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Record W3003109072

Identifying Countries at Risk of Fiscal Crisis: High-Debt Developed Countries

2019· preprint· en· W3003109072 on OpenAlexaboutno aff
Betty C. Daniel, Christos Shiamptanis

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsFiscal spaceEconomicsDebtMonetary economicsDebt crisisDebt ratioFiscal policyFiscal adjustmentValue (mathematics)External debtInternal debtMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

How large can debt get before triggering a crisis? Since debt is the expected present value of future primary surpluses, the answer depends on a country’s technical and political ability to raise future primary surpluses. However, countries do not raise the primary surplus to its peak and maintain the peak forever, the assumption implicit in the standard practice of setting maximum debt at the present value of the peak surplus. We estimate fiscal feedback rules for seven high-debt developed countries and find an increase in debt creates a sustained increase in the primary surplus, with the primary surplus reaching a peak in the future. Therefore, our implied debt limit is much lower than the standard measure. We estimate debt limits following the global financial crisis in 2008 and find substantial heterogeneity. We separate countries into risk categories based on fiscal space. Greece and Portugal eroded their fiscal space several years, prior to their fiscal crises, placing them in the highest risk category and predicting the crises that followed. Canada and Belgium maintained large enough fiscal space to achieve safe status. Other countries reduced fiscal space, with France and Spain eroding fiscal space in 2014, warning of future crises.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.298
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicFiscal Policies and Political EconomyFrench-language works237,207