Financing Health Care in Ghana: Are Ghanaians Willing to Pay Higher Taxes for Better Health Care? Findings from Afrobarometer
Bibliographic record
Abstract
Considering the recent struggle in the Ghanaian health sector, Ghanaian policy makers and donor agencies are confronted with rethinking how the health sector can be funded and sustained. In Ghana, where budgetary decisions are heavily contested and politically expensive, one option available to the government may be to raise taxes or user fees to allow for increased spending on public health care. Using Afrobarometer 2014/2015 round six survey data, this study examined whether Ghanaians would support or oppose paying higher taxes or user fees in order to increase government spending on public health care. In this study, Cross tabulation, correlation, and multiple linear regression analysis were performed to examine whether Ghanaians willingness to pay or not to pay higher tax is correlated with demographic factors, access to health services, perceptions of health care, government performance, and perceived official corruption. Findings from this study indicate that only (35%) of respondents support the payment of higher taxes or user fees even though many Ghanaians have difficulties in accessing better health and medical care. More importantly, findings from the correlation and a multiple linear regression analysis indicate that, Ghanaians support for or opposition to higher taxes/fees are powerfully influenced by perceptions of government’s performance and trustworthiness (President’s performance = 0.136**; MP performance = 0.130**; leaders serving their own interest = 0.085**; trust President = 0.147**; trust Parliament = 0.121**; trust tax department = 0.136**) rather than sociodemographic factors or difficulties in obtaining health care as well as going without medical care. Also, corruption in the Office of the President and among tax officials showed negative association with paying of higher taxes (corruption at the tax department, −0.021; p-value = 0.003 and corruption at the Office of the President, −0.005; p-value = 0.001). Therefore, improving popular access to information about taxes people owe and public spending, while reducing corruption and misuse of public monies, will help encourage voluntary compliance and enhance the government’s revenue generation in Ghana.
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".