Bridging the digital Divide: The role of educational interventions in enhancing economic growth in Ghana
Bibliographic record
Abstract
This study examines how Ghana's educational interventions mediate or moderate the effect of digitalization on economic growth. The main objective of this study is to evaluate the extent to which educational interventions affect the effectiveness of digitalization as a growth strategy. Using annual data from 1995 to 2024, the study adopts a mixed-methods econometric approach grounded in Endogenous Growth. It applies a Two-Stage Least Squares method to address endogeneity, employs mediation and moderation analyses to test interaction and transmission mechanisms, and uses a Kalman Filter to capture time-varying dynamics. The results reveal that while education (β = 2.253) and digitalization (β = 0.522) independently drive GDP growth, their interaction yields a negative and statistically significant effect (β = −0.149), indicating a misalignment between digital transformation and educational outcomes. Mediation analysis further shows that digitalization's impact on GDP rarely transmits through educational intervention (Total Effect = −0.0388), with both direct and indirect effects being statistically insignificant. However, dynamic modeling using the Kalman Filter reveals that educational interventions are the most persistent and influential factor in economic growth (β = 1.347), outperforming labor, capital, and digitalization. The findings suggest that while education remains Ghana's most potent growth driver, its structure must evolve to equip learners with digital skills. The key policy implication is that aligning educational content with digital economy demands will transform education into a synergistic engine of growth. This supports targeted implementation of Sustainable Development Goals (SDG) 4 (quality education) and SDG 8 (decent work and economic growth), ensuring inclusive and sustainable development.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".