Female Employment in MENA’s Manufacturing Sector: The Implications of Firm-Related and National Factors
Bibliographic record
Abstract
The Middle East and North Africa (MENA) region has realized significant advances toward improving women’s well-being and social status over the last few decades. However, women’s employment rate in the MENA region remains one of the lowest in the world. This paper examines the implications of firm-related and national factors for female employment rates in manufacturing firms located in the MENA region. The empirical analysis is implemented for firm-level data derived from the World Bank’s Enterprise Surveys database. It uses fractional logit and alternative models to carry out the estimations for female overall employment rates and for female non-production employment rates. The results reveal significant implications of firm-related factors, such as private foreign ownership, exporting activities, firm size, and labor composition for female employment rates. They also show that national factors, such as economic development and gender equality, promote female employment rates. There are considerable differences between the estimated marginal effects for female overall employment rates and those for female non-production employment rates. This paper provides policy-makers with important directions to design strategies aiming at enhancing women’s economic opportunities and employment rates.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".