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Record W2290962505

Wage premium in Singapore.

2010· article· en· W2290962505 on OpenAlexaboutno aff
Ruo Hui. Tan, Yan Tan, Wai Meng. Lee

Bibliographic record

VenueDR-NTU (Nanyang Technological University) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsWageLabour economicsEconomicsBusiness
DOInot available

Abstract

fetched live from OpenAlex

For several years, many economic literatures have been dedicated to estimate the wage-risk premium of workers which give insights to the attitudes of workers towards occupational risk. In a broader picture, wage-risk premiums could also be used to derive the value of a statistical life, which till today is widely debated and is a highly controversial topic. However, a large part of the existing literature was focused on western countries such as the United Kingdom, United States and Canada, and a minority on Asian countries such as Taiwan and Japan. To date, there are no such studies conducted in Singapore. This presented an interesting opportunity for us to attempt to conduct such a study in a Singapore context.
\nDuring our review of the available literature, we found two predominant methods used to investigate wage-risk premium values; the revealed preference method and the contingent valuation method.
\n(i)\tThe revealed preference method relies on actual market data to obtain the market risk premium. The most commonly used market data has been in the labour market. By regressing wages with the risk of death associated with the respective occupations, the wage premium could be estimated.
\nAlthough this method offers the advantage of using observable data in actual behaviour, several articles counter argued the disadvantages of this method. Firstly, marginal safety values were found to be widely divergent. Secondly, this method failed to capture the fact that workers make decision based on their perception of the jobs’ associated risk and not the objectively measured level of risk.
\n(ii)\tThe contingent valuation method, on the other hand, uses hypothetical situations to elicit contingent values. Because this method allows us to tailor our study to specific scenarios, the workers’ preference for safety can be measured directly. Furthermore, the fact that we could obtain directly the values that we desired, we could draw direct relationships between the variables that we wish to investigate.
\nOf the contingent valuation studies that we reviewed, we found that there were several ways to carry out the survey. One approach was the use of mail surveys sent to pre-specified target groups. However, this method presented a response bias as higher educated individuals had higher tendencies to return the surveys. Another approach, which we eventually decided upon, was to perform face-to-face interviews. This method allows us to explain and clarify for any complexities involved in our survey questionnaire.
\nWe carried out our survey over 200 Singaporeans and Singapore Permanent Residents between the ages of 20 to 59 based on the demographic of Singapore. This sample size allows us to properly execute both the Ordinary Least Squares and Tobit and Probit methods to draw the relationships between the variables. We eventually used the former method as it provided us with more significant variables.
\n 
\nWe have identified several important leads that aid us in our study.
\n(i)\tThe monthly willingness to pay and willingness to accept a higher job risk are S$668.50 and S$1868.70 respectively.
\n(ii)\tThe value of statistical life of a Singaporeans and Singapore Permanent Residents was estimated to be in the range of S$18 million to S$23.4 million, which was greater than what was found in some western countries due to environmental and cultural differences.
\n(iii)\tWe also drew some relationships between certain factors and the willingness to pay and accept values. The following variables that were found to have significant impact:
\na)\tRespondents who were themselves, or have family members and close friends being severely injured, sick or killed due to job-related accidents reported higher values,
\nb)\trespondents who were covered by life and accident insurance reported higher values,
\nc)\trespondents with higher monthly income before taxes tended to report higher values and 
\nd)\trespondents who received higher education levels reported lower values.
\nThe findings of our study give interesting insights to the attitudes towards occupational risk within the Singapore context. As this is the first ever study to be conducted in Singapore, we feel that there are opportunities for further development in this topic. We also hope that our study will arouse the interest of future research efforts that would be dedicated to uncover the policy implications.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2010
Admission routes1
Has abstractyes

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