Aiming for Growth: Is Fiscal Policy a Hit or Miss? A Peek into the OECD
Bibliographic record
Abstract
The relationship between government spending and economic growth has captured scholars and policymakers over the years. Despite studies dating back to the early mid-1900s, establishing a clear causal relationship has yet to be discovered. This paper aims to contribute to the ongoing debate on the subject, with the ultimate goal of determining the effectiveness of fiscal policy. Employing a panel-VAR (Vector AutoRegression) technique, the study utilises quarterly data on government spending and economic growth from OECD countries from 1995 to 2023. Furthermore, the analysis is segmented into two subsamples: the post-dot-com era from 2000 to 2006 and the post-financial crisis period from 2009 to 2015. Our findings suggest a negative two-way causality between GDP growth and Government spending from one quarter to the next, suggesting that fiscal policy negatively affects GDP and vice versa. The results do not change significantly between periods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".