Last 12 months. 2) Users in the last 3 months. 3) Estimated based on 2011 Residential consumer survey result and TRA analysis of the growth. 4) Individuals aged 16
and over. 5) Preliminary. 6) In the last 3 months. Population 10+.7) Residents of Canada 16 years of age or older excluding:
Residents of the Yukon, Northwest territories and Nunavut, Inmates of Institutions, Persons living on Indian Reserves,
and Full time members of the Canadian Forces. 8) Estimated based on surveys'results. Population age 5+.9) In the last 3 months.
Population 5+.10) 12+years. 11) Population 5+.Direct response from individuals 15 years and above. 12) The methodology depends basically on the number of internet users using hard indicators instead of data
survey. 13) Ghana Living Standards Survey 2012/2013. The estimate is based on weighting households who use internet by the household size over the total estimated population.
Sample weights have also been applied. The question was asked at household level. 14) All persons aged 10 and over. 15) Age 20+.
+In the last 3 months. 16) Individuals 14 years or older. 17) Break in comparability: population aged 15-74.18) Individuals aged 3
27) Reference period for computer and Internet usage is 3 months only. 28) U s. Census bureau, Table 2:
http://www. census. gov/hhes/computer/publications/2012. htm. Percentage of individuals using the Internet,
The estimate is based on weighting households who use internet by the household size over the total estimated population.
+using internet in the last 3 months. 19) Individuals aged 15 to 72 years. 20) Estimated.
Fixed (wired)- broadband subscriptions per 100 inhabitants, 2012: 1) Internet Activity Survey, June 2) Incl. fixed wireless broadband. 3) Fixed broadband in Bhutan is provided via ADSL/DSL networks only. 4) As of 2012 it includes also FTTH. 5) Expert assessment,
based on the data provided by 89.1%of operators. 6) The figure is corrected. The previous figure was 1'636'700.7) Only ADSL, excl. cable modem. 8) Speeds greater than,
or equal to, 512 Kbps. 9) By December 2012.10) Only ETL and LTC. 11) Preliminary. 12) Full VDSL. 13) Speeds equal to or greater than 144 kbit
/s. 14) Operators data/ictqatar estimate. 15) Incl. subscriptions at downstream speeds equal to, or greater than, 144 kbit/s (the number of subscriptions that are included in the 144-256 range is insignificant.
16) Q3. 17) Excl. 3203 Wimax subscriptions. 18) Excl. corporate connections. 19) Data reflect subscriptions with associated transfer rates exceeding 200 kbps
1 november 2013.2) Preliminary. 3) Internet Activity Survey, June 2013.4) Fixed broadband provided through ADSL/DSL and Fiber links. 5) Estimate,
no specific data collected for=256 kbit/s. 6) CRC estimation as of 31.12.2013.7) Estimate. 8) Data based on NRA estimates. 9) Only ADSL,
or equal to, 512 Kbps. 11 december 2013. These are the subscriptions with the minimum download speed of 512 kbps. This is as per the revised definition of Broadband in India with effect from 18th july 2013.12) December 13) ADSL and Leased lines. 14) Based on 2013q3 data
. 15) Per June 2013.16) Operators'data. 17) Incl. subscriptions at downstream speeds equal to, or greater than, 144 kbit/s (the number of subscriptions that are included in the 144-256 range is insignificant).
18) Q4 report. Definitive data (annual report) may change because quarterly reports use a smaller sample of operators than annual report. 19) Estimate.
Refers to March 2013.20) Excl. 3175 Wimax subscriptions. 21) Excl. corporate connections. 22) 2013 data is an estimate as of June 30, 2013.
Data reflect subscriptions with associated transfer rates exceeding 200 kbps in at least one direction, consistent with the reporting threshold the FCC adopted in 2000.
Wireless-broadband subscriptions per 100 inhabitants, 2012: 1) Only fixed Wimax subscriptions. 2) Internet Activity Survey, June 3) Break in comparability:
including all categories of mobile broadband. 4) Total number of EDGE/GPRS subscribers: 97 520.5) Break in comparability:
from this year incl. USB modems and dongles, mobile broadband(>256kbps at least in one direction up to HSPA+),Wimax, Pre Wimax, SID and satellite. 6) Change in definition, break in comparability. 7
) High use of mobile phones to access the internet. 8) Incl. Home Box and RLANS. 9) Break in comparability,
from this year incl. prepaid mobile-broadband subscriptions. 10) Incl. subscriptions to Wifi hotspots. 11) Methodology changed from ability to have mobile broadband to actual mobile broadband usage. 12) Satellite,
BWA and active mobile subscriptions. 13) Estimate based on partial SIT data and ITU estimates. 14) Speeds greater than,
or equal to, 512 Kbps. 15) Rightel (Tamin Telecom) has been given license to operate 3g services and started services from February 2011 (http://www. rightel. ir/).
/Data refer to the sum of fixed wireless broadband and active mobilebroadband subscriptions. 16) Incl. mobile broadband
and Wimax. 17) Estimate. 18) ETL and LTC. 19) Incl. narrowband connections. 20) Drop in mobile-broadband subscriptions
because in 2011 the operator offered free Internet access for a limited amount of time so that many people used the free service. 21) Preliminary. 22) Mobile broadband only.
Fixed wireless and satellite exist but data are not available. 23) Operators data/ictqatar estimate. 24) Refers to active mobile-broadband subscriptions only. 25.dec 26) Incl. 4125165 active mobile-broadband subscriptions plus 3203
Wimax subscriptions. Excl. satellite subscriptions. 27) Excl. satellite and fixed wireless. 28) Incl. mobile subscriptions with potential access.
Wireless-broadband subscriptions per 100 inhabitants 2013: 1) Only fixed Wimax subscriptions. 2) Preliminary. 3) Internet Activity Survey, June 2013.4) Total number of EDGE/GPRS subscribers is 112
898.5) Incl. LTE subscriptions from ENTEL. 6) Change in definition, break in comparability. 7) 2012 figures.
Still auditing the 2013 figures. 8) Incl. WCDMA, LTE, dedicated mobile-broadband and fixed wireless. 9) CRC estimation as of 31.12.2013. speeds equal to or greater than 144 kbit/s/.10) Estimate. 11) Estimate.
Incl. subscriptions to Wifi hotspots. 12) Satellite, BWA and active mobile subscriptions. 13) Incl. VSAT. 14) Speeds greater than, or equal to
512 Kbps. 15) subscriptions with minimum download speed of 512 kbps. This is as per the revised definition of Broadband in India with effect from 18th july 2013.16) Data refer to the sum of fixed wireless
broadband and active mobile-broadband subscriptions. 17) 2013 data is an estimate as of June 30,
2013.18) Incl. mobile broadband and Wimax. 19) Estimate based on 1. Standard mobile subscriptions using data services 2. Dedicated data subscriptions 3. Add on data
packages. 20) Based on 2013q3 data. 21) Per June 2013.22) Mobile broadband only. Fixed wireless and satellite exist
but data are not available. 23) Operators'data. 24) As at Dec 2013.25) Q4 report.
Definitive data (annual report) may change because quarterly reports use a smaller sample of operators than annual report. 26) Wireless Broadband services are not being offered in St vincent as yet.
We anticipate that Mobile broadband and terrestrial fixed broadband services would be in place by the end of 2014.27) OFCOM estimate. 28) Includes:
active mobile-broadband subscriptions plus 3175 Wimax. International Telecommunication Union Telecommunication Development Bureau Place des Nations CH-1211 Geneva 20 Switzerland
www. itu. int ISBN 978-92-61-14661-0 SAP id 9 7 8 9 2 6 1 1 5
2 9 1 8 3 9 4 6 4 Price: 86 CHF Printed in Switzerland Geneva, 2014 Photo credits:
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011