Distribution des revenus et développement : quelques faits stylisés
Bibliographic record
Abstract
In recent years, the relationship between income distribution and the process of development has come under increasing scrutinity. Much of the debate has focused on the hypothesis, originally advanced by Simon Kuznets, that the secular behavior of inequality follows an inverted U shaped pattern which inequality first increasing and then decreasing with development. This hypothesis has become so much a part of the conventional wisdom on this subject that it has generated considerable skepticism about the welfare implications of the development process. Indeed, on some interpretations, developing countries face the grim prospect not just of increasing relative inequality, but also of declining absolute incomes for the lower income groups. The object of the article is to re-examine the empirical basis for this hypothesis using a recent compilation of cross-country data made at the World Bank. The author uses multiple regression to estimate cross country relationships between inequality, as reflected in the income shares of various percentile groups, and selected explanatory variables reflecting different aspects of the development process. The results suggest that while there may be a secular time path for inequality which developing countries must traverse and which contains a phase of increasing inequality, there is at least no evidence that faster growing countries show higher inequality at the same level of development than slower growing countries.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".