Pan Africa Data applies a tiered forecasting framework built specifically for African market dynamics — accounting for structural informality, commodity dependence, data scarcity and market volatility that generic models designed for developed economies ignore.
Pan Africa Data aggregates, cleans and structures data from the World Bank World Development Indicators — the most comprehensive freely licensed macroeconomic and demographic dataset covering all 54 African countries. We do not create the underlying measurements. What we provide is the aggregation, cleaning, gap-filling, Africa-specific forecasting methodology and structured API delivery layer.
For forecast years beyond the World Bank's published data, we apply our proprietary tiered forecasting models — described in sections 03–06 — anchored to publicly available macroeconomic projections and structural relationships calibrated for African market dynamics.
| Source | Dataset | Coverage | Licence | Update frequency |
|---|---|---|---|---|
| World Bank data.worldbank.org |
World Development Indicators (WDI) — 90+ macroeconomic, demographic, poverty, financial inclusion and infrastructure indicators | 54 African countries, 2000–2024 | CC-BY 4.0 — free to use and redistribute with attribution | Quarterly |
| Pan Africa Data Proprietary forecast models |
Africa-specific tiered forecasting models for years beyond World Bank published data — Gini mean reversion, GDP-anchored consumption, poverty elasticity and trend models | 54 African countries, forecasts 2025–2035 | Proprietary — Pan Africa Data (Pty) Ltd | Quarterly, aligned to World Bank refresh |
Every record in the Pan Africa Data database carries a value type flag that tells you exactly what kind of data point you are working with. This is not a quality score — it is a precise description of how that value was obtained or derived. We believe transparent labelling is more useful than opaque quality indices.
Our data pipeline runs quarterly following each World Bank data refresh cycle. The pipeline fetches updated source data, recalculates gap-filled values where new measured data has become available, and re-runs forecast models with updated inputs. All historical measured values are preserved — we never overwrite source data with model outputs.
Africa presents forecasting challenges that generic models — designed for data-rich developed economies — handle poorly. Survey-based indicators are measured irregularly. History is short. Structural breaks are common. Markets are shaped by commodity prices, political cycles and demographic transitions that don't follow the same patterns as OECD economies.
Rather than applying a single extrapolation approach to all indicators, we use a five-tier framework that matches the forecasting method to the nature of each indicator.
| Tier | Method | Indicators | Rationale |
|---|---|---|---|
| Tier 1Direct source | Direct source data | GDP growth, inflation, current account balance, government debt | These indicators have authoritative projections from international institutions. Where available, we use these directly without modification. |
| Tier 2Structural | Structural relationship model | Household consumption expenditure, poverty headcount ($2.15, $3.65, $6.85), income shares | These indicators have well-established relationships with GDP growth and inequality. We model them using those relationships rather than extrapolating the indicator itself. |
| Tier 3Mean reversion | Rules-based mean reversion | Gini coefficient, income quintile shares | Inequality indicators are structurally sticky but do drift toward regional averages over time. We apply controlled mean reversion anchored to regional peers. |
| Tier 4S-curve | Logistic adoption model | Internet penetration, mobile subscriptions, bank account ownership, access to electricity | Technology and infrastructure adoption follows S-curve patterns — slow start, rapid adoption, plateau at saturation. Linear extrapolation overstates growth near saturation. |
| Tier 5Capped trend | Trend extrapolation with caps | FDI net inflows, trade volumes, remittances | These indicators are more volatile and less amenable to structural modelling. We apply trend extrapolation with hard caps to prevent implausible projections (±30% of 5-year average). |
We believe transparency about limitations is as important as the methodology itself. Clients making investment decisions or policy recommendations need to understand what our data can and cannot tell them.