Do consumers care about clean labels? Willingness to pay for simple ingredient lists and front‐of‐package labels on beef and plant‐based burgers
Bibliographic record
Abstract
Abstract We use an online hypothetical discrete choice experiment to examine willingness to pay for two dimensions of a clean label: simple ingredient lists and front‐of‐package labels. Experimental subjects were asked to choose between beef burgers, plant‐based burgers, and hybrid burgers made with beef and plant protein. The burgers had either a simple or complex ingredient list and could also be labeled as organic or an excellent source of protein. Subjects were divided into two treatments: a treatment in which ingredient lists were always visible, and a treatment in which the ingredient lists were only visible if subjects clicked on the product image (click treatment). Subjects were willing to pay a premium of $4.55–$5.58 for products with simple ingredient lists in the visible ingredient treatment (relative to base prices of $5.00 to $12.50). This premium was reduced to $1.82–$2.29 in the click treatment. Willingness to pay for the organic and excellent source of protein labels was considerably lower and was generally insignificant, ranging from ‐$0.17 (and statistically insignificant) to $0.73. Willingness to pay for simple ingredient lists and front‐of‐package labels were not correlated, suggesting that demand for these attributes does not stem from an underlying preference for clean labels.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".