Each country receives a score from 0–100 for each of the 5 skill domains. In the final tool, scores will be composite indices built from the following real indicator sources:
Data status: Scores have been computed from real data using data_refresh.py. Domain 1 & 2 values reflect actual WDI internet usage rates fetched from the World Bank API (2022–23, most recent available year per country). Domain 3 reflects published ITU GCI 2024 tier classifications. Sudan and Myanmar use regional medians as WDI has no recent reported values for these countries. The final build will add STEM enrollment and UNCTAD indicators to refine Domain 2 further.
Each country's male and female digital skills scores are computed separately using gender-disaggregated indicators:
Data status: Male scores are derived from real WDI internet usage data (via data_refresh.py). Female scores apply GSMA 2024 regional gender gap ratios — the best available free proxy, covering all 30 countries. These are regional averages (e.g. all SSA countries use the same 20% gap ratio), not country-specific values. Country-specific ratios could be refined in the final build using ILO ILOSTAT data where available.
The trend lines show how the gender digital skills gap (Male Score − Female Score) has changed from 2020 to 2025. In the final tool, this will be computed from:
Data status: Trend values are anchored to GSMA's published SSA regional trajectory: the Sub-Saharan Africa mobile internet gender gap narrowed from approximately 22% in 2020 to 15% in 2023, as reported across the GSMA Mobile Gender Gap Reports 2020–2024. Country lines are offset from this regional baseline using known policy context — Rwanda shows a steeper decline reflecting its Digital Ambition 2020 programme and GSMA Connected Women partnership; Nigeria tracks above the regional average reflecting a persistently larger gap documented in GSMA country data. The 2025 value is a one-year forward projection of the 2024 trend. Country-specific year-by-year data can be extracted from the GSMA annual report Excel downloads.
The simulator estimates how a given investment in a specific intervention type will shift a country's digital skills scores over time. The model structure is:
Effectiveness multipliers by intervention type — these represent estimated score-points gained per $1M invested per skill domain, currently set as illustrative values inspired by the literature:
Workers reached — estimated from typical programme throughput rates (bootcamp: ~2,000/M; university: ~500/M; online: ~8,000/M; PPP: ~3,000/M). Based on World Bank programme documentation.
Gender gap reduction — modelled as 35% of total skills gain flowing to female participants, based on World Bank Gender & Digital Inclusion research showing women's disproportionate benefit from targeted programmes when access barriers are addressed.
Design intent: The World Bank project brief explicitly calls for a tool to "model how different upskilling investments would affect national skills indicators over 5 years" — framing this as scenario exploration, not prediction. The effectiveness multipliers are informed estimates grounded in the literature above, intended to illustrate relative trade-offs between intervention types rather than produce precise forecasts. This is consistent with how the World Bank uses scenario tools internally for investment prioritisation discussions. Empirical calibration using Martins-Neto et al. (2025) regression outputs would be a natural refinement in the final build.
Why interactive? Complex digital development insights are often difficult to convey through static reports alone — especially when communicating to senior leadership and government officials who need to act quickly. This dashboard enables users to explore, filter, and simulate — transforming data into decisions.
This tool is Project 6 of the Georgetown University–World Bank Digital Development Partnership (Spring 2026 Data Science & Public Policy Practicum). It visualizes digital skills workforce readiness and gender-disaggregated skills gaps across 30+ developing countries, serving as a live decision-support tool for the World Bank Digital Development Team.
The dashboard uses freely available proxy sources (GitHub, Stack Overflow, LinkedIn Skills) alongside World Bank Data360, WDI, Stanford AI Index, and UNCTAD data. It is designed to be updatable as the underlying data matures — even at 60–70% data completeness.
Built by Georgetown University students · Data Science & Public Policy Practicum · Spring 2026
In collaboration with the World Bank Digital Development Team · Delivery target: Early–Mid May 2026