Gendered Leadership: The Effects of Female Development Agency Leaders on Foreign Aid Spending
Bibliographic record
Abstract
This article examines the effects of gender on the leadership of bilateral development aid agencies, particularly their official development assistance (ODA) allocations toward gender‐related programming. Drawing on earlier research on gendered leadership, the article tests the hypothesis that female director generals (DGs) and ministers responsible for aid agencies will allocate moreODAthan their male counterparts toward gender programming. This existing literature on gendered leadership is divided: some scholars argue that women and men have distinct leadership styles on account of their gender, while others argue that the only distinguishing factor is the institutional context in which they lead. Drawing on data collected on aid flows and agency leadership within the major Western aid donors of the Organisation for Economic Co‐operation and Development (OECD) Development Assistance Committee (DAC) over the period from 1995 through 2009, we use pooled time series analysis to examine the effects of gendered leadership on aid allocation. Our analysis reveals a tendency for femaleDGs and ministers to focusODAon gender‐mainstreaming programs, while maleDGs focusODAon gender‐focused programs. We argue that these divergent priorities reflect the women's desire to reform gendered power structures within their respective aid agencies, and the men's desire to maintain existing gender power structures from which they benefit.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".