helani galpaya katmandu, march, 2015lirneasia.net/wp-content/uploads/2015/03/s5_nepal_ford... ·...
TRANSCRIPT
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
150
200
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
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
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
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 (
%)
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
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
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
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?