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Record W2285602426 · doi:10.1111/coep.12167

HOW DOES SKILLS MISMATCH AFFECT REMITTANCES? A STUDY OF FILIPINO MIGRANT WORKERS

2016· article· en· W2285602426 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueContemporary Economic Policy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRemittanceInstrumental variableAffect (linguistics)Demographic economicsMigrant workersEducational attainmentUnit (ring theory)Work (physics)Labour economicsEconomicsBusinessPsychologyEconomic growthEconometrics

Abstract

fetched live from OpenAlex

In this article, unit record data on Filipino migrants are used to analyze the issue of skills mismatch, its prevalence, and its impact on remittances sent back home. Results obtained using instrumental variable techniques reveal that significant proportions of highly educated Filipino workers are employed in low‐skilled jobs overseas, with systematic variation by gender and by country of work. We find that skills mismatch impacts significantly on the migrant's remittance behavior, with effects that are differentiated between genders. Specifically, where there is mismatch in the migrant's educational attainment and the migrant's job requirement, we find significant reductions in remittances for men but not for women. ( JEL J240 , J610 , O150 )

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.996

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 head0.019
GPT teacher head0.297
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