The nexus of FDI, trade, and institutional quality: a panel data analysis of RCEP countries
Bibliographic record
Abstract
Studies have shown that the quality of institutions or public governance significantly impacts economic growth. However, the literature on international political economy continues to debate the factors that determine institutional quality and the effects of institutional quality on economic development. There is a dearth of evidence on how international political economy - such as trade, investment, and foreign aid - influences economic and political institutional change. This study examines the relationship between global trade, investment, and public governance using data collected from the 15 Regional Comprehensive Economic Partnership (RCEP) economies from 2014 to 2023. Using a dynamic panel data approach based on the Two-Step System-GMM estimator, findings show that trade openness improves the quality of economic institutions by encouraging regulatory reforms, transparency, and competitiveness. FDI produces mixed results: it boosts economic institutions in the short term but can weaken them when linked to rent-seeking and weak law enforcement environments. However, both FDI and trade have less significant or negative impacts on political institutions, emphasizing uneven democratic accountability and elite capture in several RCEP countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".