Bibliographic record
Abstract
Abstract Building on the insights from a seminal article by Kuznets (1955), this chapter examines the relationship between structural transformation and income inequality in the case of Mauritius. In addressing Mauritius’s structural transformation–inequality nexus, the authors of this chapter are particularly interested in examining two structural transitions: (1) from agriculture to industry and (2) from industry to services. The autoregressive distributed lag estimator is used to establish the long-run relationship between structural transformation and income inequality. The authors estimate a variety of models and consider various underlying drivers of inequality. Their results, in general, confirm the Kuznets hypothesis in that inequality follows an inverted U-shaped pattern. However, the effect of structural transformation on inequality seems to be more applicable for industry-driven structural transformation than for agriculture and services-driven structural transformations. As far as other key underlying variables are concerned, the labour share of GDP and government investments in human capital are found to be favourable in reducing income inequality. It follows therefore that policymakers should be mindful of sectoral differences and their underlying influences to improve income inequality and sustain long-term growth.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".