Sources of long run economic growth in Russia before and after the global financial crisis
Bibliographic record
Abstract
Although productivity decline in the global economy was observed before 2008, the global financial crisis of 2008 stimulated study of its source. In this context, recent literature mentions inefficient investments in machinery, human capital, and organizational processes. This can include skill mismatch and the lack of technology diffusion from advanced to emerging industries and firms. To what extent is this global view helpful in understanding recent productivity decline in the Russian economy? The present study reports that at least some of these sources can be observed in Russia as well. Using conventional industry growth accounting, it compares pre- and post-crisis sources of growth for the Russian economy. Specifically, it presents aggregate labor productivity growth as the sum of capital intensity and total factor productivity (TFP) growth in industries, and the contribution of labor reallocation between industries. It shows that the stagnation of 2008–2014 is more the result of the TFP decline and the deterioration of the allocation of labor than the lack of capital input. Moreover, the TFP decline started in Russia a few years before the crisis, as it did in major global economies, such as the United States, OECD countries, China, and Brazil. At the same time, relatively stable capital intensity made the Russian pattern to some degree similar to resource abundant Australia and Canada. Furthermore, the contribution of information and communications technology capital to labor productivity growth in Russia declined after 2008, which could have also hampered technology diffusion. Finally, the structure of the flow of capital services in Russia changed after 2008. Before the crisis, the contribution of machinery and equipment dominated, while after the crisis, construction provided the lion's share of capital input.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".