Lessons from the Russia-Ukraine war: Assessing the resilience of African economies to external shocks
Bibliographic record
Abstract
More than three years after Russia invaded Ukraine in February 2022, some of its global spillover effects continue to impact many low-income and lower-middle-income countries (LICs and LMICs). As the world faces new global shocks - such as aid cuts and rising US tariffs - this synthesis report draws lessons from Africa's experience during the Russia-Ukraine war to help navigate external shocks. The report finds that while the overall price shocks from the war at the continental level remain relatively low (around 0.2% of GDP), growth impacts vary significantly across countries depending on their exposure and level of resilience (e.g., policy space) to mitigate the impact of the shock. African countries that are heavily reliant on commodity imports, more integrated into global financial markets (e.g. with high levels of private capital flows and external debt), and already facing fiscal and debt vulnerabilities, have experienced more negative economic and social consequences. Within countries, the impacts have also varied, with women disproportionately affected in areas such as food security and access to clean energy. Policy interventions matter, but they come with trade-offs. Throughout the conflict, several African countries implemented monetary policy tightening to curb inflation, which, while necessary, led to higher borrowing costs that may have held back investment. Prioritising interest payments to avoid debt distress has often come at the expense of social spending. Additionally, many macroeconomic interventions - such as liquidity easing and cash transfers - have tended to benefit men more than women, further entrenching gender inequalities. The report offers several key policy levers to enhance Africa's resilience against future shocks, including: - Strengthening the capacity of central banks through well-managed sovereign wealth funds. - Utilising innovative debt instruments to address debt sustainability and close development finance gaps. - Integrating gender perspectives into shock recovery policies of central banks and ministries. - Fostering intra-African trade, regional industrialisation, regional funds and early warning systems. - Utilising international mechanisms for counter-cyclical financing, debt relief and blended finance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".