DETERMINANTS OF INTERNATIONAL MIGRATION: AN APPLIED STUDY ON SELECTED ARAB COUNTRIES (1995-2017)
Bibliographic record
Abstract
The objective of this study is to analyze the pull and push factors as determination of international migration from selected Arab countries (Algeria, Egypt, Iraq, Jordan, Lebanon, Libya, Mauritania, Morocco, Sudan, Syria, Tunisia, Yemen) to western countries (Canada, France, Germany, Britain, USA), using Unbalanced Panel Data For the period of (1995-2017). The study aimed at developing an extended gravity model to investigate economic and non-economic determinants of international immigration using negative binomial regression; this is considered as the most appropriate to estimate the relationship between the number of immigrants as a dependent variable and other explanatory variables in this study. The dependent variable is an example of a count data, which takes positive integers numbers. After examining the hypotheses of the study, the results showed that the economic factor represented by per capita income in the receiving country is the strongest attraction for migrants from Arab countries. In aWddition to the presence of former immigrants from the same immigrant country in the receiving State, the study also found an increase in the number of immigrants from Arab countries since 2011. In contrast, distance between countries and poverty in the Arab countries are the main obstacles to international migration. Keywords: International Migration, Arab Countries, Negative Binomial, Gravity Model JEL Classification: J61 DOI: https://doi.org/10.32479/ijefi.9106
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".