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Record W3194033531 · doi:10.1111/imig.12911

Non‐linear relationship between remittances and financial development in Jamaica

2021· article· en· W3194033531 on OpenAlexaff
Anupam Das, Adian McFarlane

Bibliographic record

VenueInternational Migration · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsThe King's UniversityWestern UniversityMount Royal University
Fundersnot available
KeywordsRemittanceCointegrationEconomicsDistributed lagFinancial sector developmentMonetary economicsFinancial sectorFinancial systemFinanceEconometricsEconomic growth

Abstract

fetched live from OpenAlex

Abstract We examine the relationship between financial development and remittance inflows (remittances), measured as a share of GDP, within a non‐linear framework in Jamaica over the period 1980 to 2017. By applying the autoregressive distributed lag bound testing approach to cointegration, we arrive at two key results. First, there is a long‐run cointegrating relationship running from remittances to financial development. Second, remittances and financial development are non‐linearly related. Financial development is a U‐shaped function of remittances. As remittances increase, they first have a negative impact on financial development. However, after remittances reach a threshold point, their impact on financial development is positive. Taken together, the results indicate that remittances first substitute for and then complement financial development. We provide important policy suggestions such as those that would increase remittances and those that would incentivize remittance receiving households to use these inflows in the formal financial sector.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.294
Threshold uncertainty score0.585

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.318
Teacher spread0.289 · 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

Citations18
Published2021
Admission routes1
Has abstractyes

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