Межстрановые различия в душевых ВВП и производительности труда: роль капитала, уровня технологий и природной ренты
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. Analysis concentrates on the assessment of role of the following determinants in per capita GDP gap: per hour labor productivity, number of hours worked per worker and labor-population ratio. The task of quantitative assessment of the role of such factors as human capital, capital-labor ratio and technological level (multifactor productivity) in Russia-to-developed-countries labor productivity gap is solved for the first time in literature. \nIt is shown that labor productivity difference is the main reason of Russia`s lagging behind. Next, it is found that 41-49% of 3-time labor productivity gap between Russia and developed countries (US, Canada, Germany) is explained by lower capital-to-labor ratio and the latter 47-57% of gap is due to lower technological level (multifactor productivity, MFP). Human capital level in Russia is almost the same as in developed countries, so it explains only 2-5% of labor productivity gap. \nExclusion of resource rent from GDP leads to more pessimistic estimates of Russian productivity: labor productivity drops from 35% to 27% to US level, while technological level (MFP) drops from 55% to 43% to US level in 2011 year. \nMethodological developments in data used (such as data on hours worked, human capital, resource rent and current PPPs) result in more precise estimates of Russian labor productivity and technological 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".