Remittances, Governance and Economic Growth: Empirical Evidence from MENA Region
Bibliographic record
Abstract
In this study we examine the effects of remittances and governance on economic growth in ten MENA countries. We choose these countries because they have relatively stable political situations. Using annual data from the World Bank over the period 2002-2017, we estimate panel autoregressive distributed lag (ARDL) models due to the existence of mixed levels of integration among series involved in this study. Control variables such as gross capital formation, consumption per capita and openness among others are integrated in these models. A governance composite is computed using the 6 governance indicators from the world bank. These indicators are used individually in different ARDL models with their interactions with the remittances to explore their impact on economic growth. The findings indicate a negative impact of the remittance on economic growth in the quasi-totality of the models. However, while governance composite shows a positive impact on economic growth, taking into consideration the dimensions of governance leads to conflicting results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".