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UGANDA
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.
Despite strong economic growth since 2000, many low-income countries (LICs) still face numerous macroeconomic challenges, even prior to the COVID-19 pandemic. Despite the deceleration in real GDP growth during the 2008 global financial crisis, LICs on average saw 4.5 percent of real GDP growth during 2000 to 2014, making progress in economic convergence toward higher-income countries. However, the commodity price collapse in 2014–15 hit many commodity-exporting LICs and highlighted their vulnerabilities due to the limited extent of economic diversification. Furthermore, LICs are currently facing a crisis like no other—COVID-19, which requires careful policymaking to save lives and livelihoods in LICs, informed by policy debate and thoughtful research tailored to the COVID-19 situation. There are also other challenges beyond COVID-19, such as climate change, high levels of public debt burdens, and persistent structural issues.