Locally-Led Simulation Analyses: Covid-19 Impacts and Responses for Equity in Developing Countries
Notice bibliographique
Résumé
The COVID-19 pandemic has been a global catastrophe with radical impacts triggering policy responses worldwide.1 Literature on the topic has been unanimous on the deleterious effects of this crisis on the global and national economies, and on poverty, particularly in low-income countries (Miguel and Mobarak, 2022).With varying degrees in terms of the size and nature of packages, countries have put in place measures to mitigate some of the likely devastating impacts of the pandemic.To avoid critical waste of time and resources, simultaneous efforts needed to be invested in assessing the impacts and effectiveness of these interventions, including through the development of country-adapted analytical tools that can produce periodic updates for policy adjustments.This special issue relies on locally-led simulation analyses that produced evidence that has helped local policy-makers to guide the design, or adjustment, of effective policy responses to the COVID-19 crisis.In particular, the five contributions included in this issue developed country-level tools to simulate, on an ongoing basis, the economy-wide and household impacts of the crisis as well as existing and alternative policy responses, to identify the most effective interventions.Especially in developing countries, reliable and nationally representative data are longer to collect and may not be timely.Therefore, the availability of rigorous simulation tools can help policy-makers to respond effectively to sudden economic crises, such as that generated by COVID-19 confinement measures, even when data are not readily available.The COVID-19 crisis challenges governments through the widespread nature of its impacts and the uncertainty concerning their magnitude and duration.By analyzing the likely impacts of various policy responses, simulation models provide policy-makers with valuable evidence to comprehend and respond to these challenges effectively.These simulations could be regularly updated as new data have become available and the country has progressed through the different stages of the crisis: epidemic and lockdown, gradual re-opening and full recovery.There are at least three key considerations when designing policy responses to the COVID-19 crisis and evaluating the impact of the interventions.First is the importance of identifying the sectors (industries, firms) and households/individuals that were likely to be hardest hit by the various economic and social disruptions, and estimating the nature and magnitude of their losses.These impact pathways are complex and heterogeneous across the population.With population confinement measures and the total or partial cessation of many, formal and informal, economic sectors, many workers and family enterprises have lost their sources of income.Furthermore, remittances were significantly disrupted as the pandemic has heavily impacted host countries (Europe, North America and Persian Gulf countries) of migrants sending these remittances.Finally, as a result of the decline in domestic and global production, and the disturbance of global value-added chains, production costs and consumer prices have risen while the global petroleum price was falling.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 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 source (Gemma direct ou Codex distillé), 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 ».