Investigation of Whether People Are Willing to Pay a Premium for Living in Food Swamps: A Study of Edmonton, Canada
Bibliographic record
Abstract
Extensive studies have examined how unfavorable food environments, especially food swamps (neighborhoods with oversaturated unhealthy food sources), influence people’s dietary behaviors and health. Although excess fast-food consumption may have an adverse effect on health, it also benefits consumers due to its convenience, time saving, and affordability. Therefore, people’s preference for an unhealthy food environment is not necessarily negative. Understanding how people value or disvalue unhealthy food environments is a prerequisite for developing effective policies to promote good diet habits and improve public health. Thus, this study adopts spatial hedonic pricing models to estimate people’s willingness to pay to live in food swamps. The results show that people are willing to pay a premium to live in food swamps when taking low income and low healthy-to-unhealthy food ratios into consideration. On average, a household is willing to pay a premium of C$12,309 to reside in a food swamp neighborhood. Potential reasons for the positive willingness to pay among low-income communities and households with relatively limited access to healthy food may include the unaffordability of healthy diets, preference for better tastes, and time saved in fast-food consumption. These findings can help policymakers evaluate the effectiveness of relevant policies and develop targeted strategies to improve the local food environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".