Togolese Informal Sector Workers’ Willingness to Pay for Access to Social Protection
Bibliographic record
Abstract
In Togo, the informal sector accounts for 84% of the workforce with an annual growth rate of 5%. Despite the importance of the informal sector workers in the Togolese productive activity, they do not benefit from social protection. To address this situation, Act No. 2011-006 was adopted by the Togolese National Assembly in 2011 to provide social security to informal sector workers. However, this was not applied, which means they are still not covered by social protection. This paper seeks to estimate the willingness-to-pay (WTP) of informal workers to have access to social protection services offered by National Social Security Fund (CNSS) and to analyse determinants of WTP. Data was obtained from a cross-sectional representative households’ survey involving 7,346 households in rural and urban CBMS sites in Togo. We used contingent valuation (CV) method in order to estimate the WTP. A logistic regression was used to analyse determinants of WTP. The results indicate that 84.5% of jobs in the areas studied were informal. It reveals that a significant proportion of women were engaged in informal employment wherein 88.7% were in urban areas and 94.2% were in rural areas. Also, it was interesting to note that 90.9% of informal sector workers were willing to subscribe to social protection services. Though many were willing, about 49.8% mentioned that they were only interested if the fee is below USD 2.55 per month. Moreover, it was observed that men were willing to pay for higher contribution than women. Further, more than half of the informal sector workers were interested to have occupational disease insurance while 81.9% were interested in accident work insurance. Meanwhile, a logit regression was used to estimate the relationship between the individual’s WTP and the explanatory variables, which include income, household size, age, education, gender, location, and health status. Overall, the results indicate that income and education were the key determinants of households’ WTP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".