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Africa > Uganda

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Cover IMF Working Papers
Mobile Money, Perception about Cash, and Financial Inclusion: Learning from Uganda’s Micro-Level Data
Felix F. Simione
and
Tara S Muehlschlegel
Will mobile money render cash less dominant over time in Africa? Can it promote financial inclusion? We shed light on these questions by exploring individual-level and nationally representative survey data for Uganda, a country in a region that pioneered mobile money in the world. We use the Propensity Score Matching method to robustly compare mobile money users and non-users across a range of indicators that capture individuals’ perceptions about cash, and the extent to which they remit, save, and borrow money. We present the first evidence that mobile money users, compared to non-users, are more likely to perceive cash as risky and less likely to prefer carrying large amounts of cash. We also confirm that mobile money users are more likely to receive and send remittances, save, and borrow. They also save and borrow larger amounts.
Keywords:
Developing Country; Innovation; Digital Divide; money usage; selected indicator; Propensity Score Matching method; usage treatment; deposit balance; Mobile banking; Remittances; Financial inclusion; Digital financial services; Consumer credit; Africa; Sub-Saharan Africa
Cover IMF Working Papers
Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa
Brandon Buell
,
Reda Cherif
,
Carissa Chen
,
Jiawen Tang
, and
Nils Wendt
The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.
Keywords:
model prediction; quantile plot; ML model; GDP YoY; data variable; YoY percent change; Factor models; Machine learning; Time series analysis; Spot exchange rates; Mobile banking; Africa; Sub-Saharan Africa