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Africa > Uganda

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Cover IMF Staff Country Reports
Nigeria: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper presents stylized facts about food insecurity in Nigeria, investigates its drivers in a cross-country setting, and assesses the role of policies. The paper describes regional aspects of Nigeria’s food insecurity and compares the impact of coronavirus disease 2019 and the war in Ukraine on food security in Nigeria and other countries. It also provides an overview of agricultural production and consumption in Nigeria. The paper investigates the drivers of food security using an empirical cross-country framework including demand, supply, and price factors, and offers thoughts on policies to improve agricultural yields and production. The important role of inputs is evident in the policy experience of comparator countries. Nigeria has achieved a substantial increase in agricultural production associated with its policies but some have been less successful. Import dependency for key staples has not fallen and the cost of these agricultural products remains driven by international prices. Further, central bank credit to the agricultural sector has not succeeded in increasing production beyond the stimulus of high rainfall and high food prices.
Keywords:
EIU FOOD INSECURITY index; World wheat price inflation; tax revenue mobilization; food supply; fuel subsidy reform; food price inflation; Mobile banking; Financial inclusion; Food security; Excises; Global; 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