Trade Liberalization, Selection, and Productivity in a Supply Managed Economy
Bibliographic record
Abstract
In this paper I use farm-level data from the Quebec dairy industry to estimate the relationship between productivity and participation in the Commercial Export Milk (CEM) program (2000-2003). Under the CEM program farmers could sell milk without production quota and faced a farm price that was approximately half of the domestic price under supply management. I find a positive correlation between participation in the CEM program and farm-level total factor productivity (TFP). I then use a difference-in-difference research design with inverse propensity weights to test for causality in the relationship between participation in the CEM program and TFP. I find evidence of a positive and statistically significant effect in two of four regression specifications. A number of economists have argued that the Canadian dairy industry could benefit from trade liberalization through export market growth and returns to scale in production. My results suggest that trade liberalization would also lead to additional productivity and welfare gains from farm-level selection and the direct effects from exposure to a competitive pricing environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".