Meaningful digital transformation: Preparing for the next decade
3.3 Economic impacts of broadband for the 2030 Agenda
As Iqbal Z. Quadir, the founder of GrameenPhone in Bangladesh, wrote in 2003, “for the poor, connectivity means economic opportunity.” Recent addition to economic literature in the past year continue highlight the importance of broadband infrastructure for increasing household employment and poverty alleviation through the income effects of expanding internet infrastructure.
Expanding fiber network deployment for both middle mile and access networks, for example, particularly in countries with low fiber penetration, enables greater bandwidth throughput for end-users and results in positive economic impact. From undersea and terrestrial fiber deployments across 12 African countries, researchers found that the expansion of fiber networks led to large positive effects on employment rates, increased firm entry, productivity and exports, and increases in average income.186
ITU analysis of the fifty largest countries by population in 2019 demonstrates varying densities of fiber network deployment per geographic area. Figure 27 maps countries on the basis of total kilometers of fiber network deployment (x-axis) against density of deployment (fiber kilometers per million square kilometers of country area) on the y-axis. Policy measures, such as standardizing rights-of-way policy, enabling non-discriminatory and non-exclusive sharing agreement, and supporting public investment in rural fiber deployments could accelerate deployment.
Figure 27 Total fiber deployment and fiber density by total geographic area, 2019187
In terms of mobile coverage, a new paper by the World Bank and the GSMA demonstrates how network expansion between 2010 and 2016, focused on 3G and 4G upgrades, resulted in large and positive impacts on household consumption levels.188 And that mobile broadband coverage reduced the proportion of households below the poverty line, because of effects mainly due to increasing labor participation and employment, particularly among women.
Additionally, one of the first randomized controlled trials (RCTs) focused on mobile cellular telecommunications deployment also recently published results demonstrating the statistically significant impact of basic mobile communications. Researchers at University of California and the U.S. Federal Reserve worked with implementation partners at the University of the Philippines and others to measure the impact of mobile phone service extended to isolated and previously unconnected villages in a remote part of the Philippines. Though the RCT is based on a limited number of observations in the control and treatment groups, the researchers found that the expansion of service and adoption of basic mobile phones had a large and significant impact on household income and expenditure, in part due to increases in migration, remittances and self-employment.
These studies highlight how digital infrastructure impacts various aspects of economic growth that are linked to overall social development. For example, the Sustainable Economic Development Assessment (SEDA) analysis, by Boston Consulting Group, measures objective elements of country level well-being (based on ten dimensions of income, economic stability, employment, health, education, infrastructure, equality, civil society, governance and environment). The analysis found that the ability to convert wealth (measured in GDP per capita) into improvements in well-being is clearly associated with a country’s level of technology adoption, measured by internet usage and mobile adoption.189 The higher the level of internet usage and mobile subscription, the more impact wealth and GDP per capita has on overall well-being, except for those countries already at the highest usage levels. For those countries, additional investment in digital infrastructure does not yield much more increases in overall well-being. See Figure 28.
Figure 28 Digital Infrastructure Increases and the Ability to Convert Wealth into Well-Being - Up to a Point190
Box 8: Working Group on Data, Digital and AI for Global Health
Digital and Artificial Intelligence (AI) technology offer an unprecedented opportunity to transform health systems from being reactive to preventative and even predictive. The Broadband Commission Working Group on Data, Digital and AI in Health is tasked with generating knowledge about how these technologies can advance health and care globally.
The Working Group has published a new report “Reimagining Global Health through Artificial Intelligence: A Roadmap to AI Maturity” that identifies five use cases (Population Health, Preclinical Research & Clinical Trials, Clinical Care Pathways, Patient-facing Solutions, Optimization of Health Operations) for how AI is applied to address global and public health priorities, strengthen health systems, and improve outcomes for patients. A comprehensive landscape review identified actionable recommendations for how governments and other stakeholders can create the ecosystem necessary to achieve mature integration of AI in health.
These actionable recommendations aim to enable policy makers, health organizations, civil society, the private sector and other stakeholders to capture the game-changing capabilities of AI for health. The report introduces a maturity roadmap for AI in health, which describes the stepwise progression for LMICs towards AI maturity and the enabling environment that fosters it.
Taking the six areas for AI maturity in health as its canvas to highlight benchmarks, milestones, and enablers, the roadmap maps the progressive path towards maturity for:
- People & Workforce
- Governance & Regulatory
- Data & Technology
- Design & Processes
- Partnerships & Stakeholders
- Business Models
These six areas for AI maturity comprise an ecosystem of interdependent areas: no single one can be prioritized. Rather, investments into maturity should advance the progression on all fronts. Given that countries and health systems begin or continue their AI journey from different starting points, the goal is to advance AI maturity by identifying specific gaps and existing capabilities. We describe three distinct maturity levels across areas of maturity.