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Record W4390765975 · doi:10.5539/ijef.v16n2p86

Does Budget Deficit Crowd Out Private Investment? Cote d’Ivoire As A Focus

2024· article· en· W4390765975 on OpenAlexvenueno aff
Yaya Keho

Bibliographic record

VenueInternational Journal of Economics and Finance · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)EconomicsCrowding outDeficit spendingGross private domestic investmentRevenueGovernment (linguistics)Return on investmentPublic investmentMonetary economicsOpen-ended investment companyMacroeconomicsFinanceFiscal policyProduction (economics)Debt

Abstract

fetched live from OpenAlex

This study examines the impact of budget deficit on private investment in Cote d’Ivoire. It uses data from 1975 to 2022. Using the autoregressive distributed lag approach, the results disclose a negative relationship between deficit and private investment, providing support to the crowding out hypothesis. This suggests that high deficits driven by government expenditure slow down private investment. Estimating a threshold model, the results confirm the significance of a nonlinear relationship between deficit and private investment. The results indicate that budget deficit lower than 2.3% of GDP is positively associated with private investment. However, once the budget deficit exceeds this threshold, it turns to be neutral to private investment. Since 2020, the budget deficit is higher than the threshold of 2.3%. Therefore, policy-makers are advised to take measures reducing deficit at a level conducive to investment and economic growth. Government should improve tax revenue and restrain the growth of public expenditure while enhancing its efficiency.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.283
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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