the connectivity report 2011

8/2/2019 The Connectivity Report 2011

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ConnectivityScorecard 2011

Leonard Waverman, Dean, Haskayne Business Schoool, andDirector, Berkeley Research Group

Kalyan Dasgupta, Principal, Berkeley Research Group

Janne Rajala, Managing Consultant, Communicea OY

May 5th

, 2011

With especial thanks to:Kim Jones, NSNPieter Rademeyer, NSN


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Contents

1. About this report..................................................................................................... 32. The Connectivity Scorecard Concept................................................................... 4

2.1. Connectivity...................................................................................................... 42.2. Useful connectivity......................................................................................... 42.3. What makes this Scorecard different? Connectivity and EconomicPerformance................................................................................................................. 52.4. Comparisons with other composite indices ..................................................... 8

Figure 1: Schematic representation of methodology................................................ 92.5. How useful is Connectivity? A worthwhile reminder...................................... 92.5.1. ICT, Jobs and Economic Performance.................................................... 92.5.2. ICT, Globalisation and Socio-Economic Benefits .................................. 14

3. Connectivity Scorecard 2008/09/10: What we did and what we found........... 163.1. 2008 and 2009 Scorecards............................................................................ 16Table 3: 2009 Connectivity Scorecard Results .......................................................... 183.2. The 2010 version of the Scorecard................................................................ 19

4. Connectivity Scorecard 2011: Results, Highlights and Discussion................ 214.1. 2011 Results .................................................................................................. 214.2. Highlights of results........................................................................................ 22

4.2.1. Innovation economies............................................................................ 224.2.2. Resource and Efficiency economies...................................................... 25

4.3. Innovations in 2011........................................................................................ 274.3.1. New indicators........................................................................................ 274.3.2. New Weights.......................................................................................... 30

5. Further Notes on Methodology, Inference and Data......................................... 335.1. Updated Data versus New Methodology ....................................................... 335.2. Backwards Compatibility Versus Innovation.................................................. 345.3. Statistical Significance and Clustering........................................................... 365.4. Resource and Efficiency Economies: Particular Comments ......................... 405.5. Key Data Differences With Other Composite Indices.................................... 41

5.5.1. Inclusion of soft variables .................................................................... 415.5.2. Pricing data ............................................................................................ 41

5.6. Relative versus absolute scores .................................................................... 425.7. Sensitivities of Scores to Weights.................................................................. 43

6. Conclusions .......................................................................................................... 456.1. Innovation economies .................................................................................... 456.2. Resource and Efficiency economies.............................................................. 45

7. List of Indicators: Innovation Economies.......................................................... 47


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8. List of Indicators: Resource and Efficiency Economies .................................. 529. Appendix 1: Further Data Notes.......................................................................... 56

9.1. Notes on Innovation Economy Indicators ...................................................... 569.1.1. Consumer Infrastructure Indicators........................................................ 569.1.2. Consumer Usage Indicators .................................................................. 569.1.3. Business infrastructure .......................................................................... 579.1.4. Business Usage ..................................................................................... 579.1.5. Public sector indicators .......................................................................... 58

9.2. Notes on R&E Indicators................................................................................ 589.2.1. Consumer Infrastructure Indicators........................................................ 589.2.2. Consumer Usage Indicators .................................................................. 589.2.3. Business Infrastructure Indicators.......................................................... 599.2.4. Business Usage Indicators .................................................................... 599.2.5. Public sector components...................................................................... 59

9.3. Additional general comments on the indicators ............................................. 5910. Appendix 2: Selected Methodology Q&As Discussed in 2010 Scorecard 62


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1. About this report

This report accompanies the release of the 2011 Connectivity Scorecard.

It has the following structure:

Section 2 is designed to introduce the Connectivity Scorecard. It introduces the

conceptual basis for the Connectivity Scorecard, discusses the relationship

between connectivity and economic growth, and discusses the continued

relevance of monitoring and valuing ICT performance in light of recent global

economic debates;

Section 3 presents our findings from the previous iterations of the Scorecard;

Section 4 summarises the 2011 Connectivity Scorecard, which not only

updates previous data but also incorporates data from a newer (and better) set

of sources. We have also included some new measures of connectivity that we

believe are currently more relevant than ones used in previous iterations of the

Scorecard;

Section 5 discusses some of the salient methodological features of this years

Scorecard;

Section 6 provides some concluding thoughts;

Appendix 1 provides some notes on data sources and data concepts used in

this Scorecard; and

Appendix 2 reprises the questions and answers that we had provided regarding

the methodology that went into the 2009 and 2010 editions of the Scorecard.

Extensive discussion and details regarding the methodology can be found in the 2009

and 2010 Connectivity Scorecard report, and we refer readers to the

ConnectivityScorecard.org website for this report.


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2. The Connectivity Scorecard Concept

2.1. Connectivity

It is widely accepted that most modern developed economies are Information

economies, and that a significant amount of economic growth and productivity growth in

the developed nations of Europe, North America and the Far East is driven by

information and communications technology (ICT). What is less widely accepted and

understood is that actually all economies are information economies. The free flow and

availability of information lowers the barriers to economic activity and stimulates growthand productivity in even those economies that we do not normally regard as fully

developed.

Connectivity is usually understood to be the copper wires, fibre-optic cables and

networked computers and more recently mobile phones and base stations that enable

the fast flow of information regardless of distance, at costs that are much lower than the

costs of physical travel and much lower than what these costs were just 15 or 20 years

ago. Connectivity is the key enabler of the flow of information that defines modern

economies, and it is also the key enabler of an ongoing (and sometimes overlooked)transformation in the economic fortunes of many Asian and African countries.

We define connectivity in a much broader way to embrace more than just

infrastructure and hardware. The notion of connectivity should be expanded to include

also the complementary assets (software) and skills embodied in people,

governments and businesses since it is the presence and quality of these assets that

determine just how productively the hardware and infrastructure are used.

In summary, we use the term connectivity to refer to the totality of interaction between

a nations telecommunications infrastructure, hardware, software, networks, and users

of these networks, hardware and software. Thus broadband lines, PCs, advanced

corporate data networks and advanced use of wireless data services are certainly

measures of connectivity, but so are human skills relevant to the usage of these

infrastructures, technologies and networks.

2.2. Useful connectivity

The Connectivity Scorecard that we have designed is an attempt to rank countries

according to a measure of what we call useful connectivity. Useful connectivity or


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usefully connected are phrases that we use often in this report and in our

presentational materials. They refer to the ability of connectivity to contribute to

economic growth, especially through improvements in productivity that are widely held

to be the key to sustained economic prosperity. The concept of useful connectivity is

first and foremost an attempt to recognise that the economic value generated by

connectivity depends not just on conventional measures such as broadband lines or

computers connected, but also on who is using those lines businesses or consumers

and how well they are able to use the lines (captured by measures such as user

skills, software assets, use and frequency of use of particular technologies such as the

Internet, corporate data services, mobile data services and the like).

The Scorecard aims to measure useful connectivity by making a link between

connectivity and economic performance. We next explain how this linkage is made.

2.3. What makes this Scorecard different? Connectivity andEconomic Performance

While there has been a significant body of academic research that looks at the

relationship between various elements of connectivity (Computers,

Telecommunications, or more generally ICT) on the one hand, and economic growth

and productivity on the other, the insights of this literature have not been crystallised in

existing Scorecards or Indices or rankings of the Digital Economy. However, given our

emphasis on useful connectivity, we felt that our methodology must attempt to

incorporate the findings of the academic research.

Some widely recognised facts that emerge from existing research are the following:

The US has seen more clear-cut productivity gains from ICT than has Europe,

and a major source of this US productivity advantage is the usage of ICT by

businesses that are not themselves producers of ICT for example, retailing.1

Within Europe, Nordic countries have seen a greater contribution from ICT than

other parts of Europe. Of late, however, the measurement of the U.S.

productivity miracle and the implications of this productivity miracle for U.S. jobs

has become a significant source of controversy, which we briefly discuss;

1

Robert Inklaar, Bart Van Ark and Robert McGuckin, ICT and Productivity in the United States and Europe:Where Do the Differences Come From?, Conference Board Working Paper, 03-05


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The impacts of ICT are enhanced by investment in complementary assets

such as worker training, learning and education; 2 and

Productivity growth is the key to sustained improvements in living standards

(productivity growth is one major way in which the trend rate of economic

growth the long-term potential growth rate of the economy can be

improved).

Thus in constructing a scorecard that purported to measure useful connectivity, we

wanted to take into account not just how many broadband lines were being deployed,

but who was using those broadband lines businesses or consumers? How smart

were the users of these lines for example, what proportion of the workforce was

composed of university graduates and what proportion of the workforce were

specialised in research or knowledge creation?

The results from academic research suggested that in order to construct an index or

scorecard of connectivity that actually linked connectivity to economic performance, we

needed to look at:

Whether countries were connecting up in the right places e.g., countrieswere deploying infrastructure and making use of telecommunications and ICT

in those activities and sectors that were most important to generating long-term

economic growth;

Whether investment in infrastructure was being matched by investment in

usage or skills; and

How economically beneficial investment in infrastructure was as opposed to

investment in usage and skills.

We therefore had to answer the following questions:

If the economy were to be divided into its constituent actors the government

sector, the business sector and the consumer sector how to weight the

importance of these sectors in a way that captures the role of the business

sector in terms of productivity contributions?

2Susanto Basu, John G. Fernald, Nicholas Oulton, and Sylaja Srinivasan, Does Information Technology

Explain Why Productivity Accelerated in the United States But Not the United Kingdom?, Federal ReserveBank of Chicago, Working Paper 2003-08


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How can we rank countries according to not just the availability of infrastructure

and the penetration rate of infrastructure, but also the usage level of the

infrastructure by consumers, businesses and governments? How can we

factor in the complementary investments in human and organisational capital,

particularly by businesses, i.e. in what we call useful connectivity?

Thus the Connectivity Scorecard methodology:

divided the economy into the consumer sector, the business sector, and the

government sector;

gave weights to the consumer sector, business sector and government sector

that matched their importance in economic activity;

split each of the consumer, business and government categories into

infrastructure and usage and skills components and allocated individual

measures to either of these two sub-categories; 3 and

allocated weights to the infrastructure and usage and skills categories.

A wide range of individual measures/indicators were selected, reflecting elements of

both infrastructure and usage. A full list of these indicators is presented after the tables

and figures in this section of the report.

The weighting of the infrastructure and usage and skills components was based upon

economic considerations and are unique in the literature. First, the weights for each

sector are country-specific and are drawn from national GNP accounts. The weights for

the infrastructure versus skills subcomponents used data from research into the

sources of productivity enhancement.4

3For example, education or skills measures or measures such as minutes of usage per customer wereallocated to usage and skills, while measures such as line counts or hardware investment were allocatedto infrastructure

4 For example, businesses and governments are concerned with productivity and generating growth. Thus atheoretically sensible split between infrastructure and usage and skills would look at the contribution ofinvestment in infrastructure relative to the contribution of human capital or investment in thecomplementary capital that we referred to above in generating economic growth. In practice, we useddata drawn from the Conference Board Total Economy Database 2011 that allowed us to examine therelative importance of ICT investment and improvements in labour force skills to economic growth over arecent 10-year period. For consumers, utility or consumer welfare, is of paramount importance.Consumers can subscribe to telephone service (or broadband service) based upon two considerations: (a)the value that they place on having access to the network, which is often called an option value, and (b)

the value that they derive from actual usage of that network. Unfortunately, while the economics literaturehas ample theoretical discussion on the option value of access, there is little quantitative guidance about


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Thus the selection of components (consumer, business and government), sub-

components (usage and skills, infrastructure) and weights given to the components and

sub-components reflects an ambitious attempt to capture whether countries are

investing in the right places, are matching their infrastructure investment with the right

skills, and whether they are succeeding in enabling adequate levels of both access to

and usage of key technologies.

2.4. Comparisons with other composite indices

Other composite indices that we have reviewed tend to fall into two categories:

(a) indices that look at hard variables on telecom and computer penetration, and

perhaps also at prices and usage levels; and (b) indices that look at hard variables

together with factors such as freedom, business environment economic stability and the

like.

The Connectivity Scorecard looks at substantially more than just penetration rates and

counts; it is distinguished by a focus on the business sector; it considers an extensive

array of utilisation indicators such as levels of Internet use, take-up of Internet-based

services, and use of websites by businesses. At the same time, it remains very

focussed on hard data, and although it looks at complementary factors, it avoids

drawing upon inherently subjective data points such as ratings of a countrys business

environment, level of economic freedom, vision shown by the government in

formulating ICT policy and the like. In one instance, we do rely upon components of a

composite index of E-Government performance constructed by the United Nations.

However, even that composite index is largely based upon hard information rather than

perceptions.

As with all composite indices, the Scorecard is only able to utilise the data that are

actually available to us, and particularly for measures of usage and for measures of

business connectivity the data are not ideal. Data for the developing or resource

and efficiency economies are also not ideal. All such composite indices are inevitably

coloured by the availability of data and the choice of data by the researchers

constructing the index. Such indexes are ultimately based upon subjective decisions

about which indicators to include or exclude and how to weight these indicators. That

said, there should be some logical or statistical merit to the choice of indicators and

weights. Our modest claim is that to the extent that there is subjectivity in our

the proportion of consumer welfare derived from access versus the proportion derived from usage. In the

absence of such evidence, we gave equal weights to the infrastructure and usage and skil ls sub-categories


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measurement of Connectivity, this is driven by the choice of quantitative indicators that

we used and the weights that we gave to the different components of the Scorecard. In

some other composite indices, there is not only subjectivity associated with the

weighting of different elements and the choice of individual data indicators, but there is

subjectivity associated with the indicators themselves.

The figure below is a broad schematic of the methodology and more details can be

found in Appendix 2.

Figure 1: Schematic representation of methodology5

2.5. How useful is Connectivity? A worthwhile reminder

2.5.1. ICT, Jobs and Economic Performance

For much of the early and middle-2000s, a great deal of academic and policy-making

attention was focused upon the fact that the U.S. not only was out-investing Europe and

Japan in ICT, but was able to realise a new productivity miracle that eluded old

Europe and stagnant Japan.

for consumers.5

Note: in the above figure, GDP is used somewhat loosely. In reality, the consumer share of the economywas calculated as the share of final household consumption in total use at purchasers prices (to use theterminology used in the UK Input-Output tables for 2003). Business share was gross fixed capitalformation + intermediate consumption + exports. We believe that this total use measure gives an idea ofthe supply of goods in the economy, and thus reflects not just the components of final demand but the

large amount of business-to-business activity in the economy. This in turn helps put the weight ofbusiness into perspective in terms of economic activity.


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Indeed, conventional studies find that the between the middle 1990s and the middle

2000s the U.S. had reversed its previous productivity stagnation (which lasted from the

middle 1970s to the middle 1990s), and had substantially outstripped Europe in both

economic growth and productivity growth between 1995 and 2004. Further, the

contribution of the knowledge economy (of which ICT was a major part) to this

productivity surge was more than twice that in the United States as it was in Europe.

Table 1: Economic growth and its sources, 1980-2004

EuropeanUnion UnitedStates1980-95 1995-2004 1980-1995 1995-2004

1 Market economy output (2)+(3) 1.8 2.2 3 3.7

2 Hours worked -0.6 0.7 1.4 0.6

3 Labour productivity (4)+(5)+(8) 2.4 1.5 1.5 3

Contributions from

4 Labour composition 0.3 0.2 0.2 0.3

5 Capital services per hour (6)+(7) 1.2 1 0.8 1.3

6 ICT capital per hour 0.4 0.5 0.5 0.8

7 Non-ICT capital per hour 0.8 0.5 0.2 0.4

8 Multifactor productivity 0.9 0.3 0.5 1.4

Contribution of knowledge economy 1.6 1.1 1.3 2.6

(4)+(6)+(8)

Source: Mary OMahony, Marcel P. Timmer and Bart Van Ark, The Productivity GapBetween Europe and the United States: Trends and Causes, Journal of EconomicPerspectives, Volume 22, Number 1, Winter 2008, pp.25-44.

ICTs prominent role in the U.S. productivity revival of the 1990s and 2000s was driven

by an expansion in the ICT sector itself, but equally crucially by productivity gains in

what one might term the ICT-using sectors. Among these sectors were financial

services and retailing.

Within Europe itself, some countries such as the Northern European countries and the

United Kingdom appeared to be both investing substantially in ICT and realising

significant productivity gains in what might be termed ICT-using sectors. Chief among

these sectors were financial services, retailing and other business services sectors.


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The large productivity gains in services sectors in the United States (and to some

degree in the United Kingdom) were frequently attributed to the flexible nature of the

U.S. economy which allowed for firms to reorganise themselves around new

technologies. In Europe, with its much tighter labour laws and its more conservative

management approach, such reorganisation or restructuring was viewed with suspicion

and was difficult to implement. In particular, the core European countries of Germany,

France and Italy were seen as particular laggards in putting ICT to its fullest use. For

instance, in the study by OMahony, Timmer and Van Ark, the contribution of increased

ICT capital investment to economic growth was between 0.2 and 0.5 percentage points

per year (1995-2004) in these core European economies. Taking the knowledge

economy component identified by these authors (the sum of multifactor productivity,

improvements in labour quality and ICT capital deepening), the contribution of the

knowledge economy in Italy was found to be negative. In France and Germany, the

average annual contribution of the knowledge economy was more significant1.6

percentage points per year and 1.0 percentage points per year, but this compares with

a total of 2.6 percentage points per year in the U.S., and 2.2 percentage points in the

U.K. (Finland saw an annual average contribution of 3.4 percentage points per year).

Yet following the Great Recession of 2008 and 2009, many observers and politicians

question the growth model followed by the U.K. and U.S. Political discourse betrays a

great unease with the idea of a services-based economy, and there are questions

raised about the measurement of GDP, productivity and output in a services-driven

economy. Some sectors such as construction and perhaps financial services over-

expanded during the period from 1995 to the mid-2000s. Some of the output gains that

were registered during this period proved ultimately to be unsustainable. Most

controversially, especially in the United States, is the linkage between productivity and

jobs. U.S. productivity growth has continued to outstrip that of its rivals, U.S. GDP has

regained its previous peak (unlike GDP in most other countries), but the U.S. jobless

rate has increased dramatically, much more so than in any other country. Similarly, in

the U.K., Germanyonce derided for sticking to its old economy waysis now envied

for having held on to its high-end manufacturing industries, something the U.K. is said

to have lost.

All of these current debates have resonance for the Connectivity Scorecard. Our

premise is that we (in common with the established literature on ICT and productivity)

are measuring something genuine and importantmore ICT investment and smart

usage of ICT really have produced significant and enduring economic gains. However,


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if the growth in the sectors that were most commonly identified as ICT-using sectors

was either chimerical or jobless, then clearly the role of ICT in generating genuine

economic growth can be questioned.

We address some of these issues in the detailed country write-ups that can be found on

www.connectivityscorecard.org. In particular, these issues are discussed in the

detailed write-up on the United States. Here, however, we make a few simple points

that speak to the continued relevance of ICT for economic performance in developed

countries:

The economic growth trends that were used in studies that established theimportance of ICT were long-standing growth trends. For instance, it should

not be forgotten that over the last 20 years or the last 10 years, the U.K.

economy has outperformed the Germany economy in terms of per capita GDP

growth. It is premature to suggest that all of the substantial gains made by the

U.K. during this period are likely to be reversed. The chart below shows that

Australia, the U.K., Sweden and (to a lesser extent) Canada and the U.S. all

enjoyed strong growth for many years, and all of these economies were driven

by ICT;

There are issues with respect to the measurement of productivity and output in

the services sector. There are also issues related to the proportion of final

value-added for services when a lot of the intermediate processes are

outsourced to foreign countriesthe concern is that government statistics are

misattributing productivity gains made abroad to domestic production, and thus

overstating domestic productivity. To the extent that ICT fuels the development

of globalised production processes, ICT-using globalised sectors might show

high measured productivity growth, but their growth is chimerical. However, no

definitive or even compelling analysis as to the size of this phantom GDP

effect has been offered, and thus it seems unwise to dismiss the productivity

gains recorded in ICT-intensive economies and ICT-intensive sectors in recent

years;

Higher productivity, of which ICT is a crucial driver, does not seem to be leading

to more jobs. In fact, the evidence from the United States suggests that robust

productivity growth in recent years has simply enabled firms to do more with

fewer workers. Consequently, corporate profits in the United States have


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remained extremely strong during the last two years, even as the

unemployment rate has gone from 5% to 10%. As with trade, technological

disruption likely creates winners and losers, but benefits society as a whole.

Further, the classic view has been that in the long-term, productivity is almost

everything.6 Although there is speculation about the jobless recovery and the

role of higher productivity in driving it, there is no coherent economic thinking

that suggests countries should attempt to manage productivity increases and

slow down the rollout of technology. Such policies when tried at various times

in history have been disastrous in the longer-term. It is also worth noting that

the job losses in the United States have been especially severe in the

construction sector, not noted for its ICT intensity. Indeed, it was the

construction and real estate sector rather than financial service, retail or

business services that saw a real bubble in many countries in the 2000s;

Absent continued strong investment in ICT during the recession, some 43

percent of U.S. non-residential fixed investment was ICT investment many

more jobs would have been lost and much more wealth destroyed;

Lastly, conventional statistical methods singularly fail to capture the substantial

qualitative improvements in services and applications used by everyday

consumers. Consumers are able to access content (written, video, and

otherwise) on the Internet that they were not able to access without either

incurring direct costs (paying for it) or high costs of searching for and accessing

the content. Yet this higher real consumption does not figure in GDP statistics.

6

See Krugman, Paul, The Age of Diminished Expectations: U.S. Economic Policy in the 1990s, MIT Press(Cambridge, MA and London, U.K.), 1994.


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Figure 2: GDP per capita levels (1991=100), selected OECD economies, 1991-2010

Source: Conference Board Total Economy Database, January 2011.

2.5.2. ICT, Globalisation and Socio-Economic Benefits

ICTs effects in spurring globalisation are often seen as negative for developed

countries. However, these negative viewpoints often cast globalisation in terms of a

zero-sum game, which it is not. Further, they ignore the tremendous and largely positive

effects that such ICT-driven globalisation has in terms of bringing more countries into

the global economic mainstream. Although such globalisation might be seen as a one-

way street in which rich countries lose out to poorer ones, in fact both sets of

countries could benefit. Not only do consumers in rich countries benefit from cheaper

imported goods, but the growth of places such as China and India can benefit exporters

in rich countries. Thus the recent strong economic recoveries of Germany and

Sweden, built upon exports to emerging markets, as well as the performance of U.S.

manufacturers of high-end tradable goods (firms such as Caterpillar and Boeing),

suggests the potential benefits from a larger global marketplace.

Secondly, there is a great deal of evidence now on the impact of ICT, at both the social

level and at the economic level, in developing countries. Mobile telephone networks

have been revolutionary in terms of making up for the deficiencies in physical


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transportation and fixed-line communications infrastructure. These networks have not

only helped reduce transactions costs, inefficiencies and wastes associated with a

world in which communications and transport links are costly, but they have also helped

spawn new services in areas such as mobile banking and entertainment. Jensen

(2007) provides details and calculations as to the effect of using simple mobile

telephony on the efficiency and profitability of fishermen in Southern India. 7 Other

studies similarly show the improvement in market efficiency simply as a result of the

superior information flow that mobile phones enable.

Finally, one must also consider the social aspect of mobile telephony and Internet

diffusion. In many countries, major social and political changes have been greatly

accelerated by these technology changes since these changes have greatly

decentralised the flow of information and made it much easier for citizens to

communicate directly among themselves, regardless of geographic distance. They

have also made communications between emigrant diasporas and their homelands

much cheaper and much more direct, thus facilitating the flow of knowledge and new

ideas into parts of the world which were once often years behind the leading developed

countries.

In summary, ICT remains a major contributor to economic and social change. There is

thus value to attempting to monitor the extent to which countries are adopting and

utilising key information technologies, and whether they are doing so in ways that are

likely to maximise economic growth.

7"The Digital Provide: Information (Technology), Market Performance and Welfare in theSouth Indian Fisheries Sector," Quarterly Journal of Economics, 122(3), p. 879 924, 2007.


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3. Connectivity Scorecard 2008/09/10: What we did and

what we found

3.1. 2008 and 2009 Scorecards

In January 2008 we computed two separate Connectivity Scorecards the first for a

group of 16 countries, covering mostly the innovation-driven (Tier 3) economies but

also some economies that are making the transition towards being innovation-driven:

Hungary, Poland, the Czech Republic and Korea are examples. The second

Connectivity Scorecard covered 9 countries, and uses different assessment indicators.

The countries covered by the second Scorecard are resource and efficiency-driven

economies. The terms resource and efficiency-driven and innovation-driven are

borrowed from the World Economic Forum (WEF)s classification.

We looked at the scores for each country on each indicator on a benchmarked basis

(relative to best in class in its category). In this Scorecard, the highest theoretical

score for any country is 10, although this would require the country to be the best on

every single indicator. The table below summarises the 2008 Connectivity Scorecardresults. The US and Malaysia topped their respective categories. The major surprise

in the Scorecard was the low ranking of Korea, which we discussed above.

The Connectivity Scorecard 2009 expanded coverage to 25 innovation-driven

economies and 25 resource-and-efficiency driven economies. The scoring

methodology remained the same, but there were some differences in the data sources

and indicators by which individual countries were assessed. However, no major

conceptual or methodological revisions were made to the Scorecard. We did, however,

use a new set of weights to arrive at a split between the infrastructure and usage andskills sub-components within the business and government/public sector parts of the

Scorecard. These data were obtained from Barry Bosworth of the Brookings Institution.


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Table 2: 2008 Connectivity Scorecard Results

Note: The data and indicators used to assess the resource and efficiency-driven

economies are very different from those used to assess Innovation-driven economies.

Were Malaysia to be benchmarked against the innovation-driven economies, it would

finish in the bottom spot.


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Table 3:2009Connectivity Scorecard Results

Innovation driven Connectivityeconomies score

United States 7.71Sweden 7.47Denmark 7.18Netherlands 6.75Norway 6.51United Kingdom 6.44

Canada 6.15Australia 6.14Singapore 5.99Japan 5.87Finland 5.82Ireland 5.70Germany 5.37Hong Kong SAR 5.33France 5.22New Zealand 4.85Belgium 4.65Korea 4.17

Italy 3.99Czech Republic 3.71Spain 3.49Portugal 3.02Hungary 2.72Greece 2.62Poland 2.49

Resource & efficiency Connectivitydriven economies score

Malaysia 7.07Turkey 6.71Chile 6.59South Africa 5.76Mexico 5.39Russia 5.37

Argentina 5.14Brazil 5.12Colombia 4.08Botswana 3.98Thailand 3.75Iran 3.62Ukraine 3.60Tunisia 3.50China 3.19Philippines 3.17Egypt 3.02Sri Lanka 2.87

Vietnam 2.75India 1.88Indonesia 1.87Kenya 1.75Bangladesh 1.60Pakistan 1.54Nigeria 1.30


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3.2. The 2010 version of the Scorecard

The 2010 version of the Scorecard saw a substantial revision to the indicators used for

the Innovation-driven economies that were included in the Scorecard. This revision

reflected the need to achieve greater precision in the measurement of such variables as

broadband penetration and also to capture finer distinctions in the use of particular

new technologies such as the Internet or mobile telephony. Thus we included data on

the frequency of Internet use for the first time; we also included data on ICT investment,

business use of broadband and the like, which were readily available from OECD

surveys. In 2010, the extensive use of new indicators did generate questions about

the backward compatibility of the Scorecardi.e., when the underlying indicators usedto assess performance are being continually updated, it is difficult to assess

improvements in absolute performance and also difficult to assess whether countries

are catching up or falling behind as time passes. For example, did the change in the

identity of the top-ranking country in 2010 (between Sweden and the United States)

reflect simply the impact of a different set of indicators used to assess performance, or

did it measure underlying progress between the 2009 and 2010 Scorecards?

However, we pointed out then, and we point out now, that the Scorecard has a great

deal of inherent stability built into it. That is, one would not normally expect to seedramatic changes in countries positions over the short-run or the medium-run. Over a

longer period, such as a decade, one might be better positioned to assess the true

extent of any catch up or falling behind. For example, one would not expect to see

the gap between a country that has 100 personal computers per 100 population and a

country that has only 30 personal computers per 100 population closing within a year or

two years. However, it is possible that within five years such a gap could close.

Further, with the rapid adoption of new technologies, it may be that over time personal

computers per 100 population becomes a substantially less relevant (and less well

measured) indicator of connectedness or ICT usage. The country that lagged on

personal computer adoption rates might instead become a leader in the use of mobile

phones and cellular networks to deliver some of the same services (payments, banking,

electronic communications, shopping) that were typically done over PCs and fixed

broadband networks just a scant few years ago. Further, a composite index can

always be improved by adding more information, in the same sense as the validity of a

survey can be improved by increasing the number and representativeness of people

sampled. In short the loss in terms of temporal fidelity is small compared to the gain

in validity and relevance as a result of expanding the Scorecard.


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Table 4: Connectivity Scorecard 2010 Results

Rank Country Final Score Rank Country Final Score

1 Sweden 7.95 1 Malaysia 7.14

2 United States 7.77 2 South Africa 6.18

3 Norway 7.74 3 Chile 6.06

4 Denmark 7.54 4 Argentina 5.90

5 Netherlands 7.52 5 Russia 5.82

6 Finland 7.26 6 Brazil 5.32

7 Australia 7.04 7 Turkey 5.09

8 United Kingdom 7.03 8 Mexico 5.00

9 Canada 7.02 9 Colombia 4.76

10 Japan 6.73 10 Ukraine 4.67

11 Singapore 6.68 11 Botswana 4.30

12 Ireland 6.37 12 Thailand 4.11

13 Korea 6.33 13 Tunisia 3.87

14 Hong Kong SAR 6.10 14 Iran 3.59

15 Belgium 6.08 15 Vietnam 3.42

16 New Zealand 6.07 16 Sri Lanka 3.18

17 Germany 5.77 17 China 3.14

18 France 5.65 18 Egypt 2.97

19 Czech Republic 5.03 19 Philippines 2.92

20 Spain 4.79 20 Indonesia 2.13

21 Portugal 4.45 21 India 1.82

22 Italy 4.35 22 Kenya 1.80

23 Hungary 4.31 23 Nigeria 1.78

24 Poland 4.06 24 Bangladesh 1.69

25 Greece 3.44 25 Pakistan 1.53


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4. Connectivity Scorecard 2011: Results, Highlights and

Discussion

4.1. 2011 Results

The table below summarises the baseline results that we report and refer to in the

body of this report. As with last years ranking there are separate scores for innovation-

driven and for resource and efficiency-driven economies. Although the set of indicators

used to conduct the analysis was substantially expanded, and although new and

updated weights were used, the main results for 2011 bear similarities to previous

years exercises.

Table 5: 2011 Connectivity Scorecard Results

Rank Country Final Score Rank Country

Final

Score

1 Sweden 7.84 1 Malaysia 6.61

2 United States 7.82 2 Chile 6.21

3 Denmark 7.47 3 Russia 5.68

4 Netherlands 7.45 4 Turkey 5.51

5 Norway 7.09 5 Argentina 5.46

6 United Kingdom 7.06 6 Brazil 5.14

7 Australia 6.93 7 Mexico 4.87

8 Canada 6.88 8 Ukraine 4.81

9 Finland 6.78 9 South Africa 4.68

10 Singapore 6.40 10 Colombia 4.06

11 Belgium 6.31 11 Thailand 3.68

12 Austria 6.27 12 Tunisia 2.79

13 Germany 6.27 13 Vietnam 2.73

14 Ireland 6.08 14 China 2.72

15 France 6.06 15 Iran 2.41

16 Japan 5.89 16 Philippines 2.15

17 New Zealand 5.84 17 Syria 2.11

18 Korea 5.80 18 Indonesia 2.01

19 Spain 5.09 19 Sri Lanka 2.01

20 Czech Republic 4.93 20 Egypt 1.89

21 Portugal 4.80 21 India 1.25

22 Italy 4.79 22 Pakistan 1.14

23 Hungary 4.50 23 Nigeria 1.09

24 Poland 4.26 24 Kenya 0.95

25 Greece 4.22 25 Bangladesh 0.90


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4.2. Highlights of results

4.2.1. Innovation economies

Sweden and U.S. dominate rankings

Sweden and the U.S. are in a virtual dead heat for the top spot this year. The closeness

of the U.S. and Swedish scores reflects all-round strengths in ICT, although the two

countries are not the leaders in some categories (e.g. consumer infrastructure). The

catching up between the U.S. and Sweden reflects two factors. First, the gap

between Sweden and the U.S. in years past was likely more a statistical artefact than a

reflection of any true differences in ICT performance. As such, adding new indicators to

the Scorecard always carried at least some probability that it would narrow the

differential between the two countries, or even reverse it. Second, the U.S. is showing

modest signs of catching up with the leading countries in areas of consumer

infrastructure where a few years ago it may have been farther behind3G penetration

and average broadband speeds are areas where the U.S. is improving. However, it is

important to remind readers that the Scorecard and other indices such as the

Scorecard are ultimately about numbers, and thus it is important to avoid over-

interpreting what are actually small and perhaps statistically indistinct changes inperformances.

Asian countries

Hong Kong was replaced with Austria this year, owing to data comparability issues

which continue to affect the assessment of Asian countries. Despite some problems

with comparability and representativeness of indicators for Korea and Japan, these two

countries remain in the Scorecard. Korea and Japan both fell in the rankings this year.

In both cases, these countries were penalised by the introduction of new consumer

infrastructure indicators which eroded the lead that they enjoyed on this component ofthe Scorecard. These new indicators combined with the indicators on ICT spending in

the healthcare, education and governmental sectors drove down both these countries

absolute scores and their rankings relative to other nations. Although valid questions

remain (particularly regarding Japan) as to the comparability of certain data between

countries given the very different usage patterns that might exist in South Korea and

Japan, it is important to note that (a) the Connectivity Scorecard is not simply a

measure of telecom infrastructure performance and that Japan and Korea are not

necessarily leaders in all other forms of ICT adoption and usage, and (b) in relation to

studies of ICT and productivity for these two countries, there is a well documented


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record around low productivity and the need to improve ICT adoption in the services

sector which forms the backbone of all modern economies.

Overall stability

These two nations apart, we see a great deal of stability in the Scorecard. The figure

below provides a cluster diagram that is perhaps the broadest and fairest way of

discussing the Scorecard results. We see that the same countries as in previous years

tend to form the top two or three clusters (the countries that get the scores between 6.8

and 7.8 roughly). Beyond these top three clusters, there are nine other countries that

show some strength either in specific areas of the Scorecard (e.g., Korea and Japan) or

which are generally reasonably good performers without being particularly outstanding

(France, Germany, Austria and Belgium are good examples). Within the group of

Innovation economies, the countries of Southern and Eastern Europe remain well

differentiated from the rest of the pack. It is these countries that are generally mediocre

or even weak in virtually all areas of the Scorecard. This gap between the North and

South of Europe is perhaps the true connectivity gap in the developed world.

Clustering

One way to think about the overall performance of innovation economies on the

Scorecard is that there is a gradual gradient leading from Sweden to Korea. The

cluster containing Japan and Korea is close to the preceding cluster of countries, which

is in turn close to the preceding cluster of countries. The first sharp discontinuity

between clusters occurs in the transition between the 18th

and 19th

rank countries

Korea and Spain. The last two clusters depicted in the diagram are not within shouting

distance of any other group of countries. The countries in these last two clusters have

genuine and enduring connectivity gaps.


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Figure 3: Cluster diagram for innovation economies

0

1

2

3

4

5

6

7

8

9

0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Averagescoreforgroup

Distancefrom

leading

cluster

Denmark, Netherlands

Norway,UnitedKingdom,Australia, Canada,Finland

Japan,NewZealand,Korea

Singapore,Belgium,

Austria,Germany

Hungary,Poland,Greece

Ireland, France

Spain,CzechRepublic,Portugal,Italy

Sweden,UnitedStates,


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4.2.2.Resource and Efficiency economies

Malaysia tops, South Africa slips

Malaysia continued to lead the group of Resource and Efficiency economies, although

changes to the data indicators used and the weights used had a mixed effect on

Malaysias relative position, as discussed previously. South Africa fell back several

places, penalised perhaps by the new data indicators that were used this year,

especially as these indicators whittled down what was once a very strong business

sector performance.

High degree of correlation with GDP per capita

In general, however, the Resource and Efficiency economy scores correlate very well

with per capita GDP and other measures of overall economic development. Figure 4

shows that the R-squared statistic (which measures the proportion of the variation in

scores on the Connectivity Scorecard that are explained by variations in GDP per

capita) between GDP per capita and scores attained on the Connectivity Scorecard is

0.82, compared to just 0.58 for the Innovation economies (Figure 5). Notably, the

relatively affluent Latin American region supplies a number of the top 10 countries.

Further, countries such as Russia and Turkey are also strong performers as they have

generally tended to be in years past.

China and India

China and India continue to disappoint, but this reflects the fact that these countries are

still relatively poor (at least on a nominal per capita GDP basis) and that ICT diffusion is

still very uneven within these countries. That said, there is a very substantial gap

between China and India in terms of ICT adoption and usage.

Africa and South Asia

African and South Asian countries generally bring up the rear. However, we replaced

one African nation (Botswana) with a West Asian nation (Syria) this year. The Resource

and Efficiency economies continue to show a great deal of dispersion in their scores,

and this dispersion is linked to genuine developmental differences across the sample of

25 economies considered.


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Figure 4: Correlation between Connectivity Scorecard and GDP per capita (nominal)

for Resource and Efficiency economies

Figure 5: Correlation between Connectivity Scorecard and GDP per capita (nominal)

for Innovation economies


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4.3. Innovations in 2011

4.3.1.New indicators

For both the Innovation and the Resource and Efficiency economies, a number of new

indicators were used this year. Further discussion of all of the indicators can be found

in Appendix 1. A summary of the main new developments is provided below.

Innovation economies

This year, there are 40 indicators used to assess the performance of the Innovationeconomies. This is an increase of nearly 50% from last years 28 indicators. On the

consumer infrastructure component of the Scorecard, we included data on network

coverage for the first time. That is, in previous years, we had looked only at adoption

rates of such technologies as broadband and 3G. There is reasonable, if diminishing,

variability in these adoption rates. However, the adoption rates (the term adoption

rate is synonymous with penetration) understate the ubiquity of broadband and 3G

infrastructure in most of the OECD area. Indeed, Portugal has substantially the same

availability of broadband and 3G infrastructure as Sweden, but there are differences in

the adoption levels of that infrastructure. These differences in adoption relate to a

variety of factors: consumer preferences, user skills, and affordability relative to other

communications option. This year, we decided to reflect both the availability of

infrastructure and the adoption levels of the infrastructure, as this gives a broader

picture that incorporates both network deployment and network adoption.

The impact that inclusion of broadband and 3G coverage has on average consumer

infrastructure scores is quite substantial. The score for many countries increases by

0.10 (on a 0 to 1 scale) or more. Inclusion of the coverage indicators also tends to

reduce the lead of countries such as Korea and Japan in the consumer infrastructure

category, although these countries are still some way ahead of most Western nations

in consumer infrastructure deployment. But the shrinking of that lead this year, based

on inclusion of new indicators, affected the ranking achieved by Korea and Japan.

Further we used unique user wireless penetration (the proportion of the population

that owns a wireless device) instead of the more widely-used, but sometimes very

misleading, indicator of mobile penetration per 100 population. This last measure

suffers from significant overstatement of mobile penetration in those countries where it

is common for individual subscribers to have SIM cards for more than one network.


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We also used some new indicators forconsumer usage, and for the business sub-

components of the Scorecard. Notably, we have attempted to capture the proportion

of the population that connects wirelessly from places other than work and home, on

the consumer usage side. On the business front, we have attempted to capture the

penetration rate of mobile enterprise lines that are used for data purposes. Under

business usage, we have included data on uptake of cloud computing services (which

are sometimes held to be key to the future provision of IT services within Small and

Medium Enterprises (SMEs)); we have also added data on the production rate of

doctorates in Science and Engineering, as well as the share of employment in Science

and Technology professions. These data are potentially useful indicators of the extent

to which ICT specialist or ICT user skills are demanded and/or supplied in the

economy.

In general, these new indicators have been successfully incorporated into the

Scorecard, although there are potential issues around definition and measurement, as

well as around coverage across countries. Thus the definition of an enterprise mobile

line that is used for data might tolerate a certain amount of variability in measurement

across countries; similarly, coverage for indicators such as cloud computing is not

available for all countries.

Finally, there have been a number of additions to what we now term the public sector

component of the Scorecard, which in previous years has usually been described as

the government component of the Scorecard. Specifically we used data from

WITSAs Digital Planet publication to estimate ICT spending levels (on a per capita

basis) by what were termed the government, healthcare and educational

segments. Rather surprisingly, these data show considerable dispersion among

countries, with some countries such as Korea and Japan showing surprisingly low

levels of spending across these segments. (Although healthcare and educationmight be provided privately in many parts of the developed world, especially in the U.S.,

the provision of healthcare and education often have a strong public good element to

them and are thus included in the public sector portion of the Scorecard). The impact

of these additional spending indicators on ICT spending on the scores has been

significant, and has worked to increase the level of variability across countries. This is

an interesting counterpoint to the equalising effect of including new indicators on the

consumer infrastructure component of the Scorecard. To the extent that there were

changes in relative scores and rankings between this year and last year, these were

shaped by the interplay between countries typically improved consumer infrastructure


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showing and their typically diminished public sector showing. It should be

remembered, however, that the public sector components of the Scorecard carry but a

low weight.

Resource and Efficiency economies

For the Resource and Efficiency economies, we also made significant additions to the

information base. Notably, we now have 32 indicators, up from 22 last year, an

increase of nearly 50% in the information base used. Of course, for some of these

indicators, data are not available for all countries and data coverage continues to be

something of an issue for the Resource and Efficiency section of the Scorecard.

With respect to consumer infrastructure, we have also included a measure of

wireless unique user penetration, as we did for the Innovation Economies in our

sample. However, since coverage of unique user penetration was not available for all

of the 25 economies in the Scorecard, we also used the more conventional SIM card

measure of penetration. This was done because it was important to capture at least

some measure of mobile penetration, which is a highly relevant form of ICT in many

emerging economies. On the consumer usage component, owing to reliability issues,

we dropped previous data on SMS messaging and substituted it with data on consumer

usage of email, text and instant messaging on mobile phones.

Most relevant were the new indicators on ICT imports and exports that we added to the

business infrastructure and business usage and skills components of the

Scorecard. These indicators were added because for many countries, existing data

were incomplete when it came to providing a good measure of ICT spending or ICT

investment or ICT activity that might plausibly be linked to investment and usage levels.

Owing to data constraints relating to other indicators in the business infrastructure and

business usage and skills components, we included data on ICT exports of goods

(under infrastructure), and ICT imports of goods and exports of services (under usage).

The justification for these seemingly idiosyncratic choices is as follows: high levels of

ICT exports are likely to be quite correlated with the development of a reasonably

strong ICT ecosystem. Similar to the car industry, ICT manufacturing in one area is

likely to spawn spill-over effects into ICT in other manufacturing and ICT investment in

complementary areas (for instance, an initial advantage in computer assembly might

lead to the location of mobile handset assembly in the country. This in turn will likely

have positive spill-over effects into the wider economy). Not all countries are ICT


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exporters or need to have ICT or export-led growth strategies, however. Here high

levels of ICT imports might suggest a high level of domestic demand for ICT and thus

be correlated with high levels of usage.8 High levels of ICT service exports are very

likely to correlate with the presence of a critical mass of ICT user skills in the economy,

which in turn could enable countries to support strong ICT sectors despite large

sections of the population without ICT skills.

The effect of these new indicators is to increase the dispersion between countries on

the business infrastructure and business usage components of the Scorecard.

Malaysia, for example, greatly benefits from the inclusion of ICT export and import data

in the 2011 Connectivity Scorecard.9 Other countries scores relative to Malaysias tend

to go down. On the ICT services front, the great role of ICT services exports to Indias

overall exports pushes up Indias score somewhat, but also tends to depress the

relative scores of countries that do not export much in the way of ICT services.

Finally, as with the Innovation economies, we also estimated data on ICT spending by

the healthcare, government and educational sectors. As with the Innovation economies,

these data were obtained from WITSAs Digital Planet publication. Also as with the

Innovation economies, the data tend to produce more dispersion in the Scorecard

relative to the case in which the data were not used.

Whereas with the Innovation economies, increases in average scores on consumer

infrastructure tended to balance out the effect of these data on the dispersion of scores

across countries, in the case of the R&E economies, these spending data added to

what was already a significant dispersing effect of the ICT trade indicators. For this

reason, the disparity between the top few and the remainder of the R&E economies

remains very large, as it has done in previous years.

4.3.2.New Weights

Also of note, although primarily for the Resource and Efficiency economies, are an

updated set of weighs that were used this year. As referenced in previous sections,

8In fact, a good argument could be made that the ICT imports indicator should be stuck under the business

infrastructure category since they could also be correlated with business investment. This does not,however, make much of a difference to the overall scores. Further, the extent to which imports would becorrelated with investment as distinct from usage might vary significantly from country to country.

9It could be legitimately argued that the high level of imports in Malaysias case essentially reflects the import

of components that are then assembled together into final products in Malaysia, and thus is less likely toreflect usage of ICT in Malaysia. Even without this indicator, however, Malaysia would still finish first.


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within the business and public sector components of the Scorecard we have always

sought to assign weights to infrastructure as opposed to usage and skills, based on

the relative contributions of ICT investment and improvements in labour skills to

economic growth. ICT investment or capital deepening leads to a greater stock of

infrastructure; improvements in labour composition reflect the additional impact of

having smarter workers or better trained workers. So-called growth accounting

databases such as the Conference Boards Total Economy Database now provide

detailed data on annual contributions to economic growth of ICT capital and

improvements in labour composition. This dataset now extends to many emerging

markets. Thus we were able to use weights for the Resource and Efficiency economies

that for the first time were based specifically on the relative contributions of ICT capital

and labour composition.10 We were also able to update the weights for the Innovation

economies.

The effect of the new weighting system is shown below. For most Innovation

economies, the new weights had a limited impact on their performance. This is

unsurprising given that we are only slightly updating the weights that were already

being used in previous years for these economies. For the Resource and Efficiency

economies, however, the effect was slightly more substantial.11 In these cases, there

was a substantial reweighting towards the business infrastructure component of the

Scorecard, because of (a) the high weight given to the business sector overall, (b) the

fact that ICT capital deepening has tended to contribute much more to economic growth

than improvements in labour composition in emerging economies.

10Specifically, the weights were based on an average of 2000-2009 data.

11 Specifically, the mean score for all R&E countries using the 2010 weights would have been 3.49 and themedian score would have been 2.96. Using 2011 weights, these scores are 3.31 and 2.73 respectively.

For the Innovation economies, the average score would have remained unchanged at 6.11, and themedian would have changed only slightly from 6.27 (new weights) to 6.23 (old weights).


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Table 6: Innovation economy scores using new (2011) and old (2010)

weights12

2011 Weights 2010 Weights

Rank Country Final Score Rank Country Final Score

1 Sweden 7.84 1 1 United States 7.91

2 United States 7.82 2 2 Sweden 7.85

3 Denmark 7.47 3 3 Denmark 7.49

4 Netherlands 7.45 4 4 Netherlands 7.44

5 Norway 7.09 5 5 Norway 7.34

6 United Kingdom 7.06 6 6 United Kingdom 7.09

7 Australia 6.93 7 7 Australia 6.95

8 Canada 6.88 8 8 Finland 6.90

9 Finland 6.78 9 9 Canada 6.88

10 Singapore 6.40 10 10 Singapore 6.35

11 Belgium 6.31 11 11 Belgium 6.33

12 Austria 6.27 12 12 Germany 6.27

13 Germany 6.27 13 13 Austria 6.23

14 Ireland 6.08 14 14 Ireland 6.04

15 France 6.06 15 15 France 6.00

16 Japan 5.89 16 16 Japan 5.91

17 New Zealand 5.84 17 17 New Zealand 5.91

18 Korea 5.80 18 18 Korea 5.22

19 Spain 5.09 19 19 Czech Republic 5.12

20 Czech Republic 4.93 20 20 Spain 5.09

21 Portugal 4.80 21 21 Portugal 5.00

22 Italy 4.79 22 22 Italy 4.85

23 Hungary 4.50 23 23 Hungary 4.26

24 Poland 4.26 24 24 Greece 4.21

25 Greece 4.22 25 25 Poland 4.18

12Note that these scores are reported based on applying weights to the current data, and not the 2010 data.


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Table 7: Resource and Efficiency economy scores using new (2011) and old

(2010) weights

2011 Weights 2010 Weights

Rank Country

Final

Score Rank Country

Final

Score

1 Malaysia 6.61 1 Malaysia 6.84

2 Chile 6.21 2 Chile 6.04

3 Russia 5.68 3 Argentina 5.65

4 Turkey 5.51 4 Brazil 5.41

5 Argentina 5.46 5 Russia 5.35

6 Brazil 5.14 6 Mexico 5.13

7 Mexico 4.87 7 Turkey 5.098 Ukraine 4.81 8 South Africa 5.02

9 South Africa 4.68 9 Ukraine 4.14

10 Colombia 4.06 10 Thailand 3.84

11 Thailand 3.68 11 Colombia 3.81

12 Tunisia 2.79 12 Tunisia 3.22

13 Vietnam 2.73 13 Vietnam 2.96

14 China 2.72 14 China 2.95

15 Iran 2.41 15 Syria 2.67

16 Philippines 2.15 16 Indonesia 2.51

17 Syria 2.11 17 Iran 2.47

18 Indonesia 2.01 18 Philippines 2.28

19 Sri Lanka 2.01 19 Sri Lanka 2.10

20 Egypt 1.89 20 Egypt 1.84

21 India 1.25 21 India 1.81

22 Pakistan 1.14 22 Kenya 1.73

23 Nigeria 1.09 23 Pakistan 1.72

24 Kenya 0.95 24 Nigeria 1.35

25 Bangladesh 0.90 25 Bangladesh 1.29

5. Further Notes on Methodology, Inference and Data

5.1. Updated Data versus New Methodology

It is extremely important to note that the changes discussed previously should not be

interpreted to mean that the methodology behind the Scorecard has been altered. The

distinctive feature of the first version of the Scorecard was the weighting system.

Recognising that the principal way in which connectivity impacts upon the wider

economy is through its impact on business productivity, we had sought out a weighting

system that captures this high impact of business. Thus rather than using shares of


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household consumption in GDP to weigh the consumer sector, for example, we used

the share of household consumption in total use.

The other novelty in the weighting system was the manner in which we allocated

weights to the business infrastructure and business usage and skills sub-indices.

These weights were based on the relative contribution of information technology

investment and increases in the quality of the workforce to economic growth. Where

such refined splits were not obtainable (e.g., for the resource and efficiency-driven

economies) we used data on the relative contribution of overall capital investment and

labour composition to overall economic growth. The idea behind this methodology was

to utilise some notion of how much of economic growth has been driven by

improvements in infrastructure versus how much has been driven by improvements in

the quality of the users of that infrastructure (the work-force).

The major difference between the 2009 Scorecard and the first version was that we

were fortunate to obtain a more complete set of relevant weights for the extended

sample of countries. These weights were then used in the 2010 Scorecard and then

were supplanted in 2011 by the new weights described previously. As well, the set of

indicators used to conduct the assessment has been significantly revised, updated and

expanded.

Despite these changes, and there are many, the distinctive features of the Scorecard

continue in place. These features are (a) a high weight on business, (b) a broad take

on connectivity to include not just telecom but all other forms of ICT, and (c) a

connection with the literature on ICT and productivity growth via the weighting system

and via the broad focus on ICT.

5.2. Backwards Compatibility Versus Innovation

There is limited backwards compatibility by design between Connectivity

Scorecard 2008, Connectivity Scorecard 2009, Connectivity Scorecard 2010 and

Connectivity Scorecard 2011. First and foremost, a limitation of composite indices that

produce relative rankings is that comparisons over time (inter-temporal comparisons)

are hard to make and absolute scores difficult to interpret. Connectivity is a composite

of many different attributes, many of which have different units and different

dimensions. For example, the United Nations Human Development Index (HDI) is

another composite index that uses a relative scoring system, as it must, to capture

several attributes of a country. In both these cases, it is simply very difficult to talk of


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absolute levels of human development or connectivity, the way it is possible to talk

about levels of simple measures such as height, weight or even GDP per capita.

This important conceptual point aside, Connectivity Scorecard 2008 was an ambitious

initial effort, but we felt that it was imperative to accommodate both the feedback that

we had received on Connectivity Scorecard 2008 and the continued evolution of

technology and networks. In 2009, we added 25 new countries, which created its own

set of research issues: for example, we could not continue to use some sources of data

that were used for Connectivity Scorecard 2008 as they only covered a limited subset of

the original 25 countries.

We also wanted to take into account several key realities of this technology sector. If

one looks merely at mobile penetration and broadband penetration in OECD nations,

there is a fair amount of convergence across these nations, with average penetration

rates exceeding 90 percent for mobile, and 50 percent (by households) for broadband.

However if one instead looked at the variables that reflect the most recent investment

efforts in the most recent technologies, then this picture of convergence is disrupted.

For instance, relatively few countries have high rates of fibre-to-the-home broadband

penetration (greater than 5 percent), with many countries having a virtually zero

penetration rate. 3G penetration rates are also highly variable, with most countries now

catching up with the Far East, but from a much lower level of initial penetration. In

much of the feedback and commentary that we obtained, there was a great deal of

interest in our ability to capture these forward-looking dimensions of connectivity.

Connectivity Scorecard 2009 relied on data sources that offer wider country coverage

and more up-to-date data. However, we felt that there was still a significant need to

improve upon the data for Connectivity Scorecard 2009 by using more robust

indicators. Thus, in Scorecard 2010, we eschewed use of data on average advertised

speeds and instead use data on actual speed and network performance from Akamais

State of the Internetreport for Q2 2009. Similarly, we felt that existing data on fibre

penetration might fail to capture the fact that in some countries such as the US,

Belgium, Netherlands, Canada and the UK, a high proportion of ultra-high-speed

broadband offerings will be supplied by cable operators using Docsis 3.0 technology.

Thus we used Akamais data on the proportion of IP addresses that were able to attain

a given file download speed as a proxy for the penetration of fat pipes whether Fibre-

to-the-home, Fibre-to-the-curb, or cable. On the business front, previous measures of

ICT user skills and ICT specialist skills in the workforce were supplanted with newer


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measures of workforce quality that have better comparability between countries; we

added measures of businesses with broadband access and businesses using websites.

We also added OECD data on investment in ICT and in R&D activities related to ICT by

all economic sectors, replacing data that we had previously relied upon from Digital

Planet.

We continued building on these developments in 2011. In 2011, we added several new

indicators that aimed to strengthen the Scorecard in several ways. For instance, data

on mobile data services usage by consumers and businesses, cloud computing and the

like were all added to the Scorecard to provide a better picture of adoption of

technologies that are still in a rapid growth phase. Additionally, the addition of

indicators on fixed and mobile broadband coverage and the use of unique user wireless

penetration helped to round out and bolster the consumer infrastructure section.

Similarly, we added data regarding science and technology employment, as well as

PhDs in science and engineering to bolster the human capital side of the Scorecard.

All of these are measures that improve the coverage and the quality of the Scorecard,

perhaps substantially. Yet they come at a certain cost in terms of backwards

compatibility. At present, our view is that the benefits of improving quality and coverage

outweigh the costs. The primary goal of the Scorecard is to provide as up-to-date and

relevant a snapshot of how countries are performing at a given moment in time. It is

rather more difficult to interpret the Scorecard as an absolute indicator of performance

over time.

Despite the use of new data sources and new indicators, it should be noted that the

country rankings show a great deal of stability. There were very few surprises in the

Scorecard. The results from the Scorecard are largely consistent in terms of the

broad ordering of countries with the results from other composite indices such as the

World Economic Forums Networked Readiness Index or the Economist Intelligence

Units E-Readiness Rankings.

5.3. Statistical Significance and Clustering

Absolute differences in scores (e.g., the 0.002 difference between the US and Sweden)

are admittedly difficult to analyse in any meaningful and rigorous way. Nevertheless, it

is still worth asking on the basis of rankings alone whether Sweden is really more

usefully connected than the US. The answer is that the two countries are almost

certainly not different.


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It is difficult to apply simple statistical inference to the Scorecard. However, we have

made an attempt to do precisely this to test for whether the scores for a selected

group of countries differ from the Swedish score in a statistically significant way. The

statistical testing relied upon the following intuition: there are 40 indicators that make

up the Connectivity Scorecard for each country (although there is some missing data

for many countries). The final score that any country gets is a weighted average of the

relative scores (on a 0 to 1 scale) along these 40 indicators. (The weights for each

indicator are determined by the weight given to the component of the Scorecard that

each indicator falls into. For instance, if the consumer infrastructure component for a

given country has a weight of 0.10, and there are eight indicators that make up this

component, any indicator that falls into consumer infrastructure is 0.10/8=0.0125).

The 40 indicators can be seen as 40 different measures of Connectivity, just as one

might sample 40 individual males in an effort to measure the typical height of the male

population in a city. For example, one might sample 40 males in London and 40 males

in Manchester. One might then want to test for whether the difference in the average

height of the 40 male Londoners was really different from the average height of the 40

male Manchester residents. The standard way to do this is to calculate what is known

as a t-ratio, which takes into account the magnitude of the difference in the measured

mean heights between the two cities relative to the standard error with which the two

means are measured. However, a key difference between this simple example and the

Connectivity Scorecard is that each countrys score is a weighted average and not a

simple average. We used a procedure developed in a journal article in the British

Medical Journal to devise a t-ratio that applies to the difference in weighted means.13

The table below reports the t-ratio for pairs of countries, in which Sweden is always one

member of the pair. The normal gold standard used in econometrics and statistical

inference is that a relationship or statistical pattern has to have less than a 5% chanceof being caused by random statistical variation alone. For the amount of data that we

have here, one would typically look for a t-ratio of around 2 to assure that there is less

than a 5% chance that the difference between the weighted mean scores of the

countries is the result of random statistical variation alone.14 One sees that the

13Bland, J. Martin, and Sally Kerry, Statistics Note: Weighted Comparison of Means, British Medical JournalVolume 316, 1998.

14 The intuitive explanation of this random statistical variation is to think of it in terms of some sort ofsampling error. That is, we have 40 different measures of connectivity, but there might be potentiallyhundreds more indicators that could be used to measure connectivity. To some extent, the difference thatwe see in scores between say Sweden and Denmark might be driven by the fact that we used the 40

indicators that we did, instead of the entire population of indicators. As a result, the true score of Swedenand Denmark are likely different from the scores that were obtained by using a limited set of indicators.


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difference in scores between the U.K. and Sweden, and Canada and Sweden, is

sufficient to generate a t-ratio of more than 2. Thus the difference between Sweden

and the U.K, and Sweden and Canada, meets the gold standard for statistical

significance. On the other hand, Sweden and Denmark (and given that Denmark and

the Netherlands are virtually tied), and Sweden and the Netherlands are found not to

meet this gold standard. In fact, the p-value that is reported in the table shows that

the probability that the difference between Sweden and these two countries is caused

by random statistical variation exceeds 30% (p-value of 0.34).

Table 7: Statistical testing of differences in final scores, selected countries

Note that these t-ratios are based upon comparisons of specific pairs of countries.

Thus given the variability (as measured by the standard error) inherent in the Swedishand Danish data, a difference in final score of 0.37 was still well short of acceptable

statistical significance. However, a difference of 0.37 in scores between a different pair

of countries will produce different p-values and t-ratios. That aside, however, looking at

these data one can say with some confidence that country scores that are within 0.10,

0.20 or 0.30 distance of each other are not statistically significantly different, even if one

were to relax the normal gold standard requirement for the p-value to be below 0.05,

and admit p-values of perhaps up to 0.2 or 0.25.

There are other questions about the applicability of statistical inference in these

circumstances. For one thing, the 40 indicators used are perhaps not a random

sample of indicators on connectivity; for another thing, the weights used are based on a

judgment about the importance of different components of the scorecard (e.g., business

versus consumer).

Further, we could have used 40 entirely different indicators and thus obtained a different score for eachcountry. The purpose of statistical inference is to take account of all these potential variations between

true and measured scores.15These are based upon two-tailed t-tests.

Sweden/USA Sweden/Denmark Sweden/U.K Sweden/Canada

Difference in mean score 0.02 0.37 0.78 0.96

T-Ratio 0.05 0.96 2.12 2.18

Total Degrees of Freedom 77 77 78 78

P-Values15

0.96 0.34 0.04 0.03


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The statistical tests nonetheless are saying something important about the

interpretation of scores. Notably, these tests suggest that seemingly large differences

in rankings may exaggerate the extent to which countries are really different in the level

of their performance. For instance, France is 15th with 6.06 and Belgium 11th with 6.31.

Yet the difference in scores between these countries is small enough that one could

find a different set of connectivity indicators that could overturn the relative ranking

between these countries. Overall, the limited statistical testing that we have done

suggests that differences in scores of less than at least 0.3 or 0.4 should be seen as

suggesting that two countries are relatively similar, even if this difference is large

enough to cause a difference of two, three, four or five places in rankings. For instance,

5th

placed Norway might be not be very different from 9th

placed Finland, just as 1st

placed Sweden is in reality not very different from 4th

placed Netherlands.

An intuitive explanation of the distance between countries is provided by looking at

clusters of countries, which we have addressed in passing previously.

The cluster diagram displayed previously in Figure 3 is a much less formal way of

looking at logical groupings of countries than is statistical significance. One sees for

instance that Sweden and the U.S. are very, very close in score, but that there is a

more pronounced falloff between the U.S. and the next country (Denmark). This

difference might not be statistically significant, but when one looks at how close the

U.S. is to Sweden, compared to the proximity between the U.S. and Denmark, and then

again, compared to the proximity between Denmark and the Netherlands, it seems

logical to cluster the U.S. with Sweden, and Denmark with the Netherlands. Likewise,

there is a pronounced fall-off in score (notwithstanding whether this is statistically

significant or not) between the Netherlands and Norway. However, Norway and the

U.K. are very close, and although the U.K. is farther apart from Australia than it is from

Norway, the difference is still only 0.13 points. Australia is quite close to Canada, whichis quite close to Finland. Thus Norway, the U.K., Australia, Canada and Finland might

be grouped together. Similarly, Singapore, Belgium, Austria, Germany, France and

Ireland might form a cluster. Japan is not especially far apart from France (score of

5.89 against 6.06), and it is especially close in scores to New Zealand and Korea.

However, a cluster spanning all the countries from Korea to Singapore might have too

large a span in that the difference between Singapore and Korea is relatively large

(0.6). Thus Japan, New Zealand and Korea might be split off to form a separate

cluster. Similarly, the fact that France and Ireland could be placed either in the same

cluster as Singapore, Belgium, Germany and Austria or Japan, New Zealand and


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Korea, suggests that the most neutral option is to place these countries into a small

cluster of their own.

What is very clear, and indeed very noteworthy, is the substantial difference between

Korea and Spain (0.71) in scores. Thus one would certainly not want to put Spain in

the same group as Korea. Indeed, Spain, Italy, Portugal and the Czech Republic form

another potential cluster.

Overall, the clustering analysis suggests a good deal of continuity in performance with

the connectivity report 2011

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