Oil and Africa’s macroeconomy: are structural breaks important?
Bibliographic record
Abstract
Abstract This study examines the changing impact of oil price shocks on Nigeria and South Africa. Using a structural break approach, recent studies have established the altered impact of oil price shocks consequent to the Great Moderation that commenced in the mid‐1980s. While several studies have focused on the oil–macroeconomic relationship in Africa, little attention has been paid to the variation across different periods. This study investigated the possible changing impact of oil on inflation and the real gross domestic product growth rate in the two largest African economies that are, respectively, net oil‐exporting and net importing countries. A data set from 1970 quarter 1 to 2016 quarter 4 was employed. An impulse response function was used as a referenced model while rolling impulse approach was adopted to ascertain variation across periods. Our findings show the magnitude of the impacts of oil price shocks has declined significantly since the 1990s. Several factors were adduced for the muted effects of oil in the two economies. These factors were largely driven by domestic policies and institutional reforms. Hence, not taking account of the time‐varying nature of the relationship may not provide a complete picture of the relationships between oil and macroeconomic outcomes among African 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 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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".