1. Interrogating data available from government and web sources
Rohan Samarajiva
Pokhara, February 16-19, 2019
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.

2. Data on the sector comes from multiple sources
Data on the sector comes from multiple sources. Identify methods and definitions of each
OPERATORS
Financial data
Operational data (equipment, quality)
“Supply Side”
REGULATOR/ POLICY MAKER
For decision making
Monitoring
ITU/OTHER INT’L ORGs
Raw data
Composite indices (ranking countries)
THIRD PARTY RESEARCH
Specialized Studies
CONSUMERS
- Satisfaction surveys
Complaints
“Demand Side”

3. Connectivity

4. Is connectivity increasing?
Looks impressive

5. But PK is in middle of pack when compared

6. Telecom data change: Most recent SIM/100 data matter . . .

7. Who is actually ahead?
Aided by multiple millions of SIMs deregistered in PK & SIM tax of USD 12+ in BD

8. Are the data comparable. E. g
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 “total fixed access paths in 2008” reported by IN not comparable with PK
Having quarterly data eliminates problem to a great extent
Especially important if benchmarks are used for mainstream regulatory work such as interconnection or retail tariff regulation

9. Prerequisites for comparison
Internationally accepted definitions and procedures
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 xEV-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 G mobile network technology, based on CDMA”

10. Sources of internationally accepted definitions
ITU (2010) Definitions of World Telecommunication/ICT Indicators, Geneva: ITU
Partnership on Measuring ICT for Development (2010), Core ICT Indicators 2010, Geneva: ITU
Update

11. Useful Indicators to measure connectivity
FIXED
Number of fixed lines
Number of fixed wireline phones
Number of fixed wireless phones
Total fixed line subscribers 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

12. WSIS target 10: bringing ICTs within reach of a majority of the world’s population
Four indicators:
Mobile subscriptions
Mobile use
Internet use by household
Internet use by individuals
[Note: 3 more business indicators added later (since WDTR 2010) ]
Data collected and reported for all
Can method for estimating Indicator 4 (Internet Use by Individuals) be improved?
Focus of this section

13. ‘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)
One indicator that is specifically considered important when considering the ICT sector is the proportion of individuals using the internet. This is because it is a base indicator in many other indices such as the ICT development index, knowledge economy index, network readiness index. It is also used to measure the digital divide and is one of the core indicators measured by the world summit on the information society and one of the sub indicators of the millennium development goals as well
The best method of measuring this indicator is through conducting demand-side surveys to find out the proportion of individuals using the internet. It is one of the household ICT survey indicators recommended by the Partnership on Measuring ICT for Development

14. 62.5% of countries had not conducted a demand-side survey on ICT use by 2011
But the problem is that as you can see from this slide less than 40% actually conduct demand side surveys on Internet use. While we can see that the % of countries conducting surveys are increasing, it is still very low.
Source: Measuring the Information Society 2011, ITU
Note: * Data in this chart refer to countries that have collected data on the number of households with Internet access at home through official national surveys

15. Method used to estimate the number of Internet users by ITU
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.
Above is then cross checked with other evidence (e.g. if HH access data available, Users > HH access number must be true, etc. )

16. 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
But there are many problems with this method. The multipliers are chosen at the discretion of the country administration. While this may be fare considering differences in family sizes and different practices in diverse nations, the problem is that there is no consistency or guidance on how to define the multiplier. This makes comparison between countries meaningless.
Then there are also other problems with counting Internet subscriptions. Oftentimes many countries report on the number of data enabled SIMs rather than subscriptions which are actually used to access the Internet. On the other hand, there can be under counting due to countries only reporting on the number of data subscriptions, but not counting all the pre-paid connections that do not have a specific data plan but are used to access the Internet.
There are also other difficulties due to multiple SIM ownership.
So you can see there are many problems in the method of estimating the % of Internet Users.

17. 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
Huge variance in Multipliers: 0.68 (Russia) to 500 (Afghanistan) in 2009
“Similar” countries with very different multipliers
Afghanistan - 2,000 fixed subscriptions; Multiplier=500
Burundi - 5,000 fixed subscriptions; Multiplier=13 … … … …
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

18. Proposed modest improvement

19. Proposed new methodology
% 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
So we started to look for a better more scientific estimate for the proportion of Internet Users. One of the main factors that can be seen from literature is that % of Internet Users increase with Education and Income. So we did a correlation analysis using the Education and Income components of the HDI (Human Development Index) against the % of Internet Users for countries which have already done a survey on Internet Users to findout whether there is a specific relationship. The Education and Income sub index was created using the DIY HDI Build your own index software from UNDP, and data on countreis which conducted surveys were from ITU and Research ICT Africa.

20. Strong correlation between Education_GNI Index and Internet penetration
This graph shows the strong correlation that exists between the Education Income Index vs % of Internet users.
Education_GNI Index 2011

21. 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.)

22. Else use imputed figure
Step 2: In the absence of survey data use Education_GNI Index to estimate proportion of Internet users
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
So we suggest to derive a model using countries which conducted Internet user surveys, against their Educaiton Income Index, and use this model to impute % of Internet users for countries which have not conducted a survey. Then if the country reported figure is within a +/- 7 percentage point band around the calculated estimate to use the country reported figure and else to use the imputed figure. The reason we use 7 is because we found that for countries which have actually conducted surveys the average variance was 7 percenage points.

23. Less than 30% countries show different Internet penetration rates
Overal what we see then is that most countries reported data and the new method are the same. It only changes for countries which may have reported very high or very low figures without conducting surveys and which are not based on any specific method.
The outliers in dark blue are countries from the RIA surves, Research ICT Africa, which have not been included in the ITU data set as they only use data reported from the coutry it self even though these are represenative surveys.
The red are heavy outliers and many are carribian islands. WHn looking at the carribian islands which have acually conduted surveys they show that their penertration rate is between 20 – 40 as shown by the new method and not 60 – 80 % as has been reported. SO the new method seems to be more accurate and based on evidence and is consistant. If a country does not agree, they just simply have to conduct a survey.
Nigeria
Kenya
Uganda
Rwanda
‘X’ Survey data from RIA but not same as ITU Internet penetration rate

24. Price & Affordability

25. Mobile broadband plan prices, including affordability (prepaid 500 MB), 2017 S Asia
Country
Service Provider
Price of plan (local currency, incl. tax)
Data cap (MB)
Tax %
Basket ($US)
Basket as a % of GNI per capita
Afghanistan
Roshan
350
1500
5.15
10.83
Bangladesh
Grameenphone
149
500 21
1.85
1.51
Bhutan
Bhutan Telecom 99
855 5
1.52
0.67
India
Bharti Airtel
181
1024 18
2.78
1.83
Maldives
Dhiraagu
105 6
6.82
0.86
Nepal
Ncell 70
250 13
2.68
4.07
Pakistan
Mobilink
160
2000
19.5
1.15
Sri Lanka
Dialog
238
2048
19.74
1.56
0.49

26. Mobile broadband plan prices, incl affordability (postpaid), 2017 S Asia
Country
Service Provider
Price of plan (local currency, incl. tax)
Data cap (GB)
Tax %
Basket ($US)
Basket as % of GNI per capita
Afghanistan
Roshan
350
1.5
5.15
10.83
Bangladesh
GrameenPhone
229 21
2.85
2.32
Bhutan
Bhutan Telecom
208.95
1.36 5
3.21
1.42
India
Bharti Airtel
399 20 18
6.13
4.04
Maldives
Dhiraagu
211 2 6
13.71
1.72
Nepal
Ncell
789.87 1 13
7.56
11.48
Pakistan
Jazz
999 15 32
9.47
7.19
Sri Lanka
Dialog
238.28
19.74
1.56
0.49

27. Unpacking government data

28. Age dependency is a key indicator
Broadband and digital policies have to take into account child or elderly dependency
Aggregate age dependency is not enough

29. How child & elder dependency changes over time: Bangladesh, 2006-2051