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Record W4210565278 · doi:10.1002/ijfe.2594

Debt‐to‐GDP changes and the great recession: European Periphery versus European Core

2022· article· en· W4210565278 on OpenAlexaboutno aff
Maria‐Eleni K. Agoraki, Stella Kardara, Tryphon Kollintzas, Γεώργιος Π. Κουρέτας

Bibliographic record

VenueInternational Journal of Finance & Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDebtGross domestic productRecessionReal gross domestic productInflation (cosmology)Debt-to-GDP ratioInternational economicsMonetary economicsExternal debtMacroeconomics

Abstract

fetched live from OpenAlex

Abstract In this paper, we use simple accounting schemes and counterfactual experiments, to compare the sources of changes in the public debt to GDP ratio across countries of the European Periphery (Greece, Ireland, Italy, Portugal and Spain), the European Core (Germany and France) and the other G7 countries (Canada, Japan, the United Kingdom and the United States), in two periods—2000–2007 and 2008–2015. In general, Debt‐to‐GDP ratio rose in all countries in the latter period. But the effects of total or primary fiscal deficits, inflation and real growth on the respective Debt‐to‐GDP ratio changes were different across countries in both periods. In the European Core, Ireland and the Anglo‐Saxon countries, successful countercyclical fiscal policies, tended to lower Debt‐to‐GDP ratio. However, in the other European Periphery countries and Japan, unsuccessful countercyclical policies, had the opposite effect. Since in the Euro Area (EA), monetary policy is common and fiscal policies were restricted by rules, this development suggests that differences in shock propagation mechanisms were important drivers of the observed differences in the behaviour of Debt‐to‐GDP ratio between the European Core and the EA Periphery, except Ireland. An implication of this result is that the recently announced EU transfers to the EA Periphery tied up to incentives to improve economic efficiency, seem to be the right policies to ameliorate the effects of the recession brought about by the various social distancing measures to fight the COVID‐19 pandemic, without increasing the Debt‐to‐GDP ratios of these countries.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.244
Teacher spread0.202 · 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 designNot applicable
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

Citations7
Published2022
Admission routes1
Has abstractyes

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