High-technology exports and economic growth: panel data analysis for selected OECD countriesHigh-technology exports and economic growth: panel data analysis for selected OECD countries
Bibliographic record
Abstract
This paper uses a panel cointegration model to analyse the long-term relationship between high-technology exports and economic growth in selected OECD countries in the period from 1989 to 2015. We used high-technology exports (current US$) as the dependent variable and the GDP growth rate, FDI (foreign direct investment), application of patents by residents, and gross capital formation % of GDP as explanatory variables. The export structure of countries is moving increasingly towards technology-intensive products such as ICT (information and communications technology), aerospace, computing and office equipment, electronics, chemical products, pharmaceuticals, and electrical machinery. The export structure has played an important role in the economic growth theories of many countries since the 1960s, as export growth has been associated with faster productivity and GDP growth. We aimed to find out the relationship between high-technology exports and the explanatory variables which we listed for 14 selected OECD countries (Canada, Denmark, Finland, France, Germany, Israel, Korea, the Netherlands, Norway, Switzerland, Sweden, Turkey, the UK, and the USA). According to our empirical results, there is a long-term relationship between high-technology exports and economic growth in selected OECD countries. The empirical results show that an improvement in patent applications and foreign direct investment play a decisive role in upgrading selected OECD countries’ hightech exports, while growth rate and investment play a negative role in enhancing these countries’ high-tech exports.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".