Unraveling Spillovers on Health Insurance: The Impacts of Culture and Fraud on Health Insurance Coverage
Bibliographic record
Abstract
ABSTRACT This paper adds to the literature on the determinants of health insurance by focusing especially on the spillovers from culture and fraud, along with a set of “standard” determinants. The social aspects of culture and fraud could potentially increase or decrease the propensities of individuals to purchase health insurance, and our empirical analysis informs us in this regard. For this purpose, we employ data for the year 2017 (or the closest year available) across states in the United States for most variables in the model setup. To account for year‐to‐year variability in the size of the uninsured population and the fraud data, mean annual averages over the years 2017–2022 are used. Employing OLS estimation to cross‐sectional data, the results show that cultural tightness (denoting social/cultural cohesion, measured via an index) lowers the propensities to acquire health insurance, and greater fraud (i.e., fraud reports in a US state) also undermines health insurance coverage, albeit with relatively less statistical support. The impact of higher insurance premia depressing insurance coverage is found to be consistent with intuition. The scope of the government, via Medicaid expansion to provide health coverage to certain population groups, was relatively more effective in increasing insurance coverage than the sheer size of the government (i.e., total government spending). The proximity of a state to foreign borders (Canada and Mexico) did not matter. Other things being the same, states with larger land areas faced special challenges in providing health insurance coverage. The findings have importance for the formulation of policies in the public and private sectors, and especially flesh out the crucial, and largely neglected, influence of culture on health insurance purchases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".