Determinants of Large Shifts in Official Development Aid Allocation by Major Countries
Bibliographic record
Abstract
This study provides a comparative analysis of the main determinants of large shifts in aid allocation by major donors, namely China, France, the United Kingdom, and the United States. In contrast to continuing assistance, significant year-over-year variation of allocated aid to a given recipient is considered a new and deliberate decision by the donors. Using a version of quantile regression to account for heterogeneity in the characteristics of aid recipients, we show that significant differences exist in the aid allocation strategies of the major donors. There is no conditionality attached to Chinese aid, while self-economic interests and corruption levels at home and in the recipient countries determine aid allocated by France and the U.K. to their former colonies. In addition, recipient needs affect aid from France, the U.K., and the U.S. Over the 2000-2014 period, there is no significant change in the determinants of aid allocation by China in response to various criticisms of its approach. Confronted with the growing influence of emerging donors such as China, the three major traditional donors seem to adjust their aid allocation policy towards their own economic interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".