Leland & Pyle Meet Foreign Aid? Adverse Selection and the Procyclicality of Financial Aid Flows
Bibliographic record
Abstract
Official development assistance (grants and subsidized loans from foreign aid agencies) is the main source of external finance in developing countries. These financial aid flows are positively correlated with the recipients' business cycles, which is puzzling because it reinforces already strong and costly macroeconomic fluctuations in the recipient countries. We propose an explanation related to a familiar corporate finance theory of inside equity commitments. We assume that donor agencies and recipient governments value projects differently, and that donors know less than recipients do about projects. We show that donors can make an aid recipient idientify high-return projects by conditioning aid on the recipient's committing some of its own funds to the selected projects. This commitment makes recommending bad projects costly. Contributing "counterpart funds" is more difficult during economic downturns, however - which leads to aid procyclicality. This simple model of investment financing and aid provision produces aid contracts consistent with those used by aid agencies, rationalizes observed aid flow patterns, and yields a rich set of testable empirical predictions.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".