What's in a Name? The Impact of Fair Trade Claims on Product Price
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Agribusinesses use credence claims reporting the sustainability of products and supply chains. One example, fair trade, relies on a diverse set of third party standards and certification organizations. Food marketing data are used to compare products launched between 1999 and 2013 in the coffee, tea, and chocolate categories. Out of 3,257 observations making a reference to fair trade, 2,745 were certified. The other items follow certain fair trade practices or support fair trade. Many products claim both fair trade and organic (congruent claim). Fairtrade Labeling Organizations – International (FLO‐I) certifiers dominate, but Fair Trade USA (breaking from FLO‐I in 2012) is important. A double hurdle hedonic regression model explores the relationship between claims and suggested retail price in the United States, Canada, and European Union over two periods (1999–2011 and 2012–2013). Two models are run, one aggregating non‐FLO‐I members and one accounting for each individual certifier. The models (first hurdle) are not able to identify factors explaining which products are certified. Results suggest (second hurdle) that after controlling for congruent claims, having a fair trade claim certified by certain third parties significantly raises the price (above an uncertified product). In particular FLO‐I certification leads to a higher price in all models in both periods. Conversely, there is a range of premia for non‐FLO‐I certifiers, not all statistically significant. Implications for stakeholders are advanced. [EconLit citations: D40, L15, L66].
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it