Consumers’ Willingness-to-pay for Health-enhancing Attributes in Food Products: A Meta-analysis
Bibliographic record
Abstract
Assessing potential demand for functional or healthy foods is crucial from several perspectives. First, foods with functional attributes in many cases require more expensive production process than traditional foods, for example, when the functional attribute is provided by enhancing or enriching the products with additional substances. It is necessary, then, to estimate potential demand for functional foods prior to the delivering product to the consumers. Second, the promotion of healthier food options is related to the fact that an unhealthy diet is among the four main behavioral risk factors of non-communicable diseases (NCD) and are mostly spread in low- and middle-income countries. It has been demonstrated that prices can be a barrier for healthy food consumption, especially among low-income groups of the population. From this perspective, it needs to be clearly stated if consumers indeed are ready to pay price premiums for foods aimed at improving their health. Third, market introduction of functional foods and foods with health benefits has not always been successful. Despite the importance of a healthy diet in the prevention of some diseases and sustaining well-being in general, economists and marketing researchers observe some uncertainty in consumers’ perception and acceptance of foods with health benefits. This paper investigates the body of research that has been performed so far on consumers’ valuations of healthy attributes in food products by means of a meta-analysis. It explores if variation in willingness to pay (WTP) for healthy attributes in foods that have been reported in scientific papers on the topic can be attributed to common factors related to the choice of the methodology, the place and time of data collection, the choice of the carrier product and the health benefit specified. Thus, our study contributes to the existing literature on health-enhancing foods by (i) reviewing the existing empirical evidence on consumer valuations of different healthy attributes, (ii) identifying the major underlying drivers of differences in WTP estimates via meta-analysis and (iii) deriving directions of research to be taken into account for the future developments in the field. Literature search resulted in 28 studies which provided 175 WTP estimates. Stata meta-regression command specifically designed for meta-analyses was employed. This command allows analyzing study-level data and estimates the between-study variance and the coefficients by weighted least squares when the outcome variable is continuous. The results of the meta-regression imply that the elicitation method, the carrier product, the specific health benefit, and the place of the study significantly influence variations in WTP estimates across studies. First, hypothetical methods of willingness to pay elicitation produce higher valuations compared to non-hypothetical methods like experimental auction and real purchase data. Second, with respect to the base product the results indicate that in case of dairy products (milk, yogurt, cream cheese, cheese, butter and ice cream) and fruits and vegetables the WTP estimates for a specific health attribute are significantly lower than for all other product categories included. Third, according to our results the specific health attribute “Cholesterol lowering” leads to significantly higher WTP estimates than any other health/nutrition claim. The valuations of this attribute varied from 0% to 200% with the highest values referring to the spread for lowering cholesterol. Finally, the place where the data was collected influences WTP estimates. Our results indicate that there are no significant differences between studies conducted in Europe, the United States and other regions. However, studies conducted with Canadian consumers report statistically significant lower WTP values. In general, it can be noticed that despite an established connection between diet and the development of non-communicable diseases, economics and marketing research so far fails to provide systematic view on the consumer valuations of different healthy attributes in food and, consequently, on the perspective demand for these products. Studies reviewed reported very different valuations of healthy attributes in foods. Studies also differ greatly in basically all parameters of the research: data collection, methodology, and analysis of the results. Although it seems rather difficult to draw general conclusions about consumers’ willingness to pay for healthy attributes in foods, this research summarizes the efforts performed so far and may be employed to determine the directions for future analysis.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.064 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".