Quality Choice and Market Access: Evidence from Chilean Wine Grape Production
Bibliographic record
Abstract
ABSTRACT This paper examines the determinants of becoming a producer of high‐quality wine grapes. We explore the case of wine‐grape farmers in Chile where we observe a bifurcation of farmer types.“Quasi‐subsistence” farmers produce traditional wine‐grape varieties and complement their subsistence income with cash coming from wine‐grape sale. On the other hand, we observe “entrepreneurial” farmers who produce classic varieties that have the potential to produce high‐quality wines. We study this bifurcation empirically using primary data collected during the 2011–2012 growing season. We find that wealth and cultivation ability provide economically and statistically significant explanatory power, but that buyer characteristics also matter. Our results suggest that a farmer's entry into the supply chain for high‐quality production is not an individual's choice. Rather it is a joint decision that cannot be fully understood without considering the objectives, incentives, and information of supplier and buyer. [EconLit citations: O14; Q140; L26].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".