Trends in the Distribution of Income Between Labor and Capital in Countries with a High Share of Labor in GDP
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
The work provides quantitative estimates of trends in the distribution of income between labor and capital in countries in which the share of labor in GDP exceeds the average level. The work used UN data for a set of European countries, postSoviet countries, the USA, Canada and Israel. To assess trends, linear econometric models were built depending on the share of the labor force in the GFP for the period 2012–2021. The highest level of labor share was observed in Belgium, Iceland, the Netherlands and Switzerland. The study found that Belgium, Bosnia and Herzegovina, Denmark, Spain, the Netherlands, Portugal, Finland and France have seen a decline in labor’s share of GDP. In the Netherlands and Portugal this trend is weak. Germany, Greece, Iceland, Luxembourg, the Czech Republic, Switzerland and Estonia have seen an increase in the labor share of GDP. In Germany and Switzerland this trend is weakly expressed. There are no significant trends in the redistribution of income between labor and capital in countries such as Austria, Armenia, Italy, Canada, Slovenia, the United Kingdom, the United States of America, Croatia and Sweden. Trends in the redistribution of income between labor and capital can be determined by institutional conditions in the country’s economy.
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 | 0.003 | 0.000 |
| 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 | 0.000 |
| 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