Influence of Underutilization of Production Capacities on the Dynamics of Russian GDP: An Assessment on the Basis of Production Functions
Bibliographic record
Abstract
Sustainable development of the state implies a proportional change in the key macroeconomic indicators described by standard models, one of which is the exponential production function (a special case of the Cobb-Douglas function), where the number of employees (labor) and the value of fixed assets (capital) acts as factor inputs, and GDP becomes the output, and output elasticities of production factors are estimated. This function is successfully used to analyze and predict macroeconomic processes in both developed and developing economies. The purpose of the study is to use econometric modeling—applying the exponential Cobb-Douglas production function—to identify the presence or absence of dependencies of production factors (labor, capital, etc.) on the final product output in Russia. The study shows that in Russia, GDP does not significantly depend on fixed assets. The authors hypothesized that this discrepancy is due to underutilization of production capacities and proved that to reveal the real dependence of Russian GDP production on the value of fixed assets it is necessary to adjust the indicators published by Rosstat for utilization of production capacities, which made it possible to fully use the methodological capabilities of production functions for the analysis and forecasting of macroeconomic processes in Russia.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".