Causal links between public debt and inflation in sub-Saharan African countries
Bibliographic record
Abstract
This study aims to investigate and compare both symmetric and asymmetric causal relationships between public debt and inflation across a panel of 14 sub-Saharan African countries over the period 1990 to 2021. It also examines trends in Country Policy and Institutional Assessment (CPIA) scores, particularly in the domains of debt policy and the efficiency of revenue mobilization. The analysis employs Konya's (2006) symmetric bootstrap panel causality test and an asymmetric approach developed by Yılancı and Aydın (2017), which builds on Granger and Yoon (2002) and Konya's methodology. The results reveal notable nonlinearity and considerable cross-country variation. Under the symmetric specification, causality from public debt to inflation is found in only four countries. However, when asymmetry is incorporated, this number increases to twelve. Similarly, while causality from inflation to debt is observed in four countries using the symmetric model, the asymmetric framework reveals evidence in eleven countries. These findings contribute to literature by offering a comparative perspective on the debt-inflation nexus. Moreover, the results indicate the presence of cross-sectional dependence across the panel and confirm country-specific heterogeneity. The analysis of CPIA indicators also points to varied levels of institutional capacity in public debt management and revenue mobilization across the region. Notably, Kenya's top performance in revenue mobilisation suggests that robust institutional frameworks can enhance the predictive relationship between increase public debt levels and inflation. The study's findings carry significant implications for fiscal and monetary policy in sub-Saharan Africa.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".