Decoupling of functional and household income distribution by economic growth: new findings from analysing the three‐way growth‐equity 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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