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.Follow @CDIManchester
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)|
|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.
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]
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:
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:
|“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 . . .
The spreadsheet linked below provides time-series data for India’s IT industry: software, hardware and services revenue for both export and domestic markets. Software export figures run from 1980; overall IT outputs from 1991; and detailed breakdown from 1998 including BPO (business process outsourcing) data from 2000.
Although software and IT services tend to grab the headlines, other sub-sectors are significant: with hardware worth US$9.5bn (nearer US$12bn if one includes hardware design) and BPO worth more than US$13bn in 2008/09. Total revenue for India’s IT industry in 2008/09 was US$73.4bn.
A number of charts are included in the PDF version. These show, for example:
- The phenomenal growth rate of India’s software exports, with (ten-year rolling) average annual growth never dropping below 30%, and overall exports exceeding US$36bn in 2008/09:
- The much higher growth rate of Indian IT exports compared to production for the domestic market. As a result, the share of exports in total IT output has risen from 19% in 1991/92 to 69% in 2008/09:
The source for the data used is a mixture of interviews in India and Department of Electronics/IT reports for the earlier data up to late 1990s; and the invaluable Dataquest (India) annual review of the IT industry (the “DQ Top 20″) from that point on.
I’ve entered the ITU data on broadband penetration for all countries from 1998-2008 into a Google Docs spreadsheet, and then added the Motion Chart visualiser.
To access the spreadsheet data and Google Motion chart, go to: http://spreadsheets.google.com/pub?key=tuY4rmkYVhRxKCXiltg_obg&output=html
The screenshot below gives an example. Also given below are two screenshot summary graphs derived from overview data about diffusion rates for broadband, which can be found at: http://spreadsheets.google.com/pub?key=tCyqBGWg0E8qg6qG77ltZ2g&output=html.
The most useful statistics are absolute growth rates (weighted by population), which show growth having peaked in 2005 for the richest fifth of nations, but generally still rising for the others. The percentage growth rates have been steadily declining, but mainly because those growth figures are insanely high in the first few years of broadband diffusion given the very low base they start from.
You can find similar visualisations that you can cross-match with a host of other data categories (demographics, economic/social development, and ICT diffusion) using World Bank data (http://devdata.worldbank.org/DataVisualizer/) or a graph I’ve created at Gapminder: http://bit.ly/78IWkN. But these don’t go up to 2008, and you can’t see or access the underlying data.
Note the dynamic visualisation charts will not show up on slower PCs or Internet connections.
For similar data visualisation of mobile phone penetration, see my earlier blog entry at: http://ict4dblog.wordpress.com/2009/11/30/mobile-phone-penetration-google-motion-chart-data-visualisation/
I’ve entered the ITU data on mobile phone penetration for all countries from 1998-2008 into a Google Docs spreadsheet, and then added the Motion Chart visualiser (the same engine made famous by Hans Rosling and TED, though they use the Gapminder Trendalyzer version).
Unfortunately, WordPress scripting rules mean I can’t post the active chart here. To access the spreadsheet data and Google Motion chart, you need to go to:
Screenshots below give an indicator of how you can visualise the data. The chart offers three main means to visualise (bubble, bar chart, and line graph) via tabs at the top right. You can change the axes and element colouring/size, and highlight individual countries. For bubble and bar, the main point of the chart is that you can click play (bottom left) and show how things change over time. (Note playback speed variation control, and also the ability to drag over and zoom in on parts of the chart.)
Not sure it adds a lot of analytic value but it’s engaging, helps give a sense of some overall trends, and identifies some interesting outliers. (Some older PCs and low-bandwidth connections will struggle to display.) I’ll repeat for other ITU data in later posts (e.g. broadband data visualisation here). You can find similar visualisation for mobile, Internet and a host of other development data at: http://devdata.worldbank.org/DataVisualizer (though currently up to 2007 only, no obvious access to underlying data, and the mobile data display doesn’t seem to work properly). And, finally, on a separate blog entry you can find a set of rough converters to change mobile phone subscription data to data on ownership, access, use and non-use.
How much money is spent every year on ICT4D?
We can calculate this two ways: top-down and bottom-up.
Top-Down Calculation: World Development Indicators
The World Bank’s World Development Indicators provide an entry for ICT expenditure as a % of GDP. For 2007 – the latest year available – this expenditure was 5.93% for low- and middle-income countries (covering pretty well all those we tend to call developing and transitional economies; those with GNI per capita of less than US$11,455). Given the GDP of those countries was US$14,155bn, that means spending on ICTs in all developing/transitional economies is estimated at US$840billion in 2007.
Of course this encompasses a very broad notion of ICT4D: as a random example, what Russian giant Gazprom spends on its information systems. If we exclude upper middle income countries, and set the GNI bar at US$3,705 per capita (around the level of Indonesia, Philippines); then the figure drops to US$500bn.
Including just low-income countries (GNI <US$935 per capita; akin to Paul Collier’s “bottom billion”), we have to extrapolate, and get a figure of US$57bn: about US$44 spent on ICT per head.
WITSA data (see comments) seems to confirm these calculations.
[Note: "Information and communications technology expenditures include computer hardware (computers, storage devices, printers, and other peripherals); computer software (operating systems, programming tools, utilities, applications, and internal software development); computer services (information technology consulting, computer and network systems integration, Web hosting, data processing services, and other services); and communications services (voice and data communications services) and wired and wireless communications equipment."]
Bottom-Up Calculation: Individual Organisations/Groups
A bottom-up approach would look at the ICT4D expenditure of individual organisations or groups. We can identify four main spenders: development agencies; developing country governments; the private sector; and consumers. Unfortunately, data here is a threadbare patchwork.
I will present the data I have available: if you have other/new data, please comment in order to update.
Data from the World Bank Group shows it invests around US$800m per year in specific loans and guarantees on ICTs and development, and US$1-1.5bn per year on projects with ICT components (at present, it cites a total of nearly US$8bn invested in such projects).
Some very rough estimates from personal contacts with USAID suggest perhaps something like US$800m being spent on all ICT components of development work per year. Even rougher estimates from JICA’s annual reports suggest it is spending at least US$200m per year, and probably significantly more.
Data from various bits of the International Telecommunication Union Web site suggests an expenditure of around US$60m per year on its development activities (c.20% of total expenditure). The EU’s 9th European Development Fund (2000-2007; focused on Africa, Caribbean, Pacific development) included about US$20m per year (c.US$150m in total) for ICTs. There is also expenditure on ICT4D as research collaboration under the EU’s Framework Programmes.
Canada’s IDRC spends 15.8% of its budget on ICT4D; that budget spending was C$190m in 2008, meaning a total spend of around US$25m per year on ICT4D. Korea’s International Cooperation Agency seems also to spend around US$25m per year on ICT4D; about 13% of budget.
SIDA appears to be spending around US$3m per year on ICT4D.
Delivery agencies with a specific focus on ICT4D have similarly small budgets, and will not make a significant dent on the overall figures. Examples would include IICD which spends around US$6m per year, and the Global Knowledge Partnership which may spend about half that. Quite a bit of this money would come from donor agency funds.
Developing Country Governments
Few if any developing country governments appear to compile comprehensive figures on their ICT investments. Instead, they quote expenditure that is specifically identified as “ICT”; for example, the budget of the Ministry of ICT. This will probably exclude the majority of what the government spends on ICT. For example, Ghana seems to budget ICT spending on health and education under health and education respectively, not under ICT.
Quoted annual government expenditures on ICTs via this narrow definition for some “typical” medium-/large-sized developing countries are in the few hundreds of millions of dollars. E.g. Indonesia (US$340m); Thailand (US$300m); South Africa (US$130m). These are about 0.5% of total government budget; and just under 0.1% of total GDP.
Extrapolating that latter figure, for low and lower-middle income countries, that would suggest investments of about US$7.5bn per year, but the actual figure must be significantly higher than this. As one example, the Indian government in the mid-2000s was already in the process of increasing its total spending on ICTs (i.e. not just that specifically allocated to the ICT heading) from 2% to 3% of budget: a figure of around US$3bn. By comparison, its direct spending on ICT (mostly allocated to e-government) was around one-tenth of this.
Private sector investment levels in mobile telephony are, according to the GSMA, around US$10bn per year in Africa; very roughly US$10 per capita. If at least that amount is also spent in the other developing country markets, that suggests a total investment per year of at least US$50bn.
Other ICT investments in computers, software, non-mobile-based Internet connectivity, and so forth, would be in addition to this figure.
The private sector also has specific development-oriented activities. Examples would be Microsoft Research India, and parts of Microsoft’s Unlimited Potential programme, or Intel’s World Ahead.
Individual consumers spend money on ICTs that are part of ICT4D; for example, the amount they spend on mobile telephony, and on use of telecentres.
Extrapolating figures from Nigeria, which suggest an average per capita spend on all telecommunications of US$55 per year, would indicate spending of around US$55bn per year on telecoms in Africa. Much of that, of course, would be spent by business rather than individuals, and that money then recirculates for further private sector investment. If those same figures could be extrapolated across all developing countries, they would suggest a spend of around US$300bn per year, but that is likely a significant underestimate since Nigerian expenditure will be below developing country averages. That US$55 per year is actually MORE than the US$44 figure indicated from the World Development Indicator figures, and yet it only covers telecommunications.
Alternatively, Mpogole’s work in Tanzania found rural mobile phone owners spending around US$22 per month (US$270 per year) on their mobiles. Using ITU’s 2007 figures (21% subscription rate) and the corrector discussed in a previous blog, we arrive at a spend just on mobiles of US$43 per person per year. At least, then, all these figures are not wildly dissimilar, given that spending on mobiles will be by far the major expenditure on ICT4D in developing countries.
The figures here cover some very different things. Some cover a lot of what many would regard as outside the boundaries of ICT4D. Nonetheless, it seems reasonable to conclude that hundreds of millions of US dollars per year are invested in ICT4D projects; and that tens of billions of US dollars per year are invested in ICT4D infrastructure, with consumers in developing countries spending even more on the use of ICTs, amounting on average to a few tens of US dollars per person per year.
As noted above, additions and updates are welcome.
Note: the SIDA source cited above contains, in its Chapter 4, a quick review of some of the main actors investing in ICT4D.
As widely reported, the number of mobile phone subscribers in the world passed the four billion mark at some point around the end of 2008, in a global population of around 6.7bn of whom about 80% (5.4bn) live in developing countries.
At first sight, that might suggest that 60% (4 / 6.7) of the world’s population has a mobile phone. Given (ITU stats) that the subscriber rate in industrialised countries is roughly 100 per 100 inhabitants, that might also suggest that 50% ((4 – 1.3) / 5.4) of those living in developing countries had a mobile phone at the start of 2009.
We arrive at similar figures if we extrapolate the ITU 2007 stats using the CAGR% for 2002-2007 (minus population growth for the per 100 inhabitants data), to get a 2008 figure:
- Africa: 409m mobile subscribers; 41 subscriptions per 100 inhabitants
- Americas minus US/Canada: 483m subscribers; 84 subscriptions per 100 inhabitants
- Asia minus Japan/S. Korea/Israel/Singapore: 1736m subscribers; 45 subscriptions per 100 inhabitants
- All developing countries: 2.63bn subscribers; 49 subscriptions per 100 inhabitants
BUT . . . These figures have a number of problems. I summarise below what little I have found: if you know more, please comment.
- Some individuals have multiple subscriptions
- Visitors (both foreigners (tourists, business visitors) and nationals who currently reside abroad) buy a subscription/card for their phone during a short-term visit
- Some people living in neighbouring countries may subscribe when they live close to the border within service range
- Subscriptions are counted for some period of days/months after the last use; some of these subscriptions may be on cards/phones that are now discarded
How big is this effect? Of course it varies, but a gratingly rough estimate is that in-country ownership is 75% of the subscription figure. Ewan Sutherland reports an EU-wide figure of 103 subscriptions per 100 population in 2006, but on-the-ground surveys indicating 79 per 100 own a mobile phone. Gillwald et al report 44 subscriptions per 100 population in South Africa in 2004, but on-the-ground surveys indicated 33 per 100 mobile phone ownership. de Silva et al (2008) surveyed in India in 2006 and found 9 mobiles owned per 100 respondents, compared to ITU data for the same period of 15 subscriptions per 100: a converter of 60%; in Sri Lanka, the converter was 85%. Wireless Intelligence produced a report indicating real penetration as a percentage of reported penetration for 2006 was: Romania (80%); Turkey (79%); South Africa (76%).
Within the EU, the ratios vary from about 50% (wealthy Luxembourg where multiple subscriptions are very common) to roughly 100% in France. In accession state Bulgaria, the figure was about 55%, perhaps due to it having so many foreign second home owners, nationals working overseas, and transit visitors. So the 75% estimate covers quite a wide variation that will depend on specific national conditions. We also need a lot more data from developing countries themselves (where there may be a fifth reason for overestimation according to James & Versteeg: those who own SIM cards but not phones).
Second, mobile subscription figures are underestimates of in-country mobile phone access and use for at least two reasons:
- Private mobile phones are shared with family, friends, neighbours, etc.
- Public mobile phones are accessible to large numbers of people.
How big is this effect? James & Versteeg cite an estimate of two users on average per privately-owned mobile phone on the basis of Vodafone Tanzania data; and 70 users on average per public mobile on the basis of Grameen Phone Bangladesh data. But they also note that levels of sharing seem to vary a lot between countries.
If we take the 75% figure and the “two users” figure, this would mean that usage rates of private mobile phones in developing countries are 1.5 times the cited subscription rates. So, for example, that would estimate in 2008 there were around 615m private mobile phone users in Africa: about two-thirds of the population. That’s also (citing GSM Association data) about the proportion of the population that was covered by a mobile signal.
Of course, that excludes those who use public mobiles and other public phones. Looking at de Silva et al’s Asia data, we might estimate that in the poorer developing countries, for every mobile phone owner, there are about three others who don’t own any phone, but find a way to access and use one. However, the figures vary wildly and the ratios decrease rapidly as mobile phone ownership rises. It may be better to lump all phone use together and ask our final question . . .
Levels of Non-Use
Thirdly, how many people still do not use a phone? In the 1990s, we circulated the much-quoted “fact” that half the world’s population had never made a phone call. How do things look now? Let us reduce the 350m in Africa who live outside cell phone coverage with Gillwald et al’s data that around 25% of rural populations in Africa had used public payphones in the past three months. We get roughly 250m non-phoners. About 40% of these will be under 15; we’ll exclude those, to reach 150m adult non-phoners.
In Asia, de Silva et al’s survey work from late 2006 suggested only 4.5% of adult lower-income group members in the countries they studied had not made a phone call in the previous 3 months. That suggests 120m non-phoners. And we might guess roughly the same proportion for Latin America, giving about 20m non-phoners there.
So, a very rough and ready estimate suggests about 300m adult non-phoners in developing countries. This number becomes larger if we start adding in children: around 10% of the developing world’s population (some 500m) is under 5; something like a further 1.5bn are aged 5-14. Many of them will have made phone calls, but many will not.
Finally, some additional confusions:
- The 75% conversion from subscriptions to ownership might get messed up by age demographics. Gillwald et al did draw from all age ranges to get their survey figures. It’s not clear if the EU and Bulgaria figures cited by Sutherland do the same, and the de Silva et al data does not seem to have done so. We can at least estimate that about 25% of Africa’s population and perhaps 20% of Asia’s and Latin America’s population might be seen as too young (10 or under) to own a mobile phone. That would mean, ironically, that the mobile subscription per capita figures and the actual ownership per capita of adult populations could be about the same.
- The basis for the figures is not totally clear, but in a number of surveys (some sub-Saharan African countries covered by Gillwald et al; some Asian countries covered by de Silva et al), the reported mobile ownership per capita figures were the same, or even higher, than the ITU-reported mobile subscriber per capita figure for that country. This may reflect the exact population from whom the survey data was gathered (e.g. more urban than the general population), but it may well also reflect just how ropey are the statistics on mobiles in developing countries, where ballpark figures and trends are all we can really talk about.