Assessing the macroeconomic impact of COVID-19 on the Canadian agri-food industry
Bibliographic record
Abstract
The COVID-19 pandemic caused significant changes to Canadians’ income, preferences, and purchasing behaviour, thus impacting the demand for goods and services. Industries deemed non-essential and providing consumer-facing services were severely hit, such as the foodservices sector, in which industrial GDP was down 28% from 2019. Meanwhile, sales in food retail stores increased by 10% as demand shifted from food consumed away from home to food consumed at home. Important disruptions occurred in agricultural production sectors, such as in the meat processing industry where outbreaks of coronavirus caused important economic consequences along the supply chain. This research aims at identifying the agri-food sectors that were affected by the pandemic and estimating these effects on the economy to better understand the repercussions on the Canadian food system. To do so, an interregional rectangle input-output model is used to estimate the effects from three scenarios simulating the impact of COVID-19 on food demand. The scenarios are based on three main drivers for food demand changes in 2020: higher sales in food retail stores; lower sales in the foodservice industry; and mixed effects on exports. Results suggest that the overall net impact, direct plus indirect effect, of the pandemic and policies put it place to contain the spread of the virus are estimated to be an industrial sector output reduction of $14.8 billion, a GDP reduction of $7.5 billion, and a loss of 187 thousand jobs. Moreover, the effects were uneven across industrial sectors. For example, the dairy sector and most food processing industries are estimated to be affected by increases in production, while the foodservice sector suffers important economic losses. The regional distribution of the effect is also uneven. The model estimates important reduction of industrial output in most provinces, with the exception of Saskatchewan in which results show an increase in industrial output. Issues not taken into consideration in the model are discussed. Results provide a starting point to assess the effects of the pandemic on the agri-food industry and their sectorial and regional distribution, and contribute to target policy intervention where it is most needed
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".