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Record W2996982251 · doi:10.5430/rwe.v10n5p113

The Determinations of East Asia’s Automobile Trade Using a Gravity Model

2019· article· en· W2996982251 on OpenAlexvenueno aff
Shahrun Nizam Abdul-Aziz, Normala Zulkifli, Norimah Rambeli Ramli, Noor Al-Huda Abdul Karim, Zainizam Zakariya, Norasibah Abdul Jalil

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsIndustrialisationGravity model of tradeEast AsiaForeign direct investmentMultinational corporationGovernment (linguistics)International tradeEconomicsInternational economicsPer capita incomeBilateral tradeBusinessGeographyChinaMacroeconomicsMarket economy

Abstract

fetched live from OpenAlex

The aim of this study is to investigate empirically the factors that determine the level of automobile trade in East Asian countries by taking into account government policies as well as the role of MNEs. To do so, in this study we include dummies of import substitution industrialisation (ISI) and export orientation industrialisation (EOI) policies as well as Japanese FDI as additional explanatory variables in our augmented gravity models. We found that GDPs, distance, per capita income, FTA, government policies, language and FDI are the determinants for the development of automobile industry in each country in East Asia. In the case of auto P&C, apart from economic size, the role of government through trade policy (i.e., FTA) and industrial policies as well as the role of MNCs are the major contributors to the development both exports and imports of East Asian countries. In the case of final automobiles, the role of FTA and language seems to be unimportant. Nonetheless, the role of government policies and MNCs seem to be important.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.224
GPT teacher head0.335
Teacher spread0.110 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

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