Does Female Reservation Affect Long-Term Political Outcomes? Evidence from Rural India
Bibliographic record
Abstract
Klaus Deiningera, Songqing Jinb, Hari K. Nagarajanc & Fang Xiad*a Development Research Group, World Bank, Washington, DC, USAb Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI, USAc Institute of Rural Management, Anand, Gujarat, Indiad China Center for Human Capital and Labor Market Research, Central University of Finance and Economics, Beijing, ChinaCorrespondence Address: Fang Xia, 39 Xueyuan South Road, Haidian District, Beijing, China, 100081. Email: xia.fang.fx@gmail.comAn Online Appendix is available for this article which can be accessed via the online version of this journal available at http://dx.doi.org/10.1080/00220388.2014.947279.AbstractWhile studies have explored the impacts of political quotas for females at household level, differential effects on males and females and their evolution through time have received little attention. Using nationwide data from India spanning a 15-year period, we find that, while leader quality declines, gender quotas increase the level and quality of women’s political participation, their ability to hold leaders to account, and their willingness to contribute to public goods. Key effects persist beyond the reserved period and impacts on females often materialise only with a lag.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".