How Pan Africa Data sources, cleans, forecasts and builds proprietary city-level income distribution data across all 54 African countries. Every record in our database carries a source and a value type so you always know what you are working with.
Pan Africa Data aggregates national data from reputable international institutions, cleans and standardises it across all 54 African countries, and builds proprietary sub-national extensions on top.
Primary source for historical macroeconomic and demographic data, 2000–2024. Population, GDP, inflation, unemployment, poverty, financial inclusion, trade, and infrastructure indicators.
GDP, GDP growth and inflation forecasts for 2025–2030. Used with permission from the IMF — see citation below.
GDP, consumption, poverty headcount and current account forecasts, used as a supplementary institutional anchor where IMF data does not cover a given indicator.
Population forecasts 2025–2035, World Population Prospects.
365+ remittance corridors, quarterly data 2011–2025, covering cost, fees and FX margins.
Official Development Assistance (ODA) by donor, 2000–2023, for all 54 African recipient countries.
IMF citation
Source: IMF, World Economic Outlook Database, April 2026. Used with permission. This permission does not extend to material within the IMF dataset credited to another source, and use of IMF data does not imply IMF endorsement of Pan Africa Data or its products.
Every record returned by the API and included in downloads carries a value_type field. This tells you exactly how the figure was produced.
| Type | Meaning |
|---|---|
| M | Measured — reported directly by World Bank WDI or UN, unmodified. |
| I | Interpolated — linear interpolation between two measured values where a survey-based indicator has a multi-year gap. |
| D | Derived — calculated directly from other measured indicators (e.g. GDP total derived from GDP per capita × population). |
| CF | Carried forward / external forecast — either the last known value extended forward, or an institutional forecast from IMF WEO or World Bank MPO. The source field distinguishes between these. |
| TE | Trend extrapolation — Pan Africa Data's proprietary trend model, used for indicators without an institutional forecast (e.g. internet users, electricity access, financial inclusion). |
| MODEL | Pan Africa Data model — proprietary derivations including GDP per capita (PPP and current USD), the 2031–2035 GDP extension, and all sub-national city-level income distribution data. |
The source field always names the underlying institution or model — for example "World Bank WDI", "IMF, World Economic Outlook Database, April 2026", or "Pan Africa Data — MPO growth convergence to long-run potential".
Standard global forecasting approaches — simple trend lines, single global inflation floors, unemployment trends anchored on crisis years — produce systematically wrong results for African economies. Pan Africa Data's forecast model addresses five structural issues specific to African data.
Hyperinflation spike years (above a country-specific threshold, typically 15%) are detected and excluded from trend calculations. Forecasts then converge toward central bank targets where one exists:
| Country | Target | Source |
|---|---|---|
| South Africa | 3.0% | SARB midpoint |
| Kenya | 5.0% | CBK midpoint |
| Morocco | 2.0% | Bank Al-Maghrib |
| Tunisia | 4.0% | BCT |
| Rwanda, Tanzania, Uganda | 5.0% | BNR / BoT / BoU |
| Ghana | 8.0% | BoG medium-term |
| Egypt | 7.0% | CBE |
| Nigeria | 15.0% (cap) | Structural — no formal target |
For countries without a central bank target, a global floor of 0.5% is used rather than 2.0% — this prevents CFA franc zone countries with structurally low inflation from being artificially lifted.
Forecasts revert toward country-specific structural rates rather than extrapolating from COVID-era spikes. South Africa's structural rate is 28.0%; Nigeria 5.0%; Kenya 5.5%; Ghana 4.5%; Egypt 7.5%; Morocco 10.0%; Tunisia 15.0%. Eleven countries with available structural rate estimates use this mean-reversion approach; all other countries use trend extrapolation.
Country-specific Gini floors prevent forecasts from converging toward implausibly low inequality levels. High-inequality Southern African economies (South Africa, Namibia, Botswana, Eswatini, Lesotho, Zimbabwe) retain elevated floors reflecting structural inequality; gradual improvement is modelled at approximately 2% per year toward the floor.
GDP per capita is provided in three formats:
GDP total (current US$) for 2025–2030 is taken directly from IMF WEO. The 2031–2035 extension applies a growth rate that converges linearly from the 2030 IMF growth rate toward a country-specific long-run potential growth rate — for example South Africa converges toward 1.8%, Rwanda toward 7.0%, and Nigeria toward 3.5%, reflecting differing structural growth potential across the continent.
Pan Africa Data has constructed proprietary city-level income distribution data for 509 cities across 51 African countries (Eritrea, Libya and Somalia are excluded — see limitations below) — the only dataset of this kind covering the African continent.
For each city, four income classes are provided: marginalised, low income, middle income, and high income. Each class includes population share, population count, and income bounds in current USD, constant USD, and PPP, plus a city-specific Gini coefficient.
The model starts from national income distribution anchored to World Bank WDI measured data. City-specific income relativities — derived from GDP per capita estimates, urbanisation rates, population density and economic activity — are then applied to produce city-level income bounds and class shares.
City Gini coefficients are adjusted from the national level based on each city's income relativity, with a floor of 28.0 and an adjustment of up to ±12 points from the national figure. High-income capital cities in lower-inequality countries typically show lower Gini than the national average; high-inequality Southern African cities retain elevated Gini coefficients consistent with national patterns.
Forecasts to 2035 use the same national forecast methodology described in section 3, with city-level relativities held stable over the forecast horizon.
Both per capita and household income bounds are provided for every city and income class.
Household income bounds are derived by multiplying per capita income bounds by a regional household size coefficient — for example 4.0 persons for East African urban households, 5.5 for West African urban households, calibrated using available household survey data by region.
A note on methodology choice. Survey-based household consumption data — where available — more precisely captures actual household welfare and informal economy income. Pan Africa Data's household-size-based approach prioritises consistency and forecast coverage across all 509 cities and the full 2000–2035 horizon, where survey data does not exist. For point-in-time welfare analysis in markets where household survey data is available, that data should be used as a complement to these figures.
| Country | Issue | Treatment |
|---|---|---|
| Eritrea, Libya, Somalia | Insufficient national data for reliable city-level modelling | No city-level data provided |
| South Sudan | No UN population forecast available | No GDP per capita forecast for 2025–2035 |
| Malawi | Kwacha devaluation distorts USD-denominated GDP forecasts | GDP per capita carried forward at 2024 value pending a local-currency forecast (v1.1) |
| Nigeria, Sudan, Ethiopia, Angola, Sierra Leone | Structural hyperinflation — most or all recent years excluded as spikes | Inflation forecasts dampened from last measured value toward a country-specific cap |
GDP per capita historical values for pre-2010 periods may diverge from household survey-based estimates due to methodological differences between national accounts data and consumption survey approaches. Pan Africa Data uses national accounts (WDI) as the primary source for consistency and comparability across all 54 countries.
National data is refreshed quarterly from World Bank WDI, IMF WEO (twice yearly, April and October), World Bank MPO, UN Population Division, OECD DAC and World Bank RPW. Sub-national city-level data is refreshed alongside each national update.
Questions about methodology, data sources, or a specific figure? info@panafricadata.com