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Record W7126021124 · doi:10.15353/rea.v17i4.6596

An Empirical Study on Restrictive Laws and Regulations Affecting Women’s Economic Participation

2025· article· en· W7126021124 on OpenAlexvenueno aff
Baneng Naape

Bibliographic record

VenueReview of Economic Analysis · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEmpirical researchMainstreamPovertySustainable developmentPoliticsEconomic stabilityDistribution (mathematics)

Abstract

fetched live from OpenAlex

The global emphasis on women's economic participation has grown significantly due to its vital role in promoting macroeconomic stability and advancing financial inclusion. Involving women in economic activities is essential for achieving Sustainable Development Goals, such as poverty alleviation and closing gender gaps. However, a range of regulatory, cultural, and structural barriers continue to hinder women’s ability to participate in the mainstream economy. This study aims to examine the impact of restrictive laws and regulations on women’s economic participation within the BRICS bloc. Women’s economic participation has been examined through three key dimensions: paid employment, political representation, and entrepreneurship. The findings suggest that the removal of restrictive laws and regulations is associated with increased levels of women's economic participation. It is important to acknowledge that while BRICS countries have made significant strides in dismantling legal barriers affecting women, substantial obstacles remain from both legal and regulatory perspectives that hinder women’s engagement in economic activities. Therefore, the study recommends that BRICS nations prioritize the complete removal of restrictive laws and regulations impacting various aspects of women's lives, including mobility, pay, marriage, and entrepreneurship.. Furthermore, ensuring a gender-equitable distribution of resources should be a central focus in policymaking to ensure that no one is left behind in the development agenda, particularly women and children.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.013
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.399
Teacher spread0.362 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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