The Effects of Globalization and Foreign Direct Investment on the Economic Growth of South Africa
Bibliographic record
Abstract
Developed and developing economies use globalization and foreign direct investment (FDI) to pave the way and to maximize economic growth. This study aims to investigate the impact of globalization and FDI on the economic growth of South Africa over the period from 1998 to 2022. The study employed the autoregressive distributed lag (ARDL) approach on annual data from the World Bank and the KOF index of globalization. ARDL tests reveal a long-run positive and statistically significant relationship of 12.7% in the case of economic globalization. This indicates that there is a reasonable level of the emergence of a globalized economy to integrate new and diverse systems, within internal economic growth forces that are supporting the globalization and endogenous growth theories. Political globalization is negative and statistically significant, while social globalization is positive but is used to depress long-run economic growth because of its insignificant status. The novelty of this study is to focus on the impacts of economic, social, and political globalization and FDI on the economic growth of South Africa, through direct and interactive procedures. The findings can be used by South African policymakers and other countries to prioritize reaping the benefits of globalization. These outcomes can be used to sensitize and promote policies that can attract relevant FDI, while enhancing economic growth.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".