Heterogeneity of institutions and model uncertainty in the income inequality nexus
Bibliographic record
Abstract
This study revisits the drivers of income inequality with political institutions at the core. We take a multidimensional institutional approach by defining political institutions in terms of governance, political freedom, political fragmentation and political scale. We carry out an extensive empirical analysis of the role of political institutions by decomposing it into distinct elements and providing available proxies for each dimension. Considering the difficulty and the lack of consensus and clarity regarding model selection in the literature, we follow a model averaging methodology to deal with the issue of model uncertainty and model specification that impacts the role of institutions. We combine an analysis of club convergence, a clustering mechanism according to the long term income trajectories of the countries, with Bayesian Model Averaging (BMA) to determine the most important variables that affect inequality out of a large set of potential determinants for each homogeneous country clusters in terms of their development path. Our results show that drivers of income inequality do not act the same irrespective of different economic development patterns and that there is no “one size fits all” policy prescription that links political institutions 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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".