Asymmetric interaction between government spending and terms of trade volatility
Bibliographic record
Abstract
Purpose In the literature on the effects of economic globalization, the compensation hypothesis suggests that there is a positive link between government size and external risk as governments perform a risk mitigating role to insure against productivity shocks through transfers. In contrast, the conventional wisdom hypothesis states that more openness will lower tax rates and lead to smaller government due to increased international factor mobility which undermines the ability of governments to tax. The purpose of this paper is to test the literature and present the authors' conclusions. Design/methodology/approach Using time series data for the USA, Canada, Japan and Australia over the period 1960‐2008, the authors test the asymmetric relationship between government size and terms‐of‐trade volatility by applying multivariate hidden cointegration analysis. Findings The findings show that high terms of trade volatility are positively related to government spending in the all sample countries. The effect is stronger in the case of positive movements than negative ones. Practical implications The policy implication is that the size of the public sector might play a risk‐reducing role in economies with significant amounts of external risk. In particular, public expenditure is considered to be an important fiscal policy instrument when terms of trade volatility are high. Originality/value The paper describes the first study of its kind.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".