Bibliographic record
Abstract
A recent body of empirical cross-country research has confirmed that income equality is positively related to economic growth. This paper provides an explanatory channel for this observed relationship. The novelty of its approach consists in the use of demographic channels to account for cross-country differentials in economic growth and income distribution. The paper builds upon three empirical regularities that have emerged in the recent growth literature. The first, is that when one controls for such factors as initial level of GDP per capita and education, income inequality is negatively related to long run growth. Second, income distribution is affected by age structure, with a younger working age population positively related to income inequality. Finally, age structure also plays upon the level of economic growth independent of its role through income distribution. In this paper we argue that these associations cannot be confirmed solely via the use of cross-country growth regressions. In order to determine the direction of causation one has to formalise the economic mechanisms that account for the empirical results. In our overview of the theory we analyse four models that have emerged as the most plausible transmission mechanisms linking inequality to slower growth. In each instance we demonstrate how a consideration of demographic age structure can compliment the four mainstream accounts
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".