Межстрановой анализ отраслевой производительности труда и душевого ВВП в 1991-2008 годах
Bibliographic record
Abstract
The work presents labor productivity estimates from 1991 to 2008 for 17 countries on industry ISIC. 3 – level (calculations for the whole economy-level last till 2012 year). The group of countries includes USA, Canada, Brazil, Russia, Japan, China, Australia and number of major European econo-mies. The goal is to investigate Russia`s industries comparative progress, productivity gap changes and asses possible sources for technology borrowing. In contrast to previous works analysis captures dy-namics of wider country grouping on more detailed industry level. Productivity is calculated as value added per hour worked for the following industries: agriculture, hunting, forestry and fishing (A+B), mining, electricity, gas and water supply (C+E), manufacturing (D), construction (F), wholesale and re-tail trade, hotels and restaurants (G+H), transport (I) and others (J-P). After period of transition Russia has reached own 1991`s level of productivity (per worker) and per capita GDP in 2004 and 2005-2006 years respectively. But if we measure Russia`s productivity or per capita GDP in terms of US levels, the question of reaching 1991 level has no unequivocal answer. It depends on preferred estimation methodology of catching up development, more precisely, on way of converting productivity and per capita GDP to common currency: use general accepted constant prices and constant PPP methodology (as in ((Maddison, 1995) and OECD, BLS USA, PWT calculations), or use current (benchmark) PPPs (WB data) to asses relative (to US) performance of Russia. Reasons of faster per capita GDP growth comparing to labor productivity in Russia in 2000s are explained. In 2008 productivity (per hour) gap between Russia and US was approximately threefold, com-paring with European countries – twofold. Sectoral productivity analysis shows that such a differences in some industries are even greater: up to 4 times in agriculture and transportation comparing with US and 13.5 times in mining and energy sectors (C+E I.S.I.C.) comparing with Norway. Such productivity gaps are the evidence of technological weakness of Russian economy, but also it implies the possibility of catching up by means of technology borrowing. Perspective sources for technology borrowing are identified for Russia`s industries. To asses Russia`s gap in agricultural efficiency and estimate perspective sources for technology borrowing, more detailed comparative analysis is done using indicators such as output per worker, output per hectare of arable land, labor intensity (workers per hectare of arable land), capital intensity and others.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".