Nowcasting GDP with Digital Payments: Evidence from Uganda

In developing economies, output statistics arrive with long lags and omit a large informal sector, while digital payment systems record a growing share of transactions in near real time. We assess whether these records improve GDP nowcasts in Uganda, exploiting two national systems that observe complementary segments of the economy: Mobile Money, covering retail and informal transactions, and real-time gross settlement (RTGS), covering large-value formal payments. Within a pseudo-real-time design respecting each series’ publication lag, we augment a conventional macroeconomic panel with payment data across linear and machinelearning models. Payment data cut forecast errors by up to 16 percent and rank among the most informative predictors, with up to over three times the weight of a typical macroeconomic indicator. The improvement delivered by payment data is robust to macroeconomic disturbances, such as the COVID-19 contraction. Placebo tests attribute these gains to economic content, not added predictors. Already held by central banks, payment data offer a timely, low-cost input for surveillance where conventional statistics are weakest.