International differences in labor productivity: Role of capital, technological level and resource rent
Bibliographic record
Abstract
Using level accounting methodology this article examines sources of per capita GDP and labor productivity differences between Russia and developed and developing countries. It considers the role played by the following determinants in per capita GDP gap: per hour labor productivity, number of hours worked per worker and labor-population ratio. It is shown that labor productivity difference is the main reason of Russia’s lagging behind. Factors of Russia’s low labor productivity are then estimated. It is found that 33-39% of 2.5-5-times labor productivity gap (estimated for non-oil sector) between Russia and developed countries (US, Canada, Germany, Norway) is explained by lower capital-to-labor ratio and the latter 58-65% of the gap is due to lower technological level (multifactor productivity). Human capital level in Russia is almost the same as in developed countries, so it explains only 2-4% of labor productivity gap.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".