Attaining economic growth through financial development and foreign direct investment
Bibliographic record
Abstract
Purpose The purpose of this paper is to consider the heterogeneous relationship among financial development, foreign direct investment (FDI) and economic growth, examining the possible directions of causality among them in both the short and long runs. Design/methodology/approach A sample of the G-20 countries over the period 1970–2016 is utilized. A vector error-correction model is used to consider the possible directions of causality among financial development, FDI and economic growth. Findings Results suggest a cointegrating relationship among the three series. Although short-run links among the variables are mostly non-uniform, both financial development and FDI matter in the determination of long-run economic growth. Practical implications Attention must be paid to policies that promote financial development. This, in turn, calls for fostering incentives to guarantee continued support to liberalize the economy and promoting capital openness. Additionally, financial infrastructure should be improved to improve financial innovation. The establishment of a well-developed financial market, including well-functioning banks and other financial institutions, can facilitate further investment and an easier means of raising capital to support the activities of FDI. Economic growth can ultimately be elevated through both financial development and FDI. Originality/value The study considers a sample of the G-20 countries, which have received relatively little attention in the existing literature. In addition, the study concurrently analyses the trivariate causal relationship among financial development, FDI and economic growth, a topic on which there has been a dearth of research.
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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 | 0.002 | 0.003 |
| 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".