The Limits of Foreign Aid Diplomacy: How Bureaucratic Design Shapes Aid Distribution
Bibliographic record
Abstract
How does the institutional design of a state's bureaucracy affect foreign policy? We argue that institutions can moderate bureaucrats' incentives to act in accordance with an Executive's diplomatic preferences. Where the Executive can influence budgets or career paths, bureaucrats face incentives to adopt her diplomatic goals as their own. Where agencies are shielded from Executive influence, bureaucrats are free to act independently in a bid to enhance their autonomy and their reputation for competence. To test these expectations, we develop a new measure of bureaucratic independence for the 15 aid-giving agencies in the US government. We analyze how independence affects foreign aid allocation patterns over the 1999–2010 period. We find that in “dependent” agencies, foreign aid flows track the diplomatic objectives of the president. In “independent” agencies, aid flows appear less responsive to presidential priorities and more responsive to indicators of need in the recipient country. Our results highlight limits on the diplomatic use of foreign aid and emphasize the importance of domestic institutional design. Our findings yield insight into a broad range of policy domains—including international finance, immigration, and the application of economic sanctions—where multiple government agencies are in charge of implementing foreign policy.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".