Decoupling of functional and household income distribution by economic growth: new findings from analysing the three‐way growth‐equity nexus
Bibliographic record
Abstract
Abstract This study analyses the three‐way relationship between economic growth and the two aspects of income distribution, namely, functional income distribution (labour income share) and household income distribution (Gini coefficient). One contribution of such three‐way analysis is to reveal the ‘decoupling’ pattern of the growth‐equity nexus, namely decoupling between functional income distribution and household income distribution, as it finds that economic growth tends to increase labour income share but worsen household income inequality, and also to confirm the reverse relationship that that higher labour income shares and household income inequality lead to a higher rate of economic growth. We show that these findings co‐exist with the traditional belief in the literature about the directly reinforcing relationship between functional and household income distribution. These findings are consistent with skilled labour compensated by performance‐based higher wages, which is often associated with a skill‐biassed technological change. The study confirms the same three‐way relationship in both developed and developing countries, but with several different determinants and different trends in the key variables. Given this nuanced trade‐off between economic growth and household income equality, coupled with no such trade‐off between growth and labour income share, a sensible policy prescription may be a combination of growth‐enhancing policy of increasing pre‐tax labour income share and a separate redistribution policy to decrease disposable household income inequality, which can mitigate income inequality without harming economic growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".