Economic Incentives for a Healthy Diet: A Comparison of Policies in a Canadian Context
Bibliographic record
Abstract
Abstract This paper examines the potential impact of policies to promote dietary health in the context of a tax-financed universal health care (TFUHC) system, as found in Canada and many other countries, in which a universal level of treatment is set by government policy. I construct a model in which a low quality diet raises the risk of disease, and disease in turn causes a loss of labor productivity. Low quality diets are less expensive than high quality diets, so dietary choices are worse for lower income households. The effect of disease can be partly offset by medical treatment. I show that dietary choices under TFUHC are distorted by a moral hazard problem which leads to diets that are lower in quality than is first-best. I then examine three different policy interventions to address the dietary distortion: a reduced level of treatment; risk-based premiums for health care; and a quality-based tax on food. I calibrate the model with Canadian data on type 2 diabetes and derive the net benefit and distributional impact for each policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".