Impact of the Farm Income Stabilization Insurance Program on Production Decisions in the Quebec Pork Industry: An Empirical and Theoretical Analysis
Bibliographic record
Abstract
The Farm Income Stabilization Insurance Program (ASRA) is an agricultural program implemented in several agricultural sectors in Quebec, including the pork sector. This article aims to empirically assess the effects of this program on production decisions in the pork industry in Quebec using a Vector Error Correction Model (VEC). As variables we used the pig supply, the price of pork, and stabilized income. The dataset contains information about the pork sector which cover the period 1981-2014. The annual average growth rate of the quantity offered in this period is 5.24%. The results suggest that the supply of pork is strongly correlated with lagged values of stabilized income. The results also show that there is only one long-term relationship between the three variables above-mentioned. By contrast, in the short term, an increase of one percentage point of the stabilized income leads to an increase of 0.80 percentage point of pork supply in the next period while an increase of one percentage point of pork price will result to a decrease of 0.47 percentage point of the production. Pork production decisions are dominated in short-term by the presence of ASRA program. This shows evidence that without the ASRA program, pork production would be less. These results confirm some of the criticisms of this program. Thus, through this article we suggest a compensation indicator which internalizes market signals in order to improve pork industry efficiency. Simulations of the compensation indicator were also performed. The adoption of this indicator as a measure of compensation for the ASRA program will generate an efficient production system, reduce the deficit of the program, and improve the competitiveness of pork industry. This indicator can be applied to other agricultural sectors covered by the ASRA program.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".