Trend in the Distribution of Income Between Labor and Capital in Countries with a Low Share of Labor in GDP
Bibliographic record
Abstract
The work is devoted to obtaining quantitative estimates of trends in the distribution of income between labor and capital in countries with a low share of labor in GDP. UN data was used for a set of European countries, post-Soviet countries, Israel, Canada, the USA and Turkey. The lowest labor share levels were observed in Ireland, Kyrgyzstan, Romania and Turkey. To assess trends in the share of labor in GDP on the rate of economic growth, linear econometric models of changes in the share of labor compensation in GDP by year in the period from 2012 to 2021 were built. Hungary, Ireland, Kazakhstan, Kyrgyzstan, Uzbekistan and Ukraine have seen a decline in the share of labor in GDP. In Uzbekistan, this trend is weakly expressed. 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. An increase in the share of labor in GDP is observed in countries such as Azerbaijan, Belarus, Bulgaria, Georgia, Israel, Cyprus, Latvia, Lithuania, Malta, Norway, Poland, the Russian Federation, Romania, Serbia, Slovakia, Turkmenistan and Turkey. Moreover, this trend is weakly expressed in Belarus and Turkey. There are no significant trends in the redistribution of income between labor and capital in countries such as Albania, Malta, North Macedonia, Tajikistan and Montenegro. Trends in the redistribution of income between labor and capital can be determined by institutional conditions in the country’s economy.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".