The Impact of Women Parliamentarians on Economic Growth: Modelling & Statistical Analysis of Empirical Global Data
Bibliographic record
Abstract
The impact of empowering women on economic growth is investigated through testing the influence of proportion of women candidates in parliaments. To obtain a long-term view, cross-country analysis is performed in parallel using 10 years World Bank data of 72 countries divided into the UN income-groups: 1-high, 2-upper-middle, 3-lower-middle, and 4- low-income. Statistical analysis reveals severe degree of multicollinearity. To unveil the desired connection, two approaches are implemented. Principle Component Regression is used to assess the independent impact of women parliamentarians. The results demonstrate a positive significant influence: 10% increase in female parliamentarians increase growth by 0.27%, 0.36%, 0.22%, and 0.49%, respectively. To unveil the joint influence of the considered indicators, interaction regression models are developed. The method demonstrates superior results. This work provides an empirical evidence on the positive impact of women political empowerment with respect to stimulating a sustainable economic growth.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".