Towards better GVC participation in Africa: Does infrastructural development matter?
Bibliographic record
Abstract
Global Value Chain (GVCs) offer vast opportunities for industrialization, economic diversification, and structural economic transformation. However, despite various efforts to become notable actors in the GVC, many African countries remain positioned at the lower end of the GVC spectrum. Hence, this paper investigates the potential of infrastructure to catalyse GVC participation and elevate African countries to a level comparable to developed nations. Relevant data from 38 African countries spanning 2005–2021 are analysed using the Pooled Ordinary Least Square (POLS), and the System Generalised Method of Moments (SGMM) methods. The results provide overwhelming evidence of infrastructural development's crucial role in improving forward and backward GVC participation in Africa. Specifically, information and communication technology (ICT), electricity, and water infrastructure significantly promote forward and backward GVC participation. The results are robust to alternative analytical methods. African governments, therefore, need to pursue pro-infrastructural development policies and increase infrastructural spending to improve GVC participation.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".