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Record W2796095129 · doi:10.5539/ijef.v10n5p67

Determinants of National Saving in Four West African Countries

2018· article· en· W2796095129 on OpenAlexvenueno aff
Ignatius Abasimi, Agbassou Y. A. Martin

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDistributed lagGross domestic productGross national incomeCointegrationPer capitaEconometricsReal gross domestic productShort runGross fixed capital formationMacroeconomicsDemography

Abstract

fetched live from OpenAlex

Saving is one of the preeminent integral of economic growth. The desideratum of this study is to investigate the determinants of national saving in four West African countries, namely, Ghana, Togo, Burkina Faso, and Cote d’ Ivoire. The study uses annual data from the World Bank database for the period 1997-2016. The Augmented Dickey-Fuller (ADF) test, Cumulative sum of residuals (CUSUM) test, and autoregressive distributed lag (ARDL) bounds test were used to examine the stationarity, stability, and cointegration of the variables respectively. ARDF model analysis was carried out to determine the short run and long run determinants of national saving in the studied countries. The long run results reveal that gross domestic product, per capita income and real interest rate has a statistically and significant positive effect on gross savings, were as age dependency ratio has a statistical, and insignificant negative relationship with gross saving. The short run results suggest that gross domestic product and per capita income possesses positive statistical significant effects on gross national savings.It is recommended that, in other to promote saving, growth and development, pragmatic and realistic economic policies should be formulated to strengthen all monetary and financial institutions in the respective countries.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.473

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 head0.033
GPT teacher head0.248
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations12
Published2018
Admission routes1
Has abstractyes

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