Coping with seasonality in a quarterly CGE model: COVID‐19 and U.S. agriculture<sup>*</sup>
Bibliographic record
Abstract
Most dynamic CGE models work with periods of 1 year. This limits their applicability for analysing the effects of shocks that operate over a short period or with different intensities through a year. It is relatively easy to convert an annual CGE model to shorter periodicity, for example a quarter, if we ignore seasonal differences in the pattern of economic activity, but this is not acceptable for agriculture. This paper introduces seasonal factors to the agricultural specification in a detailed quarterly CGE model of the United States. The model is then applied to analyse the effects of the COVID pandemic on U.S. farm industries. Taking account of the general features of the pandemic such as the reduction in household spending, we find that these effects are mild relative to the effects on most other industries. However, agriculture is subject to potential supply-chain disruptions. We apply our quarterly model to analyse two such possibilities: loss of labour at harvest time in Fruit & nut farms, and temporary closure of meat-processing plants. We find that these disruptions are unlikely to cause noticeable reductions in the supply of food products to U.S. households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".