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Record W2146067307 · doi:10.5539/ibr.v4n4p218

Impact of Workers’ Remittances on Financial Development in Nigeria

2011· article· en· W2146067307 on OpenAlexvenueno aff
Babatunde Olufemi Oke, Olayinka Marte Uadiale, Okwy Peter Okpala

Bibliographic record

VenueInternational Business Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLoanable fundsEconomicsNexus (standard)EstimationOrder (exchange)Consumption (sociology)Broad moneyFinancial intermediaryReal gross domestic productPer capitaMoney supplyMonetary economicsFinanceExchange rateMonetary policyInterest rate

Abstract

fetched live from OpenAlex

In this study we examine the nexus between remittances and financial development (FINDEV) in Nigeria from 1977 to 2009. Towards achieving the objective of this study, we employ both the ordinary least square estimation (OLSE) technique and the Generalized Method of Moments (GMM) estimator. Moreover, key diagnostic tests are carried out in order to ascertain model adequacy. We also use two indicators of FINDEV, namely: the ratio of money supply to GDP (m2/gdp) and the ratio of private credit to GDP (cps/gdp). The results generally indicate that remittances positively and significantly influence financial development in Nigeria, with the exception of the cps/gdp measure of FINDEV in the GMM estimation where the coefficient is insignificant. This implies that remittances augment liquid liabilities more than loanable funds in Nigeria, as remittances are likely used more for consumption purposes than for productive ventures in the country. Since remittances provide foreign exchange that is vital to both the internal and the external sectors of the economy, they should be encouraged via appropriate policy formulation and implementation. Financial intermediaries and institutions operating in Nigerian should also intensify the mobilization of remittances with the aim of making them important sources of loanable funds in the country.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.109
GPT teacher head0.430
Teacher spread0.322 · 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.

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

Citations38
Published2011
Admission routes1
Has abstractyes

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