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Enregistrement W2992494962

The Debt Index and Its Relation to Economic Activity

2013· article· en· W2992494962 sur OpenAlex
John J. Bethune

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Notice bibliographique

RevueJournal of economics and economic education research · 2013
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueFiscal Policies and Political Economy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEconomicsIndex (typography)DebtEconometricsInvestment (military)Explanatory powerRegression analysisDebt-to-GDP ratioDeficit spendingExternal debtMacroeconomicsStatisticsMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

THE DEBT INDEX There are both short run and long run issues involving the problem in the United States and elsewhere. In the short run, the deficit represents a problem for policy makers while in the long term, the national is an issue that must be addressed. To construct a debt I combine the absolute value of the annual federal budget deficit divided by federal government spending with the national divided by nominal GDP. Put simply: Deficit/Spending + Debt/GDP = Debt Index This combines the temporal aspects of our short and long term problems into one measure. The Relation of the Debt Index to Macroeconomic Variables To determine if the index had any meaningful statistical relationship with various macroeconomic variables I generated a series of correlation coefficients comparing the index with these variables on an annual basis since 1980. A major finding was that the index was highly and negatively correlated with private investment as a percent of GDP. The coefficient was -.831 and it was significant at the 99 percent confidence level. It might be expected that other measures of might be highly and/or more so correlated with investment as a percent of GDP so I ran correlations with these as well. The results are in Table I. Table I demonstrates that the relationship of the index to private investment is greater than any individual component of the index. A simple regression was run to suggest the explanatory power of the relationship between the index and investment spending. The results were: Regression Analysis: I/GDP versus Debt Index The regression equation is I/GDP = 23.8 - 6.28 Debt Index Predictor Coef SE Coef T P Constant 23.8483 0.5935 40.18 0.000 Debt Index -6.2818 0.7675 -8.19 0.000 S = 0.979482 R-Sq = 69.1% R-Sq(adj) = 68.0% While this is a fairly simplistic method of determining explanatory power it does show, according to the adjusted R-Square, that the index explains 68 percent of the movement in private investment. To determine how robust this relationship might be I used IMF data, going back to 1980, when possible, to construct a index for 15 major global economies of vary characteristics. The results can be seen in Table II. Accept where noted, all coefficients are significant at the 1 percent level in Table II as well as all of the following tables. This would indicate that the index is highly and negatively correlated with most western-style economies. This would include Sweden, where a large percentage of GDP flows through the public sector. France and New Zealand are the only economies where the relationship does not hold. Another variable exhibiting a significant relationship with the index is the unemployment rate. For the United States, the correlation coefficient is .479 and it is significant at the 1 percent level. This implies that higher levels of short term and long term correspond to higher levels of unemployment. However, in this case the deficit and deficit to government spending ratio are more highly correlated with unemployment than the index, with coefficients of .626 and .824, respectively. In contrast the national and the to GDP ratio are not significantly correlated to the unemployment rate, indicating that unemployment is related much more closely to short run difficulties in the United States. Table III shows the correlation coefficients among the various countries with respect to the index and unemployment. Unlike in the case of the USA, none of the countries that show a significant correlation coefficient have higher coefficients on the relationship with the short term deficit measures. Some, such as Greece, Canada, and Australia, show a more significant correlation with the to GDP ratio. …

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,601
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,061
Tête enseignante GPT0,327
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle