A Double-Hurdle Model of Healthcare Expenditures across Income Quintiles and Family Size: New Insights from a Household Survey
Bibliographic record
Abstract
The decision-making processes and outcomes of male and female household heads differ due to gender-based differences in preferences. In this paper, we assess the impact of this heterogeneity on household healthcare consumption in Thailand. Past studies modeling healthcare expenditures using household survey data used a gender dummy variable in regression models to control for household gender headship at the household level. Due to the endogeneity and self-selection bias in the past modelling approach, we separately modeled health expenditures for male and female household head decision makers. Using a household dataset from an earlier work, this study finds, using the double-hurdle model with dependent errors, that out-of-pocket health care spending tends to behave like a necessity across the income quintiles, household sizes, and differently for the separately modeled household gender heads. Moreover, male and female headed households responded differently to a major economic shock when adjusting household healthcare spending.
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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 | 0.001 |
| 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".