Consumer Perspectives on Fairtrade Prices
Bibliographic record
Abstract
Worldwide consumer support of disadvantaged producers from developing countries has been encouraged through a variety of options, including the acquisition of Fairtrade-certified products. Prior studies showed that consumers’ purchases of Fairtrade products are driven by moral incentives and economic factors. Among the economic factors, only cursory research attention has been paid to a key aspect influencing purchases: the specific price of the Fairtrade item as compared to the price of non-Fairtrade items in the same product category. The price difference between Fairtrade and non-Fairtrade items can range anywhere from 0% to 70% or higher. This aspect is becoming ever more important in light of recent calls toward setting a price premium level of 100%, up to 200% in some categories, to reflect the changes in producers’ working conditions and living standards triggered by the global pandemic. Our studies suggest that such increases may not have the desired outcome. We hypothesize a negative relationship between the price premium and consumers’ willingness to pay (WTP) for Fairtrade items, testing this proposition in six cross-cultural studies. Our studies involve surveys administered to cross-sectional samples of consumers from the United States, from Canada, and samples of students from large North American universities. The surveys assessed consumers’ willingness to buy Fairtrade products using 7-point Likert scales (1 = “very unlikely to choose the Fairtrade product”; 7 = “very likely to choose the Fairtrade product” at the specified price premium level), for various product categories. Linear mixed-effects models for repeated measures (within-participants data) were employed for the key analyses. The significant finding of a negative relationship between the price premium and WTP supports our Hypothesis in each study, underscoring that as the Fairtrade premium is set at increasing levels (from 0% to 2%, 10%, and 25% higher price for the Fairtrade items), consumers’ average willingness to pay is significantly diminished, decreasing from 6.43 (MWTPat0%premium) to 5.56 (MWTPat2%premium) to 4.19 (MWTPat10%premium) to 2.95 (MWTPat25%premium) in Study 1; from 6.15 (MWTPat0%premium) to 5.37 (MWTPat2%premium) to 4.08 (MWTPat10%premium) to 2.98 (MWTPat25%premium) in Study 2; from 6.16 (MWTPat0%premium) to 5.34 (MWTPat2%premium) to 3.89 (MWTPat10%premium) to 2.27 (MWTPat25%premium) in Study 3, and from 6.39 (MWTPat0%premium) to 5.62 (MWTPat2%premium) to 4.45 (MWTPat10%premium) to 3.22 (MWTPat25%premium) in Study 4. These findings highlight actionable policy implications of marketing Fairtrade products to consumers, with a focus on the price component of the marketing mix. From a theoretical perspective, our research sheds light on the less-than-straightforward consequence of Fairtrade premium increases: we propose and show that the economic model is more applicable than the contributions/donations model in the context of Fairtrade. We emphasize that Fairtrade should not be regarded conceptually as just a special case of social contributions, because general helping theory is not able to account for the particularities of Fairtrade. Also, the impact of the price premium level on Fairtrade purchases is shown to have a different direction compared to the field of cause-related marketing purchases, revealing notable conceptual and empirical distinctions between these fields.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".