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Record W3118768342 · doi:10.5430/rwe.v12n2p51

Implications of Non –productive and Productive Government Expenditure on Output and Employment: Evidence From Nigeria

2021· article· en· W3118768342 on OpenAlexvenueno aff
Abiola John Asaleye, Rotdelmwa Filibus Maimako, Henry Inegbedion, Adedoyin Isola Lawal, Charity Aremu

Bibliographic record

VenueResearch in World Economy · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsGovernment expenditureCausality (physics)Government (linguistics)Error correction modelPublic expenditureShort runJoint (building)Labour economicsCointegrationPublic financeMacroeconomicsEconometrics

Abstract

fetched live from OpenAlex

Nigerian government expenditure has been on an increasing trend over the years, and its contribution to sustainable economic development; promoting long-term output and employment has generated controversial issues in the literature. Against this background, this study analyses the impact of both productive and non-productive government expenditure on output and employment in Nigeria using the Vector Error Correction Model, The long-run equations for output and employment are established. The joint short and long-run causality was also investigated. The study shows a contrary result to theoretical predictions; Nigeria's long-run growth is not promoting by productive government expenditure. Furthermore, there is joint short and long-run causality between employment and government expenditure channels. Evidence from the output equation indicates no joint long and short-run causality. The implication of this result shows that government expenditure either productive or non-productive, has not improved the economy, although there is an increase in employment generation through the non-productive channel, which has not promoted broad-based growth. For the Nigerian government to improve the situation, the study recommends a critical assessment of public expenditure through the cost-benefit approach.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.712

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 head0.121
GPT teacher head0.327
Teacher spread0.206 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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