UN News Digital

Ashutosh Sharma/UNICEF Children at St. Columba’s School, Delhi, India, use a mobile phone

This article is brought to you thanks to the strategic cooperation of The European Sting with the World Economic Forum.

Author: Alison Gillwald, Research ICT Africa and the University of Cape Town

Much has been made of the digital underpinning of many of the UN’s Sustainable Development Goals – gender equality, good health, quality education, industry innovation, and smart and sustainable cities – and the need to set ICT sub-targets for them. The truth is we don’t have the data from developing countries, and therefore in our global statistics, to determine where we are now or to know what progress we are making towards overcoming the ‘digital divide’.

The current supply-side data collected by the International Telecommunications Union (ITU) from national administrative data doesn’t allow us to measure several basic indicators in the predominantly pre-paid mobile environment that characterizes communication services in developing countries. These indicators inform the access part of the sub-indices of the ICT Development Index (IDI). As the only global ICT dataset, it forms a crucial part of others, such as the UN E-Government Index, the World Bank’s Little Data Book on ICT, the new Facebook/Economist Intelligence Unit ‘3i’ index and the World Economic Forum’s Global Information Technology Report.

Even core ICT indicators, such as the number of mobile subscribers in developing countries, are based on the data supplied by operators to the GSMA for the Connectivity Index and to the national regulatory agencies. This administrative data is seldom audited before it’s forwarded to the ITU. This supply-side data measures the total number of active SIM cards in a market, including the growing number of SIMS used in and for the Internet of Things and other non-human purposes.

The limitations of supply-side data

Such data cannot account for the number of unique subscribers and, as such, it is unable to provide the accurate data policy-makers need to assess the progress made towards fulfilling fundamental universality objectives at the core of public policy – or the progress towards meeting SDG targets.

This is not the only data critical for policy purposes that is unavailable from supply-side data in pre-paid mobile markets. SIM data is unable to tell us anything about the user other than the country’s name where the SIM was purchased.

Even with SIM card registration we can only tell who purchased the SIM card. Disaggregating the data by sex, which is necessary to determine gender equity, is not possible from supply-side data. Furthermore, we are unable to establish the age of people, where they live and their levels of education and income – all of which is essential to the development of inclusive policies.

In pre-paid mobile markets the only way to obtain such data is through nationally representative demand-side surveys, such as the AfterAccess Survey undertaken across 16 countries in the Global South in 2017.

The comparison of mobile phone penetration data from the GSMA or ITU, now shown to be more than 100% in several African countries for example, with that from the AfterAccess Survey, highlights the difference between active SIMS and the number of people in a particular country who own/use a mobile phone at a particular time. The discrepancy in these figures can be accounted for by the number of duplicate SIM cards that individuals have. The African data shows individuals in such markets have an average of two SIM cards as part of their multiple access and affordability strategies.

The ITU recognizes this problem. It has developed manuals and provides training for countries in how to conduct the necessary demand-side surveys for measuring policy and regulation outcomes and assess progress on the global development agenda. But few developing countries have the capacity or appetite to undertake these resource-intensive data-gathering exercises, or to prioritize these over other seemingly more pressing issues.

Comparison of ITU (supply side/administrative) indicators and RIA’s nationally representative demand-side indicators
Image: RIA After Access Survey 2017

In the absence of such data collection by most governments in the Global South, the AfterAccess Survey was undertaken in Africa by RIA, in Asia by LIRNEasia and in Latin America by DIRSI. With data gathered from more than 30 000 hour-long interviews conducted in Asia and Latin America, the survey collects all of the basic indicators required by the ITU/UNCTAD-led Partnership on Measuring the Information Society as shown by the mobile phone and Internet indicators in Figure 1.

Mobile phone and Internet penetration over GDP per capita
Image: RIA After Access Survey 2017 and World Bank 2017

These indicators provide an accurate account of some of the universal indicators developed by the Partnership on Measuring the Information Society. They can be used to provide an evidence base against which best practices can be assessed in terms of real policy outcomes, not simply on whether the practice has been implemented or not. It can confirm or challenge general assumptions about gender or the relationship between GDP and internet penetration. The latter is particularly interesting because despite the complexities of the various indices identified above, they all broadly track GDP. As figure 2 shows, mobile phone penetration and internet penetration is broadly aligned with GPD per capita. But although this is true for higher income countries and the least developed countries, some interesting things happen in between.

Overall, the five Latin American countries surveyed, together with South Africa, have the highest penetration rates but South Africa has a lower internet penetration rate than any of the Latin America countries, including those with lower GDPs. Myanmar and Cambodia have not only much higher internet penetration rates than African and Asian countries with similar GDP figures, they also have higher rates than their larger GDP-per-capita counterparts, India and Nigeria.

From a policy perspective, many of the countries surveyed have adopted generic, so-called best practice reforms yet the AfterAccess data reveals varied policy outcomes. Broadband infrastructure, which indeed remains a challenge for many of the countries surveyed, is a case in point. Despite the strong supply-side measures identified as best practice by various international banks and multilateral institutions, Rwanda had an internet penetration level of only 8%.

In fact, as technology evolves from voice to data services, and over-the-top platforms, IoT and artificial intelligence become increasingly pervasive, the central policy paradox is that as we increase ICT access and use, digital inequality is also amplified. Not only is the gap between the connected and the unconnected greater, the divide between those minimally connected and those with the resources to use the internet optimally to meet their needs expands.

In low-income countries and regions, the uptake of digital technologies is retarded despite infrastructure being in place. This is a result of both the unaffordable cost of devices and services as well as the absence of requisite e-skills or even awareness, as confirmed by the After Access survey: “The reasons for this can often be found on the demand-side but remain invisible in official statistics. Household surveys can shed light on existing bottlenecks.”

The findings of the AfterAccess Survey make visible some of these demand-side challenges by providing insight to the scale and nature of the digital divide in developing countries. As we move from simple voice services and devices to more complex internet-based services the issues of digital inequality become far more complex than connectivity, requiring more comprehensive indicators and data modelling to understand the factors of inclusion and exclusion.

To redress digital inequality in the Global South, far more attention will need to be paid to measures that stimulate demand. Even where environments that are conducive to investment have been created for the extension of networks, our survey data illustrates how the socially and economically marginalized – particularly those at the intersections of class, gender and race – are unable to harness the internet to enhance their social and economic well-being. The data available shows that besides affordability, human development – particularly education and therefore income – are the primary determinants of access, intensity of use and the use of the internet for production not just consumption.

The development of relevant local content and applications in local languages, along with the enhancement of citizens’ digital literacy skills, are all important demand stimulants. Policy-makers will need to these to extend their policy lenses to if they hope to harness the benefits of the internet for all their citizens.

Large numbers of Africans will not be able to afford to be online optimally, even if GSM broadband prices were cost-based, but deploying spectrum to create and extend the Commons (unlicensed spectrum) would change this. Commercially-available, public wifi could be extended from elite urban areas, possibly through deploying poorly-utilized universal service funds or other public resources, to all public spaces. This offers a viable way to increase the intensity of use in urban areas and enhance network effects that would contribute to more inclusive digital development.