Extension of mandatory health insurance to informal sector workers in Togo
Bibliographic record
Abstract
BACKGROUND: About 90.4% of Togolese workers operate in the informal sector and account for between 20 and 30% of Togo's Gross Domestic Product. Despite their importance in the Togolese economy, informal sector workers (ISW) do not have a health insurance scheme. This paper aims to estimate the willingness-to-pay (WTP) of ISW in order to have access to Mandatory Health Insurance (MHI), and to analyze the main determinants of WTP. METHODS: This study used data from the Community-Based Monitoring System (CBMS) project implemented in 2015 by the Partnership for Economic Policy (PEP). It focusses on 4,296 ISW (2,374 in urban areas and 1,922 in rural areas, respectively). The contingent valuation method was used to determine the WTP for the MHI while the Tobit model is used to analyze its determinants. RESULTS AND DISCUSSION: Findings indicate that about 92% of ISW agreed to have access to MHI, like for formal sector workers. Overall, ISW are willing to pay 2,569 FCFA (USD 4.7) per month. ISW in the poorest quintiles are willing to allocate a higher proportion of their income (15%) to the premium than the richest quintiles (2.5%). Generally, women are more interested in MHI than men, although men are willing to pay higher premiums (3,168.9 FCFA or USD 5.8) than women (2,077 FCFA or USD 3.8). Women's lower WTP can be explained by their low levels of education and income, and a lack of employment opportunities compared to men. The gender of the head of the household, the size of the household and the education and income levels are the main determinants of WTP. CONCLUSION: We conclude that it is possible to extend MHI to ISW as long as their premiums are subsidized. The annual subsidy is estimated at 4.1% of the state current general budget or 96% of the health sector budget. In setting the premium, policy makers should take into account the MHI benefits package, subsidies from the government, and information about the WTP. It is important to emphasize that resource mobilization and management, as well as health services delivery, would be effective only in a context of improved governance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".