Financial depth and the trade openness-economic growth nexus
Bibliographic record
Abstract
Purpose The purpose of this paper is to shed light on the age-old trade-and-economic-growth controversy. The authors do so by utilizing the data relating to the G-20 countries between 1988 and 2013. Design/methodology/approach The authors seek to establish the formal statistical links between openness to trade and economic growth in the context of interactions with financial depth, gross capital formation, and foreign direct investment. The authors use a panel vector autoregressive model to obtain the estimates. The authors check for the robustness of the results. Findings The authors find that all the variables are cointegrated. That is, there is a long-run equilibrium relationship between the variables. Moreover, trade openness, financial depth, gross capital formation, and foreign direct investment are all causative factors for the economic growth of the G-20 countries in the long run. At the same time, the short-run results demonstrate that there is a myriad of causal links between these variables. Practical implications The decision makers in the G-20 countries wishing to encourage economic growth in the long run should pay close attention to trade openness, financial depth, gross capital formation, and foreign direct investment inflows to their countries. Originality/value The authors study an important group of countries over a long span of time, using advanced panel data techniques. The results demonstrate that future studies on economic growth that do not simultaneously consider trade openness, financial depth, foreign direct investment, and gross capital formation will offer biased or misguided results.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".