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The Puzzle of Digital Financial Inclusion: A Generation Game?

If we thought that financial inclusion and its digital variant are tightly correlated, we may be in for a surprise, judging from the Global Findex 2017 microdata released by the World Bank last month. Owning a bank account (financial inclusion) and owning a mobile money account (its digital variant) throw a puzzling pattern. I plot the averages of bank account ownership and mobile money account ownership in 144 countries across groups of low to high incomes economies, showing a clear separating trend. The thought is borne by 25 low income countries with the two measures of financial inclusion strongly correlated at 0.7. But as income level steps up (to middle and high incomes level) bank account shares increase while mobile money shares decrease. The final panel is flat at the bottom right: most of the 44 high income countries have more than 80% bank account shares with less than 20% mobile money account shares. The correlation? –0.2. One explanation for this negative correlation can be discounted. The digital variant is not yet a substitute for a bank account: savers cannot yet use their mobile money account on its own or as a substitute to secure property or business investment. As countries move up the economic ladder, the puzzle of separation insists on an explanation.

 

Figure 1. The puzzle of bank account ownership vs mobile money account ownership (number of countries in parentheses) Source: calculated from Global Findex 2017 microdataaccXmobXgroup

I explore an alternative here. In high income economies financial inclusion is nearly universal among adults. Not so in low and middle income economies; on the demand side lower average incomes as well as lack of trust in banks coupled with, on the supply side, weak financial infrastructures combine to leave many adults financially excluded. But the costs of financial services, such as sending and receiving money, have been pared down thanks to mobile technology, especially in low income economies. In Uganda, transfers can be made cheaply and directly from the south west to the north east without recourse to Kampala in the centre.

First in this exploration I show a map of the uneven financial inclusion around the world (https://globalfindex.worldbank.org/ accessed 31 October 2018). Map 1 shows that financial inclusion varies along levels of development. The high income economies of North America, Europe, Australia and New Zealand, are homes to adults with the majority of them having a bank account. Moreover a financial inclusion gradient is discernible with economies around the equator, where many lower and middle income economies are located, reporting lower percentages of account ownership. In particular, available data from African economies in the Global Findex and on the map show how financial inclusion is still a minority story on the continent.

 

Map 1 Financial inclusion around the world 2017, source: Global Findex 2017 report

map101

But has mobile technology made any difference to financial inclusion? It is increasingly so. A map of ownership of mobile money accounts (those who own an account and use a mobile phone to access it) tells how things have improved (Map 2). Over the last three years, some economies in East Africa such as Uganda or Kenya have accumulated owners of mobile money accounts; West African economies are treading the same path. Although it remains the case that the majority of African economies are home to the majority of adults without a mobile money account (60% or more without one).

 

Map 2 Digital financial inclusion in Sub-Saharan Africa, source: Global Findex 2017 report

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To explore further I build a non-linear multilevel model of accounts for each type of financial inclusion: in one the model explains owning a bank account, in the other owning a mobile money account. The model is non-linear because ownership is an indicator, as well as multilevel because 154,472 adults reside in 144 countries. The models account for country income groups, average national incomes, population, age, gender, education, employment, and personal incomes (quintiles). The most interesting findings relate to the associations with age and gender. I show marginal predictions of age and gender for financial inclusion below.

 

Figure 2. Marginal predictions of financial inclusion (own a bank account), calculated from the Global Findex 2017 microdata

accAgeFem

Figure 2 shows the age gradient of financial inclusion that is consistent with the life cycle effects of incomes and wealth. With age comes accumulation of wealth from earnings that needs to be stored for investment and consumption. So for both genders higher age groups have higher odds of owning a bank account (compared to the youngest age group) in a step-wise manner. The youngest (hollow point ○) and the oldest (solid point ●) form bookends to the predictions; both for men (left) and for women (right). There is also a clear gender inequality, although by age 25 women (diamond ◊, right) already have higher odds than the youngest male group. Thus financial inclusion reflects the life cycle effects of earning and saving.

 

Figure 3. Marginal predictions of digital financial inclusion (own a mobile money account), calculated from the Global Findex 2017

 

 

But the marginal predictions for digital financial inclusion do not conform at all to the life cycle effect (figure 3). Digital financial inclusion does not move lock-step with age. In contrast with traditional financial inclusion, the two oldest age groups have lower odds of owning a mobile money account; instead the highest predicted marginals are attained by the mid-30s. The solid point (● oldest group) for instance is furthest below the hollow point (○ youngest group). Here the two oldest–youngest groups do not form bookends. The gender digital divide is also sharper. For similar levels of other characteristics, no female groups have higher odds of owning a mobile money account than the youngest male group.

The strong age reversion effect (inclusion does not move in lock-step with age but reverts after age 40) suggests a generation effect. This is also consistent with the fact that many of the low income economies are still young while many of the high income economies are already ageing.

The puzzle that digital financial inclusion parts ways with financial inclusion may be driven by the generation effect. But there is no reason to expect that the life cycle effect should disappear soon. Thus the need for financial accounts around the world is likely to grow as adults age, leading to some reconciliation in paths of financial inclusion.

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Measuring the Big Data Knowledge Divide Using Wikipedia

Big data is of increasing importance; yet – like all digital technologies – it is affected by a digital divide of multiple dimensions. We set out to understand one dimension: the big data ‘knowledge divide’; meaning the way in which different groups have different levels of knowledge about big data [1,2].

To do this, we analysed Wikipedia – as a global repository of knowledge – and asked: how does people’s knowledge of big data differ by language?

An exploratory analysis of Wikipedia to understand the knowledge divide looked at differences across ten languages in production and consumption of the specific Wikipedia article entitled ‘Big Data’ in each of the languages. The figure below shows initial results:

  • The Knowledge-Awareness Indicator (KAI) measures the total number of views of the ‘Big Data’ article divided by total number of views of all articles for each language (multiplied by 100,000 to produce an easier-to-grasp number). This relates specifically to the time period 1 February – 30 April 2018.
  • ‘Total Articles’ measures the overall number of articles on all topics that were available for each language at the end of April 2018, to give a sense of the volume of language-specific material available on Wikipedia.

‘Big Data’ article knowledge-awareness, top-ten languages*

ko=Korean; zh=Chinese; fr=French; pt=Portuguese; es=Spanish; de=German; it=Italian; ru=Russian; en=English; ja=Japanese.
Note: Data analysed for 46 languages, 1 February to 30 April 2018.
* Figure shows the top-ten languages with the most views of the ‘Big Data’ article in this period.
Source: Author using data from the Wikimedia Toolforge team [3]

 

Production. Considering that Wikipedia is built as a collaborative project, the production of content and its evolution can be used as a proxy for knowledge. A divide relating to the creation of content for the ‘Big Data’ article can be measured using two indicators. First, article size in bytes: longer articles would tend to represent the curation of more knowledge. Second, number of edits: seen as representing the pace at which knowledge is changing. Larger article size and higher number of edits may allow readers to have greater and more current knowledge about big data. On this basis, we see English far ahead of other languages: articles are significantly longer and significantly more edited.

Consumption. The KAI provides a measure of the level of relative interest in accessing the ‘Big Data’ article which will also relate to level of awareness of big data. Where English was the production outlier, Korean and to a lesser extent Chinese are the consumption outliers: there appears to be significantly more relative accessing of the article on ‘Big Data’ in those languages than in others. This suggests a greater interest in and awareness of big data among readers using those languages. Assuming that accessed articles are read and understood, the KAI might also be a proxy for the readers’ level of knowledge about big data.

We can draw two types of conclusion from this work.

First, and addressing the specific research question, we see important differences between language groups; reflecting an important knowledge divide around big data. On the production side, much more is being written and updated in English about big data than in other languages; potentially hampering non-English speakers from engaging with big data; at least in relative terms. This suggests value in encouraging not just more non-English Wikipedia writing on big data, but also non-English research (and/or translation of English research) given research feeds Wikipedia writing. This value may be especially notable in relation to East Asian languages given that, on the consumption side, we found much greater relative interest and awareness of big data among Wikipedia readers.

Second, and methodologically, we can see the value of using Wikipedia to analyse knowledge divide questions. It provides a reliable source of openly-accessible, large-scale data that can be used to generate indicators that are replicable and stable over time.

This research project will continue exploring the use of Wikipedia at the country level to measure and understand the digital divide in the production and consumption of knowledge, focusing specifically on materials in Spanish.

References

[1] Andrejevic, M. (2014). ‘Big Data, Big Questions |The Big Data Divide.’ International Journal of Communication, 8.

[2] Michael, M., & Lupton, D. (2015). ‘Toward a Manifesto for the “Public Understanding of Big Data”.’ Public Understanding of Science, 25(1), 104–116. doi: 10.1177/0963662515609005

[3] Wikimedia Toolforge (2018). Available at: https://tools.wmflabs.org/

Indian IT/Software Sector Statistics: 1980-2015 Time Series Data

28 April 2015 5 comments

The spreadsheet linked below provides time-series data for India’s IT industry, updating data from an earlier blog entry on Indian IT data to 2009. Software export figures run from 1980; overall IT outputs from 1991; and detailed breakdown from 1998 including BPO (business process outsourcing) data from 2000.  Data from 2009/10 uses a different source, so changes from 2008/09 to 2009/10 are not reliable.

Link to XLS version of Indian IT data via Google docs

Main headlines:

a) Indian Software Exports

a1) Indian software exports are huge – roughly US$75bn in 2014/15 (and c.US$100bn if BPO services are included) – and continuously registering double digit annual growth.

a2) But the overall pattern of growth is slowing: the ten-year annual growth average was c.40% in 2002; c.30% in 2008; c.20% in 2014.

a3) IT software/services’ share of total exports remains roughly static: it was just under 14% in 2003/04 and just under 15% in 2013/14[1].

a4) Market diversification for Indian software remains limited.  In the early 1990s, export destinations were: US (c.60-65%), UK (c.10%), other Europe (c.10%), Aus/NZ (c.5-10%), Asia (c.0-3%)[2].  Twenty years later in 2013-14, export destinations were: North America (63%), UK (13%), other Europe (11%), Aus/NZ (4%), Asia (6%)[3].

a5) Location of production has changed.  In the early 1990s, 75% of work took place on-site, 25% in India[4].  By 2013/14, it was said that 20% of work took place on-site, 80% in India[5].  This means that net foreign exchange earnings will have risen as a proportion of gross since offshore work requires much less foreign exchange outflow than on-site working.

a6) One source[6] claims that productivity (as measured by average revenue per employee) in the Indian software sector has risen from c.US$7,000 per head in the mid-1990s, to c.US$16,000 in the late 1990s, to US$38,000 in 2014.  But my own data[7] gives a completely different picture: that productivity in the 1990s was static at just over US$30,000 per head, and thus has risen very little during the 2000s: at best by 1-2 percentage points per year.

Indian Software Exports 1980-2015

b) Domestic IT Production

b1) Although the Indian domestic IT market is large and growing, production for exports is growing faster than production for the domestic market.  As a result, the share of exports in total IT output has risen from 19% in 1991/92 to 49% in 2000/01 to 67% in 2007/08 to 81% in 2014/15.

b2) IT production for the Indian domestic market and domestic IT consumption are very different.  For example, domestic computer hardware production in 2013/14 was roughly US$3bn.  But domestic IT consumption was US$12.4bn[8].  In part, this may be because the two figures are counting different things (e.g. consumption figure includes peripherals, network kit, storage, etc).  But it likely also points to a high – and said to be growing – share of imports in Indian domestic IT consumption.

Indian IT Export Share 92-15

c) IT Sector Overall

c1) The IT sector overall in India represents just over 5% of GDP in 2014/15.

Indian IT Overall 92-15

 

[1] Mani, S. (2014) Emergence of India as the world leader in computer and information services, Economic & Political Weekly, XLIX(49), 51-61

[2] Heeks, R. (1996) India’s Software Industry, Sage, New Delhi

[3] ESC (2014) Computer Software/Services and ITeS Exports, Electronics and Computer Software Export Promotion Council, New Delhi www.escindia.in/uploads/SOFT1415.pdf

[4] Heeks (ibid.)

[5] RBI (2015) Survey on Computer Software & Information Technology Enabled Services Exports: 2013-14, Reserve Bank of India, New Delhi https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=15452

[6] Malik, A. & Nilakant, V. (2015) Context and evolution of the Indian IT industry, in: Business Models and People Management in the Indian IT Industry, A. Malik & C. Rowley (eds), Routledge, Abingdon, UK, 15-34

[7] Heeks (ibid.)

[8] Chawla, M. (2014) Indian IT hardware markets stands at $12.43bn, The Economic Times, 25 Jun http://articles.economictimes.indiatimes.com/2014-06-25/news/50856134_1_anwar-shirpurwala-biswapriya-bhattacharjee-indian-it

ICT and Economic Growth: Evidence from Kenya

26 June 2011 1 comment

Do ICTs contribute to economic growth in developing countries?

In the 1980s, Robert Solow triggered the idea of a productivity paradox, saying “You can see the computer age everywhere but in the productivity statistics.”  And for many years there was a similar developing country growth paradox: that you could increasingly see ICTs in developing countries except in the economic growth data.

That is still largely true of computers and to some extent the Internet, but much less true overall as mobiles have become the dominant form of ICTs in development.  In particular key studies such as those by Waverman et al (2005), Lee et al (2009), and Qiang (2009) have demonstrated a clear connection between mobiles and economic growth and/or between telecoms more generally and economic growth.  They all address the “endogeneity” problem: that a correlation between telecoms (indeed, all ICTs) and economic growth is readily demonstrable; but that you then have to tease out the direction of causality: economic growth of course causes increased levels of ICTs in a country (we buy more tech as we get richer); you need to try to control for that, and separate out the interesting bit: the extent to which the technology causes economic growth. 

The studies try to do this and show ICT investments cause economic growth, but they are all multi-country and provide no specific insights into the experiences of a particular developing nation.  If you know of such data, do please contribute.  Meanwhile, a recent edition of “Kenya Economic Update” provides an example.  Some overall points:

  • The ICT sector grew at an average of nearly 20% per year from 1999-2009 (by contrast, Kenya’s largest economic sector – agriculture – shrank by an annual average of nearly 2% per year).
  • The number of phone subscriptions has grown from the equivalent of one per 1,000 adults in 1999 to the equivalent of nearly one per adult in 2010; Internet usage rates for 2010 were around four per ten adults.
  • Person-to-person mobile money transactions at the end of 2010 were equivalent to around 20% of GDP with two of every three Kenyan adults being users.

But the report’s strongest claim is this: “ICT has been the main driver of Kenya’s economic growth over the last decade. … Since 2000, Kenya’s economy grew at an average of 3.7 percent. Without ICT, growth would have been a lackluster 2.8 percent—similar to the populaton growth rate—and income per capita would have stagnated”.  So ICTs were responsible for 0.9 of the 3.7% annual GDP growth, and for all of Kenya’s GDP per capita growth.  Put another way, ICTs were responsible for roughly one-quarter of Kenya’s GDP growth during the first decade of the 21st century.

Other nuggets from the report and from original World Bank data underlying the report:

  • The “ICT sector” is actually the “posts and telecommunications” sector.  Comparing figures from Research ICT Africa for mobile + fixed line + Internet/data services with those for the overall sector suggests that ICTs form by far the majority (likely greater than 90%) of that sector.  For the ICT part of the sector, latest figures for 08/09 show mobile takes a 54.8% share, fixed line takes 39.5%, with 1.8% for Internet services and 3.8% for data services (not 100% due to rounding).
  • The ICT sector in 2009 still represented only 5% of total Kenyan GDP (compared to 21% for agriculture/forestry), and growth has been volatile, at least as based on the recorded figures, ranging from 3.5% per year up to 66% per year during the first part of the decade, and from 7.9% to over 30% during the second part of the decade.  Only tourism (hotels/restaurants) was more volatile.  In six of the ten years of the 2000-2009 decade, though, ICT was Kenya’s fastest growing sector.
  • In the first half of the decade, annual investments in mobile were higher than annual revenues; but the ratio has subsequently slipped to investment averaging around half of revenue.  Investments in mobile during 2001/02 to 2009/10 are estimated at US$3.2bn (c.KSh250bn) and US$3bn in fixed phone services, with broadband, Internet and BPO investments adding perhaps another US$1bn.
  • The ICT sector provided a more than six-times-greater contribution to Kenyan GDP in 2009 compared to 1999.  Directly, the ICT sector contributed to 14% of the country’s GDP growth between 2000 and 2009 (at constant (i.e. not actual/current but accounting for inflation) prices, it grew from KSh13.7bn in 2000 to KSh71.8bn in 2009; GDP overall grew from KSh976bn to KSh1.382tn).  So the World Bank’s calculation that ICTs contributed a quarter of GDP growth during the decade also include a specific, quantified assumption about ICTs triggering growth in other sectors, in particular the financial sector.
  • Employment in the ICT sector is estimated to be around 100,000 in 2011 (c. 0.7% of the estimated 14m overall labour force).  But ICT punches above its weight in other ways: changes in mobile prices at the start of 2011 were credited with both causing the Kenyan inflation rate to drop and with potentially derailing government constitutional talks due to the substantial knock-on effects in causing tax revenues to drop since phone companies now contribute such a significant proportion of government income.

So, overall, what do we have here?  Some fairly solid evidence that ICT sector growth (predominantly due to mobiles) is making an important direct contribution to economic growth in this developing country.  And some less clear evidence that the indirect GDP growth effect of ICTs may nearly double this.  Thanks to mobile money, Kenya has seen a particularly strong take-up and economic role for ICTs, but it is fairly typical in terms of mobile investment, revenues, subscriber base, employment, etc.  In that case, it’s not too much of an extrapolation to expect that ICTs will have contributed something like one quarter of GDP growth in many developing countries during the first decade of the 21st century.  Evidence of ICT impact that development strategists and practitioners should be more aware of.

Mobile Phone Use in West Africa: Gambian Statistics

30 January 2011 9 comments

This entry reports findings from a survey of nearly 400 mobile phone users in The Gambia conducted by Fatim Badjie, who recently participated in Manchester’s MSc in ICTs for Development.

Its findings fall into six main areas:

Ownership and Costs: 83% of phone users owned their mobile; roughly 70% said that it was cheap to use a mobile.

Mobile Usage: 82% said the most-used facility on their phone was calls; 12% said it was texting; 3% said it was Internet browsing.  Overall, 38% said the service they enjoyed most was texting; 15% said Internet browsing; 8% said conference calls; 5% said video calls.  47% share their mobile with other people, sharing with an average of 3.1 other people.  That means, overall, the average mobile is used by 2.5 people (i.e. shared with 1.5 other people).  On average, users said they used their mobiles 28 times per day, and two-thirds use their mobile at least 10 times per day.

Availability and Issues: roughly 60% of users said they always had a signal and that services were available even in “inconvenient” locations (though of course Gambia is a small country).  Only 30% reported the mobile was always effective for communication and roughly one third reported they felt mobile use had become a burden to them – mostly financially but also socially or personally.  For the 55% of users who wanted improvements, these almost all related to getting 100% network coverage in the country, or wanting cheaper prices.

Impacts and Benefits: 78% felt they benefited from having a mobile particularly due to low cost of calls.  31% felt having a mobile helped them to make or get money, for example through calls from customers to go and collect money owing or, more often, calling family/friends for money (“money calls”).  58% thus felt they had come to depend on their mobile, and 78% said they could not see themselves living without one.

In terms of male-female differences:

– No real difference in rates of ownership, rates and scope of phone sharing, difficulties experienced, or dependency on mobiles.

– Slight tendency for women to have been using fixed lines rather than telecentres as a prior means of communication.

– Women use mobiles a little more than men on average per day (28.6 vs. 26.6 times).

– Less use of mobiles for Internet browsing by women than men; more use of phones for texting.

– More men (38%) than women (24%) said the mobile helped them get money and resources, though women used phones proportionately more for “money calls” than men.

My commentary would be that, overall, this is a reminder of how mature the mobile market is getting in Africa with very high rates of ownership, very high rates of usage, and signs of movement beyond basic calls/SMS: at least 15% going online via their mobiles, at least 13% using video/conference calls.  With roughly one-third saying they use mobiles to make or get money, it looks like quite a valuable financial tool: so embedded that nearly fourth-fifths of users couldn’t imagine life without it, including some who see mobiles as a “necessary burden”.

ITU estimates for 2009 (the year prior to the survey) there were 84 mobile subscriptions per 100 population in The Gambia.  Even allowing for calculations to convert from subscription data to actual ownership and use (see earlier blog entry), this means phone users were by far the bulk of the Gambian population during this survey (so skews compared to the overall population will be present but probably limited).  Given the rates of sharing reported it means that access to a mobile is virtually universal (though it must also mean that many people share their phone with others who already have one).

Noting exclusion from the survey of women (and men) who don’t use mobiles, there was relatively little difference in ownership and usage patterns between men and women.  Is that, too, a sign of market maturity?

Finally, a reminder that, even in a small country there can be significant locational differences and that “market maturity” has a rural—urban axis.  Users were surveyed in seven different parts of The Gambia but the table below compares some of the key findings for those surveyed in the capital, Banjul, and those surveyed in Bansang, a small town three-quarters of the way up-country.

  Banjul (urban) Bansang (rural)
Ownership 100% 32%
Cheap to use? 66% 84%
Access Internet via mobile 17% 0%
Use SMS texting 69% 4%
Share your mobile? 24% 86%
Average uses per day 39.8 6.7
Available in inconvenient locations? 75% 12%
Main problem (of those reporting a problem) Cost (87%) Network availability (98%)
Help you to get money/resources? 28% 40%
Calls for money 14% 44%
Live without mobile? 52% 20%

 

The data show some not unexpected differences.  In the rural location, there was much less ownership of mobiles and much more sharing; much less use of non-call services and generally much less daily use of the mobile.  Network availability is more of an issue in the rural area, but the mobile seems to be more useful for getting money and far fewer users in the rural area can imagine life without it.

You can access the results of the survey by clicking here: they also include more Gambia-specific questions about operators, services, and awareness of institutions.  Note the breakdown-by-location is very lengthy, and not provided in this document.

Global ICT Statistics on Internet Usage, Mobile, Broadband: 1998-2009

16 September 2010 6 comments

How are ICT diffusion rates changing over time in different parts of the world?  The graphs below present ITU statistics for mobile, internet and broadband, dividing countries into quintiles by GDP per capita levels, and weighting the calculated averages within each quintile by population.  They extend earlier data visualisations using Google motion chart for mobile phone and broadband penetration.

Mobile: a real sense of catch-up by the “rich” and “middle-income” countries during the mid-2000s, the former now having higher subscription rates than the “richest” countries due largely to the USA’s sub-100-per-100 statistics.  Catch-up is only just starting to happen for the poorer countries, and there’s a reminder that “mobiles are everywhere” isn’t true yet: e.g. we’ve all heard of Pakistan’s amazing growth rates but the next largest country in the bottom category is Ethiopia whose 80m citizens register less than five subscriptions per 100 people.  For fans of diffusion theory, some clear S-curve shapes on view, though a slightly worrisome dip in growth rates for the poorest countries from ’08 to ’09.  (For notes on converting mobile subscription rates to actual ownership and use rates in developing countries: see this earlier blog entry.)

Internet: still a significant gap between the richest countries and all the rest, which have a mix of larger-country stars (VietNam, China) that punch above their category averages, and dogs (South Africa, India) that fall well below.  Due to spillover effects – e.g. an Internet user passing on web-based information to a non-user – the impact of the Internet is well above what these raw figures indicate.  And poor/middle-income countries will pass the 50 users per 100 marker in just a few years.  Growth rates in the poorest countries are strong – around 25% per year – but their low base means progress is slow.

Broadband: if the Internet figures are a little salutary, then the broadband stats are more so, with the poorest countries barely figuring.  Mobiles are helping to create Development 2.0 – the ICT-enabled transformation of development processes and structures, but broadband will also be key, and it looks a very long way off for the world’s poor.  (Note poor countries average above middle-income due to China’s aggressive broadband roll-out policy, though are held back by India’s pitiful penetration rates of roughly two-thirds of a subscription per 100 people.  Middle-income countries look likely to overtake in the next year or so.)

Global Digital Gap and Digital Lag

One way to summarise the situation is to look at the difference between the poorest and richest quintile countries.  As the graph shows, the “digital gap” between average penetration rates has grown and grown during the 2000s for Internet and broadband.  For mobile it began to close from 2006 onwards, but still remains very high.

You can also measure “digital lag“: the time gap between a given average ICT penetration level in the poorest countries, and the year that was achieved in the richest countries.

Current digital lag is just under 10 years for mobile, and something like 14-15 years for Internet.  For broadband, it’s just over 10 years but the figures are so low that this may not be meaningful.

Future digital lag can be calculated by projecting growth in the poorest countries, assuming current growth rates (averaged 2004-2009) continue.  For mobile, it will be 2011 before the poorest countries reach the 75 subscriptions per 100 level that the richest countries were at in 2004; a digital lag of 7 years (though that rises to 2013 and 9 years if you extrapolate from just 2008-to-2009 growth rates).

For Internet, it will be 2019 before the poorest countries reach the 50 users per 100 level that the richest countries were at in 2002; a digital lag of 17 years.  For broadband, it will be 2020 before the poorest countries reach the 15 users per 100 level that the richest countries were at in 2005; a digital lag of 15 years (but with a wide margin for error, and calculated only on 2008-to-2009 growth rates).  Put another way, there are no signs yet of the digital lag for Internet or broadband closing over time, and not much evidence for the idea that digital lag is shortening with each new ICT innovation.

I’m sure there are other conclusions to be drawn from the data – do please go ahead.  All of the original data is available from the following spreadsheet: https://docs.google.com/leaf?id=0B-14eY3gwnmGYjVkYjIxYTQtMjQxNy00OTIxLWFlN2YtNDIwZWYzYWZlZjVk&hl=en_GB [you’ll need to log in to Google in order to guarantee access]

Public Interest in ICT4D: Web Search and News Statistics

31 August 2010 1 comment

A previous blog entry on publication of ICT4D research through academic outlets suggested that the field was growing fast. In this entry, I look at ICT4D on the web and in the news, and draw some slightly more downbeat conclusions.  These must be taken with a strong pinch of salt because the data looks somewhat cronky.  But what can Google Analytics tell us about ICT4D?

As a search term “ict4d” is insufficiently used to show up in Google Trends but it will appear in Google Insights to produce the following chart and table of web search interest over time:

Year 2004 2005 2006 2007 2008 2009 2010 (part)
Average 47.7 48.4 49.5 41.3 38.9 37.3 35.8

 

This suggests a peak of interest in ICT4D as a search term in 2006, and a small but steady decline thereafter.  A conclusion only slightly undermined by the fact that it records 100% of searches coming from the US; and the fact that the graph was a somewhat (though not greatly) different shape when I looked at it yesterday.

In any case, Google Insights data is just relative to the recorded peak of “100”.  For a guide to absolute search levels, Google Adwords suggests a global average of 5,400 searches per month using “ict4d” during mid-2009 to mid-2010 (for all countries, in English).   (For comparison, “ictd” scores 2,900 (though a bit messed-up because ictd can mean things like “implantable cardiac therapy device”), and “development informatics” scores 1,300.)  And just in case you want to feel bad about ICT4D as your chosen field, “e-government” scores 200,000 monthly searches even though it is, like, sooo 20th century as a concept.  (A joke, by the way, just in case you were considering buying my e-Gov textbook!)  I did the same search some months back: the monthly average for 2009 alone was about half the figures shown here suggesting an increase in ICT4D search activity in 2010.

Lastly we can track ICT4D and related items recorded in Google News:

Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 (est.)
“ict4d” news items 0 12 1 9 5 11 8 10 18
“ict” and “developing countries” news items 0 107 73 109 109 97 82 103 129
“broadband” and “developing countries” news items 24 42 67 76 147 119 125 125 150
“mobile” and “developing countries” news items 121 187 188 249 384 475 480 470 657

 

Looking at the individual news items recorded, there is some evidence of a WSIS effect creating mini-peaks in 2003 and 2005.  In general, the level of news items seems to have been fairly steady since 2006/2007 – it remains to be seen if the extrapolations for 2010 (showing a significant increase in interest) are borne out.

Overall conclusions are as follows:

– The base of data that Google Analytics provides is too small and uncertain to draw any strong conclusions.

– Since its first appearance around 2003 “ict4d” has been very useful for those in the field, but it has not really made it into the mainstream: something worth bearing in mind when trying to write for a mainstream audience.  News stories and searches more often use broader terms (as another exemplar, of the top 40 search terms used to find items on this blog, only 6 contain the term “ict4d”).

– The rapid recent rise in academic publication on ICT4D is not mirrored here.  There are some signs of a small peak of interest in the mid-2000s, but that might be exceeded in 2010, and the broader picture is one of fairly steady interest in ICT4D as a news and web search item during the latter half of the 2000s.

But maybe someone with better knowledge of Google stats will proffer some other conclusions . . .

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