Bibliographic record
Abstract
Providing reliable inference on inequality measures is an enduring challenge, mainly due to the complications arising from nonlinearities in their definitions and from the complex nature of the underlying distributions which are typically characterized by extremely heavy tails.The thesis is concerned with proposing non-standard asymptotic and simulation-based inference procedures for moment-based inequality measures (general entropy family of inequality indices) and quantile-based measures (quantile ratio index).Inference on both types of measures is prone to heavy-tailed distributions complications and to the ratio-induced identification issues.In addition to that, moment-based measures are subject to the so-called Bahadur-Savage impossibility problem while quantile-based measures are not.On the other hand, the main difficulty with inference on quantile-based measures is the dependence of the quantile variance on the underlying density function which involves kernel estimation and bandwidth selection.The first chapter of my thesis introduces a Fieller-type method for the Theil Index and assess its finite-sample properties by a Monte Carlo simulation study.The fact that almost all inequality indices can be written as a ratio of functions of moments Flachaire (Aix-Marseille University) for their guidance with the first two chapters.Working with them has helped me sharpen my research skills and enrich my knowledge of econometrics and income inequality.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".