Impact of Institutional Quality on Educational Attainment: The Case of Low-Income SSA Countries
Bibliographic record
Abstract
Education produces many social, political and economic outcomes including improved cognitive competences, higher wages, better health and enhanced economic status. Nations ensure the development of their population by the use of educational intervention. Despite the documented empirical correlations of education attained, there have been scanty researches exploring the impact of institutional quality in low-income SSA countries. The problem facing SSA can be ascertained in the area of weak institutions which leads to poor level of educational attainment and low level of life expectancy which have become the focus of the development agenda in the world as a whole and developing countries in general. The objective of this study is to evaluate the relationship between institutional quality and educational attainment in low-income SSA countries from 2005 to 2013. The research used secondary data sourced from World Bank governance indicators, Transparency International and Heritage Foundation. The analysis was divided into panel data using the fixed effects method (FEM) and generalized method of moments (GMM). Both the panel data analysis and the generalized method of moments of institutional quality and educational attainment indicated that most of the countries investigated exhibits mixed performance in institutional quality. The study recommends policies to reduce corruption in all levels of economic activities. In addition, rule of law need to be strengthened and the educational sector should be refined to train manpower in all aspect of human activities in the region especially the low-income countries in SSA countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".