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Record W3122569911

Explaining the MENA Paradox: Rising Educational Attainment, Yet Stagnant Female Labor Force Participation

2018· preprint· en· W3122569911 on OpenAlexaff
Ragui Assaad, Rana Hendy, Moundir Lassassi, Shaimaa Yassin

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsUnemploymentEducational attainmentEconomicsLabour economicsWagePrivate sectorMultinomial logistic regressionDemographic economicsPublic sectorInformal sectorSurvey data collectionWork (physics)Economic growthEconomy
DOInot available

Abstract

fetched live from OpenAlex

Despite rapidly rising female educational attainment and the closing if not reversal of the gender gap in education, female labor force participation rates in the MENA region remain low and stagnant, a phenomenon that has come to be known as the "MENA paradox." Even if increases in participation are observed, they are typically in the form of rising unemployment. We argue in this paper that female labor force participation among educated women in four MENA countries - Algeria, Egypt, Jordan and Tunisia - is constrained by adverse developments in the structure of employment opportunities on the demand side. Specifically, we argue that the contraction in public sector employment opportunities has not been made up by a commensurate increase in opportunities in the formal private sector, leading to increases in female unemployment or declines in participation. We use multinomial logit models estimated on annual labor force survey data by country to simulate trends in female participation in different labor market states (public sector, private wage work, non-wage work, unemployment and non-participation) for married and unmarried women of a given educational and age profile. Our results confirm that the decline in the probability of public sector employment for women with higher education is associated with either an increase in unemployment or a decline in participation.

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 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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.388
Teacher spread0.321 · 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 teacher head, 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

Citations5
Published2018
Admission routes1
Has abstractyes

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