A robust system for statistical measurement of digital development is fundamental for designing effective, evidence-based policies necessary to achieve universal and meaningful connectivity (UMC). Global initiatives like the Global Digital Compact (GDC) and the G20 Digital Economy Working Group (DEWG) have underscored the need to strengthen national statistical systems and harmonize indicators related to digital inclusion.
This report uses a UMC measurement framework that adopts a multidimensional perspective, covering six core dimensions: Connection quality, Availability for use, Affordability, Devices, Digital skills, and Safety and security. This framework is complemented by demographic, economic, and location indicators necessary for granular, disaggregated analysis. The framework, however, often needs adaptation for specific groups, such as children, whose online lives require specialized measurement methodologies and ethical protocols.
A significant challenge is strengthening national statistical capacity. ICT statistics are a relatively new domain in official statistics, and technical capacity often correlates with economic development: countries with lower GNI per capita generally have weaker statistical systems (as shown by the World Bank Statistical Performance Index, SPI). Consequently, high-income countries report ICT data more frequently (approximately every two years), while lower-income countries often report only every five years or less regularly. The Africa region lags severely, reporting on average only one indicator on household Internet access or Internet use by individuals per year, often with significant delays.
Statistical performance index and GNI per capita
Note: Based on ITU regions, markers represent countries.
Source: Own elaboration based on World Bank SPI (2023) and GNI per capita, Atlas method, 2024
Strengthening statistical capacity requires a robust institutional foundation. This includes establishing legislation for the regular and systematic collection and dissemination of ICT statistics by National Statistical Offices (NSOs) and regulators, aligning with the UN Fundamental Principles of Official Statistics. Formalized dialogue between data users and producers is essential to promote a data-driven culture, which is often lacking among policy-makers.
The main constraint, particularly for demand-side data, is financing. Conducting a nationally representative digital inclusion household survey for 15 000 households is estimated to cost approximately USD 2 to 2.5 million per country. Statistical systems rely on domestic sources, such as ordinary government budgets, sometimes supplemented by international assistance. Various sustainable financing models exist, including leveraging revenues from national digital resources (such as country-code top-level domains) or using Universal Service Funds. Currently, there is no dedicated pooled financing mechanism to support the regular implementation of digital inclusion surveys across countries, leading to fragmented and irregular data collection.
Main funding instruments for statistics
Source: ITU
To gain efficiencies, international collaboration and innovation is encouraged. NSOs can cut costs and improve comparability by using international survey tools (e.g., World Bank Survey Solutions, UNICEF MICS toolkit) and by integrating ICT modules into existing social surveys. ITU’s Expert Groups (EGH and EGTI) play a vital role in setting and maintaining harmonized methodological standards.
Furthermore, NSOs are increasingly exploring new data sources and methods to complement traditional surveys, offering timelier and more granular insights. Key examples include Mobile Phone Data (MPD), which provides near real-time, geographically detailed insights into connectivity, and geospatial technologies like satellite imagery. ITU is actively supporting the adoption of MPD, including a joint programme with the World Bank to assist at least 30 countries by 2030. The road ahead requires treating ICT statistics as a strategic public good, establishing a coordinated financing facility, and continued investment in data science skills and international standardization.