Declining labor-labor exchange rates as a cause of inequality growth
Bibliographic record
Abstract
The current trends in the capital/labor split and the impacts thereof on the growth of inequality are one of the main concerns of national governments, European Commission and international organizations like UN, ILO, IMF, OECD and WB. These trends are usually studied at the macro level of functional distribution of income, that is, among capital and labor, and less with regard to productivity, remuneration policies or some other particular factors. In this paper, we contribute to the studies of the second type, explaining the decreasing labor income share in terms of unpaid working time and underpaid hourly earnings. For this purpose, we refer to the decreasing labor–labor exchange rate, i.e. devaluation of one’s labor in exchange for other’s labor embodied in the commodities affordable for one’s earnings. We show that the productivity growth allows employers to compensate workers with always a lower labor equivalent, i.e. increasingly underpay works, maintaining however an impression of fair pay due to an increasing purchasing power of earnings. This conclusion is based on the OECD 1990–2014 data for G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and Denmark (known for the world least inequality). Then statistically significant implications for the growth of inequality are derived and some policy suggestions are formulated like taxing the enterprises with the inner Gini that surpasses the national level.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".