Social Capital and Willingness to Pay for Community Based Health Insurance: Empirical Evidence from Rural Tanzania
Bibliographic record
Abstract
This study examines the effect of social capital on willingness to pay (WTP) for health services provided through community based health insurance schemes (Community Health Fund) in Tanzania. The study covered 274 household heads. We use probit regression analysis to model the relationship between the predictors and our outcome variable. Our results have shown that with the exception of religion, all other social capital variables have a positive and significant impact on the WTP for the Community Health Fund (CHF). Specifically, membership in social organisations and networks, trust among community members and trust of community members on scheme management are positively and significantly related to WTP. On the other hand, the age, education level, household size and number of children and participation in health insurance are not predicting WTP for CHF. Taken together, these results suggest that enhancing access to health care services in the rural areas and the sustainability of CHF would require building appropriate forms of social capital at individual and community levels. Specifically, CHF may increase enrolment through the existing social organisations and associations. Similarly, CHFs may well increase their membership if the avenues for trust building are created and nurtured.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".