Farm Gate Prices for Non-Varietal Wine in Argentina: A Multilevel Comparison of the Prices Paid by Cooperatives and Investor-Oriented Firms
Bibliographic record
Abstract
Abstract In this paper, we use a unique data set of the prices paid to farmers in Argentina for grapes to examine the prices paid by non-varietal wine processing cooperatives and investor-oriented firms (IOFs). Motivated by contrasting theoretical predictions of cooperative price effects generated by the yardstick of competition and property rights theories, we apply a multilevel regression model to identify price differences at the transaction level and the departmental level. On average, farmers selling to cooperatives receive a 3.4 % lower price than farmers selling to IOFs. However, we find cooperatives pay approximately 2.4 % more in departments where cooperatives have larger market shares. We suggest that the inability of cooperatives to pay a price equal to or greater than the one paid by IOFs can be explained by the market structure for non-varietal wine in Argentina. Specifically, there is evidence that cooperative members differ from other farmers in terms of size, assets and the cost of accessing the market. We conclude that the analysis of cooperative pricing cannot solely focus on the price differential between cooperatives and IOFs, but instead must consider other factors that are important to the members.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".