The Nexus among Competitively Valued Exchange Rates, Price Level, and Growth Performance in the Turkish Economy; New Insight from the Global Value Chains
Bibliographic record
Abstract
Currently, global value chains (GVCs) are increasingly shaping the global economy, covering a growing share of international trade, GDP, and employment globally. Global trade is impacted by the emergence of GVCs in areas as diverse as commodities, electronics, and business service outsourcing, among other areas, since the countries involved in the GVCs hold some value(s) and benefit(s) from the exports of the finished product. In this study, the nexus among Competitively Valued Exchange Rates, Price level, and Growth Performance in the Turkish Economy; New insight from the GVCs is investigated using annual data from 1980 to 2020 within the framework of the ARDL bound test, Bayer and Hanck Cointegration (BHC) test, and ECM. The study results revealed that the relationship among real effective exchange rate, exports, and imports induced economic performance and external trade competitiveness particularly when directed at GVCs in both the short and long run. The study recommends that policies enhancing a 10% equilibrium convergence are required annually to competitively minimize the dependence on foreign value-added inputs by importing only world-class inputs for value addition and exports benefits in the competitive GVCs world. Furthermore, monetary policy, GVCs, and economic growth should be investigated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".