Addressing the gender gap: Impact of institutions on women’s political participation in Africa
Bibliographic record
Abstract
Women’s low political participation remains a problem in many parts of the globe. Previous research within the African context has examined the gender gap, focusing on individual-level factors. Still, the gender gap persists after controlling for the usual barriers (resource, attitudinal, social, and cultural). We complement prior studies by exploring the impact of an overlooked factor—institutions. We theorize that the gender gap in political participation in Africa depends on the specific institutional context and nature of the institutions themselves. Focusing on electoral systems, gender quotas, and their inclusive outcomes (increase in women’s numbers in national assemblies), we hypothesize that in countries with proportional (PR) electoral systems, gender quotas should encourage higher participation among women and yield small to no gender gap. Using five waves of Afrobarometer data covering 32 African countries, the multilevel regression results reveal nuanced effects of institutions on the gender gap in both electoral and non-electoral participation. First, compared with majoritarian systems, we find that PR electoral systems help erase the gender gap only for electoral participation (voting). In contrast, for non-electoral participation, PR electoral systems show no significant impact on reducing the gender gap. Second, we find no evidence to support the hypothesis that gender quotas reduce the gender gap in electoral and non-electoral participation. Third, where women’s representation in legislatures exceeds 20 percent, there is a reversal of the gender gap for voting. However, for non-electoral activity, improving women’s presence in national legislatures proves more effective in reversing the gender gap only for those belonging to a political party. For other activities, such as joining others to raise issues, protest actions, and attend community meetings, the gender gap persists but diminishes, with women holding 20–45 percent of seats.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".