Lessons and Challenges in Implementing Gender-ResponsiveBudgeting at the Local Level in Mongolia
Bibliographic record
Abstract
Gender-Responsive Budgeting (GRB) has emerged as a crucial policy instrument for promoting gender equity in public financial management. In Mongolia, despite relatively strong gender equality indicators, women remain underrepresented in leadership roles and continue to face structural barriers to accessing public services and economic opportunities. This article examines the lessons learned and challenges encountered in the local-level implementation of GRB in Mongolia from 2020 to 2023. Drawing on a pilot initiative supported by the MERIT project and implemented in four provinces—Dundgobi, Dornod, Sukhbaatar, and Tuv—the study analyzes the impact of capacity-building efforts, micro-projects, and intergovernmental collaboration on integrating gender perspectives into local governance and budgeting. Using qualitative analysis of training data, project implementation reports, and stakeholder reflections, the study identifies key enablers of GRB, such as gender-disaggregated data usage, institutional commitment, and civil servant engagement. However, it also highlights persistent challenges including limited analytical capacity, inadequate data systems, and policy fragmentation. The findings contribute to the broader discourse on public sector reform and gender mainstreaming by offering practical insights for scaling up GRB in decentralized governance contexts. The article concludes with policy recommendations to ensure the sustainability and institutionalization of GRB in Mongolia and similar transitioning economies.
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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 | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".