Causes of Restrictiveness Policies on Foreign Direct Investment in OECD and Non-OECD Countries
Bibliographic record
Abstract
Foreign direct investment (FDI) is considered a significant tool to transform modern technologies and innovation from developed to developing countries. Unfortunately, some countries impose FDI restrictions for economic, political, and social reasons. The present study assesses the role of different restrictive policies on FDI inflows in OECD and non-OECD countries. The empirical results are estimated by using panel quantile regression (PQR) at the median quantile from 1998 to 2022. It uses the all-restrictiveness policies index and its four subtypes: equity, key foreign personnel, screening and approval, and operational restrictions; the data is extracted from the OECD database. The study concluded that all restrictiveness policy indexes and their subtypes in OECD countries propose the inverted U-shaped curve. In contrast, in non-OECD countries, it shows the U-shaped relationship to determine the FDI inflows. Furthermore, this study also examines the individual countries using the marginal effect. In OECD countries, Australia, Canada, Mexico, and New Zealand have imposed higher restrictions, which reduces the FDI, while in non-OECD countries case, China, Indonesia, Malaysia, and the Philippines imposed higher restrictions, which increased FDI inflows. This study recommends that OECD countries reduce the FDI restrictiveness policies while non-OECD countries should increase it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".