What's in a Name? The Impact of Fair Trade Claims on Product Price
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].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".