Willingness to Pay for Family Health Insurance: Evidence from Baglung and Kailali Districts of Nepal
Bibliographic record
Abstract
INTRODUCTION: The Government of Nepal introduced a health insurance programme in three districts in 2016. However, it seems that there has not been systematic evidence on whether the current contribution amount (CCA) needed for enrolling in health insurance (HI), is acceptable for those who are willing to enroll. This article aims to assess the respondents' willingness to pay (WTP) for HI. METHODS: A cross-sectional study was conducted with 810 randomly selected households in Baglung and Kailali districts and the data was collected using a validated schedule. The socio-demographic characteristics were considered as independent and the WTP as dependent variables respectively. Univariate, bivariate and multivariate analysis were performed. RESULTS: Of the total respondents, 74 percent expressed that they could pay nearly three times as much as the CCA. Mean differences in WTP for HI were observed in terms of districts (p<0.001), sex of the respondents (p<0.01), household headship (p<0.05), mother tongue (p<0.001), wealth status (p<0.001), presence of chronic diseases in the family(p<0.05), enrollment in HI(p<0.01), exposure to the radio/FM(p<0.05) and TV(p<0.01), and access to health facilities (p<0.01). The lieklyhood of WTP for HI were lower in Kailali than in Baglung (b= –0.178, p<0.001); with females than with males(b= –0.076, p<0.05); and with the age group £37 years than > 37 years(b= –0.090, p<0.05). CONCLUSION: The WTP for HI was nearly three times as high as the CCA for all health services if available to them. More than one fourth of the respondents did not know about HI. Therefore, appropriate interventions are needed for awareness raising which may support the WTP as well as enrollment in HI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".