Simulating policy responses to multiple economic shocks: An experiment with combined impacts of COVID‐19 and oil price crash on Kuwait
Bibliographic record
Abstract
Abstract Researchers and policy‐makers are used to measuring impacts of an economic shock. However, multiple economic shocks generate disruption that are challenging, not just analytically but also in the interpretations of results (Pagan & Robinson, European Economic Review, 145, 2022, 104120). The disruptions come through multiple channels whose impacts were trickier to measure than emanating from those of a single shock. This study develops and applies a framework to conduct simulation experiments with multiple economic shocks. Kuwaiti data were used to simulate multiple economic shocks to the economy originating from the Corona Pandemic and the collapse of oil price, which simultaneously happened during the first quarter of 2020. As an oil exporting country, Kuwait is used to dealing with recurrent changes in oil prices in the world market as a single shock. However, unlike the oil shock, COVID‐19 has many demand and supply shocks, each with separate transmission channels. The objective here is to quantify relative contributions to overall adverse effects on GDP, and then identify policy instruments required to implement a successful recovery. A recursive dynamic economy‐wide model was formulated and calibrated. The results indicate that the GDP effects range from 35% to 11% declines from the baseline scenario depending on effectiveness of policy responses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".