Trade effects, policy responses and opportunities of COVID-19 outbreak in Africa
Notice bibliographique
Résumé
Purpose The paper is a preliminary assessment of coronavirus disease’s (COVID-19) effects on African trade, policy responses and opportunities within the limitations imposed by data and the information currently available and in the lights of other international organizations’ growth forecasts. The study was undertaken to get deeper understanding of the threats and opportunities of COVID-19 on African trade because of the existing interconnected trade networks making African countries to be more vulnerable and increasing number of restrictions and distortions among major traders. This study aims to present strong information required in underpinning sound national, regional and inter-regional policy responses to keep trade flowing. Design/methodology/approach To assess COVID-19’s effects on African trade, policy responses and opportunities, this study relied on data and information currently available from organizations such as World Trade Organization (WTO), World Bank (WB), Organisation for Economic Co-operation and Development, International Monetary Fund, European Union, International Trade Statistics and various African countries’ trade and national statistics publications. The analysis contains two main scenarios. The first, an observed effects scenario (first quarter of year 2020), looks at the observed effect of COVID-19 outbreak on trade in Africa. The second, a potential effects scenario, analyses the potential trade effects if the COVID-19 outbreak lingers and spreads more intensively than is assumed in the baseline scenario. Findings The COVID-19 outbreak affects several aspects of international trade even though the full effects of the outbreak are not yet visible in most trade data. Some leading indicators had shown that keeping trade flow can support the fight against COVID-19 as well as having damaging effect on Africa’s trade. COVID-19 had led to a deep fall in transaction, both at the international level and within-regions. Tariffs and other restrictions to imports harm the flow of critical products to African countries. Uncooperative trade policies lead to higher prices of goods in fragile and vulnerable African countries. Research limitations/implications Long term in-depth analysis of the effects of COVID-19 on trade using quantitative data is still very difficult because of paucity of data and the great level of the improbability of the trajectory of the spread of the virus. Informed assessment of the full trade impact of the pandemic on African countries is therefore still difficult. Notwithstanding, this study assesses the immediate effects and conveys the likely extent of impending African trade pains and the potential needs for assistance. Practical implications Trade in both goods and services plays a key role in overcoming the pandemic and limit its effects by providing access to essential medical goods to treat those affected, ensuring access to food, providing farmers with needed inputs, support jobs and sustain economic activity during global recession. However, temporary COVID-19 trade measures such as borders closure, export prohibition and import ban are a threat to globalization and free trade agreements engaged by some African countries. Social implications The continuous rise in COVID-19 cases is expected to trigger economic recession in Africa despite a rapid expansion and creation of new social protection programmes. The unavoidable decline in trade caused by COVID-19 is already having painful consequences on the economy, social anxiety among families, households, businesses and trade across countries in the continent. COVID-19 trade restrictions aimed at reducing the transmission of the virus have led to loss of income and jobs as well as closure of small and vulnerable businesses. Policymakers should enforce social policies that unite countries within the continents in bad times to reduce social anxiety and hardship. Originality/value Although the effects of COVID-19 outbreak on global and regional trade have received enormous attention recently, facts in the form of data have been thin particularly on African trade. This paper, to the best of the authors’ knowledge, is one of the first set of studies that provides preliminary assessment of COVID-19’s effects on trade in Africa using scenarios-building approach based on the available data and information on regional trade, complemented by those from the WTO, WB and departments of trade and statistics from various African countries such as the Nigeria Nation Bureau of Statistic and Kenyan National Bureau of Statistics.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».