Impact of National Competitiveness on Economic Growth and Income Level – Evidence From the Selected Post-Soviet Countries
Bibliographic record
Abstract
This paper investigates the impact of national competitiveness on productivity, economic growth and income per capita in the selected post-Soviet countries between 2004 and 2018. In this paper, 2019 edition of the Global Competitiveness Index (GCI), which is composed of 12 pillars such as namely institutions, infrastructure, ICT adoption, macroeconomic stability, health, skills, product market, labour market, financial system, market size, business dynamism and innovation capability, is used as a proxy for the national competitiveness and productivity for the empirical analysis purposes. The findings reveal that: (1) the GCI is highly correlated with productivity level and the selected post-Soviet countries with higher level of national competitiveness had higher long-term economic growth and income per capita, (2) Russia and Kazakhstan more benefited from rising per capita income associated with enhanced national competitiveness (or productivity growth) compared to other selected former Soviet states, (3) among the GCI factors, ICT adoption, macroeconomic stability, market size and healthy life expectancy were major levers of productivity growth that influenced the national competitiveness, positively and significantly contributing to an increase in the income level in the selected post-Soviet countries in 2004-2018 period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".