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Interrogating supply-side data

Helani Galpaya

Katmandu, March, 2015

This work was carried out with the aid of a grant from the International Development Research Centre, Canada and UKaid from the Department for International Development, UK.

Much data; many producers/publishers

OPERATORS/SUPPLIERS

-Financial data

-Operational data (equipment, quality)

-Complaints

-Transaction Generated

Data (Big Data)

REGULATOR/ POLICY

MAKER

-Network level data

for decision making

and Monitoring

-Complaints

ITU/UN/WEF/OTHER

INT’L/NTL ORGs

-Raw data

-Composite indices (ranking countries)

USERS

- Quantitative surveys

- Qualitative studies

THIRD PARTY

RESEARCH

-Specialized Studies

based on data

obtained from all

stakeholders

CONNECTIVITY

Is connectivity increasing?

-

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

70,000,000

80,000,000

90,000,000

100,000,000

2004 2005 2006 2007 2008

Pakistan Mobile SIMs: 2004 - 2008

Looks impressive

But PK is in middle of pack when compared to S Asian neighbors

-

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

300,000,000

350,000,000

2004 2005 2006 2007 2008

Mobile SIMs: 2004 - 2008

Maldives Bangladesh India Pakistan Indonesia Phillipines Sri Lanka Thailand

But telecom data changes, fast: Most recent data is a must-have

6

SIMs per 100 population

Who is actually ahead? Why?

7

Aided by multiple millions of SIMs deregistered in PK in 2008 & and change of

definition of active SIMs in India in 2011

Which indicator benchmark? Different indicators can tell different stories

Total number of mobile subscribers in India

and Bangladesh

0

50

100

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250

2002 2003 2004 2005 2006 2007

Year

In m

illi

on

s

India Bangladesh

Mobile subscribers per 100 in India and

Bangladesh

0

5

10

15

20

25

2002 2003 2004 2005 2006 2007

Year

Per

100

India Bangladesh

Mobile SIMs per 100 population, India

and Bangladesh

Mobiles SIMs: India and Bangladesh

Are the data comparable? E.g., How do you reconcile different financial years?

• Many countries Jan – Dec (calendar year) – E.g., Sri Lanka

• But many others differ – India: Apr – Mar – Pakistan : Jul – June

• So “number of Internet users in per 100 inhabitants in 2013” reported by IN not comparable with PK

• Having quarterly data eliminates problem • Especially important if benchmarks are used for

mainstream regulatory work such as interconnection or retail tariff regulation

Prerequisites for comparison

10

• Internationally accepted definitions and procedures

– ITU (2010) Definitions of World Telecommunication/ICT Indicators, Geneva: ITU

• Make sure that the definitions are adhered to

– ITU has mobile broadband definition; use is inconsistent • “Mobile broadband subscribers refer to subscribers to mobile

cellular networks with access to data communications (e.g. the Internet) at broadband speeds (here defined as greater than or equal to 256 kbit/s in one or both directions) such as WCDMA, HSDPA, CDMA2000 1xEV-DO, CDMA 2000 1xEV-DV etc, irrespective of the device used to access the Internet (handheld computer, laptop or mobile cellular telephone etc). These services are typically referred to as 3G or 3.5G and include: Wideband CDMA (W-CDMA), an IMT-2000 3G mobile network technology, based on CDMA”

And whose data do you use?

# of internet subscribers (millions), India Difference between…

Year NASSCOM data TRAI Data Ministry of

Statistics & PI

NASSCOM &

TRAI

numbers

TRAI &

Ministry

numbers

1999 0.35 0.23 - -

2000 0.65 0.95 0.943 -46% 1%

2001 1.13 3.04 2.909 -169% 4%

2002 1.763 3.42 3.239 -94% 5%

2003 3.661 3.64 3.5 1% 4%

2004 4.403 4.55 4.05 -3% 11%

2005 6.674 5.55 5.3 17% 5%

2006 6.94 5.556 - 20%

Note: Based on Financial Year – e.g. “2000” refers to April 1999 – Mar 2000

Source: NASSCOM Strategic Review 2005; TRAI; Ministry of Statistics and Program Implementation, Govt. of India

Useful Indicators to measure connectivity

FIXED • Number of fixed lines • Number of fixed wireline phones • Number of fixed wireless phones • Total fixed access paths per 100 inhabitants MOBILE • Number of mobile SIM cards • Number of mobile SIM cards – prepaid • Number of mobile SIM cards – postpaid • Total mobile SIMs per 100 inhabitants BROADBAND • Number of broadband connections per 100

inhabitants

ICT • Number of mobile users • Number of Internet users IN-COUNTRY ACCESS GROWTH • Backbone map for a country • Mobile coverage map per operator • Base station map per operator

Can the method for estimating be improved?

‘Proportion of individuals using the Internet’

• Base indicator in composite indices such as:

– NRI (Network Readiness Index)

– KEI (Knowledge Economy Index)

– IDI (ICT Development Index)

• Best measurement method recommended by ITU:

– demand-side survey on proportion of individuals using the Internet (from any location) in the last 12 months (HH7)

Various methods can be used to estimate the number of Internet users

• Internet Users = multiplier x Internet Subs (supply side)

Where

– The multiplier = a number used to reflect that each subscription is used by more than one individual (e.g. at kiosks)

– Internet subscriptions = Internet subscription of all types (speeds, technologies etc. )

• Wired, wireless etc.

Building on foundations of sand…

• Multipliers chosen at discretion of Country administrations

– Perverse incentive to use higher multiplier to show high Internet penetration in country

• Difficulties in counting Internet subscriptions include…

– Over-counting (counting all “Internet-capable” SIMs, irrespective of use)

– Under-counting (being able to only count SIMs that have subscribed to a data package; SIMs with only voice packages may use Internet, but operators cannot count; impossible for pre-paid)

– General difficulty with multiple ownership (one user with fixed and many SIM connections) leading to questionable multipliers

…

…

…

… …

…

…

…

Difficult to find rationale for multipliers

Country

Fixed Internet Subscriptions

(000s), 2009

Internet Users (000s), 2009, ITU method

ITU multiplier

Russia 88,068 59,700 0.68

Mauritius 224 290 1.3

Liberia 15 20 1.33

Liechtenstein 17 23 1.38

Hong Kong, China 3,042 4,300 1.41

Côte d'Ivoire 18 968 53.78

Sudan 44 4,200 95.24

Iraq 3 325 104.84

Uganda 30 3,200 106.67

Afghanistan 2 1,000 500

Improvement proposed by LIRNEasia • % of Internet users increase with Education and Income

components of Human Development Index (HDI) of a country – Education component - mean of years of schooling for adults and expected

years of schooling for children

– Income component- Logarithm of GNI per capita (PPP$).

– Health component of HDI is not used, due to lack of evidence that internet penetration is correlated with life expectancy

• Studied the correlation between Internet penetration rate of countries which conducted demand side surveys and the education and income components of HDI 2011 – Data on countries which have conducted demand-side surveys was obtained

from ITU and RIA

– Sub index Education_GNI Index, consisting of education and income components of the HDI index was calculated using ‘DIY HDI: Build Your Own Index’ on UNDP website. Both Education and Income were given equal weight

Strong correlation between Education_GNI Index and Internet penetration

Education_GNI Index 2011

y = 1.26e4.8x R² = 0.8

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Pro

po

rtio

n o

f in

div

idu

als

usi

ng

the

Inte

rnet

HDI_EdGNI

Step 1: If survey is available, use it since survey results are first best

• If representative survey from regional organization is available, use their data (e.g.

RIA)

• If survey from current year is not available, use previous year’s data with adjustment

– Adjust by average growth for country grouping (e.g., middle income countries etc.)

• Derive model using income and education components of Human Development Index (HDI) vs. Internet penetration rate for countries which have conducted a survey (annually after HDI report has been released)

• Use this model to impute % of Internet Users for countries which have never conducted a survey

• If Internet penetration rate provided by country administrator is within +/- 7 percentage point band around calculated estimate -> use country reported figure

• Else use imputed figure

Step 2: In the absence of survey data use Education_GNI Index to estimate proportion of Internet users

Less than 30% countries show different Internet penetration rates

‘X’ Survey data from RIA but not same as ITU Internet penetration rate

0.00

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90.00

100.00

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00

Pro

po

rtio

n o

f In

tern

et u

sers

acc

ord

ing

to I

TU 2

01

1

Proportion of Internet users according to new method 2011

Antigua & Barbuda Andorra

Kuwait Oman

Barbados

Turkmenistan

Gabon Libya

Cuba

Bahamas

PRICE & AFFORDABILITY

•In selecting an operator, consumers are likely to think about

ALL costs including Connection charge, monthly rental etc.

•ITU ICT price basket methodology takes these issues into

account and has created Broadband Baskets consisting of •Monthly cost of 1 GB use per month with at least 256kbps

connection for a period of 24 months (includes Initial Connection

Fee/24)

•Affordability measured by dividing the cost of the Broadband basket

by National average monthly GNI per capita

•RIA (Research ICT Africa) has further developed this

methodology and also measure the cost of the following

baskets in addition to the ITU basket •Monthly cost of uncapped use per month with at least 256kbps

connection for a period of 24 months.

Broadband Baskets: a realistic method of price comparison

Affordability of Fixed BB is increasing in developing countries, but still higher than developed countries

Source: ITU, Measuring the Information Society 2013, http://www.itu.int/en/ITU-

D/Statistics/Documents/publications/mis2013/MIS2013_without_Annex_4.pdf

http://www.itu.int/en/ITU-D/Statistics/Documents/publications/mis2013/MIS2013_without_Annex_4.pdf



What about other prices? E.g. BB, wholesale & retail?

With 83 footnotes in the most recent publications we did

QUALITY

Measuring BB quality: trade offs across differing approaches (who can measure?)

27

Diagnostics conducted by

Pros Cons

Service Providers (ISPs/operators)

• Easy to implement

• Results based on equipment placed in the most optimized points of the network • Not representative of the ‘actual’ speeds

Users • Represents actual user experiences

• Assumes user’s PC is virus-free, with no parallel process running etc. • Dependant on user’s willingness to participate • Large files impact user’s data limits

National Regulatory Authorities (NRAs)

• In a position to request operator involvement when necessary

• Known test locations will prompt operators to optimize networks in selected areas

Download speeds: Indonesia performs among the lowest (2014)

0

1000

2000

3000

4000

5000

6000

7000

8000

0800 H 1100 H 1500 H 1800 H 2000 H 2300 H

Do

wn

load

(kb

ps)

BSNL (1Mbps)-Bangalore,IN BSNL (4Mbps)-Chennai,IN

BSNL (4Mbps)-Delhi,IN Dhiragu (512kbps)-Male,MV

NTC (512kbps)-Kathmadu,NP PTCL (4Mbps)-Karachi,PK

SLT (2Mbps)-Colombo,LK Dialog LTE (4Mbps)-Colombo,LK

Telkom Speedy Instant (512kbps)-Jakarta,ID Internux LTE (72Mbps)-Jakarta,ID

3BB (10Mbps)-Bangkok,TH

Delivers what was promised (advertised): Indonesia is not bad (2014)

0

50

100

150

200

250

300

0800 H 1100 H 1500 H 1800 H 2000 H 2300 H

Act

ual

vs

Ad

vert

ise

d (

%)







Value for Money: Indonesia highest (2014)

0

200

400

600

800

1000

1200

1400

0800 H 1100 H 1500 H 1800 H 2000 H 2300 H

Kb

ps

per

USD







Useful Indicators to measure Quality

Voice Quality

•Call drop rates

•% of connections with good voice clarity

•Call success rate

Broadband Quality

•Broadband upload/ download speed (kbps/Mbps)

•RTT or Round Trip Time (mili-second

•Jitter (mili-second)

•Packet-Loss (as a %)

•Broadband availability (as a %)

Quality is more than download speed

+++ Highly relevant; ++ Very relevant; + Relevant; - Irrelevant

Service Download (kbps)

Upload (kbps)

Latency (Round Trip Time, RTT) (ms)

Jitter (ms)

Packet Loss (%)

Browsing (Text) ++ - ++ - -

Browsing (Media) +++ - ++ + +

Downloading +++ - - - -

Transactions - - ++ + -

Streaming media +++ - ++ ++ ++

VOIP + + +++ +++ +++

Games + + +++ ++ ++

- RTT has implications on client-server interactive systems

- Jitter adds to the ‘noise’ of the transmission

- Packet Loss affects streaming media

Source: Gonsalves, T.A & Bharadwaj, A. (2009) 32

INDUSTRY-STRUCTURE INDICATORS

Do you know a competitive market when you see one?

• “There was only ONE broadband service provider (Company A) in the country until 2008. Since then, there has been 4 service providers: A, B, C, D”. – Is this a competitive industry?

• What if we saw this?

Year A’s share B’s share C’s share D’share

2008 100% - - -

2009 95% 2% 2% 1%

2010 93% 1% 2% 2%

2011 92% .5% 2.5% 2%

2012 93% 0.25% 2.75% 2%

HHI (Hirschman Herfindahl Index) is basic measure of market concentration

• Define Market – Fixed? Mobile? Voice telephony (fixed and mobile)? Internet

Services?

– Identify market share of each operator M1, M2, M3….

– Subscriber share, revenue share, minute share?

• HHI = (M1)2+ (M2)2, (M3) 2+…+(Mn)2

• US Dept of Justice says…

– Greater than1800 concentrated market

– Between 1000-1800 moderately concentrated

– Less than 1000, concentrated

– M&A activity increasing HHI by100+ and HHI >1800 automatic review (etc.)

But what is the ‘market’? Fixed? Mobile? Voice? BB? Substitutability/elasticity

Bharti (23)

23.74%

HFCL (1)

0.11%

Shyam (1)

0.04%

MTNL (2)

1.35%

BPL (1)

0.49%Spice (2)

1.61%

Aircel (9)

4.06%

Reliance (23)

17.54%

Tata (20)

9.32%

Vodafone/Hutchison

(16)

16.90%

BSNL (21)

15.62%

Idea (11)

9.19%

BSNL 83%

MTNL

9%

Other Private 8%

India, Mar 31 2008

- Mobile HHI = 1593

- BB HHI = 3171

- Fixed HHI = 7034

India, fixed market

shares

India, BB market shares

India, mobile market

shares

Market share based on SIMs? Revenue? Minutes?

What if SMP definition was

60% for price regulation?

• Market segmented by wholesale vs. retail

– E.g., Ofcom (UK regulator) reports wholesale (BT dominated, HIGHLY concentrated) vs. retail (less concentrated, many ISPs including BT)

• Other ways

IN UNDERSTANDING THE HEALTH OF THE ICT SECTOR…

Traditionally prominance was for ‘supply side data’ • Supply side data = data produced by the

suppliers of services: MNOs, equipment manf.

• Reluctance to share, even when demanded by regulation

• Incentives to over- or under-report

• Not comparable due to lack of agreement on

definitions

– Internet user (good enough for most most people)

– ‘daily active internet user (useful to operators)

Demand-side data: great for some measures, not for others. The right mix?

• Understand perceptions, opinions, feelings, satisfaction levels

• UK broadband satisfaction survey

• Combined with crowd-sourced testing

• LIRNEasia electricity sector customer satisfaction survey in LK, IN, BD

• IN worst actual performance

• But highest satisfaction levels

Big Data: bridging the gap between expressed vs. revealed preference

• How many minutes does it take, on average, for a vehicle to get from point A to B?

• Can survey ppl reasonable estimate

• Can do traffic monitoring estimate also

• But most people have cell phones

• CDR or VLR

• Analyze the digital trace

In short, when looking at supply-side data, QUESTION EVERYTHING

Question EVERYTHING

• Who reported it? What incentives did reporter of data have?

• What was the data collection method? Is it a reasonable representation?

• Does the data in isolation say a story that’s different from the data that’s compared to others (over time, to other countries, etc.)

Where possible, triangulate (supply+demand)

COMPOSITE INDEXES

Networked Readiness Index

The Global Information Technology Report 2014 | 7

1.1: The Networked Readiness Index 2014

The business usage pillar (six variables) captures the

extent of business Internet use as well as the efforts of

the firms in an economy to integrate ICTs into an internal,

technology-savvy, innovation-conducive environment that

generates productivity gains. Consequently, this pillar

measures the firm’s technology absorption capacity as

well as its overall capacity to innovate and the production

of technology novelties measured by the number of

Patent Cooperation Treaty (PCT) patent applications.

It also measures the extent of staff training available,

which indicates the extent to which management

and employees are more capable of identifying and

developing business innovations. As we did last year,

we split the e-commerce variable to distinguish the

business-to-business dimension from the business-to-

consumer one, because some noticeable differences

between the two dimensions exist in several countries.

The government usage pillar (three variables)

provides insights into the importance that governments

place on carrying out ICT policies for competitiveness

and to enhance the well-being of their citizens, the

effort they make to implement their visions for ICT

development, and the number of government services

they provide online.

Impact subindex

The impact subindex gauges the broad economic

and social impacts accruing from ICTs to boost

competitiveness and well-being and that reflect the

transformation toward an ICT- and technology-savvy

economy and society. It includes a total of eight

variables.

The economic impacts pillar (four variables)

measures the effect of ICTs on competitiveness thanks

to the generation of technological and non-technological

innovations in the shape of patents, new products

or processes, and novel organizational practices. In

addition, it also measures the overall shift of an economy

toward more knowledge-intensive activities.

The social impacts pillar (four variables) aims to

assess the ICT-driven improvements in well-being that

result from their impacts on the environment, education,

energy consumption, health progress, or more-active

civil participation. At the moment, because of data

limitations, this pillar focuses on measuring the extent

to which governments are becoming more efficient in

the use of ICTs and provide increased online services to

their citizens, and thus improving their e-participation.

It also assesses the extent to which ICTs are present in

education, as a proxy for the potential benefits that are

associated with the use of ICTs in education.

Business and innovation environment

Political and regulatory environment

Networked

Readiness Index

AffordabilityReadiness

Infrastructure and digital content

Skills

Usage Business usage

Individual usage

Government usage

Environment

Component subindexes Pillars

Social impacts

Economic impactsImpacts

Figure 2: The Networked Readiness Index structure

© 2014 World Economic Forum

Networked Readiness Index 1.1: The Networked Readiness Index 2014

8 | The Global Information Technology Report 2014

In general, measuring the impacts of ICTs is

a complex task, and the development of rigorous

quantitative data to do so is still in its infancy. As a result,

many of the dimensions where ICTs are producing

important impacts—especially when these impacts are

not directly translated into commercial activities, as is

the case for the environment and for health—cannot yet

be covered. Therefore this subindex should be regarded

as a work in progress that will evolve to accommodate

new data on many of these dimensions as they become

available.

COMPUTATION METHODOLOGY AND DATA

In order to capture as comprehensively as possible all

relevant dimensions of societies’ networked readiness,

the NRI 2014 is composed of a mixture of quantitative

and survey data, as shown in Figure 3.

Of the 54 variables composing the NRI this year,

27—or 50 percent—are quantitative data, collected

primarily by international organizations such as

International Telecommunication Union (ITU), the World

Bank, and the United Nations. International sources

ensure the validation and comparability of data across

countries.

The remaining 27 variables capture aspects that

are more qualitative in nature or for which internationally

comparable quantitative data are not available for a large

enough number of countries, but that nonetheless are

crucial to fully measure national networked readiness.

These data come from the Executive Opinion Survey (the

Survey), which the Forum administers annually to over

15,000 business leaders in all economies included in

the Report.8 The Survey represents a unique source of

insight into many critical aspects related to the enabling

environment, such as the effectiveness of law-making

bodies and the intensity of local competition; into ICT

readiness, such as the quality of the educational system

and the accessibility of digital content; into ICT usage,

such as capacity to innovate and the importance of

government vision for ICTs; and into impacts, such as

the impact of ICTs on developing new products and

services and improving access to basic services.

The NRI’s coverage every year is determined

by the Survey coverage and data availability for

indicators obtained from other sources, mostly

international organizations. This year the Report

includes 148 economies, four more than the 2013

edition. The newly covered countries are Bhutan, Lao

PDR, and Myanmar. We have also re-instated Angola

and Tunisia into the Index, two countries that were not

included in last year’s edition. Tajikistan is not covered

in the 2014 Report because Survey data could not be

collected this year.

More details on variables included in the Index and

their computation can be found in Appendix A and in the

Technical Notes and Sources section at the end of the

Report.

THE CURRENT NETWORKED READINESS

LANDSCAPE: INSIGHTS FROM THE NRI 2014

This section provides an overview of the networked

readiness landscape of the world as assessed by the

Figure 3: Breakdown of indicators used in the Networked Readiness Index 2014 by data source

TOTAL: 54 INDICATORS

INDICATORS FROM

OTHER SOURCES

27 INDICATORS

(50%)

EXECUTIVE OPINION

SURVEY

27 INDICATORS

(50%)

© 2014 World Economic Forum

UN e-Gov survey

Expert assessment of online

Presense of 193 states

+ Data from int’l orgs

14

Ch

ap

ter

1

UNITED NATIONS E-GOVERNMENT SURVEY 2014

Chapter 1 presents an overview and broad analysis of the 2014 United Nations

E-Government Survey data. It presents e-government development at the global

and regional levels. It also analyzes the relationships of the EGDI in the Small Island

Developing States (SIDS), the Landlocked Developing States (LLDS) and the Least

Developed Countries (LDC) and explores the correlation of e-government with

other indicators like national income.

1.2. Progress at a glance

The e-government story may not be new but it is entering a new episode. Lower-

ing costs is still an important consideration in service delivery, but adding public

value is gradually taking over as the primary goal of e-government. The view

of an “e-government maturity model” no longer holds as e-government goals

are constantly evolving to meet emerging challenges and increase public value.

Emphasis is now being placed on deploying a portfolio of e-services that spans

functions, business units and geographies, at varying local or municipal levels,

thus increasing the value of service offerings to citizens by effectively adopting

disruptive technologies in an adaptive and scalable manner.

In many countries, a new governance contract is emerging to support and man-

age the service delivery model. Collaborative service delivery is now pervasive,

where governments, citizens, civil society and the private sector often work to-

gether to innovate processes and leverage new technologies. In meeting mul-

ti-faceted sustainability challenges, governments are, for example, increasingly

using open data and data analytics to improve accuracy in forecasting citizens’

demand of public utilities or to screen for irregularities in public procurement to

lower its risks. Predictive analysis is also used to identify issues before problem-

atic scenarios develop, and sentiment analysis is deployed in engaging citizens

in public consultation and decision-making processes. This shift is observed in

both developed and developing countries, with the focus on adding public value

to people’s lives in an inclusive manner.

Figure 1.1. The three components of the E-Government Development Index (EGDI)

OSI1/3

TII1/3

HCI1/3

EGDI

OSI—Online Service Index

TII—Telecommunication

Infrastructure Index

HCI—Human Capital Index

14

Ch

ap

ter

1

UNITED NATIONS E-GOVERNMENT SURVEY 2014

Chapter 1 presents an overview and broad analysis of the 2014 United Nations

E-Government Survey data. It presents e-government development at the global

and regional levels. It also analyzes the relationships of the EGDI in the Small Island

Developing States (SIDS), the Landlocked Developing States (LLDS) and the Least

Developed Countries (LDC) and explores the correlation of e-government with

other indicators like national income.

1.2. Progress at a glance

The e-government story may not be new but it is entering a new episode. Lower-

ing costs is still an important consideration in service delivery, but adding public

value is gradually taking over as the primary goal of e-government. The view

of an “e-government maturity model” no longer holds as e-government goals

are constantly evolving to meet emerging challenges and increase public value.

Emphasis is now being placed on deploying a portfolio of e-services that spans

functions, business units and geographies, at varying local or municipal levels,

thus increasing the value of service offerings to citizens by effectively adopting

disruptive technologies in an adaptive and scalable manner.

In many countries, a new governance contract is emerging to support and man-

age the service delivery model. Collaborative service delivery is now pervasive,

where governments, citizens, civil society and the private sector often work to-

gether to innovate processes and leverage new technologies. In meeting mul-

ti-faceted sustainability challenges, governments are, for example, increasingly

using open data and data analytics to improve accuracy in forecasting citizens’

demand of public utilities or to screen for irregularities in public procurement to

lower its risks. Predictive analysis is also used to identify issues before problem-

atic scenarios develop, and sentiment analysis is deployed in engaging citizens

in public consultation and decision-making processes. This shift is observed in

both developed and developing countries, with the focus on adding public value

to people’s lives in an inclusive manner.

Figure 1.1. The three components of the E-Government Development Index (EGDI)

OSI1/3

TII1/3

HCI1/3

EGDI

OSI—Online Service Index

TII—Telecommunication

Infrastructure Index

HCI—Human Capital Index

A4AI/Web Foundation: The affordability index

• Mix of subjective, perception indicators (survey) + objective data (mostly supply-side)

• No indicators of price or affordability!.

• One respondent per country in 2013 now 2

Composite indices …

• Are great for getting attention of nations

– Countries want to lead; Donors love them

• May not stand scientific interrogation?

– Why is one sub-index weighed 1/3 while another is weighted 2/3? Etc.

• Often suffer from the same issues identified in supply-side and demand-side data

– How many respondents in expert survey? What incentives?

– How old/new and how comparable is objective data?