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Record W4320917324 · doi:10.18280/ijsdp.180124

Public Debt Sustainability in Indonesia after Financial Crisis and During COVID-19 Pandemic

2023· article· en· W4320917324 on OpenAlexvenueno aff
Yozi Aulia Rahman, Dwi Rahmayani, Bayu Bagas Hapsoro

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
FundersUniversitas Negeri Semarang
KeywordsCoronavirus disease 2019 (COVID-19)PandemicFinancial crisisFinancial systemBusinessSustainabilityDebt2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)FinanceEconomicsMedicineVirologyMacroeconomicsInternal medicine

Abstract

fetched live from OpenAlex

The Developing countries are particularly vulnerable to shocks, such as the global financial crisis and the COVID-19 pandemic.The economic crisis increased external public debt to stabilize the economy and improve people's welfare.High external debt puts the debt in an unsustainable condition.This study aims to measure the debt sustainability of external public debt in Indonesia from 2008-2020.We used the Threshold Value of The Debt Sustainability Framework for Low-Income Countries (LIC-DSF) and the Solvency Rate of External Debt (SRED) as a better combination for measuring debt sustainability in Indonesia.The results showed external public debt was at a low-risk threshold after the global financial crisis.However, the impact of COVID-19 has caused the ratio of external public debt interest payment to tax revenue to be within a high-risk threshold value.The SRED value shows a minus number from 2012-2020 caused by the worsening current account balance and net capital account values.The analysis of debt sustainability may be able to encourage a prudent and sustainable the Indonesian budget management policy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.037
GPT teacher head0.268
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

Citations6
Published2023
Admission routes1
Has abstractyes

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