The Arab Spring and Its Implications for FDI Inflows to the MENA Region
Bibliographic record
Abstract
Abstract The Arab Spring (AS) marked an unprecedented event in the Middle East and North Africa (MENA) region, and it generated political and economic uncertainties and triggered violent conflicts and political rifts. This paper empirically examines the short-run and long-run effects of the AS on foreign direct investment (FDI) inflows to the MENA region and to individual MENA countries. The empirical analysis is implemented through the generalized method of moments (GMM) estimator for dynamic panel models, using different empirical specifications. The benchmark results show that the AS has led to important reductions in FDI inflows to the MENA region. A more detailed empirical analysis reveals significant variations in the AS effects on FDI inflows across MENA countries and it underscores distinct patterns over different time periods. These findings imply that governments in the MENA region are required to maintain political stability, and to adopt distinctive policies that lessen the adverse implications of the AS and that set favorable conditions for FDI inflows in the post-COVID-19 pandemic era.
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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 | 0.001 | 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) | 0.004 | 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".