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Record W4390174482 · doi:10.3390/jrfm17010009

Sovereign Debt Crisis and Fiscal Devolution

2023· article· en· W4390174482 on OpenAlexvenueno aff
Ryota Nakatani

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationDevolution (biology)EconomicsEndogeneityEconomic policyRevenueFiscal policyDebtMonetary economicsTax revenueFiscal unionFinancial systemInternational economicsMacroeconomicsFinanceMarket economyGeography

Abstract

fetched live from OpenAlex

How is the probability of a sovereign debt crisis affected by fiscal devolution? Using annual cross-country panel data from 82 advanced and developing countries, the association between fiscal decentralization and the sovereign debt crisis is investigated. We adopt an instrumental variable probit model to address potential endogeneity. The research distinguishes between tax policies and spending policies. The results reveal that local tax autonomy reduces the probability of a sovereign debt crisis. In contrast, expenditure devolution is found to increase the probability of a sovereign debt crisis. These favorable and unfavorable effects of fiscal devolution are more evident in the case of decentralization to local governments than in the case of decentralization to subnational governments. In terms of relative magnitudes, our discrete choice analysis demonstrates that the undesirable effects of expenditure decentralization are greater than the desirable effects of tax revenue decentralization. Therefore, countries should be cautious about the risks associated with fiscal devolution, particularly the contrasting impact of tax revenue and spending decentralization on the likelihood that sovereign debt crises occur.

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 score0.533
Threshold uncertainty score0.220

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 science0.0000.000
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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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