Do Political Economy Factors Influence Funding Allocations for Disaster Risk Reduction?
Bibliographic record
Abstract
Considering the importance of political economy in implementing Disaster Risk Reduction (DRR), this research investigates the significance of political economy in the distribution of DRR funding in Bangladesh. The study analysed data from self-reported surveys from 133 members of the sub-district level disaster management committee and government officials working with DRR. Employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, we find that political economy factors explain 68% of the variance in funding allocations. We also show that four categories of political economy factors—power and authority, interest and incentives, institutions, and values and ideas—are significantly influential over the distribution of DRR funding across subdistricts of Bangladesh. Our findings offer important policy implications to reduce the potential risks surrounding political economy influences in fund allocation and advance climate finance literature.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".