A Nonparametric and Semiparametric Analysis on Inequality and Development: Evidence from OECD and Non-OECD Countries
Bibliographic record
Abstract
This paper studies the income inequality and economic development relationship by using unbalanced panel data of OECD and non-OECD countries (regions) for the period 1962-2003. The nonparametric estimation results show that income inequality in OECD countries is almost on the backside of the inverted-U relationship, while non-OECD countries are approximately on the foreside, except that the relationship in both country groups shows an upturn at a high level of development. Development has an indirect effect on inequality through control variables, but the modes are different in the two country groups. The model specification tests show that the relationship is not necessarily captured by the conventional quadratic function. The cubic and fourth-degree polynomials, respectively, fit the OECD and non-OECD country groups best. Our finding is robust regardless of whether the specification uses control variables. Development plays a dominant role in mitigating 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".