How Kenya Can Track GDP Growth in Real Time Using Digital and Trade Data

An IMF and Johns Hopkins University study shows that Kenya’s GDP growth can be estimated in real time by combining monthly indicators like trade, electricity, remittances and mobile money data into a dynamic factor model. The approach delivers timely and competitive growth estimates before official figures are released, helping policymakers respond faster to economic changes.

When Kenya releases its official GDP figures, policymakers are often looking at an economy that existed months earlier. Quarterly data typically comes more than three months after the end of the period they describe. In a country exposed to global trade shifts, commodity price swings, and weather shocks, that delay can make timely decision-making difficult.

A new study by economists from the International Monetary Fund and Johns Hopkins University offers a solution: estimate growth in real time instead of waiting for official numbers. The researchers, including Nikolay Danov, Domenico Giannone, Alain Kabundi, Cedric Okou and Antonio Spilimbergo, have developed a model that predicts Kenya’s quarterly GDP before it is published. This approach, known as “nowcasting,” has been widely used in advanced economies but rarely applied in low-income countries.

How Real-Time Growth Tracking Works

The idea behind nowcasting is straightforward. Many parts of the economy move together. When trade improves, factories often produce more. When remittances rise, households tend to spend more. By tracking these patterns across different sectors, it is possible to estimate overall economic growth before GDP data are officially released.

The researchers built a model that combines 11 monthly indicators covering key areas of Kenya’s economy. These include exports and imports of goods, tea and coffee shipments, electricity production, vehicle assembly, tourism arrivals, diaspora remittances, money supply, and business confidence measured by the Purchasing Managers’ Index.

Instead of looking at each indicator separately, the model extracts common trends that link them. If several indicators point upward at the same time, the model interprets this as stronger growth. If many weaken together, the growth estimate is revised downward.

The Power of Mobile Money Data

One of the most innovative parts of the study is the use of mobile money transactions. Kenya is a global leader in digital payments, with services like M-Pesa deeply embedded in everyday life. The value of mobile money transactions is published monthly by the Central Bank of Kenya and provides a near real-time signal of consumer activity.

Mobile payments reflect spending in both the formal and informal sectors. During the COVID-19 crisis, transaction values fell sharply and then rebounded, closely tracking the broader economic slowdown and recovery. More recently, changes in transaction values have offered clues about shifts in household purchasing power.

By including mobile money data, the model captures consumption patterns that traditional statistics might miss or report too late. The study finds that digital payment figures play an important role in updating growth estimates, especially in the middle of each quarter when fresh data arrive.

Why Trade and Production Matter Most

The research highlights the central role of trade in Kenya’s economy. Imports and exports consistently have a strong influence on growth revisions. Imports are particularly important because they often include machinery, equipment and intermediate goods that support domestic production.

Electricity generation and vehicle production also stand out as reliable signals. When electricity output rises, it usually reflects increased industrial activity. Similarly, vehicle assembly provides insight into manufacturing performance.

The model does more than generate a number. It also explains why the estimate changes. Each new data release is compared with what the model expected. Only unexpected results, described as “news,” trigger revisions. For example, if exports come in stronger than predicted, the growth estimate rises. If remittances disappoint, the forecast moves lower. This transparency helps policymakers understand which sectors are driving momentum.

Strong Results and Big Implications

The researchers tested the model in real time from 2021 through 2025. As new data were added each month, the growth estimates became more accurate and moved closer to the official GDP figures. When compared with forecasts from the Central Bank of Kenya and the IMF’s World Economic Outlook, the model performed competitively. In some cases, it adjusted more quickly to new information.

Perhaps the most important message is that sophisticated real-time forecasting is possible even in data-constrained environments. Kenya’s economic indicators show enough common movement to allow a small set of statistical factors to capture the overall business cycle.

For policymakers, the benefit is clear. Instead of waiting months for official GDP data, they can rely on a continuously updated estimate based on observable monthly information. As new high-frequency data become available, the system can easily incorporate them.

In a fast-changing world, better information means better decisions. This study shows that with the right tools, Kenya can track its economic pulse in real time rather than months after the fact.