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Philip Abradu-Otoo, Ivy Acquaye, Abubakar Addy, Nana Kwame Akosah, James Attuquaye, Simon Harvey, Shalva Mkhatrishvili, Zakari Mumuni, and Valeriu Nalban
Ms. Kazuko Shirono, Esha Chhabra, Ms. Bidisha Das, Ms. Yingjie Fan, and Mr. Hector Carcel Villanova
The rapid uptake of mobile money in recent years has generated new data needs and growing interest in understanding its impact on broad money. This paper reviews mobile money trends using mobile money data from the Financial Access Survey (FAS) and examines the statistical treatment of mobile money under the IMF’s Monetary and Financial Statistics (MFS) framework. MFS guidance is straightforward in most cases, as many jurisdictions have adopted regulations which ensure that mobile money is captured in the banking system and thus in the calculation of broad money. However, in cases where mobile network operators (MNOs) act as niche financial intermediaries outside the banking regulatory perimeter and are allowed to invest their customer funds in sovereign securities and other permitted assets, mobile money liabilities may remain outside the banking system as well as monetary statistics. In that case, information on mobile money liabilities need to be collected directly from MNOs to account for mobile money as part of broad money.
Luc Eyraud, Irina Bunda, Jehann Jack, Rasmané Ouedraogo, Zhangrui Wang, and Torsten Wezel
Luc Eyraud, Irina Bunda, Jehann Jack, Rasmané Ouedraogo, Zhangrui Wang, and Torsten Wezel

Sub-Saharan African countries are facing an unprecedented health and economic crisis that is likely to severely hurt credit quality and raise non-performing loans from already high levels. Banks have a critical role to play not only during the crisis by providing temporarily relief to businesses and households, but also during the recovery by supporting economic activity and facilitating the structural transformations engaged by the pandemic.

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.