Analysis of Australia’s Fiscal Vulnerability to Crisis
Bibliographic record
Abstract
Fiscal vulnerability, like a contagion, poses a threat to financial sector stability, which can lead towards sovereign default. This study aimed to assess fiscal vulnerability to crisis by investigating the Australian economy’s gross public debt, net public debt, and net financial liabilities. We used a threshold regression model and compared results with the baseline deficit–debt framework of analysis. The results of the base model suggested that the economy is fiscally sustainable, and that the primary surplus remains unaffected by increasing levels of public debt. In contrast, the threshold regression model indicated that the increasing level of debt has eroded primary surplus below the threshold level of 30.89% of public debt to GDP. These results need further investigation. Therefore, we modified our basic threshold model to capture budget deficit and surplus as a threshold in response to changes in public debt. The results from the sequential threshold regression model using the debt to GDP ratio and primary budget surplus identifying the periods of 1991, 1992, 2008, 2009, 2011 and 2019 as times of likely vulnerability to fiscal crisis. The overall results confirmed that the primary surplus remained sustainable over the estimated threshold level of public debt in all other sample periods and these findings persisted across alternative measures of public debt.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".