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Record W3043840391 · doi:10.20448/2002.101.1.9

The Macroeconomic Effects of Public Debt: An Empirical Analysis of Evidence from Canada

2020· article· en· W3043840391 on OpenAlexaboutno aff
Charles Bahr, Alfred K. Lam

Bibliographic record

VenueJournal of Accounting Business and Finance Research · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
Fundersnot available
KeywordsDebtInternal debtDebt-to-GDP ratioDebt levels and flowsEconomicsMonetary economicsContext (archaeology)External debtExchange rateMacroeconomicsInternational economics

Abstract

fetched live from OpenAlex

Public debt refers to the amount of money which a particular country owes to the lenders either inside the country or outside the country. The lenders might be the individuals, businesses or even governments of the other countries. It might also be called as national debt or sovereign debt. Several types are associated with public debt such as domestic debt, external debt and the total debt. Public debt has critical relations with different macroeconomic factors such as economic growth, price levels and exchange rate. In this context, the current study has been conducted with the motive to explore the impact casted by public domestic debt, public external debt and total debt on the macroeconomic factors i.e. economic growth and general price level. As the study has been conducted in the context of Canada, therefore the researcher collected secondary and time series data for Canada. The collected data comprised the time of 28 years. The data regarding the variables of the study was analyzed by using different techniques and tools so that the objective of the study can be fulfilled. The results obtained from the analysis provide information that PDD has negative impact on the economic growth and positive impact on price level in short run as well. In addition, the impact of PED and TD on economic growth is positive only for shorter run and in longer run, this result is not applicable. However, the impact of PED and TD on price levels is too ambiguous to draw any conclusion.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.136
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 science0.0010.000
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 head0.159
GPT teacher head0.343
Teacher spread0.184 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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