Consumer willingness to pay for domestic ‘fair trade’: Evidence from the United States. Renewable Agriculture and Food Systems
Bibliographic record
Abstract
The success of fair trade labels for food products imported from the Global South has attracted interest from producers and activists in the Global North. Efforts are under way to develop domestic versions of fair trade in regions that include the United States, Canada and the United Kingdom. Fair trade, which is based on price premiums to support agricultural producers and workers in the Global South, has enjoyed tremendous sales growth in the past decade. Will consumers also pay a price premium to improve the conditions of those engaged in agriculture closer to home? To address this question, consumer willingness to pay for food embodying a living wage and safe working conditions for farmworkers was assessed with a national survey in the United States. The question format was a discrete choice (yes/no) response to one of four randomly selected price premiums, as applied to a hypothetical example of a pint of strawberries. Multilevel regression models indicated that respondents were willing to pay a median of 68 % more for these criteria, with frequent organic consumers and those who consider the environment when making purchases most willing to pay higher amounts. Although the results should be interpreted with caution, given the well-known gap between expressed attitudes and actual behaviors, we conclude that there is a strong potential market opportunity for domestic fair trade. Key words: fair trade, domestic, willingness to pay, consumers, ecolabels
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".