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Record W2937549485 · doi:10.1111/ecno.12145

Labor and behavior determinants of remittances in Saudi Arabia

2019· article· en· W2937549485 on OpenAlexaff
Stephen Snudden

Bibliographic record

VenueEconomic Notes · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsRemittanceEarningsEconomicsUnemploymentLabour economicsWageCommunication sourceCapital (architecture)Demographic economicsMacroeconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Abstract Saudi Arabia is the second largest sender of international remittances. These remittances constitute large foreign capital inflows to labor‐exporting remittee economies. This study is the first to structurally decompose remittance dynamics into behavioral and labor market outcomes of migrants. Remittance outflows are decomposed into migrant labor supply, unemployment and participation rates, wage earnings, and the marginal propensity to remit (MPR) out of migrant earnings. The estimates suggest that migrant labor supply is highly elastic. The important driver of remittance dynamics is the MPR, migrant wages, and the labor supply of migrants. The MPR is found to respond counter‐cyclically to foreign gross domestic product.

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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.014
GPT teacher head0.290
Teacher spread0.277 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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