Oil Price and Long-run Economic Growth in Oil-importing Developing Countries
Bibliographic record
Abstract
• Analyzed the relationship between growth in output per capita and oil prices in developing countries. • A simple theoretical framework for the relationship between long-run growth and oil price. • Used the conventional growth regressions, controlling for growth in oil price in addition to other growth determinants. • Used data on 65 net oil-importing countries and applied fixed effect panel IV regression methods. • Oil price has a statistically significant negative effect on growth in real GDP per capita. The recent spikes in oil prices are a significant setback for the world economy, which has already faced multiple challenges due to the COVID-19 pandemic. This is particularly concerning for developing countries as maintaining a sustained growth in real GDP is crucial for lifting their population out of poverty. While the short-run negative macroeconomic effects of a spike in oil prices are well established in the context of the developed world, the long-run growth effect has received little attention, especially in developing countries. Using the World Bank's development indicators database covering the period 1990 to 2020, this study aims to investigate the oil price-growth nexus in low and middle-income net oil-importing countries to shed light on how oil price increases could be a challenge for sustainable development. Specifically, we first set up a theoretical model to establish the relationship between growth in output per capita and oil price. Following the traditional growth regression approaches, we empirically estimate the causal effect of growth in oil prices on the economic growth of 65 net oil-importing developing countries using fixed effect panel IV regression methods. The empirical results confirm statistically significant negative effects of oil prices, indicating that a higher oil price reduces long-run economic growth in oil-importing developing countries. Our results on the other determinants of growth are consistent with the existing empirical growth literature. Oil-importing developing countries, therefore, must allocate resources towards alternative domestic energy sources, in addition to pursuing fuel efficiency and conservation strategies, to mitigate the negative effects of oil price fluctuations on their long-run economic output and uphold sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".