Gender wage gap in small islands: Effect of a policy framework in Mauritius
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Mauritius is classified as a high‐income small island country. However, gender inequalities are still prevalent and therefore need to be addressed. This paper analyses the attempts made by the country to improve gender parity through the adoption of a national gender policy framework (NGPF) in 2008, using difference‐in‐differences with pooled cross‐sectional data from Household Budget Surveys 2007, 2012 and 2017. The results indicate that no significant gender wage gap changes occurred in 2012 after controlling for human capital, industry, and occupation. On a longer run, the interaction of the gender and year variable was significant in 2017 only when controlling for industry and occupation. This study shows that the NGPF can help women climb the economic ladder within their occupation but does not help them increase their occupational prestige by moving to a higher‐paying occupation. More focused policies with clear action plans, for instance, those that will promote the inclusion of women in high‐level positions, will reduce the gender wage gap. Encouraging women to participate in the knowledge‐based, high‐income economy of Mauritius by overcoming the skills mismatch that pervades in sectors with fastest growth is a potential strategy for improving gender wage parity.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it