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Record W3175631523 · doi:10.3390/jrfm14070297

Analysis of Australia’s Fiscal Vulnerability to Crisis

2021· article· en· W3175631523 on OpenAlexvenueno aff
Gulasekaran Rajaguru, Safdar Ullah Khan

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
FundersWashington and Lee University
KeywordsDebtEconomicsVulnerability (computing)Deficit spendingDebt ratioDebt-to-GDP ratioInternal debtDebt levels and flowsMonetary economicsSovereign defaultFiscal adjustmentFiscal policyExternal debtMacroeconomicsSovereign debtSovereignty

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.256
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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