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Record W2978888101

الآثار الإقتصادیة لهجرة العمالة المصریة إلى الخارج [The Economic Impacts for the Egyptian Labor Emigration]

2003· article· ar· W2978888101 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMPRA Paper · 2003
Typearticle
Languagear
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationHeteroscedasticityEconomicsWageQuantile regressionUnemploymentEconometricsPopulationLabour economicsGeographyMacroeconomicsDemography
DOInot available

Abstract

fetched live from OpenAlex

Egyptian labor emigration is considered one of the changes, that led to the structural distortions in the domestic labor market in Egypt, the countries gulf were the main source of the Egyptian labor temporal emigration, while the USA, Canada, and Australia were the main source of the permanent emigration from Egypt. After the first gulf war, the Egyptian economy faced labor immigration. The study research problem, handled nature of the changes that occurred in labor market, as a direct results of the national and international economic effects. So the objective of the study is to explore the main features of the Egyptian labor emigration, and the potential impacts of the Egyptian labor immigration. The study used the regression analysis, i.e., maximum likelihood estimation (MLE) for simple regression, and simultaneous equations system by three stage least squares (3SLS), and took with considerations autocorrelation, heteroscedasticity, non normality, and multicollinearity problems, the previous econometric problems were detected by lagrange multiplier tests, and were remedied by using Pagan’s conditional least squares (CLS) of autoregression procedure, Bollerslev’s generalized autoregressive conditional heteroscedasticity (GARCH), robust regression quantile of least absolute deviation (LAD), and Hoerl's Ordinary ridge regression (ORR) respectively. The study discussed the changes and growth in the Egyptian labor market, the results indicated that there was a statistical increasing significance in the population, labor force, employed labor, unemployed labor, and the labor wages, while there was a statistical decreasing significance in unemployment rate and wage of labor during the period subject to analysis. On the other hand the results indicated that there was a statistical significance increasing in the permanent and temporal emigration. Saudi Arabia captured the most Egyptian emigration, also, Libya, Jordan, and Kuwait. In general the whole emigration increased significantly during the period of the study. Features of the Egyptian labor immigration were discussed, i.e., gender, occupation, educational status, job status, age, and the reasons of immigration to Egypt either internal or external reasons, and the potential impacts of immigration, also the positive and negative impacts for emigration from Egypt. Emigration model was estimated by (3SLS) with Newey-West’s generalized method of moments (GMM), the results indicated that, increasing unemployment rate and population led to increase emigration, while increasing the demand for domestic labor and the average annual labor wage have an effect for decreasing emigration. Finally, some recommendation from the study were mentioned, for encouragement emigration, i.e., activating and establishment the international relationships between Egypt and the neighboring countries, a diplomatic effort for emigration stabilization abroad, the search of new labor market in other countries. Also some recommendation with respect to immigration, i.e., simplification investment procedures, encouragement the industries that have an intensive human labor, and activating the training role that agree with the labor market requirements, for developing the human resources

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.003

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.

Opus teacher head0.029
GPT teacher head0.239
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it