Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa

Author/Editor:

Brandon Buell ; Reda Cherif ; Carissa Chen ; Hyeon ; Jiawen Tang ; Nils Wendt

Publication Date:

May 1, 2021

Electronic Access:

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary:

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.

Series:

Working Paper No. 2021/124

Frequency:

regular

English

Publication Date:

May 1, 2021

ISBN/ISSN:

9781513582498/1018-5941

Stock No:

WPIEA2021124

Pages:

61

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