Health Aid and Human Well-being: Exploring the Role of Donor Support in Developing Countries (Evidence from Fifty Developing Countries’ Dynamic Panel Data Analysis)
Bibliographic record
Abstract
This study aims to assess the impact of disbursed health aid on key health sector variables in 50 developing countries over 19 years (2002-2020). The variables analyzed include infant mortality rate (IMR), under-5 infant mortality rate (IMRu5), and life expectancy at birth (LifeExp). The study utilizes panel data and employs the Generalized Method of Moments (one-step and two-step GMM) for analysis. The findings reveal that health aid has a significant effect in reducing both IMR and IMRu5. A one percent increase in health aid corresponds to approximately 2.189 and 2.134 fewer infant deaths per 1000 live births and 3.497 and 2.864 fewer under-5 infant deaths per 1000 live births under one-step and two-step GMM, respectively. Additionally, a positive and statistically significant relationship exists between health aid and LifeExp. A one percent increase in health aid is associated with an increase of 0.064 and 0.076 years in LifeExp. The study also examines the impact of health aid on gender-specific health indicators. Health aid reduces both male and female IMR and IMRu5, with a more pronounced impact on male rates. Moreover, health aid has a more significant effect on improving female life expectancy than males. Furthermore, the study compares the effectiveness of multilateral and bilateral health aid. Both types of aid significantly reduce IMR and IMRu5, with bilateral aid being more effective for IMR and multilateral aid for IMRu5. Additionally, multilateral aid has a more substantial impact on enhancing life expectancy in developing countries. The main contribution of this study lies in its comprehensive analysis of the overall impact of health aid and its effects based on gender and donor characteristics. These findings emphasize the importance of Sustainable Development Goal 3 in promoting good health and well-being.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".