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Record W3173877580 · doi:10.3390/jrfm14070292

Spatial Impact of Foreign Direct Investment on Poverty Reduction in Vietnam

2021· article· en· W3173877580 on OpenAlexvenueno aff
Quynh Anh, Q Lê, Thanh Duong Nguyen, Van Anh Vu, Lan Huong Tran, Cuc Thi Thu Nguyen

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersĐại học Kinh tế Quốc dân
KeywordsForeign direct investmentPovertyPanel dataEconomicsPoverty reductionHuman capitalEconometric modelInternational economicsDevelopment economicsEconometricsMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

In this study, we analyze the spatial effect of foreign direct investment (FDI) on poverty reduction in Vietnam. This study uses the provincial-level panel data and the fixed-effects regression and the spatial econometric model to investigate empirically the impact of FDI on poverty reduction in Vietnam. The study finds that FDI has contributed to poverty reduction not only directly but also indirectly through human capital. However, FDI has indirectly worsened poverty through international trade. In addition, empirical results from the spatial econometric model show that FDI tends to decrease poverty in provinces. Finally, the study has some policy implications to decrease the negative effects of FDI on poverty reduction in Vietnam.

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)0.000
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 score0.402
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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 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

Citations37
Published2021
Admission routes1
Has abstractyes

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