Total factor productivity change in hog production and Quebec's revenue insurance program
Bibliographic record
Abstract
Abstract Quebec's hog industry is supported by a revenue insurance program that guarantees a minimum price, but it also faces strict environmental constraints. Under price volatility, risk‐averse farms may contract their output enough to produce under increasing returns. We show that the subsidy and downside risk reduction effects of the revenue insurance program tend to stimulate output and increase the likelihood of production under increasing returns. Environmental constraints that raise the cost of manure management and limit areas under cultivation also increase the likelihood of decreasing returns. Scale efficiency and technical efficiency measures are obtained through a parametric decomposition of total factor productivity (TFP) obtained from the estimation of an output distance function. As in hog studies pertaining to other countries, we found a TFP average annual growth of 5.2% between 2004 and 2012. Scale efficiency is much lower than in other countries, as per our prior about the program's distortions and environmental constraints. Integrating annual TFP gains into the setting of the minimum guaranteed price could reduce program costs by $12 million per year. About $70–80 million per year could be saved by investing in extension activities that would bring increase the level of technical efficiency of inefficient farms to the provincial average. A metatechnology frontier approach allowing for an endogenous input was also implemented to assess the robustness of the scale efficiency results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".