Global demand and supply elasticities and the impact of tariff shocks
Bibliographic record
Abstract
This study applies the Quadratic Almost Ideal Demand System (QUAIDS) to the Asian Development Bank's Multi-Region Input-Output (MRIO) dataset to estimate global demand and supply elasticities across intermediate vs. final, and domestic vs. foreign sectors. Using pooled data from 2021-2023, results show that supply is generally less responsive to income changes than demand, but more reactive to price changes, particularly for intermediate goods. Over time, demand for foreign intermediate and final goods has outpaced supply, reflecting a growing dependence on foreign inputs with low substitutability. At a more detailed sectoral level, demand elasticities exhibit stronger income and substitution effects, especially in Household final demand and intermediate Services, while supply elasticities are predominantly price-driven, with greater responsiveness in sectors such as Construction, Manufacturing, and Agriculture. These elasticities are then used to simulate welfare impacts of ongoing trade tensions between the USA and the rest of the world, using the latest bilateral tariff data. Findings indicate a global welfare loss of approximately -1.3%, with some countries, particularly those highly dependent on US imports with limited substitution options, face losses up to 5.6%. Counter-tariffs also adversely affect sanctioning countries; for example, Canada could experience revenue losses of up to 5%, while others see welfare losses ranging from 0.5-1.8%. Despite retaliatory tariffs, the USA faces minimal welfare losses. This framework presented in this paper showcases how monitoring elasticities can support with adapting policies to potential trade-related price shocks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".