The determinants of foreign direct investment: what about the potential of the Arab Maghreb countries?
Bibliographic record
Abstract
Purpose The purpose of this paper is to elucidate the main determinants of foreign direct investment (FDI) in the case of the Arab Maghreb countries. Design/methodology/approach We employ a dynamic panel analysis using the General Method of Moments for a sample composed of 105 countries over the period 1985–2018. Findings We show that FDI stability, market size, higher education enrolment, quality of institutions, distance, sharing of common border, and bilateral investment and integration agreements are the main determinants of FDI location. These determinants are neither general. The potential for attracting FDI from AMU countries is poorly exploited. FDI to the AMU is lower than estimated stock. The observed FDI to potential FDI ratio does not exceed 87%. France and Spain are the main investors in the AMU region thanks to historical and cultural links. The FDI from the United States, Canada, Germany, Belgium, and Japan are below what is expected. Originality/value The contribution of this paper is observed on the examining oh the determinants of the FDI in the Arab Maghreb countries. Our study demonstrate that the political stability can decrease investment risk in these countries. The administrations correspondingly require expanding their rules and strategies with union demonstrations which were at the beginning of the departure and closing of several foreign companies.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".