Crude Constraints: Oil Price Asymmetries and the Growth Path of Saudi Arabia’s Services Sector
Bibliographic record
Abstract
This study explores the dynamic relationship between oil dependency and the growth of the services sector in Saudi Arabia over the period 1980–2023. Using both linear Autoregressive Distributed Lag (ARDL) and nonlinear ARDL (NARDL) models, we examine whether oil price shocks exert symmetric or asymmetric effects on the share of services in GDP. The results reveal that oil rents significantly reduce the services sector’s contribution in the long run, highlighting structural dependencies within the economy. Short-run dynamics vary across macroeconomic indicators, with real oil prices showing more immediate but less persistent effects. The NARDL model provides new insights, uncovering strong asymmetries: negative oil price shocks have a greater and more significant adverse impact on services value added than positive shocks, both in the short and long term. Unit root tests support the ARDL framework, and bounds testing confirms cointegration. These findings underscore the challenges of reducing oil dependence and suggest that economic diversification strategies under Saudi Vision 2030 must account for the asymmetric vulnerabilities introduced by oil price volatility.
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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 | 0.001 | 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) | 0.000 | 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 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".