Balancing short-term recovery to long-term sustainability: evaluating the role of stimulative monetary policy in Saudi Arabia
Bibliographic record
Abstract
Purpose The paper aims to examine the impacts of expansionary monetary policies in Saudi Arabia by assessing their effectiveness in both short-term economic recovery and long-term economic sustainability by separating the associated short-term dynamic impacts from long-run equilibrium relationships. Design/methodology/approach Econometric modeling of quantitative data using a vector error correction model with exogenous variables (VEC-X) on annual time-series data from 2007 to 2022 allowed for an exploration of the relationships between monetary policy instruments and macroeconomic indicators such as employment rates, inflation and investment patterns within the Saudi context. Findings Empirical results from the VEC-X model confirm a stable long-run equilibrium relationship among the non-stationary variables. However, the short-term dynamics reveal a significant policy trade-off. While inflation responds rapidly to monetary shocks, key indicators like unemployment and investment are unresponsive in the short term. This challenges the view that monetary policy can reliably boost broad economic activity, highlighting its limited role in short-term stabilization beyond influencing prices. Research limitations/implications The primary limitation of the study is the small sample size, which constrained the VEC-X model to a single lag and precluded a more complex analysis of short-term dynamics. Future research could build upon these findings using higher-frequency data. Practical implications The study’s findings can guide the implementation of monetary policies by suggesting a coordinated approach. Whereas monetary instruments are best suited for maintaining long-term price stability, more immediate goals should be addressed with targeted fiscal and structural reforms. Originality/value This research offers a twofold contribution. Empirically, it uses the VEC-X framework to separate the short-term and long-term effects within Saudi Arabia’s unique economic structure. Theoretically, it provides evidence on the limitations of conventional monetary policy transmission in a dollar-pegged, resource-dependent economy, highlighting the need for adjustments to standard policy frameworks.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".