Consumers’ willingness to pay for organic versus all‐natural milk – Does certification make a difference?
Bibliographic record
Abstract
Abstract While there is ample evidence on consumers’ perceptions and willingness to pay (WTP) for organic food in developed markets, empirical evidence for transition economies is scarce. This paper is based on a survey of 608 Russian consumers that combined a questionnaire with a contingent valuation approach to investigate the impact of perceptions and trust on consumers’ WTP for certified organic, uncertified all‐natural and conventional milk, respectively. A between‐subject treatment design was used to analyse how consumers’ WTP responds to different information treatments. Our results suggest that most participants connect health benefits with the consumption of organic food, followed by slightly fewer respondents connecting organic production with environmental benefits. In the case of animal welfare benefits, the picture is less clear, as only 46% of respondents indicated that they agree that organic livestock production is associated with animal welfare benefits. Concerning the trust in farmer’s adherence to organic standards, a substantially higher share of respondents expressed trust in producers from the European Union versus their Russian counterparts. About 51% of respondents exhibited a positive WTP for organic milk in comparison to conventional milk. At the same time, there is no statistically significant difference in respondents’ WTP between all‐natural and organic milk. This similarity suggests that respondents do not seem to differentiate between uncertified all‐natural milk and certified organic milk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".