The Impact of Infrastructure Spending in Sub-Saharan Africa: A CGE Modeling Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we construct a standard CGE model to explore the impact of scaling up infrastructure in six African countries. As the debate on the importance of scaling up infrastructure to stimulate growth and provide a push to African economies, some analysts raise concern on financing these infrastructures after construction and that external funding of these can create major distortion and have a negative impact on the trade balance of these countries. This study aims to provide insights into this debate. It draws from the infrastructure productivity literature to postulate positive productive externalities of new infrastructure and Fay and Yepes (2003) for operating cost associated with new infrastructure. We compare various infrastructure investments funded with different fiscal tools. These investments scenarios are compared to nonproductive investment that can be interpreted as a business as usual scenario. Our results show that foreign aid does produce Dutch disease effects but the negative impacts are strongly dependent on the type of investments performed. Moreover, growth effects contribute to attenuate the negative effects.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it