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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 OpenAlexaboutno aff
Emad Abd Elmessih Shehata

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0550.019

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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