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Record W2741396535 · doi:10.59588/2243-786x.1257

Togolese Informal Sector Workers’ Willingness to Pay for Access to Social Protection

2017· article· en· W2741396535 on OpenAlexfundno aff
Esso-hanam Atake, Akoété Ega Agbodji

Bibliographic record

VenueDLSU Business & Economics Review · 2017
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsWillingness to payBusinessSocial protectionPublic economicsLabour economicsEconomicsEconomic growthMicroeconomics

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.220
GPT teacher head0.460
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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