Does Health Insurance Make You Fat?
Bibliographic record
Abstract
The prevalence of obesity has been rising dramatically in the U.S., leading to poor health and rising health care expenditures.The role of policy in addressing rising rates of obesity, however, is controversial.Policy recommendations for interventions intended to influence body weight decisions often assume the obesity creates negative externalities for the non-obese.We build on earlier work demonstrating that this argument depends on two important assumptions: 1) that the obese do not pay for their higher medical expenditures through differential payments for health care and health insurance, and 2) that body weight decisions are responsive to the incidence of medical care costs associated with obesity.In this paper, we test the latter proposition -that body weight is influenced by insurance coverage -using two approaches.First, we use data from the Rand Health Insurance Experiment, in which people were randomly assigned to varying levels of health insurance, to examine the effect of generosity of insurance coverage on body weight along the intensive coverage margin.Second, we use instrumental variables methods to estimate the effect of type of insurance coverage (private, public and none) on body weight along the extensive margin.We explicitly address the discrete nature of the endogenous indicator of health insurance coverage by estimating a nonlinear instrumental variables model.We find weak evidence that more generous insurance coverage increases body mass index.We find stronger evidence that being insured increases body mass index and obesity.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".