Efficacy of COVID-19 Macro-economic Policy Responses in Uganda
Bibliographic record
Abstract
COVID-19 has caused an unprecedented economic and health shock in Uganda, as has been the \ncase globally. After the World Health Organization announcement that COVID-19 was a global \npandemic, the government of Uganda undertook decisive measures to abate the spread of the \nvirus through adopting COVID-19 containment measures. Also, in anticipation of the distortionary \neffects of COVID-19 on Uganda’s economy through the external and domestic effects channels, \nthe government adopted an expansionary fiscal and monetary policy alongside financial sector \ninterventions. Fiscal policy interventions involved the following: tax relief measures; government \nOur Donor \nThis project is supported by the International Development Research Centre (IDRC). \nThe IDRC is a Canadian federal Crown corporation. It is part of Canada’s foreign \naffairs and development efforts and invests in knowledge, innovation, and solutions \nto improve the lives of people in the developing world. \n3 Efficacy of COVID-19 Macroeconomic Policy Responses in Uganda \nexpenditure through extending seed capital to vulnerable groups; strengthening health \nsystems; enhancing the supply of agriculture inputs through the use of e-vouchers; banning the \ndisconnection of users from utilities such as water and electricity; and payment of domestic \narrears, among others. Monetary policy interventions included reducing the central bank rate \n(CBR) to 7%, its lowest level since inception in 2011. Financial sector intervention involved credit \nrelief, asset quality support and liquidity support measures alongside supporting a reduction in \nmobile money charges. As such, this paper explores the macroeconomic impact of COVID-19 on \nUganda’s economy, the macroeconomic policy choices undertaken and, finally, inclusiveness and \nviability of the various macroeconomic policy choices undertaken. The study used high frequency \nmacroeconomic data to tease out the impact of COVID-19 on Uganda’s economy. Furthermore, \nthrough exploring the policy choices adopted, we also assess policy choice viability and extent \nof inclusiveness. The aforementioned policy interventions mitigated the extent of COVID-19 \ndistortions on Uganda’s economy. Indeed, although economic growth was slow at 2.9% in the \nfinancial year (FY) 2019/20, with especially the service and industrial sectors paying the highest \nprice, the supportive environment ensured that the industrial sector picked up quickly in the first \nquarter (Q1) of FY2020/21. The roll-out of public works in urban and peri-urban areas was aimed \nat hedging livelihoods against the impact of COVID-19 on households as a result of dampened \nproduction in the industrial and service sectors. While inflation remained subdued, the reduction \nin aggregate demand and trade disruptions suppressed inflationary pressure on food thereby \nundermining rural incomes and thus perpetuating rural poverty. Even then, the introduction of \nthe Emyooga fund1 \n and the rolling out of the e-voucher system to 10 additional districts in an \neffort to enhance the distribution of agricultural inputs are attempts to strengthen livelihoods in \nthe rural areas in the midst of COVID-19 headwinds. Interest rates were relatively low on account \nof expansionary monetary policy and confidence in Uganda’s financial sector. This was largely \non account of the Bank of Uganda’s interventions in the financial sector, which ensured a stable \nfinancial sector albeit with reduced profitability. The external sector was characterised by reduced \nforeign direct investment, tourism receipts and remittances. Overall, the policy interventions \nwere inclusive as fiscal policy was both sensitive to the formal and informal sectors (except \nfor households in urban, peri-urban and rural settings). Also, monetary and financial sector \ninterventions were sensitive to commercial banks, credit institutions and microfinance deposittaking institutions implying sensitivity to formal and informal businesses irrespective of size \nand location.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".