Heterogeneity of institutions and model uncertainty in the income inequality nexus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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 it