Locally-Led Simulation Analyses: Covid-19 Impacts and Responses for Equity in Developing Countries
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".