Explaining the Gender-Gap in Economic Activity: A Cross-Country Study
Bibliographic record
Abstract
This study aims at investigating the cross-country variation in gender-gap in economic activity. A balanced panel data for 72 countries with complete data for the period 2000-2016 were extracted from the World Bank indicators. The statistical diagnostic tests supported the use of the fixed effect model. The series was stationary at level and not co-integrated. Moreover, the null hypothesis of the appropriateness of random effect model was rejected. The estimated results assure the importance of demand side factors in dampen the gender gap in economic activity as expected such as GDP growth, gender gaps in employment, in being self-employed and unemployment, urbanization and trade openness. Surprisingly, the supply side factors such the cross-country variation in gender gap in education and fertility rates were no more important determinants in explaining the gender gap in economic activity. This study recommends governments to improve the demand-side factors that would encourage more female employment and reduce the gender gap in economic activity later.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".