The aid-income link revisited: How plausible and robust are the results?
Bibliographic record
Abstract
This study provides a re-examination of the aid-income link based on a panel data set which is downloadable at the Canadian Journal of Economics 45(1), 2012 issue. Longer time series data are available for a group of 58 countries and run from 1960 to 2007. In particular, the study aims at justifying the use of time series techniques and re-investigating whether the more recent finding that aid has an insignificant impact on per capita income is plausible and robust. Plausibility is checked by looking at the transmission channels of aid and by testing the direction of causality between aid and income and robustness is checked by investigating sub-samples of the 58 countries and by refining the model. In particular, we allow for threshold effects of aid, considering that aid might become effective after certain threshold values of other growth-determining factors have been achieved. Overall, we find that the result of an insignificant impact of aid on per capita income is robust taking a long-run perspective. Not being able to establish a significant impact of aid on income in the long run does not rule out that in the short to medium run significant positive (or negative) effects of aid can appear and, therefore, a positive short-run contribution of aid is a possible result and no counter-evidence for the findings related to the long run. In addition, we do not find any evidence that would justify the use of a model with interaction terms or with thresholds.
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 | 0.024 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".