Assessing the Impact of Education Quality on Economic Growth in OECD and CESEE Countries
Bibliographic record
Abstract
The retrospective data analysis concerning the level of per capita income evidenced that formation of an educated society created the precondition for the growth of labor productivity and economic growth. According to Gelor – Weil theory, in the 19th century, in Western Europe countries, as well as in the “Western outshoots” (the USA, Canada, Australia and New Zealand), there was a change in the attitude towards family planning, which consisted in preferring fewer well–educated children over a large number of uneducated ones. This made it possible to overcome the "Malthus trap" in these countries and enabled rapid rates of economic growth. Today, the total factor productivity of the United States is taken as a benchmark/frontier against which productivity in other countries is measured. The article presents the results of assessing the impact of the quality of education, expressed by the "Skills" indicator of the Global Competitiveness Report, which characterizes the general level of skills of the labor force, as well as the quantity and quality of education in the country, on its total factor productivity. The assessment is based on the economic growth model of Ph. Agion and P. Howitt, which determines economic growth of a certain country by its’ human capital skills, as well as by the distance of such a country to the world technology frontier. The analysis presented in the article includes both OECD countries and CESEE countries, in particular Ukraine. Based on the results, it can be concluded that OECD countries, whose total factor productivities are a minimum 7 per cent above the world technology frontier, reached this – to the great extent – through better education. CESEE countries, whose total factor productivities are at least 59 per cent below the frontier (and Ukraine is among them), should improve the quality of education to get closer to the frontier. Further analysis of the factors of education quality showed that government spending on education and quality of public and private institutions play great part in improving education both in CESEE and OECD countries. The proposed approach to assessing the factors of the quality of education can be used for further assessment of the impact of COVID–19 pandemic on the quality of education, as well as to justify the directions of state policy in the field of education aimed at ensuring economic recovery in the post–pandemic period.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".